Welcome to the Hazelcast Reference Manual. This manual includes concepts, instructions and samples to guide you on how to use Hazelcast and build Hazelcast applications.
As the reader of this manual, you must be familiar with the Java programming language and you should have installed your preferred IDE.
Throughout this manual:
Hazelcast is free provided under the Apache 2 license. Hazelcast Enterprise is commercially licensed by Hazelcast, Inc.
For more detailed information on licensing, please see the License Questions appendix.
Hazelcast is a registered trademark of Hazelcast, Inc. All other trademarks in this manual are held by their respective owners.
Support for Hazelcast is provided via GitHub, Mail Group and StackOverflow.
For information on support for Hazelcast Enterprise, please see hazelcast.com/pricing.
You can contribute to the Hazelcast code, report a bug or request an enhancement. Please see the following resources.
Below table shows the conventions used in this manual.
Convention | Description |
---|---|
bold font | - Indicates part of a sentence that require the reader's specific attention. - Also indicates property/parameter values. |
italic font | - When italicized words are enclosed with "<" and ">", indicates a variable in command or code syntax that you must replace, e.g. hazelcast-< version>.jar . - Note and Related Information texts are in italics. |
monospace |
- Indicates files, folders, class and library names, code snippets, and inline code words in a sentence. |
RELATED INFORMATION | - Indicates a resource that is relevant to the topic, usually with a link or cross-reference. |
NOTE | Indicates information that is of special interest or importance, e.g. an additional action required only in certain circumstances. |
element & attribute | Mostly used in the context of declarative configuration, i.e. configuration performed by the Hazelcast XML file. Element refers to an XML tag used to configure a Hazelcast feature. Attribute is a parameter owned by an element, contributing into the declaration of that element's configuration. Please see the following example.<port port-count="100">5701</port> In this example, port-count is an attribute of the port element. |
This chapter includes the release notes, information on how to upgrade Hazelcast from previous releases and the revision history for this document.
This section lists the new features and enhancements developed and bugs fixed for this release.
The following the new features introduced with Hazelcast 3.5 release.
SlowOperationDetector
feature, slow operations are logged and can be seen on the Hazelcast Management Center. Please see the SlowOperationDetector section and Management Center:Members section.EntryListener
has been changed to MapListener
and MapListener
has sub-interfaces to catch map/entry related events. Please see the Map Listener section for more information.3.5.3 Enhancements
The following are the enhancements performed for Hazelcast 3.5.3 release.
CacheProxy
. At the client side, cache statistics are not calculated and supported so UnsupportedOperationException
is thrown at ClientCacheProxy
[#6262].iam-role
support to Hazelcast Cloud module. So users can use Hazelcast Cloud module without configuring access keys. [#6262].3.5.1 Enhancements
The following are the enhancements performed for Hazelcast 3.5.1 release.
com.hazelcast.spi.impl.classicscheduler.ResponseThread::process
may catch throwables. When this occurs, it logs an unhelpful message, and ignores the actual exception. This method should be improved to additionally log the cause, or at least the exception class and message [#5619].min-eviction-check-millis
in the map configuration does not exist in documentation [#5614].3.5 Enhancements
This section lists the enhancements performed for Hazelcast 3.5 release.
HazelcastInstance
. For more information, please see XA Transactions.The following are the other improvements performed to solve the enhancement issues opened by the Hazelcast customers/team.
ExpiryPolicy
can be set programmatically but not declaratively [#5347].NearCacheConfig
in CacheConfig
[#5215].SlowOperationDetector
should have an option to not to log the stacktraces to the log file. There is no need to have the stacktraces written to the normal log file if the Hazelcast Management Center or the performance monitor is being used [#5043].BasicInvocationFuture
, there is no need to create an additional AtomicInteger
object. It should be
replaced with AtomicIntegerFieldUpdater
[#4408].IsStillExecutingOperation
to check if an operation is running locally. One
can directly access to the scheduler [#4407].KeyValueSource.hasNext/element/key
and Iterator.hasNext/next
should emphasize the differences between each other, i.e. the state changing behavior should be clarified [#4218].SecurityInterceptor
Implementation [#4118].executingCalls
map. The key
(and value) is a RemoteCallKey
object. The functionality provided is the ability to ask on the remote side
if an operation is still executing. For a partition-aware operation, this is not needed. When an operation is
scheduled by a partition specific operation thread, the operation can be stored in a volatile field in that
thread [#4079].TcpIpJoinerOverAWS
fails at AWS' recently launched eu-central-1 region. The reason for the fail is that the region requires v4 signatures [#3963].EntryListener
breaks the compatibility with the Camel Hazelcast component [#3859].hazelcast-spring-<
version>.xsd
should include the User Defined Services (SPI) elements and
attributes [#3565].loadAllKeys()
in map is not scalable [#2266].3.5.4 Fixes
The following are the issues solved for Hazelcast 3.5.4 release.
PER_NODE
max-size
policy is used. [#6674]migrationStarted
/migrationCompleted
were only called once when a partition migration was done. But in 3.5, these methods are called twice for each partition. [#6396]3.5.3 Fixes
The following are the issues solved for Hazelcast 3.5.3 release.
ClientInvocationFuture
may hang when the deserialized response is null. [#6363]CacheStatisticsImpl::getAverageRemoveTime()
uses the "get" count on the cache but it must use the "remove" count. [#6314]console.sh
should support changing namespaces that contain space characters. [#6307] QueueContainer
does not rollback the changes in the order that is opposite to the order of the items when they were added. It is a random order due to the fact that the changes are stored in a hashmap. [#6156]\r\n
in the body of the REST API responses causing warnings. [#6144]IList.iterator()
and listIterator()
do not support the method iterator.remove()
. [#5508]3.5.2 Fixes
The following are the issues solved for Hazelcast 3.5.2 release.
ClusteredSessionService.run()
[#6052]IMap.getAll
by-passes interceptors in the Hazelcast 3.3 and higher versions [#5775]AWSJoiner
fails for the regions except us-east-1 [#5653]sun.misc.Unsafe
class does not work on HP-UX operating system [#5518]AWSAddressTranslator
always uses the default region and this causes the HazelcastClient to be unable to join a Hazelcast AWS cluster in a non-default region [#5446]JettyWebFilterTest.java
does not fail properly [#5188]hazelcast-client.xml
, if the region is configured but host-header
is not provided, the configuration gives a default endpoint value of ec2.amazonaws.com
. It should give, for example, ec2.eu-west-1.amazonaws.com
when the region is eu-west-1 and host-header
is not provided [#4731]3.5.1 Fixes
The following are the issues solved for Hazelcast 3.5.1 release.
UpdateMapConfigOperation
to update map configurations. This operation simply replaces the map configuration of the related map container. However, this replacement has no effect for maxIdleSeconds
and timeToLiveSeconds
properties of the map configuration since they are not used in the map container directly. They are assigned to the final variables during map container creation and never touched again [#5593]DistributedObjectListener
[#5592]IMap.destroy()
but then it is immediately recreated [#5554]getApproximateMaxSize()
related to casting. Its return type is int
and this causes the map entries to be evicted all the time when, for example, the eviction policy for an IMap is set to heap percentage with the value 1% [#5516]onResponse()
calls on a MultiExecutionCallback
should be made before the method onComplete()
is called. There exists a race condition in ExecutionCallbackAdapterFactory
which permits the method onComplete()
to be called before all onResponse()
calls are made [#5490]3.5 Fixes
The following are the issues solved for Hazelcast 3.5 release.
MapLoader
exception is not logged: Exception should be logged and propagated back to the client that triggered the loading of the map [#5430]XAResource.rollback()
should not need the transaction to be in the prepared state when called from another member/client [#5401]XAResource.end()
should not need to check threadId
[#5400]IList::remove()
should publish the event REMOVED
[#5386]IllegalStateException
with wrong partition is thrown when the method IMap::getOperation()
is invoked [#5341]WrongTarget
warnings appear in the log since the operations are not sent to the replicas when a map has no backups [#5324]finalizeCombine()
is used, Hazelcast throws NullPointerException
[#5283]WanBatchReplication
causes OutOfMemoryException
when the default value for WAN Replication Batch Size (50) is used [#5280]TransactionException
instead of an XAException
on timeout [#5260]getAsync()
fails with a NegativeArraySizeException
[#5229]putTransient
actuated the MapStore unexpectedly in an environment with multiple instances [#5225]removeAttribute
will prevent any updates by the method setAttribute
in the deferred write mode [#5186]CacheEvictionConfig
class was renamed to EvictionConfig
for general usage [#5180]ICompletableFuture.onResponse()
is not deserialized [#5158]/etc/hosts
and the members are configured manually with IP addresses and while one node is running, a second node joins to the cluster 5 minutes after it started [#5072]OperationService.asyncInvokeOnPartition()
sometimes fails [#5069]SlowOperationDTO.operation
shows only the class name, not the package. This can lead to ambiguity and the actual class cannot be tracked [#5041]MessageListener
interface of ITopic [#5019]InvocationFuture.isDone
returns true
as soon as there is a response including WAIT_RESPONSE
. However, WAIT_RESPONSE
is an intermediate response, not a final one [#5002]InvocationFuture.andThen
does not deal with the null response correctly [#5001]CacheCreationTest
fails due to the multiple TestHazelcastInstanceFactory
creations in the same test [#4987]putIfAbsent
method is thrown [#4981]connectionAttemptLimit
property which internally results in int.maxValue
. However, the XSD of the Hazelcast Spring configuration requires it to be at least 1 [#4967]write-coalescing
into account [#4967]getLocalExecutorStats()
fails spuriously [#4911]NullPointerException
[#4855]System.nanoTime
in sleepAtLeast
test code [#4835]hazelcastmq.txn-topic
[#4790]ClassNotFoundException
when using WAR classes with JCache API [#4775]XmlConfigImportVariableReplacementTest
fails [#4758]size()
method for a replicated map should return 0
when the entry is removed [#4666]NullPointerException
on the CachePutBackupOperation
class [#4660]entryRemoved()
is called. In order to get the removed value, one must call the event.getValue()
instead of event.getOldValue()
[#4644]Cache.get()
[#4632]IMap.loadAllKeys()
[#4618]LocalRegionCache
cleanup is working wrongly [#4445]Hibernate
still creates multiple instances [#4374]FinalizeJoinOperation
times out if the method MapStore.loadAllKeys()
takes more than 5 seconds [#4348]ICompletableFuture
while waiting for completion latch in the cache must be checked [#4335]javax.cache.api
and Hazelcast 3.3.1 [#3792]localhost
[#3256]read()
forever [#3401]In the following sections, you can see the changes that you should take into account before upgrading to latest Hazelcast from 2.x and 3.x releases.
Map<Integer, String> customers = Hazelcast.getMap( "customers" );
with
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
// or if you already started an instance named "instance1"
// HazelcastInstance hazelcastInstance = Hazelcast.getHazelcastInstanceByName( "instance1" );
Map<Integer, String> customers = hazelcastInstance.getMap( "customers" );
Removal of lite members: With 3.0 there will be no member type as lite member. As 3.0 clients are smart client that they know in which node the data is located, you can replace your lite members with native clients.
Renaming "instance" to "distributed object": Before 3.0 there was a confusion for the term "instance". It was used for both the cluster members and the distributed objects (map, queue, topic, etc. instances). Starting 3.0, the term instance will be only used for Hazelcast instances, namely cluster members. We will use the term "distributed object" for map, queue, etc. instances. So you should replace the related methods with the new renamed ones. As 3.0 clients are smart client that they know in which node the data is located, you can replace your lite members with native clients.
public static void main( String[] args ) throws InterruptedException {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IMap map = hazelcastInstance.getMap( "test" );
Collection<Instance> instances = hazelcastInstance.getInstances();
for ( Instance instance : instances ) {
if ( instance.getInstanceType() == Instance.InstanceType.MAP ) {
System.out.println( "There is a map with name: " + instance.getId() );
}
}
}
with
public static void main( String[] args ) throws InterruptedException {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IMap map = hz.getMap( "test" );
Collection<DistributedObject> objects = hazelcastInstance.getDistributedObjects();
for ( DistributedObject distributedObject : objects ) {
if ( distributedObject instanceof IMap ) {
System.out.println( "There is a map with name: " + distributedObject.getName() );
}
}
}
com.hazelcast.core
from com.hazelcast.partition
.removeListener
methods was taking the Listener object as parameter. But, it causes confusion as same listener object may be used as parameter for different listener registrations. So we have changed the listener API. addListener
methods return you an unique ID and you can remove listener by using this ID. So you should do following replacement if needed:IMap map = hazelcastInstance.getMap( "map" );
map.addEntryListener( listener, true );
map.removeEntryListener( listener );
with
IMap map = hazelcastInstance.getMap( "map" );
String listenerId = map.addEntryListener( listener, true );
map.removeEntryListener( listenerId );
tryRemove(K key, long timeout, TimeUnit timeunit)
returns boolean indicating whether operation is successful.tryLockAndGet(K key, long time, TimeUnit timeunit)
is removed.putAndUnlock(K key, V value)
is removed.lockMap(long time, TimeUnit timeunit)
and unlockMap()
are removed.getMapEntry(K key)
is renamed as getEntryView(K key)
. The returned object's type, MapEntry class is renamed as EntryView.<merge-policy>com.hazelcast.map.merge.PassThroughMergePolicy</merge-policy>
Also MergePolicy interface has been renamed to MapMergePolicy and also returning null from the implemented merge()
method causes the existing entry to be removed.
IQueue
is configured. With Hazelcast 3.0 there will not be backing map configuration for queue. Settings like backup count will be directly configured on queue config. For queue configuration details, please see the Queue section.pause()
, resume()
, restart()
methods have been removed.AtomicNumber
class has been renamed to IAtomicLong
.await()
operation has been removed. We expect users to use await()
method with timeout parameters.ISemaphore
has been substantially changed. attach()
, detach()
methods have been removed.max-size
eviction policy was cluster_wide_map_size. In 3.x releases, default is PER_NODE. After upgrading, the max-size
should be set according to this new default, if it is not changed. Otherwise, it is likely that OutOfMemory exception may be thrown.spring-aware
element:
Before the release 3.5, Hazelcast uses SpringManagedContext
to scan SpringAware
annotations by default. This may cause some performance overhead for the users who do not use SpringAware
.
This behavior has been changed with the release of Hazelcast 3.5. SpringAware
annotations are disabled by default. By introducing the spring-aware
element, now it is possible to enable it by adding the <hz:spring-aware />
tag to the configuration. Please see the Spring Integration section.Chapter | Section | Description |
---|---|---|
Chapter 1 - Preface | Added information on how to contribute to Hazelcast. | |
Chapter 2 - What's New in Hazelcast 3.5 | Upgrading from 3.x | Added as a new section. |
Chapter 3 - Getting Started | Deploying On Amazon EC2 | Added as a new section to provide a sample deployment project. |
Chapter 4 - Hazelcast Overview | Separated from Getting Started as a new chapter. | |
Data Partitioning | Added as a new section explaining how the partitioning works in Hazelcast. | |
Chapter 5 - Hazelcast Clusters | Creating Cluster Groups | Added as a new section explaining how to separate a Hazelcast cluster. |
Chapter 6 - Distributed Data Structures | Map | The content of the section, previously read as Entry Listener improved and its name changed to Map Listener. Description of the new element write-coalescing added to the Write-Behind section.Incremental Key Loading added as a new section. Example Map Eviction Scenario added as a new section. MapPartitionLostListener added as a new section. A note stating the change in the return type of the method loadAllKeys() added to the Initialization On Startup section.Map Eviction section updated by adding the definition for the min-eviction-check-millis property. |
Replicated Map | Replicated Map Configuration added as a new section explaining the configuration elements. | |
RingBuffer | Added as a new section. | |
Reliable Topic | Added as a new section. | |
Chapter 7 - Distributed Events | The whole chapter improved by adding sections describing each listener. | |
Partition Lost Listener | Added as a new section. | |
Chapter 8 - Distributed Computing | Execution Member Selector | Added as a new section explaining how to select a cluster member on which an execution will be performed. |
Chapter 9 - Distributed Query | Paging Predicate | Added a note related to random page access. |
Chapter 11 - Transactions | Added a note related to REPEATABLE_READ isolation level. |
|
Local versus Two Phase | Added as a new section explaining the trade offs between these two transaction types. | |
Chapter 12 - Hazelcast JCache | JCache Near Cache | Added as a new section explaining the invalidation concept, eviction policies and configuration of JCache's near cache feature. |
Chapter 13 - Integrated Clustering | Added introduction paragraphs. | |
Tomcat Based Web Session Replication | Updated the Overview paragraph to include the support for Tomcat 8. | |
Web Session Replication | transient-attributes added as a new section. | |
Chapter 14 - Storage | Sizing Practices | Added as a new section. |
Chapter 15 - Hazelcast Java Client | Separated from the formerly known "Clients" chapter to be a chapter of its own. | |
Added an important note related to the new Java Client library in the chapter introduction. | ||
Hazelcast Clients Feature Comparison | Added as a new section. | |
Chapter 16 - Other Client Implementations | C++, .NET, Memcache and REST client sections separated from the formerly known "Clients" chapter. | |
Chapter 18 - Management | JMX API per Node | Two new bean definitions added to the Hazelcast Instance list (Cluster Safe State and LocalMember Safe State). |
Management Center | Added more information on the time travel data files to the Time Travel section. | |
Chapter 19 - Security | ClusterLoginModule | The Enterprise Integration section added . |
Chapter 20 - Performance | Hazelcast Performance on AWS | Added as a new section that provides best practices to improve the Hazelcast performance on Amazon Web Service. |
Back Pressure | Added as a new section. | |
SlowOperationDetector | Added as a new section explaining the SlowOperationDetector , a monitoring feature that collects information of all slow operations. |
|
Chapter 21 - Hazelcast Simulator | Added as a new chapter providing comprehensive information on the Hazelcast Simulator feature. | |
Chapter 22 - WAN | WAN Replication Queue Capacity | The previous heading title (WAN Replication Queue Size) and the system property name (hazelcast.enterprise.wanrep.queuesize ) changed to WAN Replication Queue Capacity and hazelcast.enterprise.wanrep.queue.capacity . |
Enterprise WAN Replication | Added as a new section. | |
Chapter 23 - Hazelcast Configuration | Improved by adding missing configuration elements and attributes. Added introduction paragraphs to the chapter. | |
Configuration Overview | Added a note related to the invalid configurations. | |
Using Variables | Added as a new section explaining how to use variables in declarative configuration. | |
System Properties | Added the new system properties hazelcast.unsafe.mode , hazelcast.io.input.thread.count and hazelcast.io.output.thread.count . Updated the definition of hazelcast.io.thread.count . |
|
Enterprise WAN Replication Configuration | Added as a new section describing the elements and attributes of Enterprise WAN Replication configuration. | |
Chapter 25 - License Questions | Added as a new chapter describing the license information of dependencies. | |
Chapter 26 - Common Exception Types | Added as a new chapter. | |
Chapter 27 - FAQ | Added new questions/answers. | |
Chapter 28 - Glossary | Added new glossary items. |
This chapter explains how to install Hazelcast, start a Hazelcast member and client, and gives Hazelcast configuration fundamentals.
The following sections explains the installation of Hazelcast and Hazelcast Enterprise.
You can find Hazelcast in standard Maven repositories. If your project uses Maven, you do not need to add
additional repositories to your pom.xml
or add hazelcast-<
version>.jar
file into your
classpath (Maven does that for you). Just add the following lines to your pom.xml
:
<dependencies>
<dependency>
<groupId>com.hazelcast</groupId>
<artifactId>hazelcast</artifactId>
<version>3.5.2</version>
</dependency>
</dependencies>
As an alternative, you can download and install Hazelcast yourself. You only need to:
Download hazelcast-<
version>.zip
file from www.hazelcast.org.
Unzip hazelcast-<
version>.zip
file.
Add hazelcast-<
version>.jar
file into your classpath.
There are two Maven repositories defined for Hazelcast Enterprise:
<repository>
<id>Hazelcast Private Snapshot Repository</id>
<url>https://repository-hazelcast-l337.forge.cloudbees.com/snapshot/</url>
</repository>
<repository>
<id>Hazelcast Private Release Repository</id>
<url>https://repository-hazelcast-l337.forge.cloudbees.com/release/</url>
</repository>
Hazelcast Enterprise customers may also define dependencies, a sample of which is shown below.
<dependency>
<groupId>com.hazelcast</groupId>
<artifactId>hazelcast-enterprise-tomcat6</artifactId>
<version>${project.version}</version>
</dependency>
<dependency>
<groupId>com.hazelcast</groupId>
<artifactId>hazelcast-enterprise-tomcat7</artifactId>
<version>${project.version}</version>
</dependency>
<dependency>
<groupId>com.hazelcast</groupId>
<artifactId>hazelcast-enterprise</artifactId>
<version>${project.version}</version>
</dependency>
<dependency>
<groupId>com.hazelcast</groupId>
<artifactId>hazelcast-enterprise-all</artifactId>
<version>${project.version}</version>
</dependency>
To use Hazelcast Enterprise, you need to set the license key in configuration.
<hazelcast>
...
<license-key>HAZELCAST_ENTERPRISE_LICENSE_KEY</license-key>
...
</hazelcast>
<hazelcast-client>
...
<license-key>HAZELCAST_ENTERPRISE_LICENSE_KEY</license-key>
...
</hazelcast-client>
Config config = new Config();
config.setLicenseKey( "HAZELCAST_ENTERPRISE_LICENSE_KEY" );
<hz:config>
...
<hz:license-key>HAZELCAST_ENTERPRISE_LICENSE_KEY</hz:license-key>
...
</hz:config>
-Dhazelcast.enterprise.license.key=HAZELCAST_ENTERPRISE_LICENSE_KEY
Having installed Hazelcast, you can get started.
In this short tutorial, you perform the following activities.
Let's begin.
customers
map and queue.import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import java.util.Map;
import java.util.Queue;
public class GettingStarted {
public static void main( String[] args ) {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
Map<Integer, String> customers = hazelcastInstance.getMap( "customers" );
customers.put( 1, "Joe" );
customers.put( 2, "Ali" );
customers.put( 3, "Avi" );
System.out.println( "Customer with key 1: " + customers.get(1) );
System.out.println( "Map Size:" + customers.size() );
Queue<String> queueCustomers = hazelcastInstance.getQueue( "customers" );
queueCustomers.offer( "Tom" );
queueCustomers.offer( "Mary" );
queueCustomers.offer( "Jane" );
System.out.println( "First customer: " + queueCustomers.poll() );
System.out.println( "Second customer: "+ queueCustomers.peek() );
System.out.println( "Queue size: " + queueCustomers.size() );
}
}
GettingStarted
class a second time to get the second member
started. The members form a cluster and the output is similar to the following.Members [2] {
Member [127.0.0.1:5701]
Member [127.0.0.1:5702] this
}
Now, add the hazelcast-client-
<version>
.jar
library to your classpath.
This is required to use a Hazelcast client.
The following code starts a Hazelcast Client, connects to our cluster,
and prints the size of the customers
map.
package com.hazelcast.test;
import com.hazelcast.client.config.ClientConfig;
import com.hazelcast.client.HazelcastClient;
import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.core.IMap;
public class GettingStartedClient {
public static void main( String[] args ) {
ClientConfig clientConfig = new ClientConfig();
HazelcastInstance client = HazelcastClient.newHazelcastClient( clientConfig );
IMap map = client.getMap( "customers" );
System.out.println( "Map Size:" + map.size() );
}
}
Hazelcast also offers a tool, Management Center, that enables you to monitor your cluster.
To use it, deploy the mancenter-
<version>
.war
included in the ZIP file to your web server.
You can use it to monitor your maps, queues, and other distributed data structures and members. Please
see the Management Center section for usage explanations.
By default, Hazelcast uses Multicast to discover other members that can form a cluster. If you are
working with other Hazelcast developers on the same network, you may find yourself joining their
clusters under the default settings. Hazelcast provides a way to segregate clusters within the same
network when using Multicast. Please see the Creating Cluster Groups
for more information. Alternatively, if you do not wish to use the default Multicast mechanism,
you can provide a fixed list of IP addresses that are allowed to join. Please see
the Join Configuration section for more information.
RELATED INFORMATION
You can also check the video tutorials here.
You can deploy your Hazelcast project onto Amazon EC2 environment using Third Party tools such as Vagrant and Chef.
You can find a sample deployment project (amazon-ec2-vagrant-chef
) with step by step instructions in the hazelcast-integration
folder of the hazelcast-code-samples
package. Please refer to this sample project for more information.
When Hazelcast starts up, it checks for the configuration as follows:
First, it looks for the hazelcast.config
system property. If it is set, its value is used as the path. This is useful if you want to be able to change your Hazelcast configuration: you can do this because it is not embedded within the application. You can set the config
option with the following command:
- Dhazelcast.config=
<path to the hazelcast.xml>
.
The path can be a normal one or a classpath reference with the prefix CLASSPATH
.
hazelcast.xml
file in the working directory.hazelcast.xml
exists on the classpath.hazelcast-default.xml
that comes with hazelcast.jar
.When you download and unzip hazelcast-<
version>.zip
, you will see a hazelcast.xml
in the /bin
folder. This is the declarative configuration file for Hazelcast. Part of this XML file is shown below.
<hazelcast xsi:schemaLocation="http://www.hazelcast.com/schema/config hazelcast-config-3.5.xsd"
xmlns="http://www.hazelcast.com/schema/config"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<group>
<name>dev</name>
<password>dev-pass</password>
</group>
<management-center enabled="false">http://localhost:8080/mancenter</management-center>
<network>
<port auto-increment="true" port-count="100">5701</port>
<outbound-ports>
<!--
Allowed port range when connecting to other nodes.
0 or * means use system provided port.
-->
<ports>0</ports>
</outbound-ports>
<join>
<multicast enabled="true">
<multicast-group>224.2.2.3</multicast-group>
<multicast-port>54327</multicast-port>
</multicast>
<tcp-ip enabled="false">
For most users, default configuration should be fine. If not, you can tailor this XML file according to your needs by adding/removing/modifying properties.
Besides declarative configuration, you can configure your cluster programmatically. Just instantiate a Config
object and add/remove/modify properties.
You can also use wildcards while configuring Hazelcast. Please refer to the Using Wildcard section for details.
Hazelcast also offers System Properties to tune some aspects of it. Please refer to the System Properties section for details.
RELATED INFORMATION
Please refer to the Hazelcast Configuration chapter for more information.
Hazelcast is an open source In-Memory Data Grid (IMDG). It provides elastically scalable distributed In-Memory computing, widely recognized as the fastest and most scalable approach to application performance. Hazelcast does this in open source. More importantly, Hazelcast makes distributed computing simple by offering distributed implementations of many developer friendly interfaces from Java such as Map, Queue, ExecutorService, Lock, and JCache. For example, the Map interface provides an In-Memory Key Value store which confers many of the advantages of NoSQL in terms of developer friendliness and developer productivity.
In addition to distributing data In-Memory, Hazelcast provides a convenient set of APIs to access the CPUs in your cluster for maximum processing speed. Hazelcast is designed to be lightweight and easy to use. Since Hazelcast is delivered as a compact library (JAR) and since it has no external dependencies other than Java, it easily plugs into your software solution and provides distributed data structures and distributed computing utilities.
Hazelcast is highly scalable and available (100% operational, never failing). Distributed applications can use Hazelcast for distributed caching, synchronization, clustering, processing, pub/sub messaging, etc. Hazelcast is implemented in Java and has clients for Java, C/C++, .NET and REST. Hazelcast also speaks memcache protocol. It plugs into Hibernate and can easily be used with any existing database system.
If you are looking for In-Memory speed, elastic scalability, and the developer friendliness of NoSQL, Hazelcast is a great choice.
Hazelcast is simple
Hazelcast is written in Java with no other dependencies. It exposes the same API from the familiar Java util package,
exposing the same interfaces. Just add hazelcast.jar
to your classpath, and you can quickly enjoy JVMs clustering
and you can start building scalable applications.
Hazelcast is Peer-to-Peer
Unlike many NoSQL solutions, Hazelcast is peer-to-peer. There is no master and slave; there is no single point of failure. All nodes store equal amounts of data and do equal amounts of processing. You can embed Hazelcast in your existing application or use it in client and server mode where your application is a client to Hazelcast nodes.
Hazelcast is scalable
Hazelcast is designed to scale up to hundreds and thousands of nodes. Simply add new nodes and they will automatically discover the cluster and will linearly increase both memory and processing capacity. The nodes maintain a TCP connection between each other and all communication is performed through this layer.
Hazelcast is fast
Hazelcast stores everything in-memory. It is designed to perform very fast reads and updates.
Hazelcast is redundant
Hazelcast keeps the backup of each data entry on multiple nodes. On a node failure, the data is restored from the backup and the cluster will continue to operate without downtime.
Hazelcast shards are called Partitions. By default, Hazelcast has 271 partitions. Given a key, we serialize, hash and mode it with the number of partitions to find the partition the key belongs to. The partitions themselves are distributed equally among the members of the cluster. Hazelcast also creates the backups of partitions and distributes them among nodes for redundancy.
RELATED INFORMATION
Please refer to the Data Partitioning section for more information on how Hazelcast partitions your data.
If you have an application whose main focal point is asynchronous or high performance computing and lots of task executions, then embedded deployment is very useful. In this type, nodes include both the application and data. See the below illustration.
You can have a cluster of server nodes that can be independently created and scaled. Your clients communicate with these server nodes to reach to the data on them. Hazelcast provides native clients (Java, .NET and C++), Memcache clients and REST clients. See the below illustration.
A Glance at Traditional Data Persistence
Data is at the core of software systems. In conventional architectures, a relational database persists and provides access to data. Applications are talking directly with a database which has its backup as another machine. To increase performance, tuning or a faster machine is required. This can cost a large amount of money or effort.
There is also the idea of keeping copies of data next to the database, which is performed using technologies like external key-value stores or second level caching. This helps to offload the database. However, when the database is saturated or the applications perform mostly "put" operations (writes), this approach is of no use because it insulates the database only from the "get" loads (reads). Even if the applications are read-intensive, there can be consistency problems: when data changes, what happens to the cache, and how are the changes handled? This is when concepts like time-to-live (TTL) or write-through come in.
However, in the case of TTL, if the access is less frequent then the TTL, the result will always be a cache miss. On the other hand, in the case of write-through caches; if there are more than one of these caches in a cluster, then we again have consistency issues. This can be avoided by having the nodes communicating with each other so that entry invalidations can be propagated.
We can conclude that an ideal cache would combine TTL and write-through features. And, there are several cache servers and in-memory database solutions in this field. However, those are stand-alone single instances with a distribution mechanism to an extent provided by other technologies. This brings us back to square one: we would experience saturation or capacity issues if the product is a single instance or if consistency is not provided by the distribution.
And, there is Hazelcast
Hazelcast, a brand new approach to data, is designed around the concept of distribution. Hazelcast shares data around the cluster for flexibility and performance. It is an in-memory data grid for clustering and highly scalable data distribution.
One of the main features of Hazelcast is not having a master node. Each node in the cluster is configured to be the same in terms of functionality. The oldest node (the first node created in the node cluster) manages the cluster members, i.e. automatically performs the data assignment to nodes. If the oldest node dies, the second oldest node will manage the cluster members.
Another main feature is the data being held entirely in-memory. This is fast. In the case of a failure, such as a node crash, no data will be lost since Hazelcast distributes copies of data across all the nodes of cluster.
As shown in the feature list in the Hazelcast Overview, Hazelcast supports a number of distributed data structures and distributed computing utilities. This provides powerful ways of accessing distributed clustered memory and accessing CPUs for true distributed computing.
Hazelcast's Distinctive Strengths
Finally, Hazelcast has a vibrant open source community enabling it to be continuously developed.
Hazelcast is a fit when you need:
As you read in the Sharding in Hazelcast section, Hazelcast shards are called Partitions. Partitions are memory segments, where each of those segments can contain hundreds or thousands of data entries, depending on the memory capacity of your system.
By default, Hazelcast offers 271 partitions. When you start a node, that nose owns those 271 partitions. The following illustration shows the partitions in a single node Hazelcast cluster.
When you start a second node on that cluster (creating a 2-node Hazelcast cluster), the partitions are distributed as shown in the following illustration.
In the illustration, the partitions with black text are primary partitions, and the partitions with blue text are replica partitions (backups). The first node has 135 primary partitions (black), and each of these partitions are backed up in the second node (blue). At the same time, the first node also has the replica partitions of the second node's primary partitions.
As you add more nodes, Hazelcast one-by-one moves some of the primary and replica partitions to the new nodes, making all nodes equal and redundant. Only the minimum amount of partitions will be moved to scale out Hazelcast. The following is an illustration of the partition distributions in a 4-node Hazelcast cluster.
Hazelcast distributes the partitions equally among the members of the cluster. Hazelcast creates the backups of partitions and distributes them among nodes for redundancy.
Hazelcast distributes data entries into the partitions using a hashing algorithm. Given an object key (for example, for a map) or an object name (for example, for a topic or list):
The result of this modulo - MOD(hash result, partition count) - gives the partition in which the data will be stored.
When you start a node, a partition table is created within it. This table stores the information for which partitions belong to which nodes. The purpose of this table is to make all nodes in the cluster aware of this information, making sure that each node knows where the data is.
The oldest node in the cluster (the one that started first) periodically sends the partition table to all nodes. In this way, each node in the cluster is informed about any changes to the partition ownership. The ownerships may be changed when, for example, a new node joins the cluster, or when a node leaves the cluster.
NOTE: If the oldest node goes down, the next oldest node sends the partition table information to the other nodes.
You can configure the frequency (how often) that the node sends the partition table the information by using the hazelcast.partition.table.send.interval
system property. The property is set to every 15 seconds by default.
Repartitioning is the process of redistribution of partition ownerships. Hazelcast performs the repartitioning in the following cases:
In these cases, the partition table in the oldest node is updated with the new partition ownerships.
Some example usages are listed below. Hazelcast can be used:
As a real time streamer for performance detection,
As storage for session data in web applications (enables horizontal scalability of the web application).
This chapter describes Hazelcast clusters and the ways cluster members use to form a Hazelcast cluster.
A Hazelcast cluster is a network of cluster members that run Hazelcast. Cluster members (also called nodes) automatically join together to form a cluster. This automatic joining takes place with various discovery mechanisms that the cluster members use to find each other. Hazelcast uses the following discovery mechanisms.
Each discovery mechanism is explained in the following sections.
NOTE: After a cluster is formed, communication between cluster members is always via TCP/IP, regardless of the discovery mechanism used.
With the multicast auto-discovery mechanism, Hazelcast allows cluster members to find each other using multicast communication. The cluster members do not need to know the concrete addresses of the other members, they just multicast to all the other members for listening. It depends on your environment if multicast is possible or allowed.
To set your Hazelcast to multicast auto-discovery, set the following configuration elements. Please refer to the multicast element section for the full description of the multicast discovery configuration elements.
enabled
attribute of the multicast
element to "true".multicast-group
, multicast-port
, multicast-time-to-live
, etc. to your multicast values.enabled
attribute of both tcp-ip
and aws
elements to "false".The following is an example declarative configuration.
<network>
<join>
<multicast enabled="true">
<multicast-group>224.2.2.3</multicast-group>
<multicast-port>54327</multicast-port>
<multicast-time-to-live>32</multicast-time-to-live>
<multicast-timeout-seconds>2</multicast-timeout-seconds>
<trusted-interfaces>
<interface>192.168.1.102</interface>
</trusted-interfaces>
</multicast>
<tcp-ip enabled="false">
</tcp-ip>
<aws enabled="false">
</aws>
</join>
<network>
Pay attention to the multicast-timeout-seconds
element. multicast-timeout-seconds
specifies the time in seconds that a node should wait for a valid multicast response from another node running in the network before declaring itself as the leader node (the first node joined to the cluster) and creating its own cluster. This only applies to the startup of nodes where no leader has been assigned yet. If you specify a high value to multicast-timeout-seconds
, such as 60 seconds, it means that until a leader is selected, each node will wait 60 seconds before moving on. Be careful when providing a high value. Also be careful not to set the value too low, or the nodes might give up too early and create their own cluster.
If multicast is not the preferred way of discovery for your environment, then you can configure Hazelcast to be a full TCP/IP cluster. When you configure Hazelcast to discover members by TCP/IP, you must list all or a subset of the members' hostnames and/or IP addresses as cluster members. You do not have to list all of these cluster members, but at least one of the listed members has to be active in the cluster when a new member joins.
To set your Hazelcast to be a full TCP/IP cluster, set the following configuration elements. Please refer to the tcp-ip element section for the full description of the TCP/IP discovery configuration elements.
enabled
attribute of the multicast
element to "false".enabled
attribute of the aws
element to "false".enabled
attribute of the tcp-ip
element to "true".member
elements within the tcp-ip
element.The following is an example declarative configuration.
<hazelcast>
...
<network>
...
<join>
<multicast enabled="false">
</multicast>
<tcp-ip enabled="true">
<member>machine1</member>
<member>machine2</member>
<member>machine3:5799</member>
<member>192.168.1.0-7</member>
<member>192.168.1.21</member>
</tcp-ip>
...
</join>
...
</network>
...
</hazelcast>
As shown above, you can provide IP addresses or hostnames for member
elements. You can also give a range of IP addresses, such as 192.168.1.0-7
.
Instead of providing members line by line as shown above, you also have the option to use the members
element and write comma-separated IP addresses, as shown below.
<members>192.168.1.0-7,192.168.1.21</members>
If you do not provide ports for the members, Hazelcast automatically tries the ports 5701, 5702, and so on.
By default, Hazelcast binds to all local network interfaces to accept incoming traffic. You can change this behavior using the system property hazelcast.socket.bind.any
. If you set this property to false
, Hazelcast uses the interfaces specified in the interfaces
element (please refer to the Interfaces Configuration section). If no interfaces are provided, then it will try to resolve one interface to bind from the member
elements.
Hazelcast supports EC2 Auto Discovery. It is useful when you do not want to provide or you cannot provide the list of possible IP addresses.
To configure your cluster to use EC2 Auto Discovery, set the following configuration elements. Please refer to the aws element section for the full description of the EC2 Auto Discovery configuration elements.
enabled
attribute of the multicast
element to "false", and set the enabled
attribute of the tcp-ip
element to "false".enabled
attribute of the aws
element to "true".aws
element, provide your credentials (access and secret key), your region, etc.The following is an example declarative configuration.
<join>
<multicast enabled="false">
</multicast>
<tcp-ip enabled="false">
</tcp-ip>
<aws enabled="true">
<access-key>my-access-key</access-key>
<secret-key>my-secret-key</secret-key>
<iam-role>s3access</iam-role>
<region>us-west-1</region>
<host-header>ec2.amazonaws.com</host-header>
<security-group-name>hazelcast-sg</security-group-name>
<tag-key>type</tag-key>
<tag-value>hz-nodes</tag-value>
</aws>
</join>
When needed, Hazelcast can log the events for the instances that exist in a region. To see what has happened or to trace the activities while forming the cluster, change the log level in your logging mechanism to FINEST
or DEBUG
. After this change, you can also see in the generated log whether the instances are accepted or rejected, and the reason the instances were rejected. Note that changing the log level in this way may affect the performance of the cluster. Please see the Logging Configuration section for information on logging mechanisms.
RELATED INFORMATION
You can download the white paper "Hazelcast on AWS: Best Practices for Deployment" from Hazelcast.com.
You can create cluster groups. To do this, use the group
configuration element.
By specifying a group name and group password, you can separate your clusters in a simple way. Example groupings can be by development, production, test, app, etc. The following is an example declarative configuration.
<hazelcast>
<group>
<name>app1</name>
<password>app1-pass</password>
</group>
...
</hazelcast>
You can also define the cluster groups using the programmatic configuration. A JVM can host multiple Hazelcast instances. Each Hazelcast instance can only participate in one group. Each Hazelcast instance only joins to its own group, it does not mess with other groups. The following code example creates three separate Hazelcast instances: h1
belongs to the app1
cluster, while h2
and h3
belong to the app2
cluster.
Config configApp1 = new Config();
configApp1.getGroupConfig().setName( "app1" ).setPassword( "app1-pass" );
Config configApp2 = new Config();
configApp2.getGroupConfig().setName( "app2" ).setPassword( "app2-pass" );
HazelcastInstance h1 = Hazelcast.newHazelcastInstance( configApp1 );
HazelcastInstance h2 = Hazelcast.newHazelcastInstance( configApp2 );
HazelcastInstance h3 = Hazelcast.newHazelcastInstance( configApp2 );
As mentioned in the Overview section, Hazelcast offers distributed implementations of Java interfaces. Below is the Java interface list with links to each section in this manual.
Standard utility collections:
java.util.Map
lets you read from and write to a Hazelcast map with methods like get and put.java.util.concurrent.BlockingQueue
. You can add an item in one machine and remove it from another one.java.util.Set
. It does not allow duplicate elements and does not preserve their order.java.util.concurrent.locks.Lock
. When you lock using Hazelcast Lock, the critical section that it guards is guaranteed to be executed by only one thread in the entire cluster.java.util.concurrent.Semaphore
. When performing concurrent activities, semaphores offer permits to control the thread counts.java.util.concurrent.atomic.AtomicLong
. Most of AtomicLong's operations are available. However, these operations involve remote calls and hence their performances differ from AtomicLong, due to being distributed.java.util.concurrent.atomic.AtomicReference
.AtomicLong.incrementAndGet()
.java.util.concurrent.CountDownLatch
. Hazelcast CountDownLatch is a gate keeper for concurrent activities, enabling the threads to wait for other threads to complete their operations.Common Features of all Hazelcast Data Structures:
Here is an example of how you can retrieve existing data structure instances (map, queue, set, lock, topic, etc.) and how you can listen for instance events, such as an instance being created or destroyed.
import java.util.Collection;
import com.hazelcast.config.Config;
import com.hazelcast.core.*;
public class Sample implements DistributedObjectListener {
public static void main(String[] args) {
Sample sample = new Sample();
Config config = new Config();
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance(config);
hazelcastInstance.addDistributedObjectListener(sample);
Collection<DistributedObject> distributedObjects = hazelcastInstance.getDistributedObjects();
for (DistributedObject distributedObject : distributedObjects) {
System.out.println(distributedObject.getName() + "," + distributedObject.getId());
}
}
@Override
public void distributedObjectCreated(DistributedObjectEvent event) {
DistributedObject instance = event.getDistributedObject();
System.out.println("Created " + instance.getName() + "," + instance.getId());
}
@Override
public void distributedObjectDestroyed(DistributedObjectEvent event) {
DistributedObject instance = event.getDistributedObject();
System.out.println("Destroyed " + instance.getName() + "," + instance.getId());
}
}
Hazelcast Map (IMap
) extends the interface java.util.concurrent.ConcurrentMap
and hence java.util.Map
. It is the distributed implementation of Java map. You can perfrom operations like reading and writing from/to a Hazelcast map with the well known get and put methods.
Hazelcast will partition your map entries and almost evenly distribute onto all Hazelcast members. Each member carries approximately "(1/n *
total-data) + backups", n being the number of nodes in the cluster. For example, if you have a node with 1000 objects to be stored in the cluster, and then you start a second node, each node will both store 500 objects and back up the 500 objects in the other node.
Let's create a Hazelcast instance (node) and fill a map named Capitals
with key-value pairs using the following code.
public class FillMapMember {
public static void main( String[] args ) {
HazelcastInstance hzInstance = Hazelcast.newHazelcastInstance();
Map<String, String> capitalcities = hzInstance.getMap( "capitals" );
capitalcities.put( "1", "Tokyo" );
capitalcities.put( "2", "Paris” );
capitalcities.put( "3", "Washington" );
capitalcities.put( "4", "Ankara" );
capitalcities.put( "5", "Brussels" );
capitalcities.put( "6", "Amsterdam" );
capitalcities.put( "7", "New Delhi" );
capitalcities.put( "8", "London" );
capitalcities.put( "9", "Berlin" );
capitalcities.put( "10", "Oslo" );
capitalcities.put( "11", "Moscow" );
...
...
capitalcities.put( "120", "Stockholm" )
}
}
When you run this code, a node is created with a map whose entries are distributed across the node's partitions. See the below illustration. For now, this is a single node cluster.
NOTE: Please note that some of the partitions will not contain any data entries since we only have 120 objects and the partition count is 271 by default. This count is configurable and can be changed using the system property hazelcast.partition.count
. Please see the System Properties section.
Now, let's create a second node by running the above code again. This will create a cluster with 2 nodes. This is also where backups of entries are created; remember the backup partitions mentioned in the Hazelcast Overview section. The following illustration shows two nodes and how the data and its backup is distributed.
As you see, when a new member joins the cluster, it takes ownership and loads some of the data in the cluster. Eventually, it will carry almost "(1/n *
total-data) + backups" of the data, reducing the load on other nodes.
HazelcastInstance::getMap
returns an instance of com.hazelcast.core.IMap
which extends
the java.util.concurrent.ConcurrentMap
interface. Methods like
ConcurrentMap.putIfAbsent(key,value)
and ConcurrentMap.replace(key,value)
can be used
on the distributed map, as shown in the example below.
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import java.util.concurrent.ConcurrentMap;
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
Customer getCustomer( String id ) {
ConcurrentMap<String, Customer> customers = hazelcastInstance.getMap( "customers" );
Customer customer = customers.get( id );
if (customer == null) {
customer = new Customer( id );
customer = customers.putIfAbsent( id, customer );
}
return customer;
}
public boolean updateCustomer( Customer customer ) {
ConcurrentMap<String, Customer> customers = hazelcastInstance.getMap( "customers" );
return ( customers.replace( customer.getId(), customer ) != null );
}
public boolean removeCustomer( Customer customer ) {
ConcurrentMap<String, Customer> customers = hazelcastInstance.getMap( "customers" );
return customers.remove( customer.getId(), customer );
}
All ConcurrentMap
operations such as put
and remove
might wait if the key is locked by another thread in the local or remote JVM. But, they will eventually return with success. ConcurrentMap
operations never throw a java.util.ConcurrentModificationException
.
Also see:
Hazelcast distributes map entries onto multiple JVMs (cluster members). Each JVM holds some portion of the data.
Distributed maps have 1 backup by default. If a member goes down, you do not lose data. Backup operations are synchronous, so when a map.put(key, value)
returns, it is guaranteed that the entry is replicated to one other node. For the reads, it is also guaranteed that map.get(key)
returns the latest value of the entry. Consistency is strictly enforced.
To provide data safety, Hazelcast allows you to specify the number of backup copies you want to have. That way, data on a JVM will be copied onto other JVM(s). You select the number of backup copies using the backup-count
property.
<hazelcast>
<map name="default">
<backup-count>1</backup-count>
</map>
</hazelcast>
When this count is 1, a map entry will have its backup on one other node in the cluster. If you set it to 2, then a map entry will have its backup on two other nodes. You can set it to 0 if you do not want your entries to be backed up, e.g. if performance is more important than backing up. The maximum value for the backup count is 6.
Hazelcast supports both synchronous and asynchronous backups. By default, backup operations are synchronous and configured with backup-count
. In this case, backup operations block operations until backups are successfully copied to backup nodes (or deleted from backup nodes in case of remove) and acknowledgements are received. Therefore, backups are updated before a put
operation is completed. Sync backup operations have a blocking cost which may lead to latency issues.
Asynchronous backups, on the other hand, do not block operations. They are fire & forget and do not require acknowledgements; the backup operations are performed at some point in time. Async backup is configured using the async-backup-count
property. An example is shown below.
<hazelcast>
<map name="default">
<backup-count>0</backup-count>
<async-backup-count>1</async-backup-count>
</map>
</hazelcast>
NOTE: Backups increase memory usage since they are also kept in memory.
NOTE: A map can have both sync and aysnc backups at the same time.
By default, Hazelcast has one sync backup copy. If backup-count
is set to more than 1, then each member will carry both owned entries and backup copies of other members. So for the map.get(key)
call, it is possible that the calling member has a backup copy of that key. By default, map.get(key)
will always read the value from the actual owner of the key for consistency.
It is possible to enable backup reads (read local backup entries) by setting the value of the read-backup-data
property to true. Its default value is false for strong consistency. Enabling backup reads can improve performance.
<hazelcast>
<map name="default">
<backup-count>0</backup-count>
<async-backup-count>1</async-backup-count>
<read-backup-data>true</read-backup-data>
</map>
</hazelcast>
This feature is available when there is at least 1 sync or async backup.
Unless you delete the map entries manually or use an eviction policy, they will remain in the map. Hazelcast supports policy based eviction for distributed maps. Currently supported policies are LRU (Least Recently Used) and LFU (Least Frequently Used).
Map eviction works based on the size of a partition. For example, once you specify a size using the PER_NODE
attribute for max-size
(please see Configuring Map Eviction), Hazelcast internally calculates the maximum size for every partition. Eviction process starts according to this calculated per-partition maximum size when you try to put an entry. Below section gives an example scenario.
Assume that you have the following figures:
max-size
(PER_NODE): 20000eviction-percentage
(please see Configuring Map Eviction): 10%The total number of entries here is 20000 (partition count * entry count for each partition). This means you are at the eviction threshold since you set the max-size
to 20000. When you try to put an entry:
max-size
).As a result of this eviction process, when you check the size of your map, it is ~19990 (20000 - ~10). After this eviction, subsequent put operations will not trigger the next eviction until the map size is again close to the max-size
.
NOTE: Above scenario is just an example to describe how the eviction process works. Hazelcast finds the most optimum number of entries to be evicted according to your cluster size and selected policy.
The following is an example declarative configuration for map eviction.
<hazelcast>
<map name="default">
...
<time-to-live-seconds>0</time-to-live-seconds>
<max-idle-seconds>0</max-idle-seconds>
<eviction-policy>LRU</eviction-policy>
<max-size policy="PER_NODE">5000</max-size>
<eviction-percentage>25</eviction-percentage>
<min-eviction-check-millis>100</min-eviction-check-millis>
...
</map>
</hazelcast>
Let's describe each element.
time-to-live
: Maximum time in seconds for each entry to stay in the map. If it is not 0, entries that are older than this time and not updated for this time are evicted automatically. Valid values are integers between 0 and Integer.MAX VALUE
. Default value is 0, which means infinite. If it is not 0, entries are evicted regardless of the set eviction-policy
. max-idle-seconds
: Maximum time in seconds for each entry to stay idle in the map. Entries that are idle for more than this time are evicted automatically. An entry is idle if no get
, put
or containsKey
is called. Valid values are integers between 0 and Integer.MAX VALUE
. Default value is 0, which means infinite.eviction-policy
: Valid values are described below.
max-size
will be ignored. You still can combine it with time-to-live-seconds
and max-idle-seconds
.max-size
: Maximum size of the map. When maximum size is reached, the map is evicted based on the policy defined. Valid values are integers between 0 and Integer.MAX VALUE
. Default value is 0. If you want max-size
to work, set the eviction-policy
property to a value other than NONE. Its attributes are described below.
PER_NODE
: Maximum number of map entries in each JVM. This is the default policy.
<max-size policy="PER_NODE">5000</max-size>
PER_PARTITION
: Maximum number of map entries within each partition. Storage size depends on the partition count in a JVM. This attribute should not be used often. Avoid using this attribute with a small cluster: if the cluster is small it will be hosting more partitions, and therefore map entries, than that of a larger cluster. Thus, for a small cluster, eviction of the entries will decrease performance (the number of entries is large).
<max-size policy="PER_PARTITION">27100</max-size>
USED_HEAP_SIZE
: Maximum used heap size in megabytes for each JVM.
<max-size policy="USED_HEAP_SIZE">4096</max-size>
USED_HEAP_PERCENTAGE
: Maximum used heap size percentage for each JVM. If, for example, JVM is configured to have 1000 MB and this value is 10, then the map entries will be evicted when used heap size exceeds 100 MB.
<max-size policy="USED_HEAP_PERCENTAGE">10</max-size>
FREE_HEAP_SIZE
: Minimum free heap size in megabytes for each JVM.
<max-size policy="FREE_HEAP_SIZE">512</max-size>
FREE_HEAP_PERCENTAGE
: Minimum free heap size percentage for each JVM. If, for example, JVM is configured to have 1000 MB and this value is 10, then the map entries will be evicted when free heap size is below 100 MB.
<max-size policy="FREE_HEAP_PERCENTAGE">10</max-size>
eviction-percentage
: When max-size
is reached, the specified percentage of the map will be evicted. For example, if set to 25, 25% of the entries will be evicted. Setting this property to a smaller value will cause eviction of a smaller number of map entries. Therefore, if map entries are inserted frequently, smaller percentage values may lead to overheads. Valid values are integers between 0 and 100. The default value is 25.
min-eviction-check-millis
: The minimum time in milliseconds which should elapse before checking whether a partition of the map is evictable or not. In other terms, this property specifies the frequency of the eviction process. The default value is 100. Setting it to 0 (zero) makes the eviction process run for every put operation. NOTE: When map entries are inserted frequently, the property min-eviction-check-millis
should be set to a number lower than the insertion period in order not to let any entry escape from the eviction.
<map name="documents">
<max-size policy="PER_NODE">10000</max-size>
<eviction-policy>LRU</eviction-policy>
<max-idle-seconds>60</max-idle-seconds>
</map>
In the above sample, documents
map starts to evict its entries from a member when the map size exceeds 10000 in that member. Then, the entries least recently used will be evicted. The entries not used for more than 60 seconds will be evicted as well.
The eviction policies and configurations explained above apply to all the entries of a map. The entries that meet the specified eviction conditions are evicted.
But you may want to evict some specific map entries. In this case, you can use the ttl
and timeunit
parameters of the method map.put()
. A sample code line is given below.
myMap.put( "1", "John", 50, TimeUnit.SECONDS )
The map entry with the key "1" will be evicted 50 seconds after it is put into myMap
.
The method evictAll()
evicts all keys from the map except the locked ones. If a MapStore is defined for the map, deleteAll
is not called by evictAll
. If you want to call the method deleteAll
, use clear()
.
A sample is given below.
public class EvictAll {
public static void main(String[] args) {
final int numberOfKeysToLock = 4;
final int numberOfEntriesToAdd = 1000;
HazelcastInstance node1 = Hazelcast.newHazelcastInstance();
HazelcastInstance node2 = Hazelcast.newHazelcastInstance();
IMap<Integer, Integer> map = node1.getMap(EvictAll.class.getCanonicalName());
for (int i = 0; i < numberOfEntriesToAdd; i++) {
map.put(i, i);
}
for (int i = 0; i < numberOfKeysToLock; i++) {
map.lock(i);
}
// should keep locked keys and evict all others.
map.evictAll();
System.out.printf("# After calling evictAll...\n");
System.out.printf("# Expected map size\t: %d\n", numberOfKeysToLock);
System.out.printf("# Actual map size\t: %d\n", map.size());
}
}
NOTE: Only EVICT_ALL event is fired for any registered listeners.
IMap has an in-memory-format
configuration option. By default, Hazelcast stores data into memory in binary (serialized) format. But sometimes, it can be efficient to store the entries in their object form, especially in cases of local processing like entry processor and queries. By setting in-memory-format
in map's configuration, you can decide how the data will be stored in memory. You have the following format options.
BINARY
(default): This is the default option. The data will be stored in serialized binary format. You can use this option if you mostly perform regular map operations, such as put
and get
.
OBJECT
: The data will be stored in deserialized form. This configuration is good for maps where entry processing and queries form the majority of all operations and the objects are complex ones, making the serialization cost respectively high. By storing objects, entry processing will not contain the deserialization cost.
Regular operations like get
rely on the object instance. When the OBJECT
format is used and a get
is performed, the map does not return the stored instance, but creates a clone. Therefore, this whole get
operation includes a serialization first on the node owning the instance, and then a deserialization on the node calling the instance. When the BINARY
format is used, only a deserialization is required; this is faster.
Similarly, a put
operation is faster when the BINARY
format is used. If the format was OBJECT
, map would create a clone of the instance, and there would first a serialization and then deserialization. When BINARY is used, only a deserialization is needed.
NOTE: If a value is stored in OBJECT
format, a change on a returned value does not affect the stored instance. In this case, the returned instance is not the actual one but a clone. Therefore, changes made on an object after it is returned will not reflect on the actual stored data. Similarly, when a value is written to a map and the value is stored in OBJECT
format, it will be a copy of the put
value. Therefore, changes made on the object after it is stored will not reflect on the stored data.
Hazelcast allows you to load and store the distributed map entries from/to a persistent data store such as a relational database. To do this, you can use Hazelcast's MapStore
and MapLoader
interfaces.
When you provide a MapLoader
implementation and request an entry (IMap.get()
) that does not exist in memory, MapLoader
's load
or loadAll
methods will load that entry from the data store. This loaded entry is placed into the map and will stay there until it is removed or evicted.
When a MapStore
implementation is provided, an entry is also put into a user defined data store.
NOTE: Data store needs to be a centralized system that is accessible from all Hazelcast Nodes. Persistence to local file system is not supported.
NOTE: Also note that, the MapStore
interface extends the MapLoader
interface as you can see in the interface code.
Following is a MapStore
example.
public class PersonMapStore implements MapStore<Long, Person> {
private final Connection con;
public PersonMapStore() {
try {
con = DriverManager.getConnection("jdbc:hsqldb:mydatabase", "SA", "");
con.createStatement().executeUpdate(
"create table if not exists person (id bigint, name varchar(45))");
} catch (SQLException e) {
throw new RuntimeException(e);
}
}
public synchronized void delete(Long key) {
System.out.println("Delete:" + key);
try {
con.createStatement().executeUpdate(
format("delete from person where id = %s", key));
} catch (SQLException e) {
throw new RuntimeException(e);
}
}
public synchronized void store(Long key, Person value) {
try {
con.createStatement().executeUpdate(
format("insert into person values(%s,'%s')", key, value.name));
} catch (SQLException e) {
throw new RuntimeException(e);
}
}
public synchronized void storeAll(Map<Long, Person> map) {
for (Map.Entry<Long, Person> entry : map.entrySet())
store(entry.getKey(), entry.getValue());
}
public synchronized void deleteAll(Collection<Long> keys) {
for (Long key : keys) delete(key);
}
public synchronized Person load(Long key) {
try {
ResultSet resultSet = con.createStatement().executeQuery(
format("select name from person where id =%s", key));
try {
if (!resultSet.next()) return null;
String name = resultSet.getString(1);
return new Person(name);
} finally {
resultSet.close();
}
} catch (SQLException e) {
throw new RuntimeException(e);
}
}
public synchronized Map<Long, Person> loadAll(Collection<Long> keys) {
Map<Long, Person> result = new HashMap<Long, Person>();
for (Long key : keys) result.put(key, load(key));
return result;
}
public Iterable<Long> loadAllKeys() {
return null;
}
}
NOTE: During the initial loading process, MapStore uses a thread different than the partition threads that is used by the ExecutorService. After the initialization is completed, the map.get
method looks up any inexistent value from the database in a partition thread or the map.put
method looks up the database to return the previously associated value for a key also in a partition thread.
RELATED INFORMATION
For more MapStore/MapLoader code samples please see here.
Hazelcast supports read-through, write-through, and write-behind persistence modes which are explained in below subsections.
If an entry does not exist in the memory when an application asks for it, Hazelcast asks your loader implementation to load that entry from the data store. If the entry exists there, the loader implementation gets it, hands it to Hazelcast, and Hazelcast puts it into the memory. This is read-through persistence mode.
MapStore
can be configured to be write-through by setting the write-delay-seconds
property to 0. This means the entries will be put to the data store synchronously.
In this mode, when the map.put(key,value)
call returns:
MapStore.store(key,value)
is successfully called so the entry is persisted.backup-count
is greater than 0).The same behavior goes for a map.remove(key)
call. The only difference is that MapStore.delete(key)
is called when the entry will be deleted.
If MapStore
throws an exception, then the exception will be propagated back to the original put
or remove
call in the form of RuntimeException
.
You can configure MapStore
as write-behind by setting the write-delay-seconds
property to a value bigger than 0. This means the modified entries will be put to the data store asynchronously after a configured delay.
NOTE: In write-behind mode, by default Hazelcast coalesces updates on a specific key, i.e. applies only the last update on it. But, you can set MapStoreConfig#setWriteCoalescing
to FALSE
and you can store all updates performed on a key to the data store.
NOTE: When you set MapStoreConfig#setWriteCoalescing
to FALSE
, after you reached per-node maximum write-behind-queue capacity, subsequent put operations will fail with ReachedMaxSizeException
. This exception will be thrown to prevent uncontrolled grow of write-behind queues. You can set per node maximum capacity with GroupProperty#MAP_WRITE_BEHIND_QUEUE_CAPACITY
.
In this mode, when the map.put(key,value)
call returns:
backup-count
is greater than 0).write-delay-seconds
, it can be persisted with MapStore.store(key,value)
call.The same behavior goes for the map.remove(key)
, the only difference is that MapStore.delete(key)
is called when the entry will be deleted.
If MapStore
throws an exception, then Hazelcast tries to store the entry again. If the entry still cannot be stored, a log message is printed and the entry is re-queued.
For batch write operations, which are only allowed in write-behind mode, Hazelcast will call MapStore.storeAll(map)
and MapStore.deleteAll(collection)
to do all writes in a single call.
NOTE: If a map entry is marked as dirty, i.e. it is waiting to be persisted to the MapStore
in a write-behind scenario, the eviction process forces the entry to be stored. By this way, you will have control on the number of entries waiting to be stored, and thus you can prevent a possible OutOfMemory exception.
NOTE: MapStore
or MapLoader
implementations should not use Hazelcast Map/Queue/MultiMap/List/Set operations. Your implementation should only work with your data store. Otherwise, you may get into deadlock situations.
Here is a sample configuration:
<hazelcast>
...
<map name="default">
...
<map-store enabled="true">
<class-name>com.hazelcast.examples.DummyStore</class-name>
<write-delay-seconds>60</write-delay-seconds>
<write-batch-size>1000</write-batch-size>
<write-coalescing>true</write-coalescing>
</map-store>
</map>
</hazelcast>
RELATED INFORMATION
Please refer to the Map Store section for the full Map Store configuration description.
A configuration can be applied to more than one map using wildcards (see Using Wildcard), meaning that the configuration is shared among the maps. But MapStore
does not know which entries to store when there is one configuration applied to multiple maps. To overcome this, Hazelcast provides the MapStoreFactory
interface.
Using the MapStoreFactory
interface, MapStore
s for each map can be created when a wildcard configuration is used. Sample code is shown below.
Config config = new Config();
MapConfig mapConfig = config.getMapConfig( "*" );
MapStoreConfig mapStoreConfig = mapConfig.getMapStoreConfig();
mapStoreConfig.setFactoryImplementation( new MapStoreFactory<Object, Object>() {
@Override
public MapLoader<Object, Object> newMapStore( String mapName, Properties properties ) {
return null;
}
});
If the configuration implements the MapLoaderLifecycleSupport
interface, then the user can initialize the MapLoader
implementation with the given map name, configuration properties, and the Hazelcast instance. See the following example code.
public interface MapLoaderLifecycleSupport {
/**
* Initializes this MapLoader implementation. Hazelcast will call
* this method when the map is first used on the
* HazelcastInstance. Implementation can
* initialize required resources for the implementing
* mapLoader such as reading a config file and/or creating
* database connection.
*/
void init( HazelcastInstance hazelcastInstance, Properties properties, String mapName );
/**
* Hazelcast will call this method before shutting down.
* This method can be overridden to cleanup the resources
* held by this map loader implementation, such as closing the
* database connections etc.
*/
void destroy();
}
You can use the MapLoader.loadAllKeys
API to pre-populate the in-memory map when the map is first touched/used. If MapLoader.loadAllKeys
returns NULL then nothing will be loaded. Your MapLoader.loadAllKeys
implementation can return all or some of the keys. For example, you may select and return only the hot
keys. MapLoader.loadAllKeys
is the fastest way of pre-populating the map since Hazelcast will optimize the loading process by having each node loading its owned portion of the entries.
The InitialLoadMode
configuration parameter in the class MapStoreConfig
has two values: LAZY
and EAGER
. If InitialLoadMode
is set to LAZY
, data is not loaded during the map creation. If it is set to EAGER
, the whole data is loaded while the map is created and everything becomes ready to use. Also, if you add indices to your map with the MapIndexConfig
class or the addIndex
method, then InitialLoadMode
is overridden and MapStoreConfig
behaves as if EAGER
mode is on.
Here is the MapLoader
initialization flow:
getMap()
is first called from any node, initialization will start depending on the value of InitialLoadMode
. If it is set to EAGER
, initialization starts. If it is set to LAZY
, initialization does not start but data is loaded each time a partition loading completes.MapLoader.loadAllKeys()
to get all your keys on one of the nodes.MapLoader.loadAll(keys)
.IMap.putTransient(key,value)
. NOTE: If the load mode is LAZY
and when the clear()
method is called (which triggers MapStore.deleteAll()
), Hazelcast will remove ONLY the loaded entries from your map and datastore. Since the whole data is not loaded for this case (LAZY
mode), please note that there may be still entries in your datastore.
NOTE: The return type of loadAllKeys()
is changed from Set
to Iterable
with the release of Hazelcast 3.5. MapLoader implementations from previous releases are also supported and do not need to be adapted.
If the number of keys to load is large, it is more efficient to load them incrementally than loading them all at once. To support incremental loading, MapLoader.loadAllKeys()
returns an Iterable
which can be lazily populated with results of a database query. Hazelcast iterates over the iterable and, while doing so, sends out the keys to their respective owner nodes. The Iterator
obtained from MapLoader.loadAllKeys()
may also implement the Closeable
interface in which case it is closed once the iteration is over. This is intended for releasing resources such as closing a JDBC result set.
The method loadAll
loads some or all keys into a data store in order to optimize the multiple load operations. The method has two signatures (i.e. the same method can take two different parameter lists). One signature loads the given keys and the other loads all keys. Please see the sample code below.
public class LoadAll {
public static void main(String[] args) {
final int numberOfEntriesToAdd = 1000;
final String mapName = LoadAll.class.getCanonicalName();
final Config config = createNewConfig(mapName);
final HazelcastInstance node = Hazelcast.newHazelcastInstance(config);
final IMap<Integer, Integer> map = node.getMap(mapName);
populateMap(map, numberOfEntriesToAdd);
System.out.printf("# Map store has %d elements\n", numberOfEntriesToAdd);
map.evictAll();
System.out.printf("# After evictAll map size\t: %d\n", map.size());
map.loadAll(true);
System.out.printf("# After loadAll map size\t: %d\n", map.size());
}
}
In some scenarios, you may need to modify the object after storing it into the map store.
For example, you can get an ID or version auto generated by your database and then you need to modify your object stored in the distributed map but not to break the sync between database and data grid. You can do that by implementing the PostProcessingMapStore
interface to put the modified object into the distributed map. That will cause an extra step of Serialization
, so use it only when needed. (This explanation is only valid when using the write-through
map store configuration.)
Here is an example of post processing map store:
class ProcessingStore implements MapStore<Integer, Employee>, PostProcessingMapStore {
@Override
public void store( Integer key, Employee employee ) {
EmployeeId id = saveEmployee();
employee.setId( id.getId() );
}
}
Note : Please be warned that if you are using a post processing map store in combination with entry processors, post-processed values will not be carried to backups.
Map entries in Hazelcast are partitioned across the cluster. Imagine that you are reading the key k
so many times and k
is owned by another member in your cluster. Each map.get(k)
will be a remote operation, meaning lots of network trips. If you have a map that is read-mostly, then you should consider creating a near cache for the map so that reads can be much faster and consume less network traffic. All these benefits do not come free. When using near cache, you should consider the following issues:
Near cache is highly recommended for the maps that are read-mostly. Here is a near cache configuration for a map:
<hazelcast>
...
<map name="my-read-mostly-map">
...
<near-cache>
<!--
Maximum size of the near cache. When max size is reached,
cache is evicted based on the policy defined.
Any integer between 0 and Integer.MAX_VALUE. 0 means
Integer.MAX_VALUE. Default is 0.
-->
<max-size>5000</max-size>
<!--
Maximum number of seconds for each entry to stay in the near cache. Entries that are
older than <time-to-live-seconds> will get automatically evicted from the near cache.
Any integer between 0 and Integer.MAX_VALUE. 0 means infinite. Default is 0.
-->
<time-to-live-seconds>0</time-to-live-seconds>
<!--
Maximum number of seconds each entry can stay in the near cache as untouched (not-read).
Entries that are not read (touched) more than <max-idle-seconds> value will get removed
from the near cache.
Any integer between 0 and Integer.MAX_VALUE. 0 means
Integer.MAX_VALUE. Default is 0.
-->
<max-idle-seconds>60</max-idle-seconds>
<!--
Valid values are:
NONE (no extra eviction, <time-to-live-seconds> may still apply),
LRU (Least Recently Used),
LFU (Least Frequently Used).
NONE is the default.
Regardless of the eviction policy used, <time-to-live-seconds> will still apply.
-->
<eviction-policy>LRU</eviction-policy>
<!--
Should the cached entries get evicted if the entries are changed (updated or removed).
true of false. Default is true.
-->
<invalidate-on-change>true</invalidate-on-change>
<!--
You may want also local entries to be cached.
This is useful when in memory format for near cache is different than the map's one.
By default it is disabled.
-->
<cache-local-entries>false</cache-local-entries>
</near-cache>
</map>
</hazelcast>
NOTE: Programmatically, near cache configuration is done by using the class NearCacheConfig. And this class is used both in the nodes and clients. In a client/server system, you must enable the near cache separately on the client, without needing to configure it on the server. For information on how to create a near cache on a client (native Java client), please see the Client Near Cache Configuration section. Please note that near cache configuration is specific to the node or client itself, a map in a node may not have near cache configured while the same map in a client may have.
NOTE: If you are using near cache, you should take into account that your hits to the keys in near cache are not reflected as hits to the original keys on the remote nodes; this has an impact on IMap's maximum idle seconds or time-to-live seconds expiration. Therefore, even there is a hit on a key in near cache, your original key on the remote node may expire.
NOTE: Near cache works only when you access data via map.get(k)
methods. Data returned using a predicate is not stored in the near cache
Hazelcast Distributed Map (IMap) is thread-safe to meet your thread safety requirements. When these requirements increase or you want to have more control on the concurrency, consider the following Hazelcast features and solutions.
Let's work on a sample case as shown below.
public class RacyUpdateMember {
public static void main( String[] args ) throws Exception {
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
IMap<String, Value> map = hz.getMap( "map" );
String key = "1";
map.put( key, new Value() );
System.out.println( "Starting" );
for ( int k = 0; k < 1000; k++ ) {
if ( k % 100 == 0 ) System.out.println( "At: " + k );
Value value = map.get( key );
Thread.sleep( 10 );
value.amount++;
map.put( key, value );
}
System.out.println( "Finished! Result = " + map.get(key).amount );
}
static class Value implements Serializable {
public int amount;
}
}
If the above code is run by more than one cluster member simultaneously, there will be likely a race condition. You can solve this with Hazelcast.
One way to solve the race issue is the lock mechanism provided by Hazelcast distributed map, i.e. the map.lock
and map.unlock
methods. You simply lock the entry until you are finished with it. See the below sample code.
public class PessimisticUpdateMember {
public static void main( String[] args ) throws Exception {
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
IMap<String, Value> map = hz.getMap( "map" );
String key = "1";
map.put( key, new Value() );
System.out.println( "Starting" );
for ( int k = 0; k < 1000; k++ ) {
map.lock( key );
try {
Value value = map.get( key );
Thread.sleep( 10 );
value.amount++;
map.put( key, value );
} finally {
map.unlock( key );
}
}
System.out.println( "Finished! Result = " + map.get( key ).amount );
}
static class Value implements Serializable {
public int amount;
}
}
The IMap lock will automatically be collected by the garbage collector when the lock is released and no other waiting conditions exist on the lock.
The IMap lock is reentrant, but it does not support fairness.
Another way to solve the race issue can be acquiring a predictable Lock
object from Hazelcast. This way, every value in the map can be given a lock or you can create a stripe of locks.
The Hazelcast way of optimistic locking is to use the map.replace
method. See the below sample code.
public class OptimisticMember {
public static void main( String[] args ) throws Exception {
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
IMap<String, Value> map = hz.getMap( "map" );
String key = "1";
map.put( key, new Value() );
System.out.println( "Starting" );
for ( int k = 0; k < 1000; k++ ) {
if ( k % 10 == 0 ) System.out.println( "At: " + k );
for (; ; ) {
Value oldValue = map.get( key );
Value newValue = new Value( oldValue );
Thread.sleep( 10 );
newValue.amount++;
if ( map.replace( key, oldValue, newValue ) )
break;
}
}
System.out.println( "Finished! Result = " + map.get( key ).amount );
}
static class Value implements Serializable {
public int amount;
public Value() {
}
public Value( Value that ) {
this.amount = that.amount;
}
public boolean equals( Object o ) {
if ( o == this ) return true;
if ( !( o instanceof Value ) ) return false;
Value that = ( Value ) o;
return that.amount == this.amount;
}
}
}
NOTE: Above sample code is intentionally broken.
Depending on the locking requirements, one locking strategy can be picked.
Optimistic locking is better for mostly read only systems. It has a performance boost over pessimistic locking.
Pessimistic locking is good if there are lots of updates on the same key. It is more robust than optimistic locking from the perspective of data consistency.
In Hazelcast, use IExecutorService
to submit a task to a key owner, or to a member or members. This is the recommended way to perform task executions that use pessimistic or optimistic locking techniques. IExecutorService
will have less network hops and less data over wire, and tasks will be executed very near to the data. Please refer to the Data Affinity section.
The ABA problem occurs in environments when a shared resource is open to change by multiple threads. Even if one thread sees the same value for a particular key in consecutive reads, it does not mean nothing has changed between the reads. Another thread may come and change the value, do work, and change the value back, but the first thread can think that nothing has changed.
To prevent these kind of problems, one solution is to use a version number and to check it before any write to be sure that nothing has changed between consecutive reads. Although all the other fields will be equal, the version field will prevent objects from being seen as equal. This is the optimistic locking strategy, and it is used in environments which do not expect intensive concurrent changes on a specific key.
In Hazelcast, you can apply optimistic locking strategy with the map replace
method. This method compares values in object or data forms depending on the in-memory format configuration. If the values are equal, it replaces the old value with the new one. If you want to use your defined equals
method, in-memory format should be Object
. Otherwise, Hazelcast serializes objects to binary forms and compares them.
Hazelcast keeps extra information about each map entry, such as creation time, last update time, last access time, number of hits, and version. This information is exposed to the developer via a IMap.getEntryView(key)
call. Here is an example:
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.EntryView;
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
EntryView entry = hz.getMap( "quotes" ).getEntryView( "1" );
System.out.println ( "size in memory : " + entry.getCost() );
System.out.println ( "creationTime : " + entry.getCreationTime() );
System.out.println ( "expirationTime : " + entry.getExpirationTime() );
System.out.println ( "number of hits : " + entry.getHits() );
System.out.println ( "lastAccessedTime: " + entry.getLastAccessTime() );
System.out.println ( "lastUpdateTime : " + entry.getLastUpdateTime() );
System.out.println ( "version : " + entry.getVersion() );
System.out.println ( "key : " + entry.getKey() );
System.out.println ( "value : " + entry.getValue() );
You can listen to map-wide or entry-based events by implementing a MapListener
sub-interface.
A map-wide event is fired as a result of a map-wide operation: for
example, IMap#clear
or IMap#evictAll
.
An entry-based event is fired after the operations that affect a
specific entry: for example, IMap#remove
or IMap#evict
.
Let's take a look at the following code sample. To catch an event, you should explicitly
implement a corresponding sub-interface of a MapListener
,
such as EntryAddedListener
or MapClearedListener
.
NOTE: EntryListener
interface still can be implemented,
we kept that as is due to backward compatibility reasons. However, if you need to listen to a
different event which is not available in the EntryListener
interface, you should also
implement a relevant MapListener
sub-interface.
public class Listen {
public static void main( String[] args ) {
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
IMap<String, String> map = hz.getMap( "somemap" );
map.addEntryListener( new MyEntryListener(), true );
System.out.println( "EntryListener registered" );
}
static class MyEntryListener implements EntryAddedListener<String, String>,
EntryRemovedListener<String, String>,
EntryUpdatedListener<String, String>,
EntryEvictedListener<String, String> ,
MapEvictedListener,
MapClearedListener {
@Override
public void entryAdded( EntryEvent<String, String> event ) {
System.out.println( "Entry Added:" + event );
}
@Override
public void entryRemoved( EntryEvent<String, String> event ) {
System.out.println( "Entry Removed:" + event );
}
@Override
public void entryUpdated( EntryEvent<String, String> event ) {
System.out.println( "Entry Updated:" + event );
}
@Override
public void entryEvicted( EntryEvent<String, String> event ) {
System.out.println( "Entry Evicted:" + event );
}
@Override
public void mapEvicted( MapEvent event ) {
System.out.println( "Map Evicted:" + event );
}
@Override
public void mapCleared( MapEvent event ) {
System.out.println( "Map Cleared:" + event );
}
}
}
Now, let's perform some modifications on the map entries using the following example code.
public class Modify {
public static void main( String[] args ) {
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
IMap<String, String> map = hz.getMap( "somemap");
String key = "" + System.nanoTime();
String value = "1";
map.put( key, value );
map.put( key, "2" );
map.delete( key );
}
}
If you execute the Listen
class and then the Modify
class, you get the following output
produced by the Listen
class.
entryAdded:EntryEvent {Address[192.168.1.100]:5702} key=251359212222282,
oldValue=null, value=1, event=ADDED, by Member [192.168.1.100]:5702
entryUpdated:EntryEvent {Address[192.168.1.100]:5702} key=251359212222282,
oldValue=1, value=2, event=UPDATED, by Member [192.168.1.100]:5702
entryRemoved:EntryEvent {Address[192.168.1.100]:5702} key=251359212222282,
oldValue=2, value=2, event=REMOVED, by Member [192.168.1.100]:5702
public class MyEntryListener implements EntryListener{
private Executor executor = Executors.newFixedThreadPool(5);
@Override
public void entryAdded(EntryEvent event) {
executor.execute(new DoSomethingWithEvent(event));
}
...
A map listener runs on the event threads that are also used by the other listeners: for example, the collection listeners and pub/sub message listeners. This means that the entry listeners can access other partitions. Consider this when you run long tasks, since listening to those tasks may cause the other map/event listeners to starve.
You can listen to MapPartitionLostEvent
instances by registering an implementation
of MapPartitionLostListener
, which is also a sub-interface of MapListener
.
Let`s consider the following example code:
public static void main(String[] args) {
Config config = new Config();
config.getMapConfig("map").setBackupCount(1); // might lose data if any node crashes
HazelcastInstance instance = HazelcastInstanceFactory.newHazelcastInstance(config);
IMap<Object, Object> map = instance1.getMap("map");
map.put(0, 0);
map.addPartitionLostListener(new MapPartitionLostListener() {
@Override
public void partitionLost(MapPartitionLostEvent event) {
System.out.println(event);
}
});
}
Within this example code, a MapPartitionLostListener
implementation is registered to a map
that is configured with 1 backup. For this particular map and any of the partitions in the
system, if the partition owner node and its first backup node crash simultaneously, the
given MapPartitionLostListener
receives a
corresponding MapPartitionLostEvent
. If only a single node crashes in the cluster,
there will be no MapPartitionLostEvent
fired for this map since backups for the partitions
owned by the crashed node are kept on other nodes.
Please refer to the Partition Lost Listener section for more information about partition lost detection and partition lost events.
You can add intercept operations and then execute your own business logic synchronously blocking the operations. You can change the returned value from a get
operation, change the value to be put
or cancel
operations by throwing an exception.
Interceptors are different from listeners. With listeners, you take an action after the operation has been completed. Interceptor actions are synchronous and you can alter the behavior of operation, change the values, or totally cancel it.
Map interceptors are chained, so adding the same interceptor multiple times to the same map can result in duplicate effects. This can easily happen when the interceptor is added to the map at node initialization, so that each node adds the same interceptor. When adding the interceptor in this way, be sure that the hashCode()
method is implemented to return the same value for every instance of the interceptor. It is not strictly necessary, but it is a good idea to also implement equals()
as this will ensure that the map interceptor can be removed reliably.
IMap API has two methods for adding and removing an interceptor to the map,addInterceptor
and removeInterceptor
:
/**
* Adds an interceptor for this map. Added interceptor will intercept operations
* and execute user defined methods and will cancel operations if user defined method throw exception.
*
*
* @param interceptor map interceptor
* @return id of registered interceptor
*/
String addInterceptor( MapInterceptor interceptor );
/**
* Removes the given interceptor for this map. So it will not intercept operations anymore.
*
*
* @param id registration id of map interceptor
*/
void removeInterceptor( String id );
Here is the MapInterceptor
interface:
public interface MapInterceptor extends Serializable {
/**
* Intercept the get operation before it returns a value.
* Return another object to change the return value of get(..)
* Returning null will cause the get(..) operation to return the original value,
* namely return null if you do not want to change anything.
*
*
* @param value the original value to be returned as the result of get(..) operation
* @return the new value that will be returned by get(..) operation
*/
Object interceptGet( Object value );
/**
* Called after get(..) operation is completed.
*
*
* @param value the value returned as the result of get(..) operation
*/
void afterGet( Object value );
/**
* Intercept put operation before modifying map data.
* Return the object to be put into the map.
* Returning null will cause the put(..) operation to operate as expected,
* namely no interception. Throwing an exception will cancel the put operation.
*
*
* @param oldValue the value currently in map
* @param newValue the new value to be put
* @return new value after intercept operation
*/
Object interceptPut( Object oldValue, Object newValue );
/**
* Called after put(..) operation is completed.
*
*
* @param value the value returned as the result of put(..) operation
*/
void afterPut( Object value );
/**
* Intercept remove operation before removing the data.
* Return the object to be returned as the result of remove operation.
* Throwing an exception will cancel the remove operation.
*
*
* @param removedValue the existing value to be removed
* @return the value to be returned as the result of remove operation
*/
Object interceptRemove( Object removedValue );
/**
* Called after remove(..) operation is completed.
*
*
* @param value the value returned as the result of remove(..) operation
*/
void afterRemove( Object value );
}
Example Usage:
public class InterceptorTest {
@Test
public void testMapInterceptor() throws InterruptedException {
HazelcastInstance hazelcastInstance1 = Hazelcast.newHazelcastInstance();
HazelcastInstance hazelcastInstance2 = Hazelcast.newHazelcastInstance();
IMap<Object, Object> map = hazelcastInstance1.getMap( "testMapInterceptor" );
SimpleInterceptor interceptor = new SimpleInterceptor();
map.addInterceptor( interceptor );
map.put( 1, "New York" );
map.put( 2, "Istanbul" );
map.put( 3, "Tokyo" );
map.put( 4, "London" );
map.put( 5, "Paris" );
map.put( 6, "Cairo" );
map.put( 7, "Hong Kong" );
try {
map.remove( 1 );
} catch ( Exception ignore ) {
}
try {
map.remove( 2 );
} catch ( Exception ignore ) {
}
assertEquals( map.size(), 6) ;
assertEquals( map.get( 1 ), null );
assertEquals( map.get( 2 ), "ISTANBUL:" );
assertEquals( map.get( 3 ), "TOKYO:" );
assertEquals( map.get( 4 ), "LONDON:" );
assertEquals( map.get( 5 ), "PARIS:" );
assertEquals( map.get( 6 ), "CAIRO:" );
assertEquals( map.get( 7 ), "HONG KONG:" );
map.removeInterceptor( interceptor );
map.put( 8, "Moscow" );
assertEquals( map.get( 8 ), "Moscow" );
assertEquals( map.get( 1 ), null );
assertEquals( map.get( 2 ), "ISTANBUL" );
assertEquals( map.get( 3 ), "TOKYO" );
assertEquals( map.get( 4 ), "LONDON" );
assertEquals( map.get( 5 ), "PARIS" );
assertEquals( map.get( 6 ), "CAIRO" );
assertEquals( map.get( 7 ), "HONG KONG" );
}
static class SimpleInterceptor implements MapInterceptor, Serializable {
@Override
public Object interceptGet( Object value ) {
if (value == null)
return null;
return value + ":";
}
@Override
public void afterGet( Object value ) {
}
@Override
public Object interceptPut( Object oldValue, Object newValue ) {
return newValue.toString().toUpperCase();
}
@Override
public void afterPut( Object value ) {
}
@Override
public Object interceptRemove( Object removedValue ) {
if(removedValue.equals( "ISTANBUL" ))
throw new RuntimeException( "you can not remove this" );
return removedValue;
}
@Override
public void afterRemove( Object value ) {
// do something
}
}
}
It is very easy to trigger an out of memory exception (OOME) with query based map methods, especially with large clusters or heap sizes. For example, on a 5 node cluster with 10 GB of data and 25 GB heap size per node, a single call of IMap.entrySet()
fetches 50 GB of data and crashes the calling instance.
A call of IMap.values()
may return too much data for a single node. This can also happen with a real query and an unlucky choice of predicates, especially when the parameters are chosen by a user of your application.
To prevent this, you can configure a maximum result size limit for query based operations. This is not a limit like SELECT * FROM map LIMIT 100
, which you can achieve by a Paging Predicate. A maximum result size limit for query based operations is meant to be a last line of defense to prevent your nodes from retrieving more data than they can handle.
The Hazelcast component which calculates this limit is the QueryResultSizeLimiter
.
If the QueryResultSizeLimiter
is activated, it calculates a result size limit per partition. Each QueryOperation
runs on all partitions of a node, so it collects result entries as long as the node limit is not exceeded. If that happens, a QueryResultSizeExceededException
is thrown and propagated to the calling instance.
This feature depends on an equal distribution of the data on the cluster nodes to calculate the result size limit per node. Therefore, there is a minimum value defined in QueryResultSizeLimiter.MINIMUM_MAX_RESULT_LIMIT
. Configured values below the minimum will be increased to the minimum.
In addition to the distributed result size check in the QueryOperations
, there is a local pre-check on the calling instance. If you call the method from a client, the pre-check is executed on the member which invokes the QueryOperations
.
Since the local pre-check can increase the latency of a QueryOperation
you can configure how many local partitions should be considered for the pre-check or you can deactivate the feature completely.
Besides the designated query operations, there are other operations which use predicates internally. Those method calls will throw the QueryResultSizeExceededException
as well. Please see the following matrix to see the methods that are covered by the query result size limit.
The query result size limit is configured via the following system properties.
hazelcast.query.result.size.limit
hazelcast.query.max.local.partition.limit.for.precheck
Please refer to the System Properties section for explanations of these properties.
Hazelcast distributed queue is an implementation of java.util.concurrent.BlockingQueue
. Being distributed, it enables all cluster members to interact with it. Using Hazelcast distributed queue, you can add an item in one machine and remove it from another one.
import com.hazelcast.core.Hazelcast;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.TimeUnit;
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
BlockingQueue<MyTask> queue = hazelcastInstance.getQueue( "tasks" );
queue.put( new MyTask() );
MyTask task = queue.take();
boolean offered = queue.offer( new MyTask(), 10, TimeUnit.SECONDS );
task = queue.poll( 5, TimeUnit.SECONDS );
if ( task != null ) {
//process task
}
FIFO ordering will apply to all queue operations across the cluster. User objects (such as MyTask
in the example above) that are enqueued or dequeued have to be Serializable
.
Hazelcast distributed queue performs no batching while iterating over the queue. All items will be copied locally and iteration will occur locally.
The following sample code illustrates a producer and consumer connected by a distributed queue.
Let's put one integer on the queue every second, 100 integers total.
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.core.IQueue;
public class ProducerMember {
public static void main( String[] args ) throws Exception {
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
IQueue<Integer> queue = hz.getQueue( "queue" );
for ( int k = 1; k < 100; k++ ) {
queue.put( k );
System.out.println( "Producing: " + k );
Thread.sleep(1000);
}
queue.put( -1 );
System.out.println( "Producer Finished!" );
}
}
Producer
puts a -1 on the queue to show that the put
's are finished. Now, let's create a Consumer
class that take a message from this queue, as shown below.
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.core.IQueue;
public class ConsumerMember {
public static void main( String[] args ) throws Exception {
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
IQueue<Integer> queue = hz.getQueue( "queue" );
while ( true ) {
int item = queue.take();
System.out.println( "Consumed: " + item );
if ( item == -1 ) {
queue.put( -1 );
break;
}
Thread.sleep( 5000 );
}
System.out.println( "Consumer Finished!" );
}
}
As seen in the above sample code, Consumer
waits 5 seconds before it consumes the next message. It stops once it receives -1. Also note that Consumer
puts -1 back on the queue before the loop is ended.
When you first start Producer
and then start Consumer
, items produced on the queue will be consumed from the same queue.
From the above sample code, you can see that an item is produced every second, and consumed every 5 seconds. Therefore, the consumer keeps growing. To balance the produce/consume operation, let's start another consumer. By this way, consumption is distributed to these two consumers, as seen in the sample outputs below.
The second consumer is started. After a while, here is the first consumer output:
...
Consumed 13
Consumed 15
Consumer 17
...
Here is the second consumer output:
...
Consumed 14
Consumed 16
Consumer 18
...
In the case of a lot of producers and consumers for the queue, using a list of queues may solve the queue bottlenecks. In this case, be aware that the order of the messages being sent to different queues is not guaranteed. Since in most cases strict ordering is not important, a list of queues is a good solution.
NOTE: The items are taken from the queue in the same order they were put on the queue. However, if there is more than one consumer, this order is not guaranteed.
A bounded queue is a queue with a limited capacity. When the bounded queue is full, no more items can be put into the queue until some items are taken out.
A Hazelcast distributed queue can be turned into a bounded queue by setting the capacity limit using the max-size
property.
Queue capacity can be set using the max-size
property in the configuration, as shown below. max-size
specifies the maximum size of the queue. Once the queue size reaches this value, put
operations will be blocked until the queue size goes below max-size
, that happens when a consumer removes items from the queue.
Let's set 10 as the maximum size of our sample queue in the Sample Queue Code.
<hazelcast>
...
<queue name="queue">
<max-size>10</max-size>
</queue>
...
</hazelcast>
When the producer is started, 10 items are put into the queue and then the queue will not allow more put
operations. When the consumer is started, it will remove items from the queue. This means that the producer can put
more items into the queue until there are 10 items in the queue again, at which point put
operation again become blocked.
But in this sample code, the producer is 5 times faster than the consumer. It will effectively always be waiting for the consumer to remove items before it can put more on the queue. For this sample code, if maximum throughput was the goal, it would be a good option to start multiple consumers to prevent the queue from filling up.
Hazelcast allows you to load and store the distributed queue items from/to a persistent datastore using the interface QueueStore
. If queue store is enabled, each item added to the queue will also be stored at the configured queue store. When the number of items in the queue exceeds the memory limit, the subsequent items are persisted in the queue store, they are not stored in the queue memory.
QueueStore
interface enables you to store, load, and delete items with methods like store
, storeAll
, load
and delete
. The following example class includes all of the QueueStore
methods.
public class TheQueueStore implements QueueStore<Item> {
@Override
public void delete(Long key) {
System.out.println("delete");
}
@Override
public void store(Long key, Item value) {
System.out.println("store");
}
@Override
public void storeAll(Map<Long, Item> map) {
System.out.println("store all");
}
@Override
public void deleteAll(Collection<Long> keys) {
System.out.println("deleteAll");
}
@Override
public Item load(Long key) {
System.out.println("load");
return null;
}
@Override
public Map<Long, Item> loadAll(Collection<Long> keys) {
System.out.println("loadAll");
return null;
}
@Override
public Set<Long> loadAllKeys() {
System.out.println("loadAllKeys");
return null;
}
Item
must be serializable. Following is an example queue store configuration.
<queue-store>
<class-name>com.hazelcast.QueueStoreImpl</class-name>
<properties>
<property name="binary">false</property>
<property name="memory-limit">1000</property>
<property name="bulk-load">500</property>
</properties>
</queue-store>
Let's explain the properties.
Binary: By default, Hazelcast stores the queue items in serialized form in memory. Before it inserts the queue items into datastore, it deserializes them. But if you will not reach the queue store from an external application, you might prefer that the items be inserted in binary form. You can get rid of the de-serialization step; this would be a performance optimization. The binary feature is disabled by default.
Memory Limit: This is the number of items after which Hazelcast will store items only to datastore. For example, if the memory limit is 1000, then the 1001st item will be put only to datastore. This feature is useful when you want to avoid out-of-memory conditions. The default number for memory-limit
is 1000. If you want to always use memory, you can set it to Integer.MAX_VALUE
.
Bulk Load: When the queue is initialized, items are loaded from QueueStore
in bulks. Bulk load is the size of these bulks. By default, bulk-load
is 250.
An example declarative configuration is shown below.
<hazelcast>
...
<queue name="tasks">
<max-size>10</max-size>
<backup-count>1</backup-count>
<async-backup-count>1</async-backup-count>
<empty-queue-ttl>10</empty-queue-ttl>
</queue>
</hazelcast>
Hazelcast distributed queue has one synchronous backup by default. By having this backup, when a cluster member with a queue goes down, another member having the backups will continue. Therefore, no items are lost. You can define the count of synchronous backups using the backup-count
element in the declarative configuration. A queue can also have asynchronous backups, you can define the count using the async-backup-count
element.
The max-size
element defines the maximum size of the queue. You can use the empty-queue-ttl
element when you want to purge unused or empty queues after a period of time. If you define a value (time in seconds) for this element, then your queue will be destroyed if it stays empty or unused for the time you give.
RELATED INFORMATION
Please refer to the Queue Configuration section for a full description of Hazelcast Distributed Queue configuration.
Hazelcast MultiMap
is a specialized map where you can store multiple values under a single key. Just like any other distributed data structure implementation in Hazelcast, MultiMap
is distributed and thread-safe.
Hazelcast MultiMap
is not an implementation of java.util.Map
due to the difference in method signatures. It supports most features of Hazelcast Map except for indexing, predicates and MapLoader/MapStore. Yet, like Hazelcast Map, entries are almost evenly distributed onto all cluster members. When a new member joins the cluster, the same ownership logic used in the distributed map applies.
Let's write code that puts data into a MultiMap.
public class PutMember {
public static void main( String[] args ) {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
MultiMap <String , String > map = hazelcastInstance.getMultiMap( "map" );
map.put( "a", "1" );
map.put( "a", "2" );
map.put( "b", "3" );
System.out.println( "PutMember:Done" );
}
}
Now let's print the entries in this MultiMap.
public class PrintMember {
public static void main( String[] args ) {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
MultiMap <String, String > map = hazelcastInstance.getMultiMap( "map" );
for ( String key : map.keySet() ){
Collection <String > values = map.get( key );
System.out.println( "%s -> %s\n",key, values );
}
}
}
After you run the first code sample, run the PrintMember
sample. You will see the key a
has two values, as shown below.
b -> [3]
a -> [2, 1]
When using MultiMap, the collection type of the values can be either Set or List. You configure the collection type with the valueCollectionType
parameter. If you choose Set
, duplicate and null values are not allowed in your collection and ordering is irrelevant. If you choose List
, ordering is relevant and your collection can include duplicate and null values.
You can also enable statistics for your MultiMap with the statisticsEnabled
parameter. If you enable statisticsEnabled
, statistics can be retrieved with getLocalMultiMapStats()
method.
NOTE: Currently, eviction is not supported for the MultiMap data structure.
RELATED INFORMATION
Please refer to the MultiMap Configuration section for a full description of Hazelcast Distributed MultiMap configuration.
Hazelcast Set is a distributed and concurrent implementation of java.util.Set
.
java.util.HashSet
.import com.hazelcast.core.Hazelcast;
import java.util.Set;
import java.util.Iterator;
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
Set<Price> set = hazelcastInstance.getSet( "IBM-Quote-History" );
set.add( new Price( 10, time1 ) );
set.add( new Price( 11, time2 ) );
set.add( new Price( 12, time3 ) );
set.add( new Price( 11, time4 ) );
//....
Iterator<Price> iterator = set.iterator();
while ( iterator.hasNext() ) {
Price price = iterator.next();
//analyze
}
Hazelcast Set uses ItemListener
to listen to events which occur when items are added and removed.
import java.util.Queue;
import java.util.Map;
import java.util.Set;
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.ItemListener;
import com.hazelcast.core.EntryListener;
import com.hazelcast.core.EntryEvent;
public class Sample implements ItemListener {
public static void main( String[] args ) {
Sample sample = new Sample();
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
ISet<Price> set = hazelcastInstance.getSet( "default" );
set.addItemListener( sample, true );
Price price = new Price( 10, time1 )
set.add( price );
set.remove( price );
}
public void itemAdded( Object item ) {
System.out.println( "Item added = " + item );
}
public void itemRemoved( Object item ) {
System.out.println( "Item removed = " + item );
}
}
RELATED INFORMATION
To learn more about the configuration of listeners please refer to the Listener Configurations section.
RELATED INFORMATION
Please refer to the Set Configuration section for a full description of Hazelcast Distributed Set configuration.
Hazelcast List is similar to Hazelcast Set, but Hazelcast List also allows duplicate elements.
import com.hazelcast.core.Hazelcast;
import java.util.List;
import java.util.Iterator;
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
List<Price> list = hz.getList( "IBM-Quote-Frequency" );
list.add( new Price( 10 ) );
list.add( new Price( 11 ) );
list.add( new Price( 12 ) );
list.add( new Price( 11 ) );
list.add( new Price( 12 ) );
//....
Iterator<Price> iterator = list.iterator();
while ( iterator.hasNext() ) {
Price price = iterator.next();
//analyze
}
Hazelcast List uses ItemListener
to listen to events which occur when items are added and removed.
import java.util.Queue;
import java.util.Map;
import java.util.Set;
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.ItemListener;
import com.hazelcast.core.EntryListener;
import com.hazelcast.core.EntryEvent;
public class Sample implements ItemListener{
public static void main( String[] args ) {
Sample sample = new Sample();
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IList<Price> list = hazelcastInstance.getList( "default" );
list.addItemListener( sample, true );
Price price = new Price( 10, time1 )
list.add( price );
list.remove( price );
}
public void itemAdded( Object item ) {
System.out.println( "Item added = " + item );
}
public void itemRemoved( Object item ) {
System.out.println( "Item removed = " + item );
}
}
RELATED INFORMATION
To learn more about the configuration of listeners please refer to the Listener Configurations section.
RELATED INFORMATION
Please refer to the List Configuration section for a full description of Hazelcast Distributed List configuration.
Hazelcast Ringbuffer is a distributed data structure where the data is stored in a ring-like structure. You can think of it as a circular array with a certain capacity. Each Ringbuffer has a tail and a head. The tail is where the items are added and the head is where the items are overwritten or expired. You can reach each element in a Ringbuffer using a sequence ID, which is mapped to the elements between the head and tail (inclusive) of the Ringbuffer.
Reading from Ringbuffer is very simple. Just get the current head and start reading. The method readOne
returns the item at the
given sequence or blocks if no item is available. To read the next item, the sequence is incremented by one.
Ringbuffer<String> ringbuffer = hz.getRingbuffer("rb");
long sequence = ringbuffer.headSequence();
while(true){
String item = ringbuffer.readOne(sequence);
sequence++;
... process item
}
By exposing the sequence, you can now move the item from Ringbuffer as long as the item is still available. If it is not available
any longer, StaleSequenceException
is thrown.
Adding an item to Ringbuffer is also very easy:
Ringbuffer<String> ringbuffer = hz.getRingbuffer("rb");
ringbuffer.add("someitem")
The method add
returns the sequence of the inserted item and this value will always be unique. This can sometimes be used as a
very cheap way of generating unique IDs if you are already using Ringbuffer.
Hazelcast Ringbuffer can sometimes be a better alternative than an Hazelcast IQueue. Unlike IQueue, Ringbuffer does not remove the items, it only reads items using a certain position. There are many advantages using this approach:
queue.take()
is more expensive than a ringBuffer.read(...)
.By default, a Ringbuffer is configured with a capacity of 10000 items. Internally, an array is created with exactly that size. If a time-to-live is configured, then an array of longs is also created that stores the expiration time for every item. In a lot of cases, you may want to change this number to something that fits your needs better.
Below is a declarative configuration example of a Ringbuffer with a capacity of 2000 items.
<ringbuffer name="rb">
<capacity>2000</capacity>
</ringbuffer>
Hazelcast Ringbuffer is not a partitioned data structure in its current state; its data is stored in a single partition and the replicas are stored in another partition. Therefore, create a Ringbuffer that can safely fit in a single cluster member.
Hazelcast Ringbuffer has a single synchronous backup by default. This can be controlled just like most of the other Hazelcast distributed data structures by setting the sync and async backups. In the example below, a Ringbuffer is configured with 0 sync backups and 1 async backup:
<ringbuffer name="rb">
<backup-count>0</backup-count>
<async-backup-count>1</async-backup-count>
</ringbuffer>
An async backup will probably give you better performance. However, there is a chance that the item added is lost when the member owning the primary crashes before the replication could complete. You may want to consider batching methods if you need high performance but do not want to give up on consistency.
Hazelcast Ringbuffer can be configured with a time to live seconds. Using this setting, you can control how long the items remain in the Ringbuffer before they are expired. By default, the time to live is set to 0, meaning that unless the item is overwritten, it will remain in the Ringbuffer indefinitely. If a time to live is set and an item is added, then depending on the Overflow Policy, either the oldest item is overwritten, or the call is rejected.
In the example below, a Ringbuffer is configured with a time to live of 180 seconds.
<ringbuffer name="rb">
<time-to-live-seconds>180</time-to-live-seconds>
</ringbuffer>
Using the overflow policy, you can determine what to do if the oldest item in the Ringbuffer is not old enough to expire when more items than the configured RingBuffer capacity are being added. There are currently below options available:
OverflowPolicy.OVERWRITE
: The oldest item is overwritten. OverflowPolicy.FAIL
: The call is aborted. The methods that make use of the OverflowPolicy return -1
to indicate that adding
the item has failed. Overflow policy gives fine control on what to do if the Ringbuffer is full. The policy can also be used to apply a back pressure mechanism. The following example code shows the usage of an exponential backoff.
long sleepMs = 100;
for (; ; ) {
long result = ringbuffer.addAsync(item, OverflowPolicy.FAIL).get();
if (result != -1) {
break;
}
TimeUnit.MILLISECONDS.sleep(sleepMs);
sleepMs = min(5000, sleepMs * 2);
}
Hazelcast Ringbuffer can also be configured with an in-memory format which controls the format of stored items. By default, BINARY
is used,
meaning that the object is stored in a serialized form. You can select the OBJECT
in-memory format, which is useful when filtering is
applied or when the OBJECT
in-memory format has a smaller memory footprint than BINARY
.
In the declarative configuration example below, a Ringbuffer is configured with OBJECT
in-memory format:
<ringbuffer name="rb">
<in-memory-format>BINARY</in-memory-format>
</ringbuffer>
In the previous examples, the method ringBuffer.add()
is used to add an item to the Ringbuffer. The problem with this method
is that it always overwrites and that it does not support batching. Batching can have a huge
impact on the performance. That is why the method addAllAsync
is available.
Please see the following example code.
List<String> items = Arrays.asList("1","2","3");
ICompletableFuture<Long> f = rb.addAllAsync(items, OverflowPolicy.OVERWRITE);
f.get()
In the above case, three strings are added to the Ringbuffer using the policy OverflowPolicy.OVERWRITE
. Please see the Overflow Policy section
for more information.
In the previous example the readOne
was being used. It is simple but not very efficient for the following reasons:
That is why the method readManyAsync
is available.
Please see the following example code.
ICompletableFuture<ReadResultSet<E>> readManyAsync(
long startSequence,
int minCount,
int maxCount,
IFunction<E, Boolean> filter);
This call can read a batch of items and can filter items at the source. The meaning of the arguments are given below.
startSequence
: Sequence of the first item to read.minCount
: Minimum number of items to read. If you do not want to block, set it to 0. If you do want to block for at least one item,
set it to 1.maxCount
: Maximum number of the items to retrieve. Its value cannot exceed 1000.filter
: A function that accepts an item and checks if it should be returned. If no filtering should be applied, set it to null.A full example is given below.
long sequence = rb.headSequence();
for(;;) {
ICompletableFuture<ReadResultSet<String>> f = rb.readManyAsync(sequence, 1, 10, null);
ReadResultSet<String> rs = f.get();
for (String s : rs) {
System.out.println(s);
}
sequence+=rs.readCount();
}
Please take a careful look at how the sequence is being incremented. You cannot always rely on the number of items being returned if the items are filtered out.
Hazelcast Ringbuffer provides asynchronous methods for more powerful operations like batched reading with filtering or batched writing.
To make these methods synchronous, just call the method get()
on the returned future.
Please see the following example code.
ICompletableFuture f = ringbuffer.addAsync(item, OverflowPolicy.FAIL);
f.get();
However, the ICompletableFuture
can also be used to get notified when the operation has completed. Please see the example code when you want to
get notified when a batch of reads has completed.
ICompletableFuture<ReadResultSet<String>> f = rb.readManyAsync(sequence, min, max, someFilter);
f.andThen(new ExecutionCallback<ReadResultSet<String>>() {
@Override
public void onResponse(ReadResultSet<String> response) {
for (String s : response) {
System.out.println("Received:" + s);
}
}
@Override
public void onFailure(Throwable t) {
t.printStackTrace();
}
});
The advantage of this approach: The thread that is used for the call is not blocked till the response is returned.
The following shows the declarative configuration of a Ringbuffer called rb
. The configuration is modeled after Ringbuffer defaults.
<ringbuffer name="rb">
<capacity>10000</capacity>
<backup-count>1</backup-count>
<async-backup-count>0</async-backup-count>
<time-to-live-seconds>0</time-to-live-seconds>
<in-memory-format>BINARY</in-memory-format>
</ringbuffer>
You can also configure a Ringbuffer programmatically. The following is programmatic version of the above declarative configuration.
RingbufferConfig rbConfig = new RingbufferConfig("rb")
.setCapacity(10000)
.setBackupCount(1)
.setAsyncBackupCount(0)
.setTimeToLiveSeconds(0)
.setInMemoryFormat(InMemoryFormat.BINARY);
Config config = new Config();
config.addRingbufferConfig(rbConfig);
RELATED INFORMATION
Please refer to the Ringbuffer Configuration section for more information on configuring the Ringbuffer.
Hazelcast provides a distribution mechanism for publishing messages that are delivered to multiple subscribers. This is also known as a publish/subscribe (pub/sub) messaging model. Publishing and subscribing operations are cluster wide. When a member subscribes to a topic, it is actually registering for messages published by any member in the cluster, including the new members that joined after you add the listener.
NOTE: Publish operation is async. It does not wait for operations to run in remote nodes, it works as fire and forget.
import com.hazelcast.core.Topic;
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.MessageListener;
public class Sample implements MessageListener<MyEvent> {
public static void main( String[] args ) {
Sample sample = new Sample();
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
ITopic topic = hazelcastInstance.getTopic( "default" );
topic.addMessageListener( sample );
topic.publish( new MyEvent() );
}
public void onMessage( Message<MyEvent> message ) {
MyEvent myEvent = message.getMessageObject();
System.out.println( "Message received = " + myEvent.toString() );
if ( myEvent.isHeavyweight() ) {
messageExecutor.execute( new Runnable() {
public void run() {
doHeavyweightStuff( myEvent );
}
} );
}
}
// ...
private final Executor messageExecutor = Executors.newSingleThreadExecutor();
}
Topic has two statistic variables that you can query. These values are incremental and local to the member.
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
ITopic<Object> myTopic = hazelcastInstance.getTopic( "myTopicName" );
myTopic.getLocalTopicStats().getPublishOperationCount();
myTopic.getLocalTopicStats().getReceiveOperationCount();
getPublishOperationCount
and getReceiveOperationCount
returns the total number of published and received messages since the start of this node, respectively. Please note that these values are not backed up, so if the node goes down, these values will be lost.
You can disable this feature with topic configuration. Please see the Topic Configuration section.
NOTE: These statistics values can be also viewed in Management Center. Please see the Topics section.
Each node has a list of all registrations in the cluster. When a new node is registered for a topic, it sends a registration message to all members in the cluster. Also, when a new node joins the cluster, it will receive all registrations made so far in the cluster.
The behavior of a topic varies depending on the value of the configuration parameter globalOrderEnabled
.
globalOrderEnabled
is disabled:Messages are ordered, i.e. listeners (subscribers) process the messages in the order that the messages are published. If cluster member M publishes messages m1, m2, m3, ..., mn to a topic T, then Hazelcast makes sure that all of the subscribers of topic T will receive and process m1, m2, m3, ..., mn in the given order.
Here is how it works. Let's say that we have three nodes (node1, node2 and node3) and that node1 and node2 are registered to a topic named news
. Note that all three nodes know that node1 and node2 are registered to news
.
In this example, node1 publishes two messages: a1
and a2
, and node3 publishes two messages: c1
and c2
. When node1 and node3 publish a message, they will check their local list for registered nodes, and they will discover that node1 and node2 are in their lists, then they will fire messages to those nodes. One possible order of the messages received can be the following.
node1 -> c1
, b1
, a2
, c2
node2 -> c1
, c2
, a1
, a2
globalOrderEnabled
is enabled:When enabled, globalOrderEnabled
guarantees that all nodes listening to the same topic will get its messages in the same order.
Here is how it works. Let's say that we have three nodes (node1, node2 and node3) and that node1 and node2 are registered to a topic named news
. Note that all three nodes know that node1 and node2 are registered to news
.
In this example, node1 publishes two messages: a1
and a2
, and node3 publishes two messages: c1
and c2
. When a node publishes messages over the topic news
, it first calculates which partition the news
ID corresponds to. Then it sends an operation to the owner of the partition for that node to publish messages. Let's assume that news
corresponds to a partition that node2 owns. node1 and node3 first sends all messages to node2. Assume that the messages are published in the following order:
node1 -> a1
, c1
, a2
, c2
node2 then publishes these messages by looking at registrations in its local list. It sends these messages to node1 and node2 (it makes a local dispatch for itself).
node1 -> a1
, c1
, a2
, c2
node2 -> a1
, c1
, a2
, c2
This way, we guarantee that all nodes will see the events in the same order.
In both cases, there is a StripedExecutor
in EventService that is responsible for dispatching the received message. For all events in Hazelcast, the order that events are generated and the order they are published to the user are guaranteed to be the same via this StripedExecutor
.
In StripedExecutor
, there are as many threads as are specified in the property hazelcast.event.thread.count
(default is 5). For a specific event source (for a particular topic name), hash of that source's name % 5 gives the ID of the responsible thread. Note that there can be another event source (entry listener of a map, item listener of a collection, etc.) corresponding to the same thread. In order not to make other messages to block, heavy processing should not be done in this thread. If there is time consuming work that needs to be done, the work should be handed over to another thread. Please see the Sample Topic Code section.
Declarative Configuration:
<hazelcast>
...
<topic name="yourTopicName">
<global-ordering-enabled>true</global-ordering-enabled>
<statistics-enabled>true</statistics-enabled>
<message-listeners>
<message-listener>MessageListenerImpl</message-listener>
</message-listeners>
</topic>
...
</hazelcast>
Programmatic Configuration:
TopicConfig topicConfig = new TopicConfig();
topicConfig.setGlobalOrderingEnabled( true );
topicConfig.setStatisticsEnabled( true );
topicConfig.setName( "yourTopicName" );
MessageListener<String> implementation = new MessageListener<String>() {
@Override
public void onMessage( Message<String> message ) {
// process the message
}
};
topicConfig.addMessageListenerConfig( new ListenerConfig( implementation ) );
HazelcastInstance instance = Hazelcast.newHazelcastInstance()
Default values are:
global-ordering
is false, meaning that by default, there is no guarantee of global order.
statistics
is true, meaning that by default, statistics are calculated.
Topic related but not topic specific configuration parameters:
hazelcast.event.queue.capacity
: default value is 1,000,000hazelcast.event.queue.timeout.millis
: default value is 250hazelcast.event.thread.count
: default value is 5
RELATED INFORMATION
For description of these parameters, please see the Global Event Configuration section.
RELATED INFORMATION
Please refer to the Topic Configuration section for a full description of Hazelcast Distributed Topic configuration.
Reliable Topic data structure has been introduced with the release of Hazelcast 3.5. The Reliable Topic makes use of the same ITopic
interface
as a regular topic. The main difference is that it is backed up by the RingBuffer (also introduced with Hazelcast
3.5) data structure. The following are the advantages of this approach:
ITopic
gets its own RingBuffer; if there is a topic with a very fast producer, it will not lead to problems at the topic that runs at a slower pace.ITopic
is shared with other data structures (e.g. collection listeners),
you can run into isolation problems. This does not happen with the Reliable ITopic
.import com.hazelcast.core.Topic;
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.MessageListener;
public class Sample implements MessageListener<MyEvent> {
public static void main( String[] args ) {
Sample sample = new Sample();
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
ITopic topic = hazelcastInstance.getReliableTopic( "default" );
topic.addMessageListener( sample );
topic.publish( new MyEvent() );
}
public void onMessage( Message<MyEvent> message ) {
MyEvent myEvent = message.getMessageObject();
System.out.println( "Message received = " + myEvent.toString() );
}
}
The Reliable ITopic
can be configured using its RingBuffer. If there is a Reliable Topic with name Foo
, then this topic can
be configured by adding a ReliableTopicConfig
for a RingBuffer with the name Foo
. By default, a RingBuffer does not have any TTL (time to live) and
it has a limited capacity; you may want to change the configuration.
By default, the Reliable ITopic
uses a shared thread pool. If you need a better isolation, you can configure a custom executor on the
ReliableTopicConfig
.
Because the reads on a RingBuffer are not destructive, it is easy to apply batching. ITopic
uses read batching and reads
10 items at a time (if available) by default.
The Reliable ITopic
provides control and a way to deal with slow consumers. It is unwise to keep events for a slow consumer in memory
indefinitely since you do not know when it is going to catch up. The size of the RingBuffer can be controlled using its capacity. For the cases when a RingBuffer runs out of its capacity, you can specify the following policies for the TopicOverloadPolicy
configuration:
DISCARD_OLDEST
: Overwrite the oldest item, no matter if a TTL is set. In this case the fast producer supersedes a slow consumerDISCARD_NEWEST
: Discard the newest item.BLOCK
: Wait until the items are expired in the RingBuffer.FAIL
: Immediately throw TopicOverloadException
if there is no space in the RingBuffer.ILock is the distributed implementation of java.util.concurrent.locks.Lock
. Meaning if you lock using an ILock, the critical
section that it guards is guaranteed to be executed by only one thread in the entire cluster. Even though locks are great for synchronization, they can lead to problems if not used properly. Also note that Hazelcast Lock does not support fairness.
A few warnings when using locks:
import com.hazelcast.core.Hazelcast;
import java.util.concurrent.locks.Lock;
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
Lock lock = hazelcastInstance.getLock( "myLock" );
lock.lock();
try {
// do something here
} finally {
lock.unlock();
}
tryLock
with a timeout value. You can
set a high value (normally it should not take that long) for tryLock
. You can check the return value of tryLock
as follows:if ( lock.tryLock ( 10, TimeUnit.SECONDS ) ) {
try {
// do some stuff here..
} finally {
lock.unlock();
}
} else {
// warning
}
You can also avoid indefinitely waiting threads by using lock with lease time: the lock will be released in the given lease time. Lock can be safely unlocked before the lease time expires. Note that the unlock operation can
throw an IllegalMonitorStateException
if lock is released because the lease time expires. If that is the case, critical section guarantee is broken.
Please see the below example.
lock.lock( 5, TimeUnit.SECONDS )
try {
// do some stuff here..
} finally {
try {
lock.unlock();
} catch ( IllegalMonitorStateException ex ){
// WARNING Critical section guarantee can be broken
}
}
Locks are re-entrant: the same thread can lock multiple times on the same lock. Note that for other threads to be
able to require this lock, the owner of the lock must call unlock
as many times as the owner called lock
.
In the split-brain scenario, the cluster behaves as if it were two different clusters. Since two separate clusters are not aware of each other, two nodes from different clusters can acquire the same lock. For more information on places where split brain syndrome can be handled, please see split brain syndrome.
Locks are not automatically removed. If a lock is not used anymore, Hazelcast will not automatically garbage collect the lock.
This can lead to an OutOfMemoryError
. If you create locks on the fly, make sure they are destroyed.
Hazelcast IMap also provides locking support on the entry level with the method IMap.lock(key)
. Although the same infrastructure
is used, IMap.lock(key)
is not an ILock and it is not possible to expose it directly.
ICondition
is the distributed implementation of the notify
, notifyAll
and wait
operations on the Java object. You can use it to synchronize
threads across the cluster. More specifically, you use ICondition
when a thread's work depends on another thread's output. A good example
can be producer/consumer methodology.
Please see the below code snippets for a sample producer/consumer implementation.
Producer thread:
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
Lock lock = hazelcastInstance.getLock( "myLockId" );
ICondition condition = lock.newCondition( "myConditionId" );
lock.lock();
try {
while ( !shouldProduce() ) {
condition.await(); // frees the lock and waits for signal
// when it wakes up it re-acquires the lock
// if available or waits for it to become
// available
}
produce();
condition.signalAll();
} finally {
lock.unlock();
}
Consumer thread:
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
Lock lock = hazelcastInstance.getLock( "myLockId" );
ICondition condition = lock.newCondition( "myConditionId" );
lock.lock();
try {
while ( !canConsume() ) {
condition.await(); // frees the lock and waits for signal
// when it wakes up it re-acquires the lock if
// available or waits for it to become
// available
}
consume();
condition.signalAll();
} finally {
lock.unlock();
}
Hazelcast IAtomicLong
is the distributed implementation of java.util.concurrent.atomic.AtomicLong
. It offers most of AtomicLong's operations such as get
, set
, getAndSet
, compareAndSet
and incrementAndGet
. Since IAtomicLong is a distributed implementation, these operations involve remote calls and hence their performances differ from AtomicLong.
The following sample code creates an instance, increments it by a million, and prints the count.
public class Member {
public static void main( String[] args ) {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IAtomicLong counter = hazelcastInstance.getAtomicLong( "counter" );
for ( int k = 0; k < 1000 * 1000; k++ ) {
if ( k % 500000 == 0 ) {
System.out.println( "At: " + k );
}
counter.incrementAndGet();
}
System.out.printf( "Count is %s\n", counter.get() );
}
}
When you start other instances with the code above, you will see the count as member count times a million.
You can send functions to an IAtomicLong. Function
is a Hazelcast owned, single method interface. The following sample Function
implementation doubles the original value.
private static class Add2Function implements Function <Long, Long> {
@Override
public Long apply( Long input ) {
return input + 2;
}
}
You can use the following methods to execute functions on IAtomicLong.
apply
: It applies the function to the value in IAtomicLong without changing the actual value and returning the result.alter
: It alters the value stored in the IAtomicLong by applying the function. It will not send back a result.alterAndGet
: It alters the value stored in the IAtomicLong by applying the function, storing the result in the IAtomicLong and returning the result.getAndAlter
: It alters the value stored in the IAtomicLong by applying the function and returning the original value.The following sample code includes these methods.
public class Member {
public static void main( String[] args ) {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IAtomicLong atomicLong = hazelcastInstance.getAtomicLong( "counter" );
atomicLong.set( 1 );
long result = atomicLong.apply( new Add2Function() );
System.out.println( "apply.result: " + result);
System.out.println( "apply.value: " + atomicLong.get() );
atomicLong.set( 1 );
atomicLong.alter( new Add2Function() );
System.out.println( "alter.value: " + atomicLong.get() );
atomicLong.set( 1 );
result = atomicLong.alterAndGet( new Add2Function() );
System.out.println( "alterAndGet.result: " + result );
System.out.println( "alterAndGet.value: " + atomicLong.get() );
atomicLong.set( 1 );
result = atomicLong.getAndAlter( new Add2Function() );
System.out.println( "getAndAlter.result: " + result );
System.out.println( "getAndAlter.value: " + atomicLong.get() );
}
}
The reason for using a function instead of a simple code line like atomicLong.set(atomicLong.get() + 2));
is that the IAtomicLong read and write operations are not atomic. Since IAtomicLong
is a distributed implementation, those operations can be remote ones, which may lead to race problems. By using functions, the data is not pulled into the code, but the code is sent to the data. This makes it more scalable.
NOTE: IAtomicLong has 1 synchronous backup and no asynchronous backups. Its backup count is not configurable.
Hazelcast ISemaphore is the distributed implementation of java.util.concurrent.Semaphore
. Semaphores offer permits to control the thread counts in the case of performing concurrent activities. To execute a concurrent activity, a thread grants a permit or waits until a permit becomes available. When the execution is completed, the permit is released.
NOTE: Semaphore with a single permit may be considered as a lock. But unlike the locks, when semaphores are used, any thread can release the permit and semaphores can have multiple permits.
NOTE: Hazelcast Semaphore does not support fairness.
When a permit is acquired on ISemaphore:
InstanceDestroyedException
is thrown.The following sample code uses an IAtomicLong
resource 1000 times, increments the resource when a thread starts to use it, and decrements it when the thread completes.
public class SemaphoreMember {
public static void main( String[] args ) throws Exception{
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
ISemaphore semaphore = hazelcastInstance.getSemaphore( "semaphore" );
IAtomicLong resource = hazelcastInstance.getAtomicLong( "resource" );
for ( int k = 0 ; k < 1000 ; k++ ) {
System.out.println( "At iteration: " + k + ", Active Threads: " + resource.get() );
semaphore.acquire();
try {
resource.incrementAndGet();
Thread.sleep( 1000 );
resource.decrementAndGet();
} finally {
semaphore.release();
}
}
System.out.println("Finished");
}
}
Let's limit the concurrent access to this resource by allowing at most 3 threads. You can configure it declaratively by setting the initial-permits
property, as shown below.
<semaphore name="semaphore">
<initial-permits>3</initial-permits>
</semaphore>
NOTE: If there is a shortage of permits while the semaphore is being created, value of this property can be set to a negative number.
If you execute the above SemaphoreMember
class 5 times, the output will be similar to the following:
At iteration: 0, Active Threads: 1
At iteration: 1, Active Threads: 2
At iteration: 2, Active Threads: 3
At iteration: 3, Active Threads: 3
At iteration: 4, Active Threads: 3
As can be seen, the maximum count of concurrent threads is equal or smaller than 3. If you remove the semaphore acquire/release statements in SemaphoreMember
, you will see that there is no limitation on the number of concurrent usages.
Hazelcast also provides backup support for ISemaphore
. When a member goes down, another member can take over the semaphore with the permit information (permits are automatically released when a member goes down). To enable this, configure synchronous or asynchronous backup with the properties backup-count
and async-backup-count
(by default, synchronous backup is already enabled).
A sample configuration is shown below.
<semaphore name="semaphore">
<initial-permits>3</initial-permits>
<backup-count>1</backup-count>
</semaphore>
NOTE: If high performance is more important (than not losing the permit information), you can disable the backups by setting backup-count
to 0.
RELATED INFORMATION
Please refer to the Semaphore Configuration section for a full description of Hazelcast Distributed Semaphore configuration.
The IAtomicLong
is very useful if you need to deal with a long, but in some cases you need to deal with a reference. That is why Hazelcast also supports the IAtomicReference
which is the distributed version of the java.util.concurrent.atomic.AtomicReference
.
Here is an IAtomicReference example.
public class Member {
public static void main(String[] args) {
Config config = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(config);
IAtomicReference<String> ref = hz.getAtomicReference("reference");
ref.set("foo");
System.out.println(ref.get());
System.exit(0);
}
}
When you execute the above example, you will see the following output.
foo
Just like IAtomicLong
, IAtomicReference
has methods that accept a 'function' as an argument, such as alter
, alterAndGet
, getAndAlter
and apply
. There are two big advantages of using these methods:
There are some issues you need to know, described below.
IAtomicReference
works based on the byte-content and not on the object-reference. If you use the compareAndSet
method, do not change to original value because its serialized content will then be different.
It is also important to know that if you rely on Java serialization, sometimes (especially with hashmaps) the same object can result in different binary content.IAtomicReference
will always have 1 synchronous backup.IAtomicReference
; but be careful with introducing a data-race. IAtomicReference
is binary
. The receiving side does not need to have the class definition available, unless it needs to be deserialized on the other side (e.g. because a method like 'alter' is executed). This deserialization is done for every call that needs to have the object instead of the binary content, so be careful with expensive object graphs that need to be deserialized.apply
method. With the apply
method, the whole object does not need to be sent over the line, only the information that is relevant.Hazelcast ICountDownLatch
is the distributed implementation of java.util.concurrent.CountDownLatch
. As you may know, CountDownLatch
is considered to be a gate keeper for concurrent activities. It enables the threads to wait for other threads to complete their operations.
The following code samples describe the mechanism of ICountDownLatch
. Assume that there is a leader process and there are follower processes that will wait until the leader completes. Here is the leader:
public class Leader {
public static void main( String[] args ) throws Exception {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
ICountDownLatch latch = hazelcastInstance.getCountDownLatch( "countDownLatch" );
System.out.println( "Starting" );
latch.trySetCount( 1 );
Thread.sleep( 30000 );
latch.countDown();
System.out.println( "Leader finished" );
latch.destroy();
}
}
Since only a single step is needed to be completed as a sample, the above code initializes the latch with 1. Then, the code sleeps for a while to simulate a process and starts the countdown. Finally, it clears up the latch. Let's write a follower:
public class Follower {
public static void main( String[] args ) throws Exception {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
ICountDownLatch latch = hazelcastInstance.getCountDownLatch( "countDownLatch" );
System.out.println( "Waiting" );
boolean success = latch.await( 10, TimeUnit.SECONDS );
System.out.println( "Complete: " + success );
}
}
The follower class above first retrieves ICountDownLatch
and then calls the await
method to enable the thread to listen for the latch. The method await
has a timeout value as a parameter. This is useful when countDown
method fails. To see ICountDownLatch
in action, start the leader first and then start one or more followers. You will see that the followers will wait until the leader completes.
In a distributed environment, the counting down cluster member may go down. In this case, all listeners are notified immediately and automatically by Hazelcast. The state of the current process just before the failure should be verified and 'how to continue now' should be decided (e.g. restart all process operations, continue with the first failed process operation, throw an exception, etc.).
Although the ICountDownLatch
is a very useful synchronization aid, you will probably not use it on a daily basis. Unlike Java’s implementation, Hazelcast’s ICountDownLatch
count can be re-set after a countdown has finished but not during an active count.
NOTE: ICountDownLatch has 1 synchronous backup and no asynchronous backups. Its backup count is not configurable. Also, the count cannot be re-set during an active count, it should be re-set after the countdown is finished.
Hazelcast IdGenerator is used to generate cluster-wide unique identifiers. Generated identifiers are long type primitive values between 0 and Long.MAX_VALUE
.
ID generation occurs almost at the speed of AtomicLong.incrementAndGet()
. A group of 1 million identifiers is allocated for each cluster member. In the background, this allocation takes place with an IAtomicLong
incremented by 1 million. Once a cluster member generates IDs (allocation is done), IdGenerator
increments a local counter. If a cluster member uses all IDs in the group, it will get another 1 million IDs. By this way, only one time of network traffic is needed, meaning that 999,999 identifiers are generated in memory instead of over the network. This is fast.
Let's write a sample identifier generator.
public class IdGeneratorExample {
public static void main( String[] args ) throws Exception {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IdGenerator idGen = hazelcastInstance.getIdGenerator( "newId" );
while (true) {
Long id = idGen.newId();
System.err.println( "Id: " + id );
Thread.sleep( 1000 );
}
}
}
Let's run the above code two times. The output will be similar to the following.
Members [1] {
Member [127.0.0.1]:5701 this
}
Id: 1
Id: 2
Id: 3
Members [2] {
Member [127.0.0.1]:5701
Member [127.0.0.1]:5702 this
}
Id: 1000001
Id: 1000002
Id: 1000003
You can see that the generated IDs are unique and counting upwards. If you see duplicated identifiers, it means your instances could not form a cluster.
NOTE: Generated IDs are unique during the life cycle of the cluster. If the entire cluster is restarted, IDs start from 0 again or you can initialize to a value using the init()
method of IdGenerator.
NOTE: IdGenerator has 1 synchronous backup and no asynchronous backups. Its backup count is not configurable.
A replicated map is a weakly consistent, distributed key-value data structure provided by Hazelcast.
All other data structures are partitioned in design. A replicated map does not partition data (it does not spread data to different cluster members); instead, it replicates the data to all nodes.
This leads to higher memory consumption. However, a replicated map has faster read and write access since the data are available on all nodes and writes take place on local nodes, eventually being replicated to all other nodes.
Weak consistency compared to eventually consistency means that replication is done on a best efforts basis. Lost or missing updates are neither tracked nor resent. This kind of data structure is suitable for immutable objects, catalogue data, or idempotent calculable data (like HTML pages).
Replicated map nearly fully implements the java.util.Map
interface, but it lacks the methods from java.util.concurrent.ConcurrentMap
since
there are no atomic guarantees to writes or reads.
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import java.util.Collection;
import java.util.Map;
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
Map<String, Customer> customers = hazelcastInstance.getReplicatedMap("customers");
customers.put( "1", new Customer( "Joe", "Smith" ) );
customers.put( "2", new Customer( "Ali", "Selam" ) );
customers.put( "3", new Customer( "Avi", "Noyan" ) );
Collection<Customer> colCustomers = customers.values();
for ( Customer customer : colCustomers ) {
// process customer
}
HazelcastInstance::getReplicatedMap
returns com.hazelcast.core.ReplicatedMap
which, as stated above, extends the
java.util.Map
interface.
The com.hazelcast.core.ReplicatedMap
interface has some additional methods for registering entry listeners or retrieving values in an expected order.
NOTE: Replicated Map is in the beta stage.
A replicated map does not support ordered writes! In case of a conflict caused by two nodes simultaneously written to the same key, a vector clock algorithm resolves and decides on one of the values.
Due to the weakly consistent nature and the previously mentioned behaviors of replicated map, there is a chance of reading stale data at any time. There is no read guarantee like there is for repeatable reads.
Replicated Map offers a distributed java.util.Map::clear
implementation, but due to the asynchronous nature and the
weakly consistency of this implementation, there is no point in time where you can say the map is empty. Every node
applies that to its local dataset in "a near point in time".
If you need a definite point in time to empty the map, you may want to consider using a lock around the clear
operation.
You can simulate the clear
method by locking your user codebase and executing a remote operation that
uses DistributedObject::destroy
to destroy the node's own proxy and storage of the Replicated Map. A new proxy instance
and storage will be created on the next retrieval of the Replicated Map using HazelcastInstance::getReplicatedMap
.
You will have to reallocate the Replicated Map in your code. Afterwards, just release the lock when finished.
There are several technical design decisions for configurable behavior.
Initial provisioning
If a new member joins, there are two ways you can handle the initial provisioning that is executed to replicate all existing values to the new member.
First, you can have an async fill up, which does not block reads while the fill up operation is underway. That way, you have immediate access on the new member, but it will take time until all values are eventually accessible. Not yet replicated values are returned as non-existing (null). Write operations to already existing keys during this async phase can be lost, since the vector clock for an entry might not be initialized by another member yet, and it might be seen as an old update by other members.
Second, you can perform a synchronous initial fill up, which blocks every read or write access to the map until the fill up operation is finished. Use this way with caution since it might block your application from operating.
Replication delay
By default, the replication of values is delayed by 100 milliseconds when no current waiting replication is found. This collects multiple updates and minimizes the operations overhead on replication. A hard limit of 1000 replications
is built into the system to prevent OutOfMemory
situations where you put lots of data into the replicated map in a very
short time.
The delay is configurable. A value of "0" means immediate replication. You can configure the trade off between
replication overhead and the time for the value to be replicated.
Concurrency Level
The concurrency level configuration defines the number of mutexes and segments inside the replicated map storage.
A mutex/segment is chosen by calculating the hashCode
of the key and using the module by the concurrency level. If multiple
keys fall into the same mutex, they will wait for other mutex holders on the same mutex to finish their operation.
For a high amount of values, or for high contention on the mutexes, this value can be changed.
Replicated Map can be configured using the following two ways (as with most other features in Hazelcast):
hazelcast.xml
.You can declare your Replicated Map configuration in the Hazelcast configuration file hazelcast.xml
. You can use the configuration to tune the behavior of the internal replication algorithm, such as the replication delay which batches up the replication
for better network utilization. See the following example declarative configuration.
<replicatedmap name="default">
<in-memory-format>BINARY</in-memory-format>
<concurrency-level>32</concurrency-level>
<replication-delay-millis>100</replication-delay-millis>
<async-fillup>true</async-fillup>
<statistics-enabled>true</statistics-enabled>
<entry-listeners>
<entry-listener include-value="true">
com.hazelcast.examples.EntryListener
</entry-listener>
</entry-listeners>
</replicatedmap>
in-memory-format
: Defines the internal storage format. Please see the In-Memory Format section. The default value is BINARY
.concurrency-level
: Number of parallel mutexes to minimize the contention on the keys. The default value is 32, which is a good number for lots of applications. If higher contention is seen on writes to values inside the replicated map, this value can be adjusted according to the needs.replication-delay-millis
: Defines the period in milliseconds after a put is executed that the put value is replicated to other nodes. During this time, multiple puts can be operated and the values are cached up to be sent all at once. This increases the latency for eventual consistency, but it lowers the I/O operations. The default value is 100ms before a replication is operated. If replication-delay-millis
is set to 0, no delay is used (not cached) and all values are replicated one by one.async-fillup
: Defines if the replicated map is available for reads before the initial replication is completed. The default value is true
. If set to false
(i.e. synchronous initial fill up), no exception will be thrown when the replicated map is not yet ready, but the call will block until it is finished.statistics-enabled
: If set to true
, the statistics such as cache hits and misses are collected. The default value is false
.entry-listener
: The value of this element is the full canonical classname of the EntryListener
implementation.entry-listener#include-value
: This attribute defines if the event will include the value or not. Sometimes the key is enough to react on an event. In those situations, setting this value to false
will save a deserialization cycle. The default value is true
.entry-listener#local
: This attribute is not used for Replicated Map since listeners are always local.You can use the Config API for programmatic configuration, as you can for all other data structures in Hazelcast. You must create the configuration upfront, when you instantiate the HazelcastInstance
.
A basic example on how to configure the Replicated Map using the programmatic approach is shown in the following snippet.
Config config = new Config();
ReplicatedMapConfig replicatedMapConfig =
config.getReplicatedMapConfig( "default" );
replicatedMapConfig.setInMemoryFormat( InMemoryFormat.BINARY );
replicatedMapConfig.setConcurrencyLevel( 32 );
All properties that can be configured using the declarative configuration are also available using programmatic configuration by transforming the tag names into getter or setter names.
Currently, two in-memory-format
values are usable with the Replicated Map.
OBJECT
(default): The data will be stored in deserialized form. This configuration is the default choice since
the data replication is mostly used for high speed access. Please be aware that changing the values without a Map::put
is
not reflected on the other nodes but is visible on the changing nodes for later value accesses.
BINARY
: The data is stored in serialized binary format and has to be deserialized on every request. This
option offers higher encapsulation since changes to values are always discarded as long as the newly changed object is
not explicitly Map::put
into the map again.
A com.hazelcast.core.EntryListener
used on a Replicated Map serves the same purpose as it would on other
data structures in Hazelcast. You can use it to react on add, update, and remove operations. Replicated maps do not yet support eviction.
The fundamental difference in Replicated Map behavior, compared to the other data structures, is that an EntryListener only reflects changes on local data. Since replication is asynchronous, all listener events are fired only when an operation is finished on a local node. Events can fire at different times on different nodes.
import com.hazelcast.core.EntryEvent;
import com.hazelcast.core.EntryListener;
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.core.ReplicatedMap;
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
ReplicatedMap<String, Customer> customers =
hazelcastInstance.getReplicatedMap( "customers" );
customers.addEntryListener( new EntryListener<String, Customer>() {
@Override
public void entryAdded( EntryEvent<String, Customer> event ) {
log( "Entry added: " + event );
}
@Override
public void entryUpdated( EntryEvent<String, Customer> event ) {
log( "Entry updated: " + event );
}
@Override
public void entryRemoved( EntryEvent<String, Customer> event ) {
log( "Entry removed: " + event );
}
@Override
public void entryEvicted( EntryEvent<String, Customer> event ) {
// Currently not supported, will never fire
}
});
customers.put( "1", new Customer( "Joe", "Smith" ) ); // add event
customers.put( "1", new Customer( "Ali", "Selam" ) ); // update event
customers.remove( "1" ); // remove event
You can register for Hazelcast entry events so you will be notified when those events occur. Event Listeners are cluster-wide: when a listener is registered in one member of cluster, it is actually registered for events that originated at any member in the cluster. When a new member joins, events originated at the new member will also be delivered.
An Event is created only if you registered an event listener. If no listener is registered, then no event will be created. If you provided a predicate when you registered the event listener, pass the predicate before sending the event to the listener (node/client).
As a rule of thumb, your event listener should not implement heavy processes in its event methods which block the thread for a long time. If needed, you can use ExecutorService
to transfer long running processes to another thread and thus offload the current listener thread.
Hazelcast offers the following event listeners:
HazelcastInstance
lifecycle events.IMap
and MultiMap
entry events (please refer to the Map Listener section).IQueue
, ISet
and IList
item events (please refer to the Event Registration and Configuration parts of the sections Set and List).ITopic
message events.The Membership Listener allows to get notified for the following events.
The following is an example Membership Listener class.
public class ClusterMembershipListener
implements MembershipListener {
public void memberAdded(MembershipEvent membershipEvent) {
System.err.println("Added: " + membershipEvent);
}
public void memberRemoved(MembershipEvent membershipEvent) {
System.err.println("Removed: " + membershipEvent);
}
public void memberAttributeChanged(MemberAttributeEvent memberAttributeEvent) {
System.err.println("Member attribute changed: " + memberAttributeEvent);
}
}
When a respective event is fired, the membership listener outputs the addresses of the members that joined and left, and also which attribute changed on which member.
The Distributed Object Listener notifies when a distributed object is created or destroyed throughout the cluster.
The following is an example Distributed Object Listener class.
public class Sample implements DistributedObjectListener {
public static void main(String[] args) {
Sample sample = new Sample();
Config config = new Config();
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance(config);
hazelcastInstance.addDistributedObjectListener(sample);
Collection<DistributedObject> distributedObjects = hazelcastInstance.getDistributedObjects();
for (DistributedObject distributedObject : distributedObjects) {
System.out.println(distributedObject.getName() + "," + distributedObject.getId());
}
}
@Override
public void distributedObjectCreated(DistributedObjectEvent event) {
DistributedObject instance = event.getDistributedObject();
System.out.println("Created " + instance.getName() + "," + instance.getId());
}
@Override
public void distributedObjectDestroyed(DistributedObjectEvent event) {
DistributedObject instance = event.getDistributedObject();
System.out.println("Destroyed " + instance.getName() + "," + instance.getId());
}
}
When a respective event is fired, the distributed object listener outputs the event type, and the name, service (for example, if a Map service provides the distributed object, than it is a Map object), and ID of the object.
The Migration Listener notifies for the following events:
The following is an example Migration Listener class.
public class ClusterMigrationListener implements MigrationListener {
@Override
public void migrationStarted(MigrationEvent migrationEvent) {
System.err.println("Started: " + migrationEvent);
}
@Override
public void migrationCompleted(MigrationEvent migrationEvent) {
System.err.println("Completed: " + migrationEvent);
}
@Override
public void migrationFailed(MigrationEvent migrationEvent) {
System.err.println("Failed: " + migrationEvent);
}
}
When a respective event is fired, the migration listener outputs the partition ID, status of the migration, the old member and the new member. The following is an example output.
Started: MigrationEvent{partitionId=98, oldOwner=Member [127.0.0.1]:5701,
newOwner=Member [127.0.0.1]:5702 this}
Hazelcast provides fault-tolerance by keeping multiple copies of your data. For each partition, one of your nodes become owner and some of the other nodes become replica nodes based on your configuration. Nevertheless, data loss may occur if a few nodes crash simultaneously.
Let`s consider the following example with three nodes: N1, N2, N3 for a given partition-0. N1 is owner of partition-0, N2 and N3 are the first and second replicas respectively. If N1 and N2 crash simultaneously, partition-0 loses its data that is configured with less than 2 backups. For instance, if we configure a map with 1 backup, that map loses its data in partition-0 since both owner and first replica of partition-0 have crashed. However, if we configure our map with 2 backups, it does not lose any data since a copy of partition-0's data for the given map also resides in N3.
The Partition Lost Listener notifies for possible data loss occurrences with the information of how many replicas are lost for a partition. It listens to PartitionLostEvent
instances. Partition lost events are dispatched per partition.
Partition loss detection is done after a node crash is detected by the other nodes and the crashed node is removed from the cluster. Please note that false-positive PartitionLostEvent
instances may be fired on partial network split errors.
The following is an example of Partition Lost Listener.
public class ConsoleLoggingPartitionLostListener implements PartitionLostListener {
@Override
public void partitionLost(PartitionLostEvent event) {
System.out.println(event);
}
}
When a PartitionLostEvent
is fired, the partition lost listener given above outputs the partition ID, the replica index that is lost and the node that has detected the partition loss. The following is an example output.
com.hazelcast.partition.PartitionLostEvent{partitionId=242, lostBackupCount=0,
eventSource=Address[192.168.2.49]:5701}
The Lifecycle Listener notifies for the following events:
The following is an example Lifecycle Listener class.
public class NodeLifecycleListener implements LifecycleListener {
@Override
public void stateChanged(LifecycleEvent event) {
System.err.println(event);
}
}
This listener is local to an individual node. It notifies the application that uses Hazelcast about the events mentioned above for a particular node.
The Item Listener is used by the Hazelcast IQueue
, ISet
and IList
interfaces. It notifies when an item is added or removed.
The following is an example Item Listener class.
public class Sample implements ItemListener {
public static void main( String[] args ) {
Sample sample = new Sample();
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
ISet<Price> set = hazelcastInstance.getSet( "default" );
set.addItemListener( sample, true );
Price price = new Price( 10, time1 )
set.add( price );
set.remove( price );
}
public void itemAdded( Object item ) {
System.out.println( "Item added = " + item );
}
public void itemRemoved( Object item ) {
System.out.println( "Item removed = " + item );
}
}
The Message Listener is used by the ITopic
interface. It notifies when a message is received for the registered topic.
The following is an example Message Listener class.
public class Sample implements MessageListener<MyEvent> {
public static void main( String[] args ) {
Sample sample = new Sample();
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
ITopic topic = hazelcastInstance.getTopic( "default" );
topic.addMessageListener( sample );
topic.publish( new MyEvent() );
}
public void onMessage( Message<MyEvent> message ) {
MyEvent myEvent = message.getMessageObject();
System.out.println( "Message received = " + myEvent.toString() );
if ( myEvent.isHeavyweight() ) {
messageExecutor.execute( new Runnable() {
public void run() {
doHeavyweightStuff( myEvent );
}
} );
}
}
The Client Listener is used by the Hazelcast nodes. It notifies the nodes when a client is connected to or disconnected from the cluster.
NOTE: You can also add event listeners to a Hazelcast client. Please refer to Client Listenerconfig for the related information.
You can add event listeners to a Hazelcast Java client. You can configure the following listeners to listen to the events on the client side. Please see the respective sections under the Event Listeners for Hazelcast Nodes section for example code.
RELATED INFORMATION
Please refer to the Client Listenerconfig section for more information.
RELATED INFORMATION
Please refer to the Listener Configurations section for a configuration wrap-up of event listeners.
hazelcast.event.queue.capacity
: default value is 1000000hazelcast.event.queue.timeout.millis
: default value is 250hazelcast.event.thread.count
: default value is 5A striped executor in each node controls and dispatches the received events. This striped executor also guarantees the event order. For all events in Hazelcast, the order in which events are generated and the order in which they are published are guaranteed for given keys. For map and multimap, the order is preserved for the operations on the same key of the entry. For list, set, topic and queue, the order is preserved for events on that instance of the distributed data structure.
You achieve the order guarantee by making only one thread responsible for a particular set of events (entry events of a key in a map, item events of a collection, etc.) in StripedExecutor
.
If the event queue reaches its capacity (hazelcast.event.queue.capacity
) and the last item cannot be put into the event queue for the period specified in hazelcast.event.queue.timeout.millis
, these events will be dropped with a warning message, such as "EventQueue overloaded".
If event listeners perform a computation that takes a long time, the event queue can reach its maximum capacity and lose events. For map and multimap, you can configure hazelcast.event.thread.count
to a higher value so that fewer collisions occur for keys, and therefore worker threads will not block each other in StripedExecutor
. For list, set, topic and queue, you should offload heavy work to another thread. To preserve order guarantee, you should implement similar logic with StripedExecutor
in the offloaded thread pool.
RELATED INFORMATION
Please refer to the Listener Configurations section on how to configure each listener.
From Wikipedia: Distributed computing refers to the use of distributed systems to solve computational problems. In distributed computing, a problem is divided into many tasks, each of which is solved by one or more computers.
One of the coolest features of Java 1.5 is the Executor framework, which allows you to asynchronously execute your tasks (logical units of work), such as database query, complex calculation, and image rendering.
The default implementation of this framework (ThreadPoolExecutor
) is designed to run within a single JVM. In distributed systems, this implementation is not desired since you may want a task submitted in one JVM and processed in another one. Hazelcast offers IExecutorService
for you to use in distributed environments: it implements java.util.concurrent.ExecutorService
to serve the applications requiring computational and data processing power.
With IExecutorService
, you can execute tasks asynchronously and perform other useful tasks. If your task execution takes longer than expected, you can cancel the task execution. Tasks should be Serializable
since they will be distributed.
In the Java Executor framework, you implement tasks two ways: Callable or Runnable.
java.util.concurrent.Callable
.java.util.concurrent.Runnable
.In Hazelcast, when you implement a task as java.util.concurrent.Callable
(a task that returns a value), you implement Callable and Serializable.
Below is an example of a Callable.
import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.core.HazelcastInstanceAware;
import com.hazelcast.core.IMap;
import java.io.Serializable;
import java.util.concurrent.Callable;
public class SumTask
implements Callable<Integer>, Serializable, HazelcastInstanceAware {
private transient HazelcastInstance hazelcastInstance;
public void setHazelcastInstance( HazelcastInstance hazelcastInstance ) {
this.hazelcastInstance = hazelcastInstance;
}
public Integer call() throws Exception {
IMap<String, Integer> map = hazelcastInstance.getMap( "map" );
int result = 0;
for ( String key : map.localKeySet() ) {
System.out.println( "Calculating for key: " + key );
result += map.get( key );
}
System.out.println( "Local Result: " + result );
return result;
}
}
Another example is the Echo callable below. In its call() method, it returns the local member and the input passed in. Remember that instance.getCluster().getLocalMember()
returns the local member and toString()
returns the member's address (IP + port) in String form, just to see which member actually executed the code for our example. Of course, the call()
method can do and return anything you like.
import java.util.concurrent.Callable;
import java.io.Serializable;
public class Echo implements Callable<String>, Serializable {
String input = null;
public Echo() {
}
public Echo(String input) {
this.input = input;
}
public String call() {
Config cfg = new Config();
HazelcastInstance instance = Hazelcast.newHazelcastInstance(cfg);
return instance.getCluster().getLocalMember().toString() + ":" + input;
}
}
To execute a task with the executor framework:
ExecutorService
instance (generally via Executors
).Future
. Future
object to retrieve the result as shown in the code example below.Below, the Echo task is executed.
ExecutorService executorService = Executors.newSingleThreadExecutor();
Future<String> future = executorService.submit( new Echo( "myinput") );
//while it is executing, do some useful stuff
//when ready, get the result of your execution
String result = future.get();
Please note that the Echo callable in the above code sample also implements a Serializable interface, since it may be sent to another JVM to be processed.
NOTE: When a task is deserialized, HazelcastInstance needs to be accessed. To do this, the task should implement HazelcastInstanceAware
interface. Please see the HazelcastInstanceAware Interface section for more information.
In Hazelcast, when you implement a task as java.util.concurrent.runnable
(a task that does not return a value), you implement Runnable and Serializable.
Below is Runnable example code. It is a task that waits for some time and echoes a message.
public class EchoTask implements Runnable, Serializable {
private final String msg;
public EchoTask( String msg ) {
this.msg = msg;
}
@Override
public void run() {
try {
Thread.sleep( 5000 );
} catch ( InterruptedException e ) {
}
System.out.println( "echo:" + msg );
}
}
To execute the task:
HazelcastInstance
.Now let's write a class that submits and executes these echo messages. Executor is retrieved from HazelcastInstance
and 1000 echo tasks are submitted.
public class MasterMember {
public static void main( String[] args ) throws Exception {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IExecutorService executor = hazelcastInstance.getExecutorService( "exec" );
for ( int k = 1; k <= 1000; k++ ) {
Thread.sleep( 1000 );
System.out.println( "Producing echo task: " + k );
executor.execute( new EchoTask( String.valueOf( k ) ) );
}
System.out.println( "EchoTaskMain finished!" );
}
}
By default, Executor is configured to have 8 threads in the pool. You can change that with the pool-size
property in the declarative configuration (hazelcast.xml
). An example is shown below (using the above Executor).
<executor-service name="exec">
<pool-size>1</pool-size>
</executor-service>
RELATED INFORMATION
Please refer to the Executor Service Configuration section for a full description of Hazelcast Distributed Executor Service configuration.
You can scale the Executor service both vertically (scale up) and horizontally (scale out).
To scale up, you should improve the processing capacity of the JVM. You can do this by increasing the pool-size
property mentioned in the Executor Thread Configuration section (i.e., increasing the thread count). However, please be aware of your JVM's capacity. If you think it cannot handle such an additional load caused by increasing the thread count, you may want to consider improving the JVM's resources (CPU, memory, etc.). As an example, set the pool-size
to 5 and run the above MasterMember
. You will see that EchoTask
is run as soon as it is produced.
To scale out, more JVMs should be added instead of increasing only one JVM's capacity. In reality, you may want to expand your cluster by adding more physical or virtual machines. For example, in the EchoTask example in the Runnable section, you can create another Hazelcast instance. That instance will automatically get involved in the executions started in MasterMember
and start processing.
The distributed executor service is a distributed implementation of java.util.concurrent.ExecutorService
. It allows you to execute your code in the cluster. In this section, the code examples are based on the Echo class above (please note that the Echo class is Serializable
). The code examples show how Hazelcast can execute your code (Runnable, Callable
):
import com.hazelcast.core.Member;
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.IExecutorService;
import java.util.concurrent.Callable;
import java.util.concurrent.Future;
import java.util.Set;
public void echoOnTheMember( String input, Member member ) throws Exception {
Callable<String> task = new Echo( input );
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IExecutorService executorService =
hazelcastInstance.getExecutorService( "default" );
Future<String> future = executorService.submitToMember( task, member );
String echoResult = future.get();
}
public void echoOnTheMemberOwningTheKey( String input, Object key ) throws Exception {
Callable<String> task = new Echo( input );
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IExecutorService executorService =
hazelcastInstance.getExecutorService( "default" );
Future<String> future = executorService.submitToKeyOwner( task, key );
String echoResult = future.get();
}
public void echoOnSomewhere( String input ) throws Exception {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IExecutorService executorService =
hazelcastInstance.getExecutorService( "default" );
Future<String> future = executorService.submit( new Echo( input ) );
String echoResult = future.get();
}
public void echoOnMembers( String input, Set<Member> members ) throws Exception {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IExecutorService executorService =
hazelcastInstance.getExecutorService( "default" );
Map<Member, Future<String>> futures = executorService
.submitToMembers( new Echo( input ), members );
for ( Future<String> future : futures.values() ) {
String echoResult = future.get();
// ...
}
}
NOTE: You can obtain the set of cluster members via HazelcastInstance#getCluster().getMembers()
call.
A task in the code you execute in a cluster might take longer than expected. If you cannot stop/cancel that task, it will keep eating your resources.
To cancel a task, you can use the standard Java executor framework's cancel()
API. This framework encourages us to code and design for cancellations, a highly ignored part of software development.
The Fibonacci callable class below calculates the Fibonacci number for a given number. In the calculate
method, we check if the current thread is interrupted so that the code can respond to cancellations once the execution is started.
public class Fibonacci<Long> implements Callable<Long>, Serializable {
int input = 0;
public Fibonacci() {
}
public Fibonacci( int input ) {
this.input = input;
}
public Long call() {
return calculate( input );
}
private long calculate( int n ) {
if ( Thread.currentThread().isInterrupted() ) {
return 0;
}
if ( n <= 1 ) {
return n;
} else {
return calculate( n - 1 ) + calculate( n - 2 );
}
}
}
The fib()
method below submits the Fibonacci calculation task above for number 'n' and waits a maximum of 3 seconds for the result. If the execution does not completed in 3 seconds, future.get()
will throw a TimeoutException
and upon catching it, we cancel the execution, saving some CPU cycles.
long fib( int n ) throws Exception {
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IExecutorService es = hazelcastInstance.getExecutorService();
Future future = es.submit( new Fibonacci( n ) );
try {
return future.get( 3, TimeUnit.SECONDS );
} catch ( TimeoutException e ) {
future.cancel( true );
}
return -1;
}
fib(20)
will probably take less than 3 seconds. However, fib(50)
will take much longer. (This is not an example for writing better Fibonacci calculation code, but for showing how to cancel a running execution that takes too long.) The method future.cancel(false)
can only cancel execution before it is running (executing), but future.cancel(true)
can interrupt running executions if your code is able to handle the interruption. If you are willing to cancel an already running task, then your task should be designed to handle interruptions. If the calculate (int n)
method did not have the (Thread.currentThread().isInterrupted())
line, then you would not be able to cancel the execution after it is started.
You can use the ExecutionCallback
offered by Hazelcast to asynchronously be notified when the execution is done.
Let's use the Fibonacci series to explain this. The example code below is the calculation that will be executed. Note that it is Callable and Serializable.
public class Fibonacci<Long> implements Callable<Long>, Serializable {
int input = 0;
public Fibonacci() {
}
public Fibonacci( int input ) {
this.input = input;
}
public Long call() {
return calculate( input );
}
private long calculate( int n ) {
if (n <= 1) {
return n;
} else {
return calculate( n - 1 ) + calculate( n - 2 );
}
}
}
The example code below submits the Fibonacci calculation to ExecutionCallback
and prints the result asynchronously. ExecutionCallback
has the methods onResponse
and onFailure
. In this example code, onResponse
is called upon a valid response and prints the calculation result, whereas onFailure
is called upon a failure and prints the stacktrace.
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.ExecutionCallback;
import com.hazelcast.core.IExecutorService;
import java.util.concurrent.Future;
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IExecutorService es = hazelcastInstance.getExecutorService();
Callable<Long> task = new Fibonacci( 10 );
es.submit(task, new ExecutionCallback<Long> () {
@Override
public void onResponse( Long response ) {
System.out.println( "Fibonacci calculation result = " + response );
}
@Override
public void onFailure( Throwable t ) {
t.printStackTrace();
}
};
As previously mentioned, it is possible to indicate where in the Hazelcast cluster the Runnable
or Callable
is executed. Usually, you will execute these in the cluster based on the location of a key, set of keys or just allow Hazelcast to select a member.
If want more control over where your code runs, you can use the MemberSelector
interface. For example, you may want certain tasks to run only on certain members, or you may wish to implement some form of custom load balancing regime. The MemberSelector
is an interface that you can implement and then provide to the IExecutorService
when you submit or execute.
The select(Member)
method is called for every available member in the cluster and it is up to the implementation to decide if the member is going to be used or not.
In a simple example shown below, we select the cluster members based on the presence of an attribute.
public class MyMemberSelector implements MemberSelector {
public boolean select(Member member) {
return Boolean.TRUE.equals(member.getAttribute("my.special.executor"));
}
}
Hazelcast supports entry processing. An entry processor is a function that executes your code on a map entry in an atomic way.
An entry processor is a good option if you perform bulk processing on an IMap
. Usually, you perform a loop of keys: executing IMap.get(key)
, mutating the value, and finally putting the entry back in the map using IMap.put(key,value)
. If you perform this process from a client or from a member where the keys do not exist, you effectively perform 2 network hops for each update: the first to retrieve the data and the second to update the mutated value.
If you are doing the above, you should consider using entry processors. An entry processor executes a read and updates upon the member where the data resides. This eliminates the costly network hops described previously.
An entry processor enables fast in-memory operations on your map without you having to worry about locks or concurrency issues. It can be applied to a single map entry or to all map entries. It supports choosing target entries using predicates. You do not need any explicit lock on entry: Hazelcast locks the entry, runs the EntryProcessor, and then unlocks the entry.
Hazelcast sends the entry processor to each cluster member and these members apply it to map entries. Therefore, if you add more members, your processing is completed faster.
If entry processing is the major operation for a map and if the map consists of complex objects, you should use OBJECT
as the in-memory-format
to minimize serialization cost. By default, the entry value is stored as a byte array (BINARY
format). When it is stored as an object (OBJECT
format), then the entry processor is applied directly on the object. In that case, no serialization or deserialization is performed. But if there is a defined event listener, a new entry value will be serialized when passing to the event publisher service.
NOTE: When in-memory-format
is OBJECT
, old value of the updated entry will be null.
The methods below are in the IMap interface for entry processing.
executeOnKey
processes an entry mapped by a key.executeOnKeys
processes entries mapped by a collection of keys.submitToKey
processes an entry mapped by a key while listening to event status.executeOnEntries
processes all entries in a map.executeOnEntries
can also process all entries in a map with a defined predicate./**
* Applies the user defined EntryProcessor to the entry mapped by the key.
* Returns the object which is the result of the process() method of EntryProcessor.
*/
Object executeOnKey( K key, EntryProcessor entryProcessor );
/**
* Applies the user defined EntryProcessor to the entries mapped by the collection of keys.
* Returns the results mapped by each key in the collection.
*/
Map<K, Object> executeOnKeys( Set<K> keys, EntryProcessor entryProcessor );
/**
* Applies the user defined EntryProcessor to the entry mapped by the key with
* specified ExecutionCallback to listen to event status and return immediately.
*/
void submitToKey( K key, EntryProcessor entryProcessor, ExecutionCallback callback );
/**
* Applies the user defined EntryProcessor to all entries in the map.
* Returns the results mapped by each key in the map.
*/
Map<K, Object> executeOnEntries( EntryProcessor entryProcessor );
/**
* Applies the user defined EntryProcessor to the entries in the map which satisfies
provided predicate.
* Returns the results mapped by each key in the map.
*/
Map<K, Object> executeOnEntries( EntryProcessor entryProcessor, Predicate predicate );
NOTE: Entry Processors run via Operation Threads that are dedicated to specific partitions. Therefore, with long running Entry Processor executions, other partition operations such as map.put(key)
cannot be processed. With this in mind, it is a good practice to make your Entry Processor executions as quick as possible.
EntryProcessor
InterfaceThe following is the EntryProcessor
interface:
public interface EntryProcessor<K, V> extends Serializable {
Object process( Map.Entry<K, V> entry );
EntryBackupProcessor<K, V> getBackupProcessor();
}
NOTE: If you want to execute a task on a single key, you can also use executeOnKeyOwner
provided by Executor Service. But, in this case, you need to perform a lock and serialization.
When using executeOnEntries
method, if the number of entries is high and you do need the results, then returning null in process()
method is a good practice. By this way, results of the processing is not stored in the map and hence out of memory errors are eliminated.
If your code modifies the data, then you should also provide a processor for backup entries. This is required to prevent the primary map entries from having different values than the backups; it causes the entry processor to be applied both on the primary and backup entries.
public interface EntryBackupProcessor<K, V> extends Serializable {
void processBackup( Map.Entry<K, V> entry );
}
NOTE: There is a possibility that an Entry Processor can see that a key exists but its backup processor may not find it at the run time due to an unsent backup of a previous operation (e.g. a previous put operation). In those situations, Hazelcast internally/eventually will synchronize those owner and backup partitions so you will not lose any data. When coding an EntryBackupProcessor
, you should take that case into account, otherwise NullPointerException
can be seen since Map.Entry.getValue()
may return null
.
The EntryProcessorTest class has the following methods.
testMapEntryProcessor
puts one map entry and calls executeOnKey
to process that map entry.testMapEntryProcessor
puts all the entries in a map and calls executeOnEntries
to process
all the entries.The static class IncrementingEntryProcessor
creates an entry processor to process the map
entries in the EntryProcessorTest class. It creates the entry processor class by:
EntryProcessor
and EntryBackupProcessor
.java.io.Serializable
interface.EntryProcessor
methods process
and getBackupProcessor
.EntryBackupProcessor
method processBackup
.public class EntryProcessorTest {
@Test
public void testMapEntryProcessor() throws InterruptedException {
Config config = new Config().getMapConfig( "default" )
.setInMemoryFormat( MapConfig.InMemoryFormat.OBJECT );
HazelcastInstance hazelcastInstance1 = Hazelcast.newHazelcastInstance( config );
HazelcastInstance hazelcastInstance2 = Hazelcast.newHazelcastInstance( config );
IMap<Integer, Integer> map = hazelcastInstance1.getMap( "mapEntryProcessor" );
map.put( 1, 1 );
EntryProcessor entryProcessor = new IncrementingEntryProcessor();
map.executeOnKey( 1, entryProcessor );
assertEquals( map.get( 1 ), (Object) 2 );
hazelcastInstance1.getLifecycleService().shutdown();
hazelcastInstance2.getLifecycleService().shutdown();
}
@Test
public void testMapEntryProcessorAllKeys() throws InterruptedException {
StaticNodeFactory factory = new StaticNodeFactory( 2 );
Config config = new Config().getMapConfig( "default" )
.setInMemoryFormat( MapConfig.InMemoryFormat.OBJECT );
HazelcastInstance hazelcastInstance1 = factory.newHazelcastInstance( config );
HazelcastInstance hazelcastInstance2 = factory.newHazelcastInstance( config );
IMap<Integer, Integer> map = hazelcastInstance1
.getMap( "mapEntryProcessorAllKeys" );
int size = 100;
for ( int i = 0; i < size; i++ ) {
map.put( i, i );
}
EntryProcessor entryProcessor = new IncrementingEntryProcessor();
Map<Integer, Object> res = map.executeOnEntries( entryProcessor );
for ( int i = 0; i < size; i++ ) {
assertEquals( map.get( i ), (Object) (i + 1) );
}
for ( int i = 0; i < size; i++ ) {
assertEquals( map.get( i ) + 1, res.get( i ) );
}
hazelcastInstance1.getLifecycleService().shutdown();
hazelcastInstance2.getLifecycleService().shutdown();
}
static class IncrementingEntryProcessor
implements EntryProcessor, EntryBackupProcessor, Serializable {
public Object process( Map.Entry entry ) {
Integer value = (Integer) entry.getValue();
entry.setValue( value + 1 );
return value + 1;
}
public EntryBackupProcessor getBackupProcessor() {
return IncrementingEntryProcessor.this;
}
public void processBackup( Map.Entry entry ) {
entry.setValue( (Integer) entry.getValue() + 1 );
}
}
}
NOTE: You should explicitly call setValue
method of Map.Entry
when modifying data in Entry Processor. Otherwise, Entry Processor will be accepted as read-only.
NOTE: An Entry Processor instance is not thread safe. If you are storing partition specific state between invocations, be sure to register this in a thread-local. An Entry Processor instance can be used by multiple partition threads.
You can use the AbstractEntryProcessor
class when the same processing will be performed both on the primary and backup map entries (i.e. the same logic applies to them). If you use Entry Processor, you need to apply the same logic to the backup entries separately. The AbstractEntryProcessor
class makes this primary/backup processing easier.
The code below shows the Hazelcast AbstractEntryProcessor
class. You can use it to create your own Abstract Entry Processor.
public abstract class AbstractEntryProcessor <K, V>
implements EntryProcessor <K, V> {
private final EntryBackupProcessor <K,V> entryBackupProcessor;
public AbstractEntryProcessor() {
this(true);
}
public AbstractEntryProcessor(boolean applyOnBackup) {
if ( applyOnBackup ) {
entryBackupProcessor = new EntryBackupProcessorImpl();
} else {
entryBackupProcessor = null;
}
}
@Override
public abstract Object process(Map.Entry<K, V> entry);
@Override
public final EntryBackupProcessor <K, V> getBackupProcessor() {
return entryBackupProcessor;
}
private class EntryBackupProcessorImpl implements EntryBackupProcessor <K,V>{
@Override
public void processBackup(Map.Entry<K, V> entry) {
process(entry);
}
}
}
In the above code, the method getBackupProcessor
returns an EntryBackupProcessor
instance. This means the same processing will be applied to both the primary and backup entries. If you want to apply the processing only upon the primary entries, then make the getBackupProcessor
method return null.
NOTE: Beware of the null issue described at the note in the Processing Backup Entries section. Due to a yet unsent backup from a previous operation, an EntryBackupProcessor
may temporarily receive null
from Map.Entry.getValue()
even though the value actually exists in the map. If you decide to use AbstractEntryProcessor
, make sure your code logic is not sensitive to null values, or you may encounter NullPointerException
during runtime.
Distributed queries access data from multiple data sources stored on either the same or different computers.
Hazelcast partitions your data and spreads it across cluster of servers. You can iterate over the map entries and look for certain entries (specified by predicates) you are interested in. However, this is not very efficient because you will have to bring the entire entry set and iterate locally. Instead, Hazelcast allows you to run distributed queries on your distributed map.
If you add new members to the cluster, the partition count for each member is reduced and hence the time spent by each member on iterating its entries is reduced. Therefore, the above querying approach is highly scalable. Another reason it is highly scalable is the pool of partition threads that evaluates the entries concurrently in each member. The network traffic is also reduced since only filtered data is sent to the requester.
Hazelcast offers the following APIs for distributed query purposes:
Assume that you have an "employee" map containing values of Employee
objects, as coded below.
import java.io.Serializable;
public class Employee implements Serializable {
private String name;
private int age;
private boolean active;
private double salary;
public Employee(String name, int age, boolean live, double price) {
this.name = name;
this.age = age;
this.active = live;
this.salary = price;
}
public Employee() {
}
public String getName() {
return name;
}
public int getAge() {
return age;
}
public double getSalary() {
return salary;
}
public boolean isActive() {
return active;
}
}
Now, let's look for the employees who are active and have an age less than 30 using the aforementioned APIs (Criteria API and Distributed SQL Query). The following subsections describe each query mechanism for this example.
NOTE: When using Portable objects, if one field of an object exists on one node but does not exist on another one, Hazelcast does not throw an unknown field exception. Instead, Hazelcast treats that predicate, which tries to perform a query on an unknown field, as an always false predicate.
Criteria API is a programming interface offered by Hazelcast that is similar to the Java Persistence Query Language (JPQL). Below is the code for the above example query.
import com.hazelcast.core.IMap;
import com.hazelcast.query.Predicate;
import com.hazelcast.query.PredicateBuilder;
import com.hazelcast.query.EntryObject;
import com.hazelcast.config.Config;
IMap<String, Employee> map = hazelcastInstance.getMap( "employee" );
EntryObject e = new PredicateBuilder().getEntryObject();
Predicate predicate = e.is( "active" ).and( e.get( "age" ).lessThan( 30 ) );
Set<Employee> employees = map.values( predicate );
In the above example code, predicate
verifies whether the entry is active and its age
value is less than 30. This predicate
is
applied to the employee
map using the map.values(predicate)
method. This method sends the predicate to all cluster members
and merges the results coming from them. Since the predicate is communicated between the members, it needs to
be serializable.
NOTE: Predicates can also be applied to keySet
, entrySet
and localKeySet
of Hazelcast distributed
map.
The Predicates
class offered by Hazelcast includes many operators for your query requirements. Some of them are
explained below.
equal
: checks if the result of an expression is equal to a given value.notEqual
: checks if the result of an expression is not equal to a given value.instanceOf
: checks if the result of an expression has a certain type.like
: checks if the result of an expression matches some string pattern. % (percentage sign) is placeholder for many
characters, (underscore) is placeholder for only one character.greaterThan
: checks if the result of an expression is greater than a certain value.greaterEqual
: checks if the result of an expression is greater than or equal to a certain value.lessThan
: checks if the result of an expression is less than a certain value.lessEqual
: checks if the result of an expression is less than or equal to a certain value.between
: checks if the result of an expression is between 2 values (this is inclusive).in
: checks if the result of an expression is an element of a certain collection.isNot
: checks if the result of an expression is false.regex
: checks if the result of an expression matches some regular expression.
RELATED INFORMATION
Please see the Predicates class for all predicates provided.
Predicates can be joined using the and
, or
and not
operators, as shown in the below examples.
public Set<Person> getWithNameAndAge( String name, int age ) {
Predicate namePredicate = Predicates.equal( "name", name );
Predicate agePredicate = Predicates.equal( "age", age );
Predicate predicate = Predicates.and( namePredicate, agePredicate );
return personMap.values( predicate );
}
public Set<Person> getWithNameOrAge( String name, int age ) {
Predicate namePredicate = Predicates.equal( "name", name );
Predicate agePredicate = Predicates.equal( "age", age );
Predicate predicate = Predicates.or( namePredicate, agePredicate );
return personMap.values( predicate );
}
public Set<Person> getNotWithName( String name ) {
Predicate namePredicate = Predicates.equal( "name", name );
Predicate predicate = Predicates.not( namePredicate );
return personMap.values( predicate );
}
You can simplify predicate usage with the PredicateBuilder
class, which offers simpler predicate building. Please see the
below example code which selects all people with a certain name and age.
public Set<Person> getWithNameAndAgeSimplified( String name, int age ) {
EntryObject e = new PredicateBuilder().getEntryObject();
Predicate agePredicate = e.get( "age" ).equal( age );
Predicate predicate = e.get( "name" ).equal( name ).and( agePredicate );
return personMap.values( predicate );
}
com.hazelcast.query.SqlPredicate
takes the regular SQL where
clause. Here is an example:
IMap<Employee> map = hazelcastInstance.getMap( "employee" );
Set<Employee> employees = map.values( new SqlPredicate( "active AND age < 30" ) );
AND/OR: <expression> AND <expression> AND <expression>...
active AND age>30
active=false OR age = 45 OR name = 'Joe'
active AND ( age > 20 OR salary < 60000 )
Equality: =, !=, <, <=, >, >=
<expression> = value
age <= 30
name = "Joe"
salary != 50000
BETWEEN: <attribute> [NOT] BETWEEN <value1> AND <value2>
age BETWEEN 20 AND 33 ( same as age >= 20 AND age <= 33 )
age NOT BETWEEN 30 AND 40 ( same as age < 30 OR age > 40 )
LIKE: <attribute> [NOT] LIKE 'expression'
The %
(percentage sign) is placeholder for multiple characters, an _
(underscore) is placeholder for only one character.
name LIKE 'Jo%'
(true for 'Joe', 'Josh', 'Joseph' etc.)name LIKE 'Jo_'
(true for 'Joe'; false for 'Josh')name NOT LIKE 'Jo_'
(true for 'Josh'; false for 'Joe')name LIKE 'J_s%'
(true for 'Josh', 'Joseph'; false 'John', 'Joe')IN: <attribute> [NOT] IN (val1, val2,...)
age IN ( 20, 30, 40 )
age NOT IN ( 60, 70 )
active AND ( salary >= 50000 OR ( age NOT BETWEEN 20 AND 30 ) )
age IN ( 20, 30, 40 ) AND salary BETWEEN ( 50000, 80000 )
Hazelcast provides paging for defined predicates. With its PagingPredicate
class, you can
get a collection of keys, values, or entries page by page by filtering them with predicates and giving the size of the pages. Also, you
can sort the entries by specifying comparators.
In the example code below, the greaterEqual
predicate gets values from the "students" map. This predicate has a filter
to retrieve the objects with a "age" greater than or equal to 18. Then a PagingPredicate
is
constructed in which the page size is 5, so there will be 5 objects in each page.
The first time the values are called creates the first page. You can get the subsequent pages by using the nextPage()
method of PagingPredicate
and querying the map again with the updated PagingPredicate
.
IMap<Integer, Student> map = hazelcastInstance.getMap( "students" );
Predicate greaterEqual = Predicates.greaterEqual( "age", 18 );
PagingPredicate pagingPredicate = new PagingPredicate( greaterEqual, 5 );
// Retrieve the first page
Collection<Student> values = map.values( pagingPredicate );
...
// Set up next page
pagingPredicate.nextPage();
// Retrieve next page
values = map.values( pagingPredicate );
...
If a comparator is not specified for PagingPredicate
, but you want to get a collection of keys or values page by page, this collection must be an instance of Comparable
(i.e. it must implement java.lang.Comparable
). Otherwise, the java.lang.IllegalArgument
exception is thrown.
Paging Predicate, also known as Order & Limit, is not supported in Transactional Context.
NOTE: Currently, random page accessing is not supported.
RELATED INFORMATION
Please refer to the Javadoc for all predicates.
Hazelcast distributed queries will run on each member in parallel and only results will return the conn. When a query runs on a
member, Hazelcast will iterate through the entire owned entries and find the matching ones. This can be made faster by indexing
the mostly queried fields, just like you would do for your database. Indexing will add overhead for each write
operation but queries will be a lot faster. If you query your map a lot, make sure to add indexes for the most frequently
queried fields. For example, if your active and age < 30
query, make sure you add index for active
and
age
fields. Here is how to do it.
IMap map = hazelcastInstance.getMap( "employees" );
// ordered, since we have ranged queries for this field
map.addIndex( "age", true );
// not ordered, because boolean field cannot have range
map.addIndex( "active", false );
IMap.addIndex(fieldName, ordered)
is used for adding index. For each indexed field, if you have ranged queries such as age>30
,
age BETWEEN 40 AND 60
, then you should set the ordered
parameter to true
. Otherwise, set it to false
.
Also, you can define IMap
indexes in configuration. An example is shown below.
<map name="default">
...
<indexes>
<index ordered="false">name</index>
<index ordered="true">age</index>
</indexes>
</map>
You can also define IMap
indexes using programmatic configuration, as in the example below.
mapConfig.addMapIndexConfig( new MapIndexConfig( "name", false ) );
mapConfig.addMapIndexConfig( new MapIndexConfig( "age", true ) );
The following is the Spring declarative configuration for the same sample.
<hz:map name="default">
<hz:indexes>
<hz:index attribute="name"/>
<hz:index attribute="age" ordered="true"/>
</hz:indexes>
</hz:map>
NOTE: Non-primitive types to be indexed should implement Comparable
.
You can change the size of the thread pool dedicated to query operations using the pool-size
property. Below is an example of that declarative configuration.
<executor-service name="hz:query">
<pool-size>100</pool-size>
</executor-service>
Below is an example of the equivalent programmatic configuration.
Config cfg = new Config();
cfg.getExecutorConfig("hz:query").setPoolSize(100);
You have likely heard about MapReduce ever since Google released its research white paper on this concept. With Hadoop as the most common and well known implementation, MapReduce gained a broad audience and made it into all kinds of business applications dominated by data warehouses.
MapReduce is a software framework for processing large amounts of data in a distributed way. Therefore, the processing is normally spread over several machines. The basic idea behind MapReduce is to map your source data into a collection of key-value pairs and reducing those pairs, grouped by key, in a second step towards the final result.
The main idea can be summarized with the following steps.
Use Cases
The best known examples for MapReduce algorithms are text processing tools, such as counting the word frequency in large texts or websites. Apart from that, there are more interesting examples of use cases listed below.
This section will give a deeper insight on the MapReduce pattern and helps you understand the semantics behind the different MapReduce phases and how they are implemented in Hazelcast.
In addition to this, the following sections compare Hadoop and Hazelcast MapReduce implementations to help adopters with Hadoop backgrounds to quickly get familiar with Hazelcast MapReduce.
The flowchart below demonstrates the basic workflow of the word count example (distributed occurrences analysis) mentioned in the MapReduce section. From left to right, it iterates over all the entries of a data structure (in this case an IMap). In the mapping phase, it splits the sentence into single words and emits a key-value pair per word: the word is the key, 1 is the value. In the next phase, values are collected (grouped) and transported to their corresponding reducers, where they are eventually reduced to a single key-value pair, the value being the number of occurrences of the word. At the last step, the different reducer results are grouped up to the final result and returned to the requester.
In pseudo code, the corresponding map and reduce function would look like the following. A Hazelcast code example will be shown in the next section.
map( key:String, document:String ):Void ->
for each w:word in document:
emit( w, 1 )
reduce( word:String, counts:List[Int] ):Int ->
return sum( counts )
As seen in the workflow example, a MapReduce process consists of multiple phases. The original MapReduce pattern describes two phases (map, reduce) and one optional phase (combine). In Hazelcast, these phases are either only existing virtually to explain the data flow or are executed in parallel during the real operation while the general idea is still persisting.
(K x V)* -> (L x W)*
[(k1, v1), ..., (kn, vn)] -> [(l1, w1), ..., (lm, wm)]
Mapping Phase
The mapping phase iterates all key-value pairs of any kind of legal input source. The mapper then analyzes the input pairs and emits zero or more new key-value pairs.
K x V -> (L x W)*
(k, v) -> [(l1, w1), ..., (ln, wn)]
Combine Phase
In the combine phase, multiple key-value pairs with the same key are collected and combined to an intermediate result before being send to the reducers. Combine phase is also optional in Hazelcast, but is highly recommended to lower the traffic.
In terms of the word count example, this can be explained using the sentences "Saturn is a planet but the Earth is a planet, too". As shown above, we would send two key-value pairs (planet, 1). The registered combiner now collects those two pairs and combines them into an intermediate result of (planet, 2). Instead of two key-value pairs sent through the wire, there is now only one for the key "planet".
The pseudo code for a combiner is similar to the reducer.
combine( word:String, counts:List[Int] ):Void ->
emit( word, sum( counts ) )
Grouping / Shuffling Phase
The grouping or shuffling phase only exists virtually in Hazelcast since it is not a real phase; emitted key-value pairs with the same key are always transferred to the same reducer in the same job. They are grouped together, which is equivalent to the shuffling phase.
Reducing Phase
In the reducing phase, the collected intermediate key-value pairs are reduced by their keys to build the final by-key result. This value can be a sum of all the emitted values of the same key, an average value, or something completely different, depending on the use case.
Here is a reduced representation of this phase.
L x W* -> X*
(l, [w1, ..., wn]) -> [x1, ..., xn]
Producing the Final Result
This is not a real MapReduce phase, but it is the final step in Hazelcast after all reducers are notified that reducing has finished. The original job initiator then requests all reduced results and builds the final result.
The Internet is full of useful resources to find deeper information on MapReduce. Below is a short collection of more introduction material. In addition, there are books written about all kinds of MapReduce patterns and how to write a MapReduce function for your use case. To name them all is out of scope of this documentation.
This section explains the basics of the Hazelcast MapReduce framework. While walking through the different API classes, we will build the word count example that was discussed earlier and create it step by step.
The Hazelcast API for MapReduce operations consists of a fluent DSL-like configuration syntax to build and submit jobs. JobTracker
is the basic entry point to all MapReduce operations and is retrieved from com.hazelcast.core.HazelcastInstance
by calling getJobTracker
and supplying the name of the required JobTracker
configuration. The configuration for JobTracker
s will be discussed later, for now we focus on the API itself.
In addition, the complete submission part of the API is built to support a fully reactive way of programming.
To give an easy introduction to people used to Hadoop, we created the class names to be as familiar as possible to their counterparts on Hadoop. That means while most users will recognize a lot of similar sounding classes, the way to configure the jobs is more fluent due to the DSL-like styled API.
While building the example, we will go through as many options as possible, e.g. we create a specialized JobTracker
configuration (at the end). Special JobTracker
configuration is not required, because for all other Hazelcast features you can use "default" as the configuration name. However, special configurations offer better options to predict behavior of the framework execution.
The full example is available here as a ready to run Maven project.
JobTracker
creates Job instances, whereas every instance of com.hazelcast.mapreduce.Job
defines a single MapReduce configuration. The same Job can be submitted multiple times, no matter if it is executed in parallel or after the previous execution is finished.
NOTE: After retrieving the JobTracker
, be aware that it should only be used with data structures derived from the same HazelcastInstance. Otherwise, you can get unexpected behavior.
To retrieve a JobTracker
from Hazelcast, we will start by using the "default" configuration for convenience reasons to show the basic way.
import com.hazelcast.mapreduce.*;
JobTracker jobTracker = hazelcastInstance.getJobTracker( "default" );
JobTracker
is retrieved using the same kind of entry point as most other Hazelcast features. After building the cluster connection, you use the created HazelcastInstance to request the configured (or default) JobTracker
from Hazelcast.
The next step will be to create a new Job
and configure it to execute our first MapReduce request against cluster data.
As mentioned in the JobTracker section, a Job is created using the retrieved JobTracker
instance. A Job defines exactly one configuration of a MapReduce task. Mapper, combiner and reducers will be defined per job but since the Job instance is only a configuration, it is possible to be submitted multiple times, no matter if executions happening in parallel or one after the other.
A submitted job is always identified using a unique combination of the JobTracker
's name and a jobId generated on submit-time. The way for retrieving the jobId will be shown in one of the later sections.
To create a Job, a second class com.hazelcast.mapreduce.KeyValueSource
is necessary. We will have a deeper look at the KeyValueSource class in the next section, for now it is enough to know that it is used to wrap any kind of data or data structure into a well defined set of key-value pairs.
Below example code is a direct follow up of the example of the JobTracker section and reuses the already created HazelcastInstance and JobTracker
instances.
We start by retrieving an instance of our data map and create the Job instance afterwards. Implementations used to configure the Job will be discussed while walking further through the API documentation, they are not yet discussed.
NOTE: Since the Job class is highly dependent upon generics to support type safety, the generics change over time and may not be assignment compatible to old variable types. To make use of the full potential of the fluent API, we recommend you use fluent method chaining as shown in this example to prevent the need for too many variables.
IMap<String, String> map = hazelcastInstance.getMap( "articles" );
KeyValueSource<String, String> source = KeyValueSource.fromMap( map );
Job<String, String> job = jobTracker.newJob( source );
ICompletableFuture<Map<String, Long>> future = job
.mapper( new TokenizerMapper() )
.combiner( new WordCountCombinerFactory() )
.reducer( new WordCountReducerFactory() )
.submit();
// Attach a callback listener
future.andThen( buildCallback() );
// Wait and retrieve the result
Map<String, Long> result = future.get();
As seen above, we create the Job instance and define a mapper, combiner, reducer and eventually submit the request to the cluster. The submit
method returns an ICompletableFuture that can be used to attach our callbacks or just to wait for the result to be processed in a blocking fashion.
There are more options available for job configurations such as defining a general chunk size or on what keys the operation will operate. For more information, please refer to the Javadoc matching your Hazelcast version.
KeyValueSource
is able to either wrap Hazelcast data structures (like IMap, MultiMap, IList, ISet) into key-value pair input sources, or build your own custom key-value input source. The latter option makes it possible to feed Hazelcast MapReduce with all kinds of data, such as just-in-time downloaded web page contents or data files. People familiar with Hadoop will recognize similarities with the Input class.
You can imagine a KeyValueSource
as a bigger java.util.Iterator
implementation. Whereas most methods are required to be implemented, the getAllKeys
method is optional to implement. If implementation is able to gather all keys upfront, it should be implemented and isAllKeysSupported
must return true
. That way, Job configured KeyPredicates are able to evaluate keys upfront before sending them to the cluster. Otherwise, they are serialized and transferred as well, to be evaluated at execution time.
As shown in the example above, the abstract KeyValueSource
class provides a number of static methods to easily wrap Hazelcast data structures into KeyValueSource
implementations already provided by Hazelcast. The data structures' generics are inherited into the resulting KeyValueSource
instance. For data structures like IList or ISet, the key type is always String. While mapping, the key is the data structure's name whereas
the value type and value itself are inherited from the IList or ISet itself.
// KeyValueSource from com.hazelcast.core.IMap
IMap<String, String> map = hazelcastInstance.getMap( "my-map" );
KeyValueSource<String, String> source = KeyValueSource.fromMap( map );
// KeyValueSource from com.hazelcast.core.MultiMap
MultiMap<String, String> multiMap = hazelcastInstance.getMultiMap( "my-multimap" );
KeyValueSource<String, String> source = KeyValueSource.fromMultiMap( multiMap );
// KeyValueSource from com.hazelcast.core.IList
IList<String> list = hazelcastInstance.getList( "my-list" );
KeyValueSource<String, String> source = KeyValueSource.fromList( list );
// KeyValueSource from com.hazelcast.core.ISet
ISet<String> set = hazelcastInstance.getSet( "my-set" );
KeyValueSource<String, String> source = KeyValueSource.fromSet( set );
PartitionIdAware
The com.hazelcast.mapreduce.PartitionIdAware
interface can be implemented by the KeyValueSource
implementation if the underlying data set is aware of the Hazelcast partitioning schema (as it is for all internal data structures). If this interface is implemented, the same KeyValueSource
instance is reused multiple times for all partitions on the cluster node. As a consequence, the close
and open
methods are also executed
multiple times but once per partitionId.
Using the Mapper
interface, you will implement the mapping logic. Mappers can transform, split, calculate, aggregate data from data sources. In Hazelcast, it is also possible to integrate data from more than the KeyValueSource data source by implementing com.hazelcast.core.HazelcastInstanceAware
and requesting additional maps, multimaps, list, sets.
The mappers map
function is called once per available entry in the data structure. If you work on distributed data structures that operate in a partition based fashion, then multiple mappers work in parallel on the different cluster nodes, on the nodes' assigned partitions. Mappers then prepare and maybe transform the input key-value pair and emit zero or more key-value pairs for reducing phase.
For our word count example, we retrieve an input document (a text document) and we transform it by splitting the text into the available words. After that, as discussed in the pseudo code, we emit every single word with a key-value pair with the word as the key and 1 as the value.
A common implementation of that Mapper
might look like the following example:
public class TokenizerMapper implements Mapper<String, String, String, Long> {
private static final Long ONE = Long.valueOf( 1L );
@Override
public void map(String key, String document, Context<String, Long> context) {
StringTokenizer tokenizer = new StringTokenizer( document.toLowerCase() );
while ( tokenizer.hasMoreTokens() ) {
context.emit( tokenizer.nextToken(), ONE );
}
}
}
The code splits the mapped texts into their tokens, iterates over the tokenizer as long as there are more tokens, and emits a pair per word. Note that we're not yet collecting multiple occurrences of the same word, we just fire every word on its own.
LifecycleMapper / LifecycleMapperAdapter
The LifecycleMapper interface or its adapter class LifecycleMapperAdapter can be used to make the Mapper implementation lifecycle aware. That means it will be notified when mapping of a partition or set of data begins and when the last entry was mapped.
Only special algorithms might need those additional lifecycle events to prepare, clean up, or emit additional values.
As stated in the introduction, a Combiner is used to minimize traffic between the different cluster nodes when transmitting mapped values from mappers to the reducers. It does this by aggregating multiple values for the same emitted key. This is a fully optional operation, but using it is highly recommended.
Combiners can be seen as an intermediate reducer. The calculated value is always assigned back to the key for which the combiner initially was created. Since combiners are created per emitted key, the Combiner implementation itself is not defined in the jobs configuration; instead, a CombinerFactory is created that is able to create the expected Combiner instance.
Because Hazelcast MapReduce is executing mapping and reducing phase in parallel, the Combiner implementation must be able to deal with chunked data. Therefore, you must reset its internal state whenever you call finalizeChunk
. Calling that method creates a chunk of intermediate data to be grouped (shuffled) and sent to the reducers.
Combiners can override beginCombine
and finalizeCombine
to perform preparation or cleanup work.
For our word count example, we are going to have a simple CombinerFactory and Combiner implementation similar to the following example.
public class WordCountCombinerFactory
implements CombinerFactory<String, Long, Long> {
@Override
public Combiner<Long, Long> newCombiner( String key ) {
return new WordCountCombiner();
}
private class WordCountCombiner extends Combiner<Long, Long> {
private long sum = 0;
@Override
public void combine( Long value ) {
sum++;
}
@Override
public Long finalizeChunk() {
return sum;
}
@Override
public void reset() {
sum = 0;
}
}
}
The Combiner must be able to return its current value as a chunk and reset the internal state by setting sum
back to 0. Since combiners are always called from a single thread, no synchronization or volatility of the variables is necessary.
Reducers do the last bit of algorithm work. This can be aggregating values, calculating averages, or any other work that is expected from the algorithm.
Since values arrive in chunks, the reduce
method is called multiple times for every emitted value of the creation key. This also can happen multiple times per chunk if no Combiner implementation was configured for a job configuration.
In difference of the combiners, a reducers finalizeReduce
method is only called once per reducer (which means once per key). Therefore, a reducer does not need to reset its internal state at any time.
Reducers can override beginReduce
to perform preparation work.
For our word count example, the implementation will look similar to the following code example.
public class WordCountReducerFactory implements ReducerFactory<String, Long, Long> {
@Override
public Reducer<Long, Long> newReducer( String key ) {
return new WordCountReducer();
}
private class WordCountReducer extends Reducer<Long, Long> {
private volatile long sum = 0;
@Override
public void reduce( Long value ) {
sum += value.longValue();
}
@Override
public Long finalizeReduce() {
return sum;
}
}
}
Different from combiners, reducers tend to switch threads if running out of data to prevent blocking threads from the JobTracker
configuration. They are rescheduled at a later point when new data to be processed arrives but unlikely to be executed on the same thread as before. As of Hazelcast version 3.3.3 the guarantee for memory visibility on the new thread is ensured by the framework. This means the previous requirement for making fields volatile is dropped.
A Collator is an optional operation that is executed on the job emitting node and is able to modify the finally reduced result before returned to the user's codebase. Only special use cases are likely to use collators.
For an imaginary use case, we might want to know how many words were all over in the documents we analyzed. For this case, a Collator implementation can be given to the submit
method of the Job instance.
A collator would look like the following snippet:
public class WordCountCollator implements Collator<Map.Entry<String, Long>, Long> {
@Override
public Long collate( Iterable<Map.Entry<String, Long>> values ) {
long sum = 0;
for ( Map.Entry<String, Long> entry : values ) {
sum += entry.getValue().longValue();
}
return sum;
}
}
The definition of the input type is a bit strange, but because Combiner and Reducer implementations are optional, the input type heavily depends on the state of the data. As stated above, collators are non-typical use cases and the generics of the framework always help in finding the correct signature.
A KeyPredicate
can be used to pre-select whether or not a key should be selected for mapping in the mapping phase. If the KeyValueSource
implementation is able to know all keys prior to execution, the keys are filtered before the operations are divided among the different cluster nodes.
A KeyPredicate
can also be used to select only a special range of data (e.g. a time-frame) or similar use cases.
A basic KeyPredicate
implementation that only maps keys containing the word "hazelcast" might look like the following code example:
public class WordCountKeyPredicate implements KeyPredicate<String> {
@Override
public boolean evaluate( String s ) {
return s != null && s.toLowerCase().contains( "hazelcast" );
}
}
You can retrieve a TrackableJob
instance after submitting a job. It is requested from the JobTracker
using the unique jobId (per JobTracker
). It can be used to get runtime statistics of the job. The information available is limited to the number of processed (mapped) records and the processing state of the different partitions or nodes (if KeyValueSource
is not PartitionIdAware).
To retrieve the jobId after submission of the job, use com.hazelcast.mapreduce.JobCompletableFuture
instead of the com.hazelcast.core.ICompletableFuture
as the variable type for the returned future.
The example code below gives a quick introduction on how to retrieve the instance and the runtime data. For more information, please have a look at the Javadoc corresponding your running Hazelcast version.
IMap<String, String> map = hazelcastInstance.getMap( "articles" );
KeyValueSource<String, String> source = KeyValueSource.fromMap( map );
Job<String, String> job = jobTracker.newJob( source );
JobCompletableFuture<Map<String, Long>> future = job
.mapper( new TokenizerMapper() )
.combiner( new WordCountCombinerFactory() )
.reducer( new WordCountReducerFactory() )
.submit();
String jobId = future.getJobId();
TrackableJob trackableJob = jobTracker.getTrackableJob(jobId);
JobProcessInformation stats = trackableJob.getJobProcessInformation();
int processedRecords = stats.getProcessedRecords();
log( "ProcessedRecords: " + processedRecords );
JobPartitionState[] partitionStates = stats.getPartitionStates();
for ( JobPartitionState partitionState : partitionStates ) {
log( "PartitionOwner: " + partitionState.getOwner()
+ ", Processing state: " + partitionState.getState().name() );
}
NOTE: Caching of the JobProcessInformation does not work on Java native clients since current values are retrieved while retrieving the instance to minimize traffic between executing node and client.
The JobTracker
configuration is used to setup behavior of the Hazelcast MapReduce framework.
Every JobTracker
is capable of running multiple MapReduce jobs at once; one configuration is meant as a shared resource for all jobs created by the same JobTracker
. The configuration gives full control over the expected load behavior and thread counts to be used.
The following snippet shows a typical JobTracker
configuration. We will discuss the configuration properties one by one:
<jobtracker name="default">
<max-thread-size>0</max-thread-size>
<!-- Queue size 0 means number of partitions * 2 -->
<queue-size>0</queue-size>
<retry-count>0</retry-count>
<chunk-size>1000</chunk-size>
<communicate-stats>true</communicate-stats>
<topology-changed-strategy>CANCEL_RUNNING_OPERATION</topology-changed-strategy>
</jobtracker>
com.hazelcast.mapreduce.TopologyChangedException
).RELATED INFORMATION
Please refer to the MapReduce Jobtracker Configuration section for a full description of Hazelcast MapReduce JobTracker configuration (includes an example programmatic configuration).
This section explains some of the internals of the MapReduce framework. This is more advanced information. If you're not interested in how it works internally, you might want to skip this section.
To understand the following technical internals, we first have a short look at what happens in terms of an example workflow.
As a simple example, think of an IMap<String, Integer>
and emitted keys having the same types. Imagine you have a three node cluster and you initiate the MapReduce job on the first node. After you requested the JobTracker from your running / connected Hazelcast, we submit the task and retrieve the ICompletableFuture which gives us a chance to wait for the result to be calculated or to add a callback (and being more reactive).
The example expects that the chunk size is 0 or 1, so an emitted value is directly sent to the reducers. Internally, the job is prepared, started, and executed on all nodes as shown below. The first node acts as the job owner (job emitter).
Node1 starts MapReduce job
Node1 emits key=Foo, value=1
Node1 does PartitionService::getKeyOwner(Foo) => results in Node3
Node2 emits key=Foo, value=14
Node2 asks jobOwner (Node1) for keyOwner of Foo => results in Node3
Node1 sends chunk for key=Foo to Node3
Node3 receives chunk for key=Foo and looks if there is already a Reducer,
if not creates one for key=Foo
Node3 processes chunk for key=Foo
Node2 sends chunk for key=Foo to Node3
Node3 receives chunk for key=Foo and looks if there is already a Reducer and uses
the previous one
Node3 processes chunk for key=Foo
Node1 send LastChunk information to Node3 because processing local values finished
Node2 emits key=Foo, value=27
Node2 has cached keyOwner of Foo => results in Node3
Node2 sends chunk for key=Foo to Node3
Node3 receives chunk for key=Foo and looks if there is already a Reducer and uses
the previous one
Node3 processes chunk for key=Foo
Node2 send LastChunk information to Node3 because processing local values finished
Node3 finishes reducing for key=Foo
Node1 registers its local partitions are processed
Node2 registers its local partitions are processed
Node1 sees all partitions processed and requests reducing from all nodes
Node1 merges all reduced results together in a final structure and returns it
The flow is quite complex but extremely powerful since everything is executed in parallel. Reducers do not wait until all values are emitted, but they immediately begin to reduce (when first chunk for an emitted key arrives).
Beginning with the package level, there is one basic package: com.hazelcast.mapreduce
. This includes the external API and the impl package which itself contains the internal implementation.
KeyValueSource
implementations and abstract base and support classes for the exposed API.And now to the technical walk-through: a MapReduce Job is always retrieved from a named JobTracker
, which is implemented in NodeJobTracker
(extends AbstractJobTracker
) and is configured using the configuration DSL. All of the internal implementation is completely ICompletableFuture-driven and mostly non-blocking in design.
On submit, the Job creates a unique UUID which afterwards acts as a jobId and is combined with the JobTracker's name to be uniquely identifiable inside the cluster. Then, the preparation is sent around the cluster and every member prepares its execution by creating a JobSupervisor, MapCombineTask, and ReducerTask. The job-emitting JobSupervisor gains special capabilities to synchronize and control JobSupervisors on other nodes for the same job.
If preparation is finished on all nodes, the job itself is started by executing a StartProcessingJobOperation on every node. This initiates a MappingPhase implementation (defaults to KeyValueSourceMappingPhase) and starts the actual mapping on the nodes.
The mapping process is currently a single threaded operation per node, but will be extended to run in parallel on multiple partitions (configurable per Job) in future versions. The Mapper is now called on every available value on the partition and eventually emits values. For every emitted value, either a configured CombinerFactory is called to create a Combiner or a cached one is used (or the default CollectingCombinerFactory is used to create Combiners). When the chunk limit is reached on a node, a IntermediateChunkNotification is prepared by collecting emitted keys to their corresponding nodes. This is either done by asking the job owner to assign members or by an already cached assignment. In later versions, a PartitionStrategy might also be configurable.
The IntermediateChunkNotification is then sent to the reducers (containing only values for this node) and is offered to the ReducerTask. On every offer, the ReducerTask checks if it is already running and if not, it submits itself to the configured ExecutorService (from the JobTracker configuration).
If reducer queue runs out of work, the ReducerTask is removed from the ExecutorService to not block threads but eventually will be resubmitted on next chunk of work.
On every phase, the partition state is changed to keep track of the currently running operations. A JobPartitionState can be in one of the following states with self-explanatory titles: [WAITING, MAPPING, REDUCING, PROCESSED, CANCELLED]
. If you have a deeper interest of these states, look at the Javadoc.
Eventually (or hopefully), all JobPartitionStates reach the state of PROCESSED. Then, the job emitter's JobSupervisor asks all nodes for their reduced results and executes a potentially offered Collator. With this Collator, the overall result is calculated before it removes itself from the JobTracker, doing some final cleanup and returning the result to the requester (using the internal TrackableJobFuture).
If a job is cancelled while execution, all partitions are immediately set to the CANCELLED state and a CancelJobSupervisorOperation is executed on all nodes to kill the running processes.
While the operation is running in addition to the default operations, some more operations like ProcessStatsUpdateOperation (updates processed records statistics) or NotifyRemoteExceptionOperation (notifies the nodes that the sending node encountered an unrecoverable situation and the Job needs to be cancelled - e.g. NullPointerException inside of a Mapper) are executed against the job owner to keep track of the process.
Based on the Hazelcast MapReduce framework, Aggregators are ready-to-use data aggregations. These are typical operations like sum up values, finding minimum or maximum values, calculating averages, and other operations that you would expect in the relational database world.
Aggregation operations are implemented, as mentioned above, on top of the MapReduce framework and all operations can be achieved using pure MapReduce calls. However, using the Aggregation feature is more convenient for a big set of standard operations.
This section will quickly guide you through the basics of the Aggregations framework and some of its available classes. We also will implement a first base example.
Aggregations are available on both types of map interfaces, com.hazelcast.core.IMap
and com.hazelcast
.core.MultiMap
, using
the aggregate
methods. Two overloaded methods are available that customize resource management of the
underlying MapReduce framework by supplying a custom configured
com.hazelcast.mapreduce.JobTracker
instance. To find out how to
configure the MapReduce framework, please see the JobTracker Configuration section. We will
later see another way to configure the automatically used MapReduce framework if no special JobTracker
is supplied.
To make Aggregations more convenient to use and future proof, the API is heavily optimized for Java 8 and future versions. The API is still fully compatible with any Java version Hazelcast supports (Java 6 and Java 7). The biggest difference is how you work with the Java generics: on Java 6 and 7, the process to resolve generics is not as strong as on Java 8 and upcoming Java versions. In addition, the whole Aggregations API has full Java 8 Project Lambda (or Closure, JSR 335) support.
For illustration of the differences in Java 6 and 7 in comparison to Java 8, we will have a quick look at code examples for both. After that, we will focus on using Java 8 syntax to keep examples short and easy to understand, and we will see some hints as to what the code looks like in Java 6 or 7.
The first example will produce the sum of some int
values stored in a Hazelcast IMap. This example does not use much of the functionality of the Aggregations framework, but it will show the main difference.
IMap<String, Integer> personAgeMapping = hazelcastInstance.getMap( "person-age" );
for ( int i = 0; i < 1000; i++ ) {
String lastName = RandomUtil.randomLastName();
int age = RandomUtil.randomAgeBetween( 20, 80 );
personAgeMapping.put( lastName, Integer.valueOf( age ) );
}
With our demo data prepared, we can see how to produce the sums in different Java versions.
Since Java 6 and 7 are not as strong on resolving generics as Java 8, you need to be a bit more verbose with the code you write. You might also consider using raw types, but breaking the type safety to ease this process.
For a short introduction on what the following code example means, look at the source code comments. We will later dig deeper into the different options.
// No filter applied, select all entries
Supplier<String, Integer, Integer> supplier = Supplier.all();
// Choose the sum aggregation
Aggregation<String, Integer, Integer> aggregation = Aggregations.integerSum();
// Execute the aggregation
int sum = personAgeMapping.aggregate( supplier, aggregation );
With Java 8, the Aggregations API looks simpler because Java 8 can resolve the generic parameters for us. That means the above lines of Java 6/7 example code will end up in just one easy line on Java 8.
int sum = personAgeMapping.aggregate( Supplier.all(), Aggregations.integerSum() );
As mentioned before, the Aggregations implementation is based on the Hazelcast MapReduce framework and therefore you might find
overlaps in their APIs. One overload of the aggregate
method can be supplied with
a JobTracker
which is part of the MapReduce framework.
If you implement your own aggregations, you will use a mixture of the Aggregations and the MapReduce API. If you will implement your own aggregation, e.g. to make the life of colleagues easier, please read the Implementing Aggregations section.
For the full MapReduce documentation please see the MapReduce section.
We now look into the possible options of what can be achieved using the Aggregations API. To work on some deeper examples, let's quickly have a look at the available classes and interfaces and discuss their usage.
The com.hazelcast.mapreduce.aggregation.Supplier
provides filtering and data extraction to the aggregation operation.
This class already provides a few different static methods to achieve the most common cases. Supplier.all()
accepts all incoming values and does not apply any data extraction or transformation upon them before supplying them to
the aggregation function itself.
For filtering data sets, you have two different options by default. You can either supply a com.hazelcast.query.Predicate
if you want to filter on values and / or keys, or you can supply a com.hazelcast.mapreduce.KeyPredicate
if you can decide directly on the data
key without the need to deserialize the value.
As mentioned above, all APIs are fully Java 8 and Lambda compatible. Let's have a look on how we can do basic filtering using those two options.
First, we have a look at a KeyPredicate
and only accept people whose last name is "Jones".
Supplier<...> supplier = Supplier.fromKeyPredicate(
lastName -> "Jones".equalsIgnoreCase( lastName )
);
class JonesKeyPredicate implements KeyPredicate<String> {
public boolean evaluate( String key ) {
return "Jones".equalsIgnoreCase( key );
}
}
Using the standard Hazelcast Predicate
interface, you can also filter based on the value of a data entry. In the following example, you can
only select values which are divisible by 4 without a remainder.
Supplier<...> supplier = Supplier.fromPredicate(
entry -> entry.getValue() % 4 == 0
);
class DivisiblePredicate implements Predicate<String, Integer> {
public boolean apply( Map.Entry<String, Integer> entry ) {
return entry.getValue() % 4 == 0;
}
}
As well as filtering, Supplier
can also extract or transform data before providing it
to the aggregation operation itself. The following example shows how to transform an input value to a string.
Supplier<String, Integer, String> supplier = Supplier.all(
value -> Integer.toString(value)
);
You can see a Java 6 / 7 example in the Aggregations Examples section.
Apart from the fact we transformed the input value of type int
(or Integer) to a string, we can see that the generic information
of the resulting Supplier
has changed as well. This indicates that we now have an aggregation working on string values.
Another feature of Supplier
is its ability to chain multiple filtering rules. Let's combine all of the
above examples into one rule set:
Supplier<String, Integer, String> supplier =
Supplier.fromKeyPredicate(
lastName -> "Jones".equalsIgnoreCase( lastName ),
Supplier.fromPredicate(
entry -> entry.getValue() % 4 == 0,
Supplier.all( value -> Integer.toString(value) )
)
);
Last but not least, you might prefer to (or need to) implement your Supplier
based on special
requirements. This is a very basic task. The Supplier
abstract class has just one method.
NOTE: Due to a limitation of the Java Lambda API, you cannot implement abstract classes using Lambdas. Instead it is recommended that you create a standard named class.
class MyCustomSupplier extends Supplier<String, Integer, String> {
public String apply( Map.Entry<String, Integer> entry ) {
Integer value = entry.getValue();
if (value == null) {
return null;
}
return value % 4 == 0 ? String.valueOf( value ) : null;
}
}
Supplier
s are expected to return null from the apply
method whenever the input value should not be mapped to the aggregation
process. This can be used, as in the example above, to implement filter rules directly. Implementing filters using the
KeyPredicate
and Predicate
interfaces might be more convenient.
To use your own Supplier
, just pass it to the aggregate method or use it in combination with other Supplier
s.
int sum = personAgeMapping.aggregate( new MyCustomSupplier(), Aggregations.count() );
Supplier<String, Integer, String> supplier =
Supplier.fromKeyPredicate(
lastName -> "Jones".equalsIgnoreCase( lastName ),
new MyCustomSupplier()
);
int sum = personAgeMapping.aggregate( supplier, Aggregations.count() );
The com.hazelcast.mapreduce.aggregation.Aggregation
interface defines the aggregation operation itself. It contains a set of
MapReduce API implementations like Mapper
, Combiner
, Reducer
, and Collator
. These implementations are normally unique to
the chosen Aggregation
. This interface can also be implemented with your aggregation operations based on MapReduce calls. For
more information, refer to Implementing Aggregations section.
The com.hazelcast.mapreduce.aggregation.Aggregations
class provides a common predefined set of aggregations. This class
contains type safe aggregations of the following types:
Those aggregations are similar to their counterparts on relational databases and can be equated to SQL statements as set out below.
Calculates an average value based on all selected values.
map.aggregate( Supplier.all( person -> person.getAge() ),
Aggregations.integerAvg() );
SELECT AVG(person.age) FROM person;
Calculates a sum based on all selected values.
map.aggregate( Supplier.all( person -> person.getAge() ),
Aggregations.integerSum() );
SELECT SUM(person.age) FROM person;
Finds the minimal value over all selected values.
map.aggregate( Supplier.all( person -> person.getAge() ),
Aggregations.integerMin() );
SELECT MIN(person.age) FROM person;
Finds the maximal value over all selected values.
map.aggregate( Supplier.all( person -> person.getAge() ),
Aggregations.integerMax() );
SELECT MAX(person.age) FROM person;
Returns a collection of distinct values over the selected values
map.aggregate( Supplier.all( person -> person.getAge() ),
Aggregations.distinctValues() );
SELECT DISTINCT person.age FROM person;
Returns the element count over all selected values
map.aggregate( Supplier.all(), Aggregations.count() );
SELECT COUNT(*) FROM person;
We used the com.hazelcast.mapreduce.aggregation.PropertyExtractor
interface before when we had a look at the example
on how to use a Supplier
to transform a value to another type. It can also be used to extract attributes from values.
class Person {
private String firstName;
private String lastName;
private int age;
// getters and setters
}
PropertyExtractor<Person, Integer> propertyExtractor = (person) -> person.getAge();
class AgeExtractor implements PropertyExtractor<Person, Integer> {
public Integer extract( Person value ) {
return value.getAge();
}
}
In this example, we extract the value from the person's age attribute. The value type changes from Person to Integer
which is reflected in the generics information to stay type safe.
PropertyExtractor
s are meant to be used for any kind of transformation of data. You might even want to have multiple
transformation steps chained one after another.
As stated before, the easiest way to configure the resources used by the underlying MapReduce framework is to supply a JobTracker
to the aggregation call itself by passing it to either IMap::aggregate
or MultiMap::aggregate
.
There is another way to implicitly configure the underlying used JobTracker
. If no specific JobTracker
was
passed for the aggregation call, internally one will be created using the following naming specifications:
For IMap
aggregation calls the naming specification is created as:
hz::aggregation-map-
and the concatenated name of the map.For MultiMap
it is very similar:
hz::aggregation-multimap-
and the concatenated name of the MultiMap.Knowing that (the specification of the name), we can configure the JobTracker
as expected
(as described in the Jobtracker section) using the naming spec we just learned. For more information on configuration of the
JobTracker
, please see the JobTracker Configuration section.
To finish this section, let's have a quick example for the above naming specs:
IMap<String, Integer> map = hazelcastInstance.getMap( "mymap" );
// The internal JobTracker name resolves to 'hz::aggregation-map-mymap'
map.aggregate( ... );
MultiMap<String, Integer> multimap = hazelcastInstance.getMultiMap( "mymultimap" );
// The internal JobTracker name resolves to 'hz::aggregation-multimap-mymultimap'
multimap.aggregate( ... );
For the final example, imagine you are working for an international company and you have an employee database stored in Hazelcast
IMap
with all employees worldwide and a MultiMap
for assigning employees to their certain locations or offices. In addition,
there is another IMap
which holds the salary per employee.
Let's have a look at our data model:
class Employee implements Serializable {
private String firstName;
private String lastName;
private String companyName;
private String address;
private String city;
private String county;
private String state;
private int zip;
private String phone1;
private String phone2;
private String email;
private String web;
// getters and setters
}
class SalaryMonth implements Serializable {
private Month month;
private int salary;
// getters and setters
}
class SalaryYear implements Serializable {
private String email;
private int year;
private List<SalaryMonth> months;
// getters and setters
public int getAnnualSalary() {
int sum = 0;
for ( SalaryMonth salaryMonth : getMonths() ) {
sum += salaryMonth.getSalary();
}
return sum;
}
}
The two IMap
s and the MultiMap
are keyed by the string of email. They are defined as follows:
IMap<String, Employee> employees = hz.getMap( "employees" );
IMap<String, SalaryYear> salaries = hz.getMap( "salaries" );
MultiMap<String, String> officeAssignment = hz.getMultiMap( "office-employee" );
So far, we know all the important information to work out some example aggregations. We will look into some deeper implementation details and how we can work around some current limitations that will be eliminated in future versions of the API.
Let's start with a very basic example. We want to know the average salary of all of our employees. To do this,
we need a PropertyExtractor
and the average aggregation for type Integer
.
IMap<String, SalaryYear> salaries = hazelcastInstance.getMap( "salaries" );
PropertyExtractor<SalaryYear, Integer> extractor =
(salaryYear) -> salaryYear.getAnnualSalary();
int avgSalary = salaries.aggregate( Supplier.all( extractor ),
Aggregations.integerAvg() );
That's it. Internally, we created a MapReduce task based on the predefined aggregation and fired it up immediately. Currently, all
aggregation calls are blocking operations, so it is not yet possible to execute the aggregation in a reactive way (using
com.hazelcast.core.ICompletableFuture
) but this will be part of an upcoming version.
The following example is a little more complex. We only want to have our US based employees selected into the average salary calculation, so we need to execute some kind of a join operation between the employees and salaries maps.
class USEmployeeFilter implements KeyPredicate<String>, HazelcastInstanceAware {
private transient HazelcastInstance hazelcastInstance;
public void setHazelcastInstance( HazelcastInstance hazelcastInstance ) {
this.hazelcastInstance = hazelcastInstance;
}
public boolean evaluate( String email ) {
IMap<String, Employee> employees = hazelcastInstance.getMap( "employees" );
Employee employee = employees.get( email );
return "US".equals( employee.getCountry() );
}
}
Using the HazelcastInstanceAware
interface, we get the current instance of Hazelcast injected into our filter and we can perform data
joins on other data structures of the cluster. We now only select employees that work as part of our US offices into the
aggregation.
IMap<String, SalaryYear> salaries = hazelcastInstance.getMap( "salaries" );
PropertyExtractor<SalaryYear, Integer> extractor =
(salaryYear) -> salaryYear.getAnnualSalary();
int avgSalary = salaries.aggregate( Supplier.fromKeyPredicate(
new USEmployeeFilter(), extractor
), Aggregations.integerAvg() );
For our next example, we will do some grouping based on the different worldwide offices. Currently, a group aggregator is not yet available, so we need a small workaround to achieve this goal. (In later versions of the Aggregations API this will not be required because it will be available out of the box in a much more convenient way.)
Again, let's start with our filter. This time, we want to filter based on an office name and we need to do some data joins to achieve this kind of filtering.
A short tip: to minimize the data transmission on the aggregation we can use Data Affinity rules to influence the partitioning of data. Be aware that this is an expert feature of Hazelcast.
class OfficeEmployeeFilter implements KeyPredicate<String>, HazelcastInstanceAware {
private transient HazelcastInstance hazelcastInstance;
private String office;
// Deserialization Constructor
public OfficeEmployeeFilter() {
}
public OfficeEmployeeFilter( String office ) {
this.office = office;
}
public void setHazelcastInstance( HazelcastInstance hazelcastInstance ) {
this.hazelcastInstance = hazelcastInstance;
}
public boolean evaluate( String email ) {
MultiMap<String, String> officeAssignment = hazelcastInstance
.getMultiMap( "office-employee" );
return officeAssignment.containsEntry( office, email );
}
}
Now we can execute our aggregations. As mentioned before, we currently need to do the grouping on our own by executing multiple aggregations in a row.
Map<String, Integer> avgSalariesPerOffice = new HashMap<String, Integer>();
IMap<String, SalaryYear> salaries = hazelcastInstance.getMap( "salaries" );
MultiMap<String, String> officeAssignment =
hazelcastInstance.getMultiMap( "office-employee" );
PropertyExtractor<SalaryYear, Integer> extractor =
(salaryYear) -> salaryYear.getAnnualSalary();
for ( String office : officeAssignment.keySet() ) {
OfficeEmployeeFilter filter = new OfficeEmployeeFilter( office );
int avgSalary = salaries.aggregate( Supplier.fromKeyPredicate( filter, extractor ),
Aggregations.integerAvg() );
avgSalariesPerOffice.put( office, avgSalary );
}
After the previous example, we want to end this section by executing one final and easy aggregation. We want to know how many employees we currently have on a worldwide basis. Before reading the next lines of example code, you can try to do it on your own to see if you understood how to execute aggregations.
IMap<String, Employee> employees = hazelcastInstance.getMap( "employees" );
int count = employees.size();
Ok, after that quick joke, we look at the real two code lines:
IMap<String, Employee> employees = hazelcastInstance.getMap( "employees" );
int count = employees.aggregate( Supplier.all(), Aggregations.count() );
We now have an overview of how to use aggregations in real life situations. If you want to do your colleagues a favor, you might want to write your own additional set of aggregations. If so, then read the next section, Implementing Aggregations.
This section explains how to implement your own aggregations in your own application. It is meant to be an advanced section, so if you do not intend to implement your own aggregation, you might want to stop reading here and come back later when you need to know how to implement your own aggregation.
The main interface for making your own aggregation is com.hazelcast.mapreduce.aggregation.Aggregation
. It consists of four
methods.
interface Aggregation<Key, Supplied, Result> {
Mapper getMapper(Supplier<Key, ?, Supplied> supplier);
CombinerFactory getCombinerFactory();
ReducerFactory getReducerFactory();
Collator<Map.Entry, Result> getCollator();
}
An Aggregation
implementation is just defining a MapReduce task with a small difference: the Mapper
is always expected to work on a Supplier
that filters and / or transforms the mapped input value to some output value.
getMapper
and getReducerFactory
are expected to return non-null values. getCombinerFactory
and getCollator
are
optional operations and do not need to be implemented. If you can decide to implement them depending on the use case you want
to achieve.
For more information on how you implement mappers, combiners, reducers, and collators, refer to the MapReduce section.
For best speed and traffic usage, as mentioned in the MapReduce section, you should add a Combiner
to your aggregation
whenever it is possible to do some kind of pre-reduction step.
Your implementation also should use DataSerializable
or IdentifiedDataSerializable
for best compatibility and speed / stream-size
reasons.
Continuous query enables you to listen to the modifications performed on specific map entries. It is an entry listener with predicates. Please see the Map Listener section for information on how to add entry listeners to a map.
As an example, let's listen to the changes made on an employee with the surname "Smith". First, let's create the Employee
class.
import java.io.Serializable;
public class Employee implements Serializable {
private final String surname;
public Employee(String surname) {
this.surname = surname;
}
@Override
public String toString() {
return "Employee{" +
"surname='" + surname + '\'' +
'}';
}
}
Then, let's create the continuous query by adding the entry listener with the surname
predicate.
import com.hazelcast.core.*;
import com.hazelcast.query.SqlPredicate;
public class ContinuousQuery {
public static void main(String[] args) {
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
IMap<String, String> map = hz.getMap("map");
map.addEntryListener(new MyEntryListener(),
new SqlPredicate("surname=smith"), true);
System.out.println("Entry Listener registered");
}
static class MyEntryListener
implements EntryListener<String, String> {
@Override
public void entryAdded(EntryEvent<String, String> event) {
System.out.println("Entry Added:" + event);
}
@Override
public void entryRemoved(EntryEvent<String, String> event) {
System.out.println("Entry Removed:" + event);
}
@Override
public void entryUpdated(EntryEvent<String, String> event) {
System.out.println("Entry Updated:" + event);
}
@Override
public void entryEvicted(EntryEvent<String, String> event) {
System.out.println("Entry Evicted:" + event);
}
@Override
public void mapEvicted(MapEvent event) {
System.out.println("Map Evicted:" + event);
}
}
}
And now, let's play with the employee "smith" and see how that employee will be listened to.
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.core.IMap;
public class Modify {
public static void main(String[] args) {
HazelcastInstance hz = Hazelcast.newHazelcastInstance();
IMap<String, Employee> map = hz.getMap("map");
map.put("1", new Employee("smith"));
map.put("2", new Employee("jordan"));
System.out.println("done");
System.exit(0);
}
}
When you first run the class ContinuousQuery
and then run Modify
, you will see output similar to the listing below.
entryAdded:EntryEvent {Address[192.168.178.10]:5702} key=1,oldValue=null,
value=Person{name= smith }, event=ADDED, by Member [192.168.178.10]:5702
NOTE: This feature is supported for Hazelcast Enterprise 3.5 or higher.
This feature is used to cache the result of a continuous query. After construction of a continuous query cache, all changes on underlying IMap
is immediately reflected to this cache as a stream of events.
Therefore, this cache will be an always up to date view of the IMap
.
This feature is beneficial when you need to query the distributed IMap
data in a very frequent and fast way. By using continuous query cache, the result of the query will be always ready and local to the application.
You can access this continuous query cache from the server and client side respectively as shown below.
QueryCacheConfig queryCacheConfig = new QueryCacheConfig("cache-name");
queryCacheConfig.getPredicateConfig().setImplementation(new OddKeysPredicate());
MapConfig mapConfig = new MapConfig("map-name");
mapConfig.addQueryCacheConfig(queryCacheConfig);
Config config = new Config();
config.addMapConfig(mapConfig);
HazelcastInstance node = Hazelcast.newHazelcastInstance(config);
IEnterpriseMap<Integer, String> map = (IEnterpriseMap) node.getMap("map-name");
QueryCache<Integer, String> cache = map.getQueryCache("cache-name");
QueryCacheConfig queryCacheConfig = new QueryCacheConfig("cache-name");
queryCacheConfig.getPredicateConfig().setImplementation(new OddKeysPredicate());
ClientConfig clientConfig = new ClientConfig();
clientConfig.addQueryCacheConfig("map-name", queryCacheConfig);
HazelcastInstance client = HazelcastClient.newHazelcastClient(clientConfig);
IEnterpriseMap<Integer, Integer> clientMap = (IEnterpriseMap) client.getMap("map-name");
QueryCache<Integer, Integer> cache = clientMap.getQueryCache("cache-name");
IMap
data during construction of continuous query cache according to the supplied predicate via QueryCacheConfig#setPopulate
.QueryCacheConfig#setEvictionConfig
.QueryCache#addEntryListener
.Events on IMap
are guaranteed to be reflected to this cache in the happening order. Note that this happening order is a partition order so you can only expect ordered events from the same partition. Any loss of event can be listened via EventLostListener
and it can be recoverable with QueryCache#tryRecover
method. If your buffer size on the node side is big enough, you can recover from a possible event loss scenario.
At the moment, setting the size of QueryCacheConfig#setBufferSize
is the only option for recovery because the events which feed continuous query cache have no backups.
Below snippet can be used for recovery case.
QueryCache queryCache = map.getQueryCache("cache-name", new SqlPredicate("this > 20"), true);
queryCache.addEntryListener(new EventLostListener() {
@Override
public void eventLost(EventLostEvent event) {
queryCache.tryRecover();
}
}, false);
Event batching and coalescing.
QueryCache#get
from the underlying IMap
. This will help
to decrease initial population time if the values are very big in size.
In the case of special/custom needs, Hazelcast's SPI (Service Provider Interface) module allows users to develop their own distributed data structures and services.
Throughout this section, we create a distributed counter that will be the guide to reveal the Hazelcast SPI usage.
Here is our counter.
public interface Counter{
int inc(int amount);
}
This counter will have the following features:
All these features will be realized with the steps below. In each step, a new functionality to this counter will be added.
To have the counter as a functioning distributed object, we need a class. This class (named CounterService in the following sample) will be the gateway between Hazelcast internals and the counter, allowing us to add features to the counter. In the following sample, the class CounterService
is created. Its lifecycle will be managed by Hazelcast.
CounterService
should implement the interface com.hazelcast.spi.ManagedService
as shown below.
import com.hazelcast.spi.ManagedService;
import com.hazelcast.spi.NodeEngine;
import java.util.Properties;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentMap;
public class CounterService implements ManagedService {
private NodeEngine nodeEngine;
@Override
public void init( NodeEngine nodeEngine, Properties properties ) {
System.out.println( "CounterService.init" );
this.nodeEngine = nodeEngine;
}
@Override
public void shutdown( boolean terminate ) {
System.out.println( "CounterService.shutdown" );
}
@Override
public void reset() {
}
}
As can be seen from the code, CounterService
implements the following methods.
init
: This is called when CounterService
is initialized. NodeEngine
enables access to Hazelcast internals such as HazelcastInstance
and PartitionService
. Also, the object Properties
will provide us with the ability to create our own properties.shutdown
: This is called when CounterService
is shutdown. It cleans up the resources.reset
: This is called when cluster members are faced with the Split-Brain issue. This occurs when disconnected members that have created their own cluster are merged back into the main cluster. Services can also implement the SplitBrainHandleService
to indicate that they can take part in the merge process. For CounterService
we are going to implement as a no-op.Now, we need to enable the class CounterService
. The declarative way of doing this is shown below.
<network>
<join><multicast enabled="true"/> </join>
</network>
<services>
<service enabled="true">
<name>CounterService</name>
<class-name>CounterService</class-name>
</service>
</services>
CounterService
is declared within the services
configuration element.
enabled
attribute as true
enables the service.name
attribute defines the name of the service. It should be a unique name (CounterService
in our case) since it will be looked up when a remote call is made. Note that the value of this attribute will be sent at each request, and that a longer name
value means more data (de)serialization. A good practice is to give an understandable name with the shortest possible length.class-name
is the class name of the service (CounterService
in our case). The class should have a no-arg constructor. Otherwise, the object cannot be initialized.Note that multicast is enabled as the join mechanism. In the later sections for the CounterService
example, we will see why.
RELATED INFORMATION
Please refer to the Services Configuration section for a full description of Hazelcast SPI configuration.
The init
method for CounterService
takes the Properties
object as an argument. This means we can add properties to the service that are passed to the method init
. You can add properties declaratively as shown below.
<service enabled="true">
<name>CounterService</name>
<class-name>CounterService</class-name>
<properties>
<someproperty>10</someproperty>
</properties>
</service>
If you want to parse a more complex XML, you can use the interface com.hazelcast.spi.ServiceConfigurationParser
. It gives you access to the XML DOM tree.
Now, let's start a HazelcastInstance
as shown below, which will start the CounterService
.
import com.hazelcast.core.Hazelcast;
public class Member {
public static void main(String[] args) {
Hazelcast.newHazelcastInstance();
}
}
Once it is started, the CounterService#init
method prints the following output.
CounterService.init
Once the HazelcastInstance is shutdown (for example with Ctrl+C), the CounterService#shutdown
method prints the following output.
CounterService.shutdown
In the previous sections for the CounterService
example, we started CounterService
as part of a HazelcastInstance startup.
Now, let's connect the Counter
interface to CounterService
and perform a remote call to the cluster member hosting the counter data. Then, we will return a dummy result.
Remote calls are performed via a proxy in Hazelcast. Proxies expose the methods at the client side. Once a method is called, proxy creates an operation object, sends this object to the cluster member responsible from executing that operation, and then sends the result.
First, we need to make the Counter
interface a distributed object by extending the DistributedObject
interface, as shown below.
import com.hazelcast.core.DistributedObject;
public interface Counter extends DistributedObject {
int inc(int amount);
}
Now, we need to make the CounterService
class implement not only the ManagedService
interface, but also the interface com.hazelcast.spi.RemoteService
. This way, a client will be able to get a handle of a counter proxy.
import com.hazelcast.core.DistributedObject;
import com.hazelcast.spi.ManagedService;
import com.hazelcast.spi.NodeEngine;
import com.hazelcast.spi.RemoteService;
import java.util.Properties;
public class CounterService implements ManagedService, RemoteService {
public static final String NAME = "CounterService";
private NodeEngine nodeEngine;
@Override
public DistributedObject createDistributedObject(String objectName) {
return new CounterProxy(objectName, nodeEngine, this);
}
@Override
public void destroyDistributedObject(String objectName) {
// for the time being a no-op, but in the later examples this will be implemented
}
@Override
public void init(NodeEngine nodeEngine, Properties properties) {
this.nodeEngine = nodeEngine;
}
@Override
public void shutdown(boolean terminate) {
}
@Override
public void reset() {
}
}
The CounterProxy
returned by the method createDistributedObject
is a local representation to (potentially) remote managed data and logic.
NOTE: Note that caching and removing the proxy instance are done outside of this service.
Now, it is time to implement the CounterProxy
as shown below.
import com.hazelcast.spi.AbstractDistributedObject;
import com.hazelcast.spi.InvocationBuilder;
import com.hazelcast.spi.NodeEngine;
import com.hazelcast.util.ExceptionUtil;
import java.util.concurrent.Future;
public class CounterProxy extends AbstractDistributedObject<CounterService> implements Counter {
private final String name;
public CounterProxy(String name, NodeEngine nodeEngine, CounterService counterService) {
super(nodeEngine, counterService);
this.name = name;
}
@Override
public String getServiceName() {
return CounterService.NAME;
}
@Override
public String getName() {
return name;
}
@Override
public int inc(int amount) {
NodeEngine nodeEngine = getNodeEngine();
IncOperation operation = new IncOperation(name, amount);
int partitionId = nodeEngine.getPartitionService().getPartitionId(name);
InvocationBuilder builder = nodeEngine.getOperationService()
.createInvocationBuilder(CounterService.NAME, operation, partitionId);
try {
final Future<Integer> future = builder.invoke();
return future.get();
} catch (Exception e) {
throw ExceptionUtil.rethrow(e);
}
}
}
CounterProxy
is a local representation of remote data/functionality. It does not include the counter state. Therefore, the method inc
should be invoked on the cluster member hosting the real counter. You can invoke it using Hazelcast SPI; then it will send the operations to the correct member and return the results.
Let's dig deeper into the method inc
.
IncOperation
with a given name
and amount
.name
; by this way, all operations for a given name will result in the same partition ID.InvocationBuilder
where the connection between operation and partition is made.InvocationBuilder
and wait for its result. This waiting is performed with a future.get()
. In our case, timeout is not important. However, it is a good practice to use a timeout for a real system since operations should complete in a certain amount of time. Hazelcast's ExceptionUtil
is a good solution when it comes to dealing with execution exceptions. When the execution of the operation fails with an exception, an ExecutionException
is thrown and handled with the method ExceptionUtil.rethrow(Throwable)
.
If it is an InterruptedException
, we have two options: Either propagating the exception or just using the ExceptionUtil.rethrow
for all exceptions. Please see below sample.
try {
final Future<Integer> future = invocation.invoke();
return future.get();
} catch(InterruptedException e){
throw e;
} catch(Exception e){
throw ExceptionUtil.rethrow(e);
}
Now, let's write the IncOperation
. It implements PartitionAwareOperation
interface, meaning that it will be executed on the partition that hosts the counter.
import com.hazelcast.nio.ObjectDataInput;
import com.hazelcast.nio.ObjectDataOutput;
import com.hazelcast.spi.AbstractOperation;
import com.hazelcast.spi.PartitionAwareOperation;
import java.io.IOException;
class IncOperation extends AbstractOperation implements PartitionAwareOperation {
private String objectId;
private int amount, returnValue;
// Important to have a no-arg constructor for deserialization
public IncOperation() {
}
public IncOperation(String objectId, int amount) {
this.amount = amount;
this.objectId = objectId;
}
@Override
public void run() throws Exception {
System.out.println("Executing " + objectId + ".inc() on: " + getNodeEngine().getThisAddress());
returnValue = 0;
}
@Override
public boolean returnsResponse() {
return true;
}
@Override
public Object getResponse() {
return returnValue;
}
@Override
protected void writeInternal(ObjectDataOutput out) throws IOException {
super.writeInternal(out);
out.writeUTF(objectId);
out.writeInt(amount);
}
@Override
protected void readInternal(ObjectDataInput in) throws IOException {
super.readInternal(in);
objectId = in.readUTF();
amount = in.readInt();
}
}
The method run
does the actual execution. Since IncOperation
will return a response, the method returnsResponse
returns true
. If your method is asynchronous and does not need to return a response, it is better to return false
since it will be faster. The actual response is stored in the field returnValue
; you can retrieve it with the method getResponse
.
There are two more methods in the above code: writeInternal
and readInternal
. Since IncOperation
needs to be serialized, these two methods should be overwritten, and hence, objectId
and amount
will be serialized and available when those operations are executed.
For the deserialization, note that the operation must have a no-arg constructor.
Now, let's run our code.
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import java.util.UUID;
public class Member {
public static void main(String[] args) {
HazelcastInstance[] instances = new HazelcastInstance[2];
for (int k = 0; k < instances.length; k++)
instances[k] = Hazelcast.newHazelcastInstance();
Counter[] counters = new Counter[4];
for (int k = 0; k < counters.length; k++)
counters[k] = instances[0].getDistributedObject(CounterService.NAME, k+"counter");
for (Counter counter : counters)
System.out.println(counter.inc(1));
System.out.println("Finished");
System.exit(0);
}
}
Once run, you will see the output as below.
Executing 0counter.inc() on: Address[192.168.1.103]:5702
0
Executing 1counter.inc() on: Address[192.168.1.103]:5702
0
Executing 2counter.inc() on: Address[192.168.1.103]:5701
0
Executing 3counter.inc() on: Address[192.168.1.103]:5701
0
Finished
Note that counters are stored in different cluster members. Also note that increment is not active for now since the value remains as 0.
Until now, we have performed the basics to get this up and running. In the next section, we will make a real counter, cache the proxy instances and deal with proxy instance destruction.
Let's create a Container for every partition in the system. This container will contain all counters and proxies.
import java.util.HashMap;
import java.util.Map;
class Container {
private final Map<String, Integer> values = new HashMap();
int inc(String id, int amount) {
Integer counter = values.get(id);
if (counter == null) {
counter = 0;
}
counter += amount;
values.put(id, counter);
return counter;
}
public void init(String objectName) {
values.put(objectName,0);
}
public void destroy(String objectName) {
values.remove(objectName);
}
...
}
Hazelcast guarantees that a single thread will be active in a single partition. Therefore, when accessing a container, concurrency control will not be an issue.
The code in our example uses a Container
instance per partition approach. With this approach, there will not be any mutable shared state between partitions. This approach also makes operations on partitions simpler since you do not need to filter out data that does not belong to a certain partition.
Let's integrate the Container
in the CounterService
, as shown below.
import com.hazelcast.spi.ManagedService;
import com.hazelcast.spi.NodeEngine;
import com.hazelcast.spi.RemoteService;
import java.util.HashMap;
import java.util.Map;
import java.util.Properties;
public class CounterService implements ManagedService, RemoteService {
public final static String NAME = "CounterService";
Container[] containers;
private NodeEngine nodeEngine;
@Override
public void init(NodeEngine nodeEngine, Properties properties) {
this.nodeEngine = nodeEngine;
containers = new Container[nodeEngine.getPartitionService().getPartitionCount()];
for (int k = 0; k < containers.length; k++)
containers[k] = new Container();
}
@Override
public void shutdown(boolean terminate) {
}
@Override
public CounterProxy createDistributedObject(String objectName) {
int partitionId = nodeEngine.getPartitionService().getPartitionId(objectName);
Container container = containers[partitionId];
container.init(objectName);
return new CounterProxy(objectName, nodeEngine, this);
}
@Override
public void destroyDistributedObject(String objectName) {
int partitionId = nodeEngine.getPartitionService().getPartitionId(objectName);
Container container = containers[partitionId];
container.destroy(objectName);
}
@Override
public void reset() {
}
public static class Container {
final Map<String, Integer> values = new HashMap<String, Integer>();
private void init(String objectName) {
values.put(objectName, 0);
}
private void destroy(String objectName){
values.remove(objectName);
}
}
}
We create a container for every partition with the method init
. Then we create the proxy with the method createDistributedObject
. And finally, we need to remove the value of the object with the method destroyDistributedObject
, otherwise we may get an OutOfMemory exception.
As the last step in creating a Container, we connect the method IncOperation.run
to the Container, as shown below.
import com.hazelcast.nio.ObjectDataInput;
import com.hazelcast.nio.ObjectDataOutput;
import com.hazelcast.spi.AbstractOperation;
import com.hazelcast.spi.PartitionAwareOperation;
import java.io.IOException;
import java.util.Map;
class IncOperation extends AbstractOperation implements PartitionAwareOperation {
private String objectId;
private int amount, returnValue;
public IncOperation() {
}
public IncOperation(String objectId, int amount) {
this.amount = amount;
this.objectId = objectId;
}
@Override
public void run() throws Exception {
System.out.println("Executing " + objectId + ".inc() on: " + getNodeEngine().getThisAddress());
CounterService service = getService();
CounterService.Container container = service.containers[getPartitionId()];
Map<String, Integer> valuesMap = container.values;
Integer counter = valuesMap.get(objectId);
counter += amount;
valuesMap.put(objectId, counter);
returnValue = counter;
}
@Override
public boolean returnsResponse() {
return true;
}
@Override
public Object getResponse() {
return returnValue;
}
@Override
protected void writeInternal(ObjectDataOutput out) throws IOException {
super.writeInternal(out);
out.writeUTF(objectId);
out.writeInt(amount);
}
@Override
protected void readInternal(ObjectDataInput in) throws IOException {
super.readInternal(in);
objectId = in.readUTF();
amount = in.readInt();
}
}
partitionId
has a range between 0 and partitionCount and can be used as an index for the container array. Therefore, you can use partitionId
to retrieve the container, and once the container has been retrieved, you can access the value.
Let's run the following sample code.
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
public class Member {
public static void main(String[] args) {
HazelcastInstance[] instances = new HazelcastInstance[2];
for (int k = 0; k < instances.length; k++)
instances[k] = Hazelcast.newHazelcastInstance();
Counter[] counters = new Counter[4];
for (int k = 0; k < counters.length; k++)
counters[k] = instances[0].getDistributedObject(CounterService.NAME, k+"counter");
System.out.println("Round 1");
for (Counter counter: counters)
System.out.println(counter.inc(1));
System.out.println("Round 2");
for (Counter counter: counters)
System.out.println(counter.inc(1));
System.out.println("Finished");
System.exit(0);
}
}
The output will be as follows. It indicates that we have now a basic distributed counter up and running.
Round 1
Executing 0counter.inc() on: Address[192.168.1.103]:5702
1
Executing 1counter.inc() on: Address[192.168.1.103]:5702
1
Executing 2counter.inc() on: Address[192.168.1.103]:5701
1
Executing 3counter.inc() on: Address[192.168.1.103]:5701
1
Round 2
Executing 0counter.inc() on: Address[192.168.1.103]:5702
2
Executing 1counter.inc() on: Address[192.168.1.103]:5702
2
Executing 2counter.inc() on: Address[192.168.1.103]:5701
2
Executing 3counter.inc() on: Address[192.168.1.103]:5701
2
Finished
In the previous section, we created a real distributed counter. Now, we need to make sure that the content of the partition containers is migrated to different cluster members when a member joins or leaves the cluster. To make this happen, first we need to add three new methods (applyMigrationData
, toMigrationData
and clear
) to the Container
, as shown below.
import java.util.HashMap;
import java.util.Map;
class Container {
private final Map<String, Integer> values = new HashMap();
int inc(String id, int amount) {
Integer counter = values.get(id);
if (counter == null) {
counter = 0;
}
counter += amount;
values.put(id, counter);
return counter;
}
void clear() {
values.clear();
}
void applyMigrationData(Map<String, Integer> migrationData) {
values.putAll(migrationData);
}
Map<String, Integer> toMigrationData() {
return new HashMap(values);
}
public void init(String objectName) {
values.put(objectName,0);
}
public void destroy(String objectName) {
values.remove(objectName);
}
}
toMigrationData
: This method is called when Hazelcast wants to start the partition migration from the member owning the partition. The result of the toMigrationData
method is the partition data in a form that can be serialized to another member.applyMigrationData
: This method is called when migrationData
(created by the method toMigrationData
) will be applied to the member that will be the new partition owner.clear
: This method is called when the partition migration is successfully completed and the old partition owner gets rid of all data in the partition. This method is also called when the partition migration operation fails and the to-be-the-new partition owner needs to roll back its changes.After you add these three methods to the Container
, you need to create a CounterMigrationOperation
class that transfers migrationData
from one member to another and calls the method applyMigrationData
on the correct partition of the new partition owner. A sample is shown below.
import com.hazelcast.nio.ObjectDataInput;
import com.hazelcast.nio.ObjectDataOutput;
import com.hazelcast.spi.AbstractOperation;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
public class CounterMigrationOperation extends AbstractOperation {
Map<String, Integer> migrationData;
public CounterMigrationOperation() {
}
public CounterMigrationOperation(Map<String, Integer> migrationData) {
this.migrationData = migrationData;
}
@Override
public void run() throws Exception {
CounterService service = getService();
Container container = service.containers[getPartitionId()];
container.applyMigrationData(migrationData);
}
@Override
protected void writeInternal(ObjectDataOutput out) throws IOException {
out.writeInt(migrationData.size());
for (Map.Entry<String, Integer> entry : migrationData.entrySet()) {
out.writeUTF(entry.getKey());
out.writeInt(entry.getValue());
}
}
@Override
protected void readInternal(ObjectDataInput in) throws IOException {
int size = in.readInt();
migrationData = new HashMap<String, Integer>();
for (int i = 0; i < size; i++)
migrationData.put(in.readUTF(), in.readInt());
}
}
NOTE: During a partition migration, no other operations are executed on the related partition.
We need to make our CounterService
class implement the MigrationAwareService
interface. This will let Hazelcast know that the CounterService
can perform partition migration. See the below code.
import com.hazelcast.core.DistributedObject;
import com.hazelcast.partition.MigrationEndpoint;
import com.hazelcast.spi.*;
import java.util.Map;
import java.util.Properties;
public class CounterService implements ManagedService, RemoteService, MigrationAwareService {
public final static String NAME = "CounterService";
Container[] containers;
private NodeEngine nodeEngine;
@Override
public void init(NodeEngine nodeEngine, Properties properties) {
this.nodeEngine = nodeEngine;
containers = new Container[nodeEngine.getPartitionService().getPartitionCount()];
for (int k = 0; k < containers.length; k++)
containers[k] = new Container();
}
@Override
public void shutdown(boolean terminate) {
}
@Override
public DistributedObject createDistributedObject(String objectName) {
int partitionId = nodeEngine.getPartitionService().getPartitionId(objectName);
Container container = containers[partitionId];
container.init(objectName);
return new CounterProxy(objectName, nodeEngine,this);
}
@Override
public void destroyDistributedObject(String objectName) {
int partitionId = nodeEngine.getPartitionService().getPartitionId(objectName);
Container container = containers[partitionId];
container.destroy(objectName);
}
@Override
public void beforeMigration(PartitionMigrationEvent e) {
//no-op
}
@Override
public void clearPartitionReplica(int partitionId) {
Container container = containers[partitionId];
container.clear();
}
@Override
public Operation prepareReplicationOperation(PartitionReplicationEvent e) {
if (e.getReplicaIndex() > 1) {
return null;
}
Container container = containers[e.getPartitionId()];
Map<String, Integer> data = container.toMigrationData();
return data.isEmpty() ? null : new CounterMigrationOperation(data);
}
@Override
public void commitMigration(PartitionMigrationEvent e) {
if (e.getMigrationEndpoint() == MigrationEndpoint.SOURCE) {
Container c = containers[e.getPartitionId()];
c.clear();
}
//todo
}
@Override
public void rollbackMigration(PartitionMigrationEvent e) {
if (e.getMigrationEndpoint() == MigrationEndpoint.DESTINATION) {
Container c = containers[e.getPartitionId()];
c.clear();
}
}
@Override
public void reset() {
}
}
With the MigrationAwareService
interface, some additional methods are exposed. For example, the method prepareMigrationOperation
returns all the data of the partition that is going to be moved.
The method commitMigration
commits the data, meaning in this case, it clears the partition container of the old owner.
We can run the following code.
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
public class Member {
public static void main(String[] args) throws Exception {
HazelcastInstance[] instances = new HazelcastInstance[3];
for (int k = 0; k < instances.length; k++)
instances[k] = Hazelcast.newHazelcastInstance();
Counter[] counters = new Counter[4];
for (int k = 0; k < counters.length; k++)
counters[k] = instances[0].getDistributedObject(CounterService.NAME, k + "counter");
for (Counter counter : counters)
System.out.println(counter.inc(1));
Thread.sleep(10000);
System.out.println("Creating new members");
for (int k = 0; k < 3; k++) {
Hazelcast.newHazelcastInstance();
}
Thread.sleep(10000);
for (Counter counter : counters)
System.out.println(counter.inc(1));
System.out.println("Finished");
System.exit(0);
}
}
And we get the following output.
Executing 0counter.inc() on: Address[192.168.1.103]:5702
Executing backup 0counter.inc() on: Address[192.168.1.103]:5703
1
Executing 1counter.inc() on: Address[192.168.1.103]:5703
Executing backup 1counter.inc() on: Address[192.168.1.103]:5701
1
Executing 2counter.inc() on: Address[192.168.1.103]:5701
Executing backup 2counter.inc() on: Address[192.168.1.103]:5703
1
Executing 3counter.inc() on: Address[192.168.1.103]:5701
Executing backup 3counter.inc() on: Address[192.168.1.103]:5703
1
Creating new members
Executing 0counter.inc() on: Address[192.168.1.103]:5705
Executing backup 0counter.inc() on: Address[192.168.1.103]:5703
2
Executing 1counter.inc() on: Address[192.168.1.103]:5703
Executing backup 1counter.inc() on: Address[192.168.1.103]:5704
2
Executing 2counter.inc() on: Address[192.168.1.103]:5705
Executing backup 2counter.inc() on: Address[192.168.1.103]:5704
2
Executing 3counter.inc() on: Address[192.168.1.103]:5704
Executing backup 3counter.inc() on: Address[192.168.1.103]:5705
2
Finished
You can see that the counters have moved. 0counter
moved from 192.168.1.103:5702 to 192.168.1.103:5705 and it is incremented correctly. Our counters can now move around in the cluster. You will see the counters will be redistributed once you add or remove a cluster member.
Finally, we make sure that the data of counter is available on another node when a member goes down. We need to have the IncOperation
class implement the BackupAwareOperation
interface contained in the SPI package. See the following code.
class IncOperation extends AbstractOperation
implements PartitionAwareOperation, BackupAwareOperation {
...
@Override
public int getAsyncBackupCount() {
return 0;
}
@Override
public int getSyncBackupCount() {
return 1;
}
@Override
public boolean shouldBackup() {
return true;
}
@Override
public Operation getBackupOperation() {
return new IncBackupOperation(objectId, amount);
}
}
The methods getAsyncBackupCount
and getSyncBackupCount
specify the count for asynchronous and synchronous backups. Our sample has one synchronous backup and no asynchronous backups. In the above code, counts of the backups are hard-coded, but they can also be passed to IncOperation
as parameters.
The method shouldBackup
specifies whether our Operation needs a backup or not. For our sample, it returns true
, meaning the Operation will always have a backup even if there are no changes. Of course, in real systems, we want to have backups if there is a change. For IncOperation
for example, having a backup when amount
is null would be a good practice.
The method getBackupOperation
returns the operation (IncBackupOperation
) that actually performs the backup creation; the backup itself is an operation and will run on the same infrastructure.
If a backup should be made and getSyncBackupCount
returns 3, then three IncBackupOperation
instances are created and sent to the three machines containing the backup partition. If fewer machines are available, then backups need to be created. Hazelcast will just send a smaller number of operations.
Now, let's have a look at the IncBackupOperation
.
public class IncBackupOperation
extends AbstractOperation implements BackupOperation {
private String objectId;
private int amount;
public IncBackupOperation() {
}
public IncBackupOperation(String objectId, int amount) {
this.amount = amount;
this.objectId = objectId;
}
@Override
protected void writeInternal(ObjectDataOutput out) throws IOException {
super.writeInternal(out);
out.writeUTF(objectId);
out.writeInt(amount);
}
@Override
protected void readInternal(ObjectDataInput in) throws IOException {
super.readInternal(in);
objectId = in.readUTF();
amount = in.readInt();
}
@Override
public void run() throws Exception {
CounterService service = getService();
System.out.println("Executing backup " + objectId + ".inc() on: "
+ getNodeEngine().getThisAddress());
Container c = service.containers[getPartitionId()];
c.inc(objectId, amount);
}
}
NOTE: Hazelcast will also make sure that a new IncOperation for that particular key will not be executed before the (synchronous) backup operation has completed.
Let's see the backup functionality in action with the following code.
public class Member {
public static void main(String[] args) throws Exception {
HazelcastInstance[] instances = new HazelcastInstance[2];
for (int k = 0; k < instances.length; k++)
instances[k] = Hazelcast.newHazelcastInstance();
Counter counter = instances[0].getDistributedObject(CounterService.NAME, "counter");
counter.inc(1);
System.out.println("Finished");
System.exit(0);
}
}
Once it is run, the following output will be seen.
Executing counter0.inc() on: Address[192.168.1.103]:5702
Executing backup counter0.inc() on: Address[192.168.1.103]:5701
Finished
As it can be seen, both IncOperation
and IncBackupOperation
are executed. Notice that these operations have been executed on different cluster members to guarantee high availability.
WaitNotifyService
is an interface offered by SPI for the objects (e.g. Lock, Semaphore) to be used when a thread needs to wait for a lock to be released.
WaitNotifyService
keeps a list of waiters. For each notify operation:
Each waiter can sit on a wait-notify queue for, at most, its operation's call timeout. For example, by default, each waiter can wait here for at most 1 minute. There is a continuous task that scans expired/timed-out waiters and invalidates them with CallTimeoutException
. Each waiter on the remote side should retry and keep waiting if it still wants to wait. This is a liveness check for remote waiters.
This way, it is possible to distinguish an unresponsive node and a long (~infinite) wait. On the caller side, if the waiting thread does not get a response for either a call timeout or for more than 2 times the call-timeout, it will exit with OperationTimeoutException
.
Note that this behavior breaks the fairness. Hazelcast does not support fairness for any of the data structures with blocking operations (i.e. lock and semaphore).
You can use Hazelcast in transactional context.
You create a TransactionContext
to begin, commit, and rollback a transaction. You can obtain transaction-aware instances of queues, maps, sets, lists, multimaps via TransactionContext
, work with them, and commit/rollback in one shot.
Hazelcast supports two types of transactions: LOCAL (One Phase) and TWO_PHASE. With the type, you have influence over how much guarantee you get when a member crashes while a transaction is committing. The default behavior is TWO_PHASE.
LOCAL: Unlike the name suggests, LOCAL is a two phase commit. First, all cohorts are asked to prepare; if everyone agrees, then all cohorts are asked to commit. A problem can happen during the commit phase: if one or more members crash, then the system could be left in an inconsistent state.
TWO_PHASE: The TWO_PHASE commit is more than the classic two phase commit (if you want a regular two phase commit, use local). Before TWO_PHASE commits, it copies the commit log to other members, so in case of member failure, another member can complete the commit.
import java.util.Queue;
import java.util.Map;
import java.util.Set;
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.Transaction;
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
TransactionOptions options = new TransactionOptions()
.setTransactionType( TransactionType.LOCAL );
TransactionContext context = hazelcastInstance.newTransactionContext( options )
context.beginTransaction();
TransactionalQueue queue = context.getQueue( "myqueue" );
TransactionalMap map = context.getMap( "mymap" );
TransactionalSet set = context.getSet( "myset" );
try {
Object obj = queue.poll();
//process obj
map.put( "1", "value1" );
set.add( "value" );
//do other things..
context.commitTransaction();
} catch ( Throwable t ) {
context.rollbackTransaction();
}
In a transaction, operations will not be executed immediately. Their changes will be local to the TransactionContext
until committed. However, they will ensure the changes via locks.
For the above example, when map.put
is executed, no data will be put to the map but the key will get locked for changes. While committing, operations will be executed, the value will be put to the map, and the key will be unlocked.
Isolation level in Hazelcast Transactions is READ_COMMITTED
. If you are in a transaction, you can read the data in your transaction and the data that is already committed. If you are not in a transaction, you can only read the committed data.
NOTE: The REPEATABLE_READ isolation level can also be exercised using the method getForUpdate()
of TransactionalMap
.
Implementation is different for queue/set/list and map/multimap. For queue operations (offer, poll), offered and/or polled objects are copied to the owner member in order to safely commit/rollback. For map/multimap, Hazelcast first acquires the locks for the write operations (put, remove) and holds the differences (what is added/removed/updated) locally for each transaction. When the transaction is set to commit, Hazelcast will release the locks and apply the differences. When rolling back, Hazelcast will release the locks and discard the differences.
MapStore
and QueueStore
does not participate in transactions. Hazelcast will suppress exceptions thrown by store in a transaction. Please refer to the XA Transactions section for further information.
As it can be understood from the above Transaction Interface section, when you choose LOCAL as the transaction type, Hazelcast tracks all changes you make locally in a commit log, i.e. list of changes. In this case, all the other members are asked to agree that the commit can succeed and once it is agreed, Hazelcast starts to write the changes. However, if the member that initiates the commit crashes after it has written to at least one member (but has not completed writing to all other members), your system may be left in an inconsistent state.
On the other hand, if you choose TWO_PHASE as the transaction type, the commit log is again tracked locally but it is copied to another cluster member. Therefore, when a failure happens (e.g. the member initiating the commit crashes), you still have the commit log in another member and that member can complete the commit. However, copying the commit log to another member makes the TWO_PHASE approach slow.
Consequently, it is recommended that you choose LOCAL as the transaction type if you want a better performance and TWO_PHASE if reliability of your system is more important than the performance.
XA describes the interface between the global transaction manager and the local resource manager. XA allows multiple resources (such as databases, application servers, message queues, transactional caches, etc.) to be accessed within the same transaction, thereby preserving the ACID properties across applications. XA uses a two-phase commit to ensure that all resources either commit or rollback any particular transaction consistently (all do the same).
By implementing the XAResource
interface, Hazelcast provides XA transactions. You can obtain the HazelcastXAResource
instance via HazelcastInstance
.
Below is example code that uses Atomikos for transaction management.
UserTransactionManager tm = new UserTransactionManager();
tm.setTransactionTimeout(60);
tm.begin();
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
HazelcastXAResource xaResource = hazelcastInstance.getXAResource();
Transaction transaction = tm.getTransaction();
transaction.enlistResource(xaResource);
// other resources (database, app server etc...) can be enlisted
try {
TransactionContext context = xaResource.getTransactionContext();
TransactionalMap map = context.getMap("m");
map.put("key", "value");
// other resource operations
transaction.delistResource(xaResource, XAResource.TMSUCCESS);
tm.commit();
} catch (Exception e) {
tm.rollback();
}
Hazelcast can be integrated into J2EE containers via the Hazelcast Resource Adapter (hazelcast-jca-rar-
version.rar
). After proper configuration, Hazelcast can participate in standard J2EE transactions.
<%@page import="javax.resource.ResourceException" %>
<%@page import="javax.transaction.*" %>
<%@page import="javax.naming.*" %>
<%@page import="javax.resource.cci.*" %>
<%@page import="java.util.*" %>
<%@page import="com.hazelcast.core.*" %>
<%@page import="com.hazelcast.jca.*" %>
<%
UserTransaction txn = null;
HazelcastConnection conn = null;
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
try {
Context context = new InitialContext();
txn = (UserTransaction) context.lookup( "java:comp/UserTransaction" );
txn.begin();
HazelcastConnectionFactory cf = (HazelcastConnectionFactory)
context.lookup ( "java:comp/env/HazelcastCF" );
conn = cf.getConnection();
TransactionalMap<String, String> txMap = conn.getTransactionalMap( "default" );
txMap.put( "key", "value" );
txn.commit();
} catch ( Throwable e ) {
if ( txn != null ) {
try {
txn.rollback();
} catch ( Exception ix ) {
ix.printStackTrace();
};
}
e.printStackTrace();
} finally {
if ( conn != null ) {
try {
conn.close();
} catch (Exception ignored) {};
}
}
%>
Please see our sample application for J2EE Integration.
Deploying and configuring the Hazelcast resource adapter is no different than configuring any other resource adapter since the Hazelcast resource adapter is a standard JCA one. However, resource adapter installation and configuration is container specific, so please consult your J2EE vendor documentation for details. The most common steps are:
hazelcast-
version.jar
and hazelcast-jca-
version.jar
to the container's classpath. Usually there is a lib directory that is loaded automatically by the container on startup.hazelcast-jca-rar-
version.rar
. Usually there is some kind of a deploy directory. The name of the directory varies by container.hazelcast-jca-rar-
version.rar
. Besides container specific configurations, set the JNDI name for the Hazelcast resource.web.xml
and/or ejb-jar.xml
to let the container know that your application will use the Hazelcast resource and define the resource reference.hazelcast-
version.jar
and hazelcast-jca-
version.jar
into the GLASSFISH_HOME/glassfish/
domains/domain1/lib/ext/
folder.hazelcast-jca-rar-
version.rar
into GLASSFISH_HOME/glassfish/domains/domain1/autodeploy/
folder.web.xml
file.<resource-ref>
<res-ref-name>HazelcastCF</res-ref-name>
<res-type>com.hazelcast.jca.ConnectionFactoryImpl</res-type>
<res-auth>Container</res-auth>
</resource-ref>
Notice that we did not have to put sun-ra.xml
into the RAR file since it already comes with the hazelcast-ra-
version.rar
file.
If the Hazelcast resource is used from EJBs, you should configure ejb-jar.xml
for resource reference and JNDI definitions, just like for the web.xml
file.
hazelcast-
version.jar
and hazelcast-jca-
version.jar
into the JBOSS_HOME/server/deploy/
default/lib
folder.hazelcast-jca-rar-
version.rar
into the JBOSS_HOME/server/deploy/default/deploy
folder.hazelcast-ds.xml
file containing the following content in the JBOSS_HOME/server/deploy/default/deploy
folder. Make sure to set the rar-name
element to hazelcast-ra-
version.rar
.<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE connection-factories
PUBLIC "-//JBoss//DTD JBOSS JCA Config 1.5//EN"
"http://www.jboss.org/j2ee/dtd/jboss-ds_1_5.dtd">
<connection-factories>
<tx-connection-factory>
<local-transaction/>
<track-connection-by-tx>true</track-connection-by-tx>
<jndi-name>HazelcastCF</jndi-name>
<rar-name>hazelcast-jca-rar-<version>.rar</rar-name>
<connection-definition>
javax.resource.cci.ConnectionFactory
</connection-definition>
</tx-connection-factory>
</connection-factories>
web.xml
file.<resource-ref>
<res-ref-name>HazelcastCF</res-ref-name>
<res-type>com.hazelcast.jca.ConnectionFactoryImpl</res-type>
<res-auth>Container</res-auth>
</resource-ref>
jboss-web.xml
file.<resource-ref>
<res-ref-name>HazelcastCF</res-ref-name>
<jndi-name>java:HazelcastCF</jndi-name>
</resource-ref>
If the Hazelcast resource is used from EJBs, you should configure ejb-jar.xml
and jboss.xml
for resource reference and JNDI definitions.
Deployment on JBoss AS 7 or JBoss EAP 6 is a fairly straightforward process. The steps you perform are shown below. The only non-trivial step is the creation of a new JBoss module with Hazelcast libraries.
<jboss_home>/modules/system/layers/base/com/hazelcast/main
.hazelcast-
<version>.jar
and hazelcast-jca-
<version>.jar
into the folder you created in the previous step.Create the file module.xml
and place it in the same folder. This file should have the following content.
<module xmlns="urn:jboss:module:1.0" name="com.hazelcast">
<resources>
<resource-root path="."/>
<resource-root path="hazelcast-<version>.jar"/>
<resource-root path="hazelcast-jca-<version>.jar"/>
</resources>
<dependencies>
<module name="sun.jdk"/>
<module name="javax.api"/>
<module name="javax.resource.api"/>
<module name="javax.validation.api"/>
<module name="org.jboss.ironjacamar.api"/>
</dependencies>
</module>
At this point, you have a new JBoss module with Hazelcast in it. You can now start JBoss and deploy the hazelcast-jca-rar-
<version>.rar
file via JBoss CLI or Administration Console.
Once the Hazelcast Resource Adapter is deployed, you can start using it. The easiest way is to let a container inject ConnectionFactory
into your beans.
package com.hazelcast.examples.rar;
import com.hazelcast.core.TransactionalMap;
import com.hazelcast.jca.HazelcastConnection;
import javax.annotation.Resource;
import javax.resource.ResourceException;
import javax.resource.cci.ConnectionFactory;
import java.util.logging.Level;
import java.util.logging.Logger;
@javax.ejb.Stateless
public class ExampleBean implements ExampleInterface {
private final static Logger log = Logger.getLogger(ExampleBean.class.getName());
@Resource(mappedName = "java:/HazelcastCF")
protected ConnectionFactory connectionFactory;
public void insert(String key, String value) {
HazelcastConnection hzConn = null;
try {
hzConn = getConnection();
TransactionalMap<String,String> txmap = hzConn.getTransactionalMap("txmap");
txmap.put(key, value);
} finally {
closeConnection(hzConn);
}
}
private HazelcastConnection getConnection() {
try {
return (HazelcastConnection) connectionFactory.getConnection();
} catch (ResourceException e) {
throw new RuntimeException("Error while getting Hazelcast connection", e);
}
}
private void closeConnection(HazelcastConnection hzConn) {
if (hzConn != null) {
try {
hzConn.close();
} catch (ResourceException e) {
log.log(Level.WARNING, "Error while closing Hazelcast connection.", e);
}
}
}
}
This chapter describes the basics of JCache: the standardized Java caching layer API. The JCache caching API is specified by the Java Community Process (JCP) as Java Specification Request (JSR) 107.
Caching keeps data in memory that either are slow to calculate/process or originate from another underlying backend system whereas caching is used to prevent additional request round trips for frequently used data. In both cases, caching could be used to gain performance or decrease application latencies.
Starting with Hazelcast release 3.3.1, a specification compliant JCache implementation is offered. To show our commitment to this important specification the Java world was waiting for over a decade, we do not just provide a simple wrapper around our existing APIs but implemented a caching structure from ground up to optimize the behavior to the needs of JCache. As mentioned before, the Hazelcast JCache implementation is 100% TCK (Technology Compatibility Kit) compliant and therefore passes all specification requirements.
In addition to the given specification, we added some features like asynchronous versions of almost all operations to give the user extra power.
This chapter gives a basic understanding of how to configure your application and how to setup Hazelcast to be your JCache provider. It also shows examples of basic JCache usage as well as the additionally offered features that are not part of JSR-107. To gain a full understanding of the JCache functionality and provided guarantees of different operations, read the specification document (which is also the main documentation for functionality) at the specification page of JSR-107:
https://www.jcp.org/en/jsr/detail?id=107
This sub-chapter shows what is necessary to provide the JCache API and the Hazelcast JCache implementation for your application. In addition, it demonstrates the different configuration options as well as a description of the configuration properties.
To provide your application with this JCache functionality, your application needs the JCache API inside its classpath. This API is the bridge between the specified JCache standard and the implementation provided by Hazelcast.
The way to integrate the JCache API JAR into the application classpath depends on the build system used. For Maven, Gradle, SBT, Ivy and many other build systems, all using Maven based dependency repositories, perform the integration by adding the Maven coordinates to the build descriptor.
As already mentioned, next to the default Hazelcast coordinates that might be already part of the application, you have to add JCache coordinates.
For Maven users, the coordinates look like the following code:
<dependency>
<groupId>javax.cache</groupId>
<artifactId>cache-api</artifactId>
<version>1.0.0</version>
</dependency>
With other build systems, you might need to describe the coordinates in a different way.
To activate Hazelcast as the JCache provider implementation, add either hazelcast-all.jar
or
hazelcast.jar
to the classpath (if not already available) by either one of the following Maven snippets.
If you use hazelcast-all.jar
:
<dependency>
<groupId>com.hazelcast</groupId>
<artifactId>hazelcast-all</artifactId>
<version>3.4</version>
</dependency>
If you use hazelcast.jar
:
<dependency>
<groupId>com.hazelcast</groupId>
<artifactId>hazelcast</artifactId>
<version>3.4</version>
</dependency>
The users of other build systems have to adjust the way of defining the dependency to their needs.
When the users want to use Hazelcast clients to connect to a remote cluster, the hazelcast-client.jar
dependency is also required
on the client side applications. This JAR is already included in hazelcast-all.jar
. Or, you can add it to the classpath using the following
Maven snippet:
<dependency>
<groupId>com.hazelcast</groupId>
<artifactId>hazelcast</artifactId>
<version>3.4</version>
</dependency>
For other build systems, e.g. ANT, the users have to download these dependencies from either the JSR-107 specification and Hazelcast community website (http://www.hazelcast.org) or from the Maven repository search page (http://search.maven.org).
Before moving on to configuration, let's have a look at a basic introductory example. The following code shows how to use the Hazelcast JCache integration inside an application in an easy but typesafe way.
// Retrieve the CachingProvider which is automatically backed by
// the chosen Hazelcast server or client provider
CachingProvider cachingProvider = Caching.getCachingProvider();
// Create a CacheManager
CacheManager cacheManager = cachingProvider.getCacheManager();
// Create a simple but typesafe configuration for the cache
CompleteConfiguration<String, String> config =
new MutableConfiguration<String, String>()
.setTypes( String.class, String.class );
// Create and get the cache
Cache<String, String> cache = cacheManager.createCache( "example", config );
// Alternatively to request an already existing cache
// Cache<String, String> cache = cacheManager
// .getCache( name, String.class, String.class );
// Put a value into the cache
cache.put( "world", "Hello World" );
// Retrieve the value again from the cache
String value = cache.get( "world" );
// Print the value 'Hello World'
System.out.println( value );
Although the example is simple, let's go through the code lines one by one.
First of all, we retrieve the javax.cache.spi.CachingProvider
using the static method from
javax.cache.Caching::
getCachingManager
which automatically picks up Hazelcast as the underlying JCache implementation, if
available in the classpath. This way the Hazelcast implementation of a CachingProvider
will automatically start a new Hazelcast
node or client (depending on the chosen provider type) and pick up the configuration from either the command line parameter
or from the classpath. We will show how to use an existing HazelcastInstance
later in this chapter, for now we keep it simple.
In the next line, we ask the CachingProvider
to return a javax.cache.CacheManager
. This is the general application's entry
point into JCache. The CachingProvider
creates and manages named caches.
The next few lines create a simple javax.cache.configuration.MutableConfiguration
to configure the cache before actually
creating it. In this case, we only configure the key and value types to make the cache typesafe which is highly recommended and
checked on retrieval of the cache.
To create the cache, we call javax.cache.CacheManager::createCache
with a name for the cache and the previously created
configuration; the call returns the created cache. If you need to retrieve a previously created cache, you can use the corresponding method overload javax.cache.CacheManager::getCache
. If the cache was created using type parameters, you must retrieve the cache afterward using the type checking version of getCache
.
The following lines are simple put
and get
calls from the java.util.Map
interface. The
javax.cache.Cache::put
has a void
return type and does not return the previously assigned value of the key. To imitate the
java.util.Map::put
method, the JCache cache has a method called getAndPut
.
Hazelcast JCache provides two different ways of cache configuration:
hazelcast.xml
or hazelcast-client.xml
.You can declare your JCache cache configuration using the hazelcast.xml
or hazelcast-client.xml
configuration files. Using this declarative configuration makes the creation of the javax.cache.Cache
fully transparent and automatically ensures internal thread safety. You do not need a call to javax.cache.Cache::createCache
in this case: you can retrieve the cache using
javax.cache.Cache::getCache
overloads and by passing in the name defined in the configuration for the cache.
To retrieve the cache defined in the declaration files, you need only perform a simple call (example below) because the cache is created automatically by the implementation.
CachingProvider cachingProvider = Caching.getCachingProvider();
CacheManager cacheManager = cachingProvider.getCacheManager();
Cache<Object, Object> cache = cacheManager
.getCache( "default", Object.class, Object.class );
Note that this section only describes the JCache provided standard properties. For the Hazelcast specific properties, please see the ICache Configuration section.
<cache name="default">
<key-type class-name="java.lang.Object" />
<value-type class-name="java.lang.Object" />
<statistics-enabled>false</statistics-enabled>
<management-enabled>false</management-enabled>
<read-through>true</read-through>
<write-through>true</write-through>
<cache-loader-factory
class-name="com.example.cache.MyCacheLoaderFactory" />
<cache-writer-factory
class-name="com.example.cache.MyCacheWriterFactory" />
<expiry-policy-factory
class-name="com.example.cache.MyExpirePolicyFactory" />
<cache-entry-listeners>
<cache-entry-listener old-value-required="false" synchronous="false">
<cache-entry-listener-factory
class-name="com.example.cache.MyEntryListenerFactory" />
<cache-entry-event-filter-factory
class-name="com.example.cache.MyEntryEventFilterFactory" />
</cache-entry-listener>
...
</cache-entry-listeners>
</cache>
key-type#class-name
: The fully qualified class name of the cache key type, defaults to java.lang.Object
.value-type#class-name
: The fully qualified class name of the cache value type, defaults to java.lang.Object
.statistics-enabled
: If set to true, statistics like cache hits and misses are collected. Its default value is false.management-enabled
: If set to true, JMX beans are enabled and collected statistics are provided - It doesn't automatically enables statistics collection, defaults to false.read-through
: If set to true, enables read-through behavior of the cache to an underlying configured javax.cache.integration.CacheLoader
which is also known as lazy-loading, defaults to false.write-through
: If set to true, enables write-through behavior of the cache to an underlying configured javax.cache.integration.CacheWriter
which passes any changed value to the external backend resource, defaults to false.cache-loader-factory#class-name
: The fully qualified class name of the javax.cache.configuration.Factory
implementation providing a javax.cache.integration.CacheLoader
instance to the cache.cache-writer-factory#class-name
: The fully qualified class name of the javax.cache.configuration.Factory
implementation providing a javax.cache.integration.CacheWriter
instance to the cache.expiry-policy-factory#-class-name
: The fully qualified class name of the javax.cache.configuration.Factory
implementation providing a javax.cache.expiry.ExpiryPolicy
instance to the cache.cache-entry-listener
: A set of attributes and elements, explained below, to describe a javax.cache.event.
CacheEntryListener
.cache-entry-listener#old-value-required
: If set to true, previously assigned values for the affected keys will be sent to the javax.cache.event.CacheEntryListener
implementation. Setting this attribute to true creates additional traffic, defaults to false.cache-entry-listener#synchronous
: If set to true, the javax.cache.event.CacheEntryListener
implementation will be called in a synchronous manner, defaults to false.cache-entry-listener/entry-listener-factory#class-name
: The fully qualified class name of the javax.cache.configuration.Factory
implementation providing a javax.cache.event.CacheEntryListener
instance.cache-entry-listener/entry-event-filter-factory#class-name
: The fully qualified class name of the javax.cache.configuration.Factory
implementation providing a javax.cache.event.
CacheEntryEventFilter
instance.
NOTE: The JMX MBeans provided by Hazelcast JCache show statistics of the local node only.
To show the cluster-wide statistics, the user should collect statistic information from all nodes and accumulate them to
the overall statistics.
To configure the JCache programmatically:
javax.cache.configuration.MutableConfiguration
if you will use
only the JCache standard configuration,com.hazelcast.config.CacheConfig
for a deeper Hazelcast integration.com.hazelcast.config.CacheConfig
offers additional options that are specific to Hazelcast like asynchronous and synchronous backup counts.
Both classes share the same supertype interface javax.cache.configuration.
CompleteConfiguration
which is part of the JCache
standard.
NOTE: To stay vendor independent, try to keep your code as near as possible to the standard JCache API. We recommend you to use declarative configuration
and use the javax.cache.configuration.Configuration
or javax.cache.configuration.CompleteConfiguration
interfaces in
your code only when you need to pass the configuration instance throughout your code.
If you don't need to configure Hazelcast specific properties, it is recommended that you instantiate
javax.cache.configuration.MutableConfiguration
and that you use the setters to configure Hazelcast as shown in the example in the
Quick Example section. Since the configurable properties are the same as the ones explained in the
JCache Declarative Configuration section, they are not mentioned here. For Hazelcast specific
properties, please read the ICache Configuration section section.
Use JCache providers to create caches for a specification compliant implementation. Those providers abstract the platform specific behavior and bindings, and provide the different JCache required features.
Hazelcast has two types of providers. Depending on your application setup and the cluster topology, you can use the Client Provider (used from Hazelcast clients) or the Server Provider (used by cluster nodes).
You configure the JCache javax.cache.spi.CachingProvider
by either specifying the provider at the command line or by declaring the provider inside the Hazelcast configuration XML file. For more information on setting properties in this XML
configuration file, please see the JCache Declarative Configuration section.
Hazelcast implements a delegating CachingProvider
that can automatically be configured for either client or server mode and that
delegates to the real underlying implementation based on the user's choice. It is recommended that you use this CachingProvider
implementation.
The delegating CachingProvider
s fully qualified class name is:
com.hazelcast.cache.HazelcastCachingProvider
To configure the delegating provider at the command line, add the following parameter to the Java startup call, depending on the chosen provider:
-Dhazelcast.jcache.provider.type=[client|server]
By default, the delegating CachingProvider
is automatically picked up by the JCache SPI and provided as shown above. In cases where multiple javax.cache.spi.CachingProvider
implementations reside on the classpath (like in some Application
Server scenarios), you can select a CachingProvider
by explicitly calling Caching::getCachingProvider
overloads and providing them using the canonical class name of the provider to be used. The class names of server and client providers
provided by Hazelcast are mentioned in the following two subsections.
NOTE: Hazelcast advises that you use the Caching::getCachingProvider
overloads to select a
CachingProvider
explicitly. This ensures that uploading to later environments or Application Server versions doesn't result in
unexpected behavior like choosing a wrong CachingProvider
.
For more information on cluster topologies and Hazelcast clients, please see the Hazelcast Topology section.
For cluster topologies where Hazelcast light clients are used to connect to a remote Hazelcast cluster, use the Client Provider to configure JCache.
The Client Provider provides the same features as the Server Provider. However, it does not hold data on its own but instead delegates requests and calls to the remotely connected cluster.
The Client Provider can connect to multiple clusters at the same time. This can be achieved by scoping the client side
CacheManager
with different Hazelcast configuration files. For more information, please see the
Scopes and Namespaces section.
For requesting this CachingProvider
using Caching#getCachingProvider( String )
or
Caching#getCachingProvider( String, ClassLoader )
, use the following fully qualified class name:
com.hazelcast.client.cache.impl.HazelcastClientCachingProvider
If a Hazelcast node is embedded into an application directly and the Hazelcast client is not used, the Server Provider is required. In this case, the node itself becomes a part of the distributed cache and requests and operations are distributed directly across the cluster by its given key.
The Server Provider provides the same features as the Client provider, but it keeps data in the local Hazelcast node and also distributes non-owned keys to other direct cluster members.
Like the Client Provider, the Server Provider is able to connect to multiple clusters at the same time. This can be achieved by scoping the client side CacheManager
with different Hazelcast configuration files. For more
information please see the Scopes and Namespaces section.
To request this CachingProvider
using Caching#getCachingProvider( String )
or
Caching#getCachingProvider( String, ClassLoader )
, use the following fully qualified class name:
com.hazelcast.cache.impl.HazelcastServerCachingProvider
This section explains the JCache API by providing simple examples and use cases. While walking through the examples, we will have a look at a couple of the standard API classes and see how these classes are used.
The code in this subsection creates a small account application by providing a caching layer over an imagined database abstraction. The database layer will be simulated using single demo data in a simple DAO interface. To show the difference between the "database" access and retrieving values from the cache, a small waiting time is used in the DAO implementation to simulate network and database latency.
Before we implement the JCache caching layer, let's have a quick look at some basic classes we need for this example.
The User
class is the representation of a user table in the database. To keep it simple, it has just two properties:
userId
and username
.
public class User {
private int userId;
private String username;
// Getters and setters
}
The DAO interface is also kept easy in this example. It provides a simple method to retrieve (find) a user by its userId
.
public interface UserDAO {
User findUserById( int userId );
boolean storeUser( int userId, User user );
boolean removeUser( int userId );
Collection<Integer> allUserIds();
}
To show most of the standard features, the configuration example is a little more complex.
// Create javax.cache.configuration.CompleteConfiguration subclass
CompleteConfiguration<Integer, User> config =
new MutableConfiguration<Integer, User>()
// Configure the cache to be typesafe
.setTypes( Integer.class, User.class )
// Configure to expire entries 30 secs after creation in the cache
.setExpiryPolicyFactory( FactoryBuilder.factoryOf(
new AccessedExpiryPolicy( new Duration( TimeUnit.SECONDS, 30 ) )
) )
// Configure read-through of the underlying store
.setReadThrough( true )
// Configure write-through to the underlying store
.setWriteThrough( true )
// Configure the javax.cache.integration.CacheLoader
.setCacheLoaderFactory( FactoryBuilder.factoryOf(
new UserCacheLoader( userDao )
) )
// Configure the javax.cache.integration.CacheWriter
.setCacheWriterFactory( FactoryBuilder.factoryOf(
new UserCacheWriter( userDao )
) )
// Configure the javax.cache.event.CacheEntryListener with no
// javax.cache.event.CacheEntryEventFilter, to include old value
// and to be executed synchronously
.addCacheEntryListenerConfiguration(
new MutableCacheEntryListenerConfiguration<Integer, User>(
new UserCacheEntryListenerFactory(),
null, true, true
)
);
Let's go through this configuration line by line.
First, we set the expected types for the cache, which is already known from the previous example. On the next line, an
javax.cache.expiry.ExpirePolicy
is configured. Almost all integration ExpirePolicy
implementations are configured using
javax.cache.configuration.Factory
instances. Factory
and FactoryBuilder
are explained later in this chapter.
The next two lines configure the thread that will be read-through and write-through to the underlying backend resource that is configured
over the next few lines. The JCache API offers javax.cache.integration.CacheLoader
and javax.cache.integration.CacheWriter
to
implement adapter classes to any kind of backend resource, e.g. JPA, JDBC, or any other backend technology implementable in Java.
The interfaces provides the typical CRUD operations like create
, get
, update
, delete
and some bulk operation versions of those
common operations. We will look into the implementation of those implementations later.
The last configuration setting defines entry listeners based on sub-interfaces of javax.cache.event.CacheEntryListener
. This
config does not use a javax.cache.event.CacheEntryEventFilter
since the listener is meant to be fired on every change that
happens on the cache. Again we will look in the implementation of the listener in later in this chapter.
A full running example that is presented in this
subsection is available in the
code samples repository.
The application is built to be a command line app. It offers a small shell to accept different commands. After startup, you can
enter help
to see all available commands and their descriptions.
In the Quick Example section, we have already seen a couple of the base classes and explained how those work. Following are quick descriptions of them.
javax.cache.Caching
:
The access point into the JCache API. It retrieves the general CachingProvider
backed by any compliant JCache
implementation, such as Hazelcast JCache.
javax.cache.spi.CachingProvider
:
The SPI that is implemented to bridge between the JCache API and the implementation itself. Hazelcast nodes and clients use different providers chosen as seen in the Provider Configuration section which enable the JCache API to interact with Hazelcast clusters.
When a javax.cache.spi.CachingProvider::getCacheManager
overload is used that takes a java.lang.ClassLoader
argument, this
classloader will be part of the scope of the created java.cache.Cache
and it is not possible to retrieve it on other nodes.
We advise not to use those overloads, those are not meant to be used in distributed environments!
javax.cache.CacheManager
:
The CacheManager
provides the capability to create new and manage existing JCache caches.
NOTE: A javax.cache.Cache
instance created with key and value types in the configuration
provides a type checking of those types at retrieval of the cache. For that reason, all non-types retrieval methods like
getCache
throw an exception because types cannot be checked.
javax.cache.configuration.Configuration
, javax.cache.configuration.MutableConfiguration
:
These two classes are used to configure a cache prior to retrieving it from a CacheManager
. The Configuration
interface,
therefore, acts as a common super type for all compatible configuration classes such as MutableConfiguration
.
Hazelcast itself offers a special implementation (com.hazelcast.config.CacheConfig
) of the Configuration
interface which
offers more options on the specific Hazelcast properties that can be set to configure features like synchronous and asynchronous
backups counts or selecting the underlying In Memory Format of the cache. For more information on this
configuration class, please see the reference in JCache Programmatic Configuration section.
javax.cache.Cache
:
This interface represents the cache instance itself. It is comparable to java.util.Map
but offers special operations dedicated
to the caching use case. Therefore, for example javax.cache.Cache::put
, unlike java.util.Map::put
, does not return the old
value previously assigned to the given key.
NOTE: Bulk operations on the Cache
interface guarantee atomicity per entry but not over
all given keys in the same bulk operations since no transactional behavior is applied over the whole batch process.
The javax.cache.configuration.Factory
implementations are used to configure features like
CacheEntryListener
, ExpirePolicy
and CacheLoader
s or CacheWriter
s. These factory implementations are required to distribute the
different features to nodes in a cluster environment like Hazelcast. Therefore, these factory implementations have to be serializable.
Factory
implementations are easy to do: they follow the default Provider- or Factory-Pattern. The sample class
UserCacheEntryListenerFactory
shown below implements a custom JCache Factory
.
public class UserCacheEntryListenerFactory
implements Factory<CacheEntryListener<Integer, User>> {
@Override
public CacheEntryListener<Integer, User> create() {
// Just create a new listener instance
return new UserCacheEntryListener();
}
}
To simplify the process for the users, JCache API offers a set of helper methods collected in
javax.cache.
configuration.FactoryBuilder
. In the above configuration example, FactoryBuilder::factoryOf
is used to create a
singleton factory for the given instance.
javax.cache.integration.CacheLoader
loads cache entries from any external backend resource. If the cache is
configured to be read-through
, then CacheLoader::load
is called transparently from the cache when the key or the value is not
yet found in the cache. If no value is found for a given key, it returns null.
If the cache is not configured to be read-through
, nothing is loaded automatically. However, the user code must call javax.cache.Cache::loadAll
to load data for the given set of keys into the cache.
For the bulk load operation (loadAll()
), some keys may not be found in the returned result set. In this case, a javax.cache.integration.CompletionListener
parameter can be used as an asynchronous callback after all the key-value pairs are loaded because loading many key-value pairs can take lots of time.
Let's look at the UserCacheLoader
implementation.
public class UserCacheLoader
implements CacheLoader<Integer, User>, Serializable {
private final UserDao userDao;
public UserCacheLoader( UserDao userDao ) {
// Store the dao instance created externally
this.userDao = userDao;
}
@Override
public User load( Integer key ) throws CacheLoaderException {
// Just call through into the dao
return userDao.findUserById( key );
}
@Override
public Map<Integer, User> loadAll( Iterable<? extends Integer> keys )
throws CacheLoaderException {
// Create the resulting map
Map<Integer, User> loaded = new HashMap<Integer, User>();
// For every key in the given set of keys
for ( Integer key : keys ) {
// Try to retrieve the user
User user = userDao.findUserById( key );
// If user is not found do not add the key to the result set
if ( user != null ) {
loaded.put( key, user );
}
}
return loaded;
}
}
The implementation is quite straight forward. An important note is that
any kind of exception has to be wrapped into javax.cache.integration.CacheLoaderException
.
A javax.cache.integration.CacheWriter
is used to update an external backend resource. If the cache is configured to be
write-through
this process is executed transparently to the users code otherwise at the current state there is no way to trigger
writing changed entries to the external resource to a user defined point in time.
If bulk operations throw an exception, java.util.Collection
has to be cleaned of all successfully written keys so
the cache implementation can determine what keys are written and can be applied to the cache state.
public class UserCacheWriter
implements CacheWriter<Integer, User>, Serializable {
private final UserDao userDao;
public UserCacheWriter( UserDao userDao ) {
// Store the dao instance created externally
this.userDao = userDao;
}
@Override
public void write( Cache.Entry<? extends Integer, ? extends User> entry )
throws CacheWriterException {
// Store the user using the dao
userDao.storeUser( entry.getKey(), entry.getValue() );
}
@Override
public void writeAll( Collection<Cache.Entry<...>> entries )
throws CacheWriterException {
// Retrieve the iterator to clean up the collection from
// written keys in case of an exception
Iterator<Cache.Entry<...>> iterator = entries.iterator();
while ( iterator.hasNext() ) {
// Write entry using dao
write( iterator.next() );
// Remove from collection of keys
iterator.remove();
}
}
@Override
public void delete( Object key ) throws CacheWriterException {
// Test for key type
if ( !( key instanceof Integer ) ) {
throw new CacheWriterException( "Illegal key type" );
}
// Remove user using dao
userDao.removeUser( ( Integer ) key );
}
@Override
public void deleteAll( Collection<?> keys ) throws CacheWriterException {
// Retrieve the iterator to clean up the collection from
// written keys in case of an exception
Iterator<?> iterator = keys.iterator();
while ( iterator.hasNext() ) {
// Write entry using dao
delete( iterator.next() );
// Remove from collection of keys
iterator.remove();
}
}
}
Again the implementation is pretty straight forward and also as above all exceptions thrown by the external resource, like
java.sql.SQLException
has to be wrapped into a javax.cache.integration.CacheWriterException
. Note this is a different
exception from the one thrown by CacheLoader
.
With javax.cache.processor.EntryProcessor
, you can apply an atomic function to a cache entry. In a distributed
environment like Hazelcast, you can move the mutating function to the node that owns the key. If the value
object is big, it might prevent traffic by sending the object to the mutator and sending it back to the owner to update it.
By default, Hazelcast JCache sends the complete changed value to the backup partition. Again, this can cause a lot of traffic if the object is big. Another option to prevent this is part of the Hazelcast ICache extension. Further information is available at BackupAwareEntryProcessor.
An arbitrary number of arguments can be passed to the Cache::invoke
and Cache::invokeAll
methods. All of those arguments need
to be fully serializable because in a distributed environment like Hazelcast, it is very likely that these arguments have to be passed around the cluster.
public class UserUpdateEntryProcessor
implements EntryProcessor<Integer, User, User> {
@Override
public User process( MutableEntry<Integer, User> entry, Object... arguments )
throws EntryProcessorException {
// Test arguments length
if ( arguments.length < 1 ) {
throw new EntryProcessorException( "One argument needed: username" );
}
// Get first argument and test for String type
Object argument = arguments[0];
if ( !( argument instanceof String ) ) {
throw new EntryProcessorException(
"First argument has wrong type, required java.lang.String" );
}
// Retrieve the value from the MutableEntry
User user = entry.getValue();
// Retrieve the new username from the first argument
String newUsername = ( String ) arguments[0];
// Set the new username
user.setUsername( newUsername );
// Set the changed user to mark the entry as dirty
entry.setValue( user );
// Return the changed user to return it to the caller
return user;
}
}
NOTE: By executing the bulk Cache::invokeAll
operation, atomicity is only guaranteed for a
single cache entry. No transactional rules are applied to the bulk operation.
NOTE: JCache EntryProcessor
implementations are not allowed to call
javax.cache.Cache
methods; this prevents operations from deadlocking between different calls.
In addition, when using a Cache::invokeAll
method, a java.util.Map
is returned that maps the key to its
javax.cache.processor.EntryProcessorResult
, and which itself wraps the actual result or a thrown
javax.cache.processor.EntryProcessorException
.
The javax.cache.event.CacheEntryListener
implementation is straight forward. CacheEntryListener
is a super-interface which is used as a marker for listener classes in JCache. The specification brings a set of sub-interfaces.
CacheEntryCreatedListener
: Fires after a cache entry is added (even on read-through by a CacheLoader
) to the cache.CacheEntryUpdatedListener
: Fires after an already existing cache entry was updates.CacheEntryRemovedListener
: Fires after a cache entry was removed (not expired) from the cache.CacheEntryExpiredListener
: Fires after a cache entry has been expired. Expiry does not have to be parallel process, it is only required to be executed on the keys that are requested by Cache::get
and some other operations. For a full table of expiry please see the https://www.jcp.org/en/jsr/detail?id=107 point 6. To configure CacheEntryListener
, add a javax.cache.configuration.CacheEntryListenerConfiguration
instance to
the JCache configuration class, as seen in the above example configuration. In addition listeners can be configured to be
executed synchronously (blocking the calling thread) or asynchronously (fully running in parallel).
In this example application, the listener is implemented to print event information on the console. That visualizes what is going on in the cache.
public class UserCacheEntryListener
implements CacheEntryCreatedListener<Integer, User>,
CacheEntryUpdatedListener<Integer, User>,
CacheEntryRemovedListener<Integer, User>,
CacheEntryExpiredListener<Integer, User> {
@Override
public void onCreated( Iterable<CacheEntryEvent<...>> cacheEntryEvents )
throws CacheEntryListenerException {
printEvents( cacheEntryEvents );
}
@Override
public void onUpdated( Iterable<CacheEntryEvent<...>> cacheEntryEvents )
throws CacheEntryListenerException {
printEvents( cacheEntryEvents );
}
@Override
public void onRemoved( Iterable<CacheEntryEvent<...>> cacheEntryEvents )
throws CacheEntryListenerException {
printEvents( cacheEntryEvents );
}
@Override
public void onExpired( Iterable<CacheEntryEvent<...>> cacheEntryEvents )
throws CacheEntryListenerException {
printEvents( cacheEntryEvents );
}
private void printEvents( Iterable<CacheEntryEvent<...>> cacheEntryEvents ) {
Iterator<CacheEntryEvent<...>> iterator = cacheEntryEvents.iterator();
while ( iterator.hasNext() ) {
CacheEntryEvent<...> event = iterator.next();
System.out.println( event.getEventType() );
}
}
}
In JCache, javax.cache.expiry.ExpirePolicy
implementations are used to automatically expire cache entries based on different rules.
Expiry timeouts are defined using javax.cache.expiry.Duration
, which is a pair of java.util.concurrent.TimeUnit
, which
describes a time unit and a long, defining the timeout value. The minimum allowed TimeUnit
is TimeUnit.MILLISECONDS
.
The long value durationAmount
must be equal or greater than zero. A value of zero (or Duration.ZERO
) indicates that the
cache entry expires immediately.
By default, JCache delivers a set of predefined expiry strategies in the standard API.
AccessedExpiryPolicy
: Expires after a given set of time measured from creation of the cache entry, the expiry timeout is updated on accessing the key.CreatedExpiryPolicy
: Expires after a given set of time measured from creation of the cache entry, the expiry timeout is never updated.EternalExpiryPolicy
: Never expires, this is the default behavior, similar to ExpiryPolicy
to be set to null.ModifiedExpiryPolicy
: Expires after a given set of time measured from creation of the cache entry, the expiry timeout is updated on updating the key.TouchedExpiryPolicy
: Expires after a given set of time measured from creation of the cache entry, the expiry timeout is updated on accessing or updating the key.Because EternalExpirePolicy
does not expire cache entries, it is still possible to evict values from memory if an underlying
CacheLoader
is defined.
Hazelcast provides extension methods to Cache API through the interface com.hazelcast.cache.ICache
.
It has two sets of extensions:
ExpiryPolicy
parameter to apply on that specific operation.As mentioned before, a CacheManager
can be scoped in the case of client to connect to multiple clusters, or in the case of an embedded node, a CacheManager
can be scoped to join different clusters at the same time. This process is called scoping. To apply it, request
a CacheManager
by passing a java.net.URI
instance to CachingProvider::getCacheManager
. The java.net.URI
instance must point to either a Hazelcast configuration or to the name of a named
com.hazelcast.core.HazelcastInstance
instance.
NOTE: Multiple requests for the same java.net.URI
result in returning a CacheManager
instance that shares the same HazelcastInstance
as the CacheManager
returned by the previous call.
To connect or join different clusters, apply a configuration scope to the CacheManager
. If the same URI
is
used to request a CacheManager
that was created previously, those CacheManager
s share the same underlying HazelcastInstance
.
To apply a configuration scope, pass in the path of the configuration file using the location property
HazelcastCachingProvider#HAZELCAST_CONFIG_LOCATION
(which resolves to hazelcast.config.location
) as a mapping inside a
java.util.Properties
instance to the CachingProvider#getCacheManager(uri, classLoader, properties)
call.
Here is an example of using Configuration Scope.
CachingProvider cachingProvider = Caching.getCachingProvider();
// Create Properties instance pointing to a Hazelcast config file
Properties properties = new Properties();
properties.setProperty( HazelcastCachingProvider.HAZELCAST_CONFIG_LOCATION,
"classpath://my-configs/scoped-hazelcast.xml" );
URI cacheManagerName = new URI( "my-cache-manager" );
CacheManager cacheManager = cachingProvider
.getCacheManager( cacheManagerName, null, properties );
Here is an example using HazelcastCachingProvider::propertiesByLocation
helper method.
CachingProvider cachingProvider = Caching.getCachingProvider();
// Create Properties instance pointing to a Hazelcast config file
String configFile = "classpath://my-configs/scoped-hazelcast.xml";
Properties properties = HazelcastCachingProvider
.propertiesByLocation( configFile );
URI cacheManagerName = new URI( "my-cache-manager" );
CacheManager cacheManager = cachingProvider
.getCacheManager( cacheManagerName, null, properties );
The retrieved CacheManager
is scoped to use the HazelcastInstance
that was just created and was configured using the given XML
configuration file.
Available protocols for config file URL include classpath://
to point to a classpath location, file://
to point to a filesystem
location, http://
an https://
for remote web locations. In addition, everything that does not specify a protocol is recognized
as a placeholder that can be configured using a system property.
String configFile = "my-placeholder";
Properties properties = HazelcastCachingProvider
.propertiesByLocation( configFile );
Can be set on the command line by:
-Dmy-placeholder=classpath://my-configs/scoped-hazelcast.xml
NOTE: No check is performed to prevent creating multiple CacheManager
s with the same cluster
configuration on different configuration files. If the same cluster is referred from different configuration files, multiple
cluster members or clients are created.
NOTE: The configuration file location will not be a part of the resulting identity of the
CacheManager
. An attempt to create a CacheManager
with a different set of properties but an already used name will result in
undefined behavior.
A CacheManager
can be bound to an existing and named HazelcastInstance
instance. If the instanceName
is specified in com.hazelcast.config.Config
, it can be used directly by passing it to CachingProvider
implementation. Otherwise (instanceName
not set or instance is a client instance) instance name must be get over HazelcastInstance
instance via String getName()
method to pass the CachingProvider
implementation. Please note that instanceName
is not configurable for the client side HazelcastInstance
instance and is auto-generated by using group name (if it is specified). In general, String getName()
method over HazelcastInstance
is more safe and preferable way for getting name of instance. Multiple CacheManager
s created using an equal java.net.URI
will share the same HazelcastInstance
.
A named scope is applied nearly the same way as the configuration scope: pass in the instance name using the HazelcastCachingProvider#HAZELCAST_INSTANCE_NAME
(which resolves to hazelcast.instance.name
) property as a mapping inside a java.util.Properties
instance to the CachingProvider#getCacheManager(uri, classLoader, properties)
call.
Here is an example of Named Instance Scope with specified name.
Config config = new Config();
config.setInstanceName( "my-named-hazelcast-instance" );
// Create a named HazelcastInstance
Hazelcast.newHazelcastInstance( config );
CachingProvider cachingProvider = Caching.getCachingProvider();
// Create Properties instance pointing to a named HazelcastInstance
Properties properties = new Properties();
properties.setProperty( HazelcastCachingProvider.HAZELCAST_INSTANCE_NAME,
"my-named-hazelcast-instance" );
URI cacheManagerName = new URI( "my-cache-manager" );
CacheManager cacheManager = cachingProvider
.getCacheManager( cacheManagerName, null, properties );
Here is an example of Named Instance Scope with auto-generated name.
Config config = new Config();
// Create a auto-generated named HazelcastInstance
HazelcastInstance instance = Hazelcast.newHazelcastInstance( config );
String instanceName = instance.getName();
CachingProvider cachingProvider = Caching.getCachingProvider();
// Create Properties instance pointing to a named HazelcastInstance
Properties properties = new Properties();
properties.setProperty( HazelcastCachingProvider.HAZELCAST_INSTANCE_NAME,
instanceName );
URI cacheManagerName = new URI( "my-cache-manager" );
CacheManager cacheManager = cachingProvider
.getCacheManager( cacheManagerName, null, properties );
Here is an example of Named Instance Scope with auto-generated name on client instance.
ClientConfig clientConfig = new ClientConfig();
ClientNetworkConfig networkConfig = clientConfig.getNetworkConfig();
networkConfig.addAddress("127.0.0.1", "127.0.0.2");
// Create a client side HazelcastInstance
HazelcastInstance instance = HazelcastClient.newHazelcastClient( clientConfig );
String instanceName = instance.getName();
CachingProvider cachingProvider = Caching.getCachingProvider();
// Create Properties instance pointing to a named HazelcastInstance
Properties properties = new Properties();
properties.setProperty( HazelcastCachingProvider.HAZELCAST_INSTANCE_NAME,
instanceName );
URI cacheManagerName = new URI( "my-cache-manager" );
CacheManager cacheManager = cachingProvider
.getCacheManager( cacheManagerName, null, properties );
Here is an example using HazelcastCachingProvider::propertiesByInstanceName
method.
Config config = new Config();
config.setInstanceName( "my-named-hazelcast-instance" );
// Create a named HazelcastInstance
Hazelcast.newHazelcastInstance( config );
CachingProvider cachingProvider = Caching.getCachingProvider();
// Create Properties instance pointing to a named HazelcastInstance
Properties properties = HazelcastCachingProvider
.propertiesByInstanceName( "my-named-hazelcast-instance" );
URI cacheManagerName = new URI( "my-cache-manager" );
CacheManager cacheManager = cachingProvider
.getCacheManager( cacheManager, null, properties );
NOTE: The instanceName
will not be a part of the resulting identity of the CacheManager
.
An attempt to create a CacheManager
with a different set of properties but an already used name will result in undefined behavior.
The java.net.URI
s that don't use the above mentioned Hazelcast specific schemes are recognized as namespacing. Those
CacheManager
s share the same underlying default HazelcastInstance
created (or set) by the CachingProvider
, but they cache with the
same names but differently namespaces on CacheManager
level, and therefore won't share the same data. This is useful where multiple
applications might share the same Hazelcast JCache implementation (e.g. on application or OSGi servers) but are developed by
independent teams. To prevent interfering on caches using the same name, every application can use its own namespace when
retrieving the CacheManager
.
Here is an example of using namespacing.
CachingProvider cachingProvider = Caching.getCachingProvider();
URI nsApp1 = new URI( "application-1" );
CacheManager cacheManagerApp1 = cachingProvider.getCacheManager( nsApp1, null );
URI nsApp2 = new URI( "application-2" );
CacheManager cacheManagerApp2 = cachingProvider.getCacheManager( nsApp2, null );
That way both applications share the same HazelcastInstance
instance but not the same caches.
Besides Scopes and Namespaces, which are implemented using the URI feature of the
specification, all other extended operations are required to retrieve the com.hazelcast.cache.ICache
interface instance from
the JCache javax.cache.Cache
instance. For Hazelcast, both interfaces are implemented on the same object instance. It
is recommended that you stay with the specification way to retrieve the ICache
version, since ICache
might be subject to change without notification.
To retrieve or unwrap the ICache
instance, you can execute the following code snippet:
CachingProvider cachingProvider = Caching.getCachingProvider();
CacheManager cacheManager = cachingProvider.getCacheManager();
Cache<Object, Object> cache = cacheManager.getCache( ... );
ICache<Object, Object> unwrappedCache = cache.unwrap( ICache.class );
After unwrapping the Cache
instance into an ICache
instance, you have access to all of the following operations, e.g.
Async Operations and Additional Methods.
As mentioned in the JCache Declarative Configuration section, the Hazelcast ICache extension offers additional configuration properties over the default JCache configuration. These additional properties include internal storage format, backup counts and eviction policy.
The declarative configuration for ICache is a superset of the previously discussed JCache configuration:
<cache>
<!-- ... default cache configuration goes here ... -->
<backup-count>1</backup-count>
<async-backup-count>1</async-backup-count>
<in-memory-format>BINARY</in-memory-format>
<eviction size="10000" max-size-policy="ENTRY_COUNT" eviction-policy="LRU" />
</cache>
backup-count
: The number of synchronous backups. Those backups are executed before the mutating cache operation is finished. The mutating operation is blocked. backup-count
default value is 1.async-backup-count
: The number of asynchronous backups. Those backups are executed asynchronously so the mutating operation is not blocked and it will be done immediately. async-backup-count
default value is 0. in-memory-format
: Defines the internal storage format. For more information, please see the In Memory Format section. Default is BINARY
.eviction
: Defines the used eviction strategies and sizes for the cache. For more information on eviction, please see the JCache Eviction.size
: The maximum number of records or maximum size in bytes depending on the max-size-policy
property. Size can be any integer between 0
and Integer.MAX_VALUE
. Default max-size-policy is ENTRY_COUNT
and default size is 10.000
.max-size-policy
: The size policy property defines a maximum size. If maximum size is reached, the cache is evicted based on the eviction policy. Default max-size-policy is ENTRY_COUNT
and default size is 10.000
. The following eviction policies are available:ENTRY_COUNT
: Maximum number of cache entries in the cache. Available on heap based cache record store only.USED_NATIVE_MEMORY_SIZE
: Maximum used native memory size in megabytes for each instance. Available on High-Density Memory cache record store only.USED_NATIVE_MEMORY_PERCENTAGE
: Maximum used native memory size percentage for each instance. Available on High-Density Memory cache record store only.FREE_NATIVE_MEMORY_SIZE
: Maximum free native memory size in megabytes for each instance. Available on High-Density Memory cache record store only.FREE_NATIVE_MEMORY_PERCENTAGE
: Maximum free native memory size percentage for each instance. Available on High-Density Memory cache record store only.eviction-policy
: The defined eviction policy to compare values with to find the best matching eviction candidate. Default is LRU
.LRU
: Less Recently Used - finds the best eviction candidate based on the lastAccessTime.LFU
: Less Frequently Used - finds the best eviction candidate based on the number of hits.Since javax.cache.configuration.MutableConfiguration
misses the above additional configuration properties, Hazelcast ICache extension
provides an extended configuration class called com.hazelcast.config.CacheConfig
. This class is an implementation of javax.cache.configuration.CompleteConfiguration
and all the properties shown above can be configured
using its corresponding setter methods.
NOTE: At the client side, ICache can be configured only programmatically.
As another addition of Hazelcast ICache over the normal JCache specification, Hazelcast provides asynchronous versions of almost
all methods, returning a com.hazelcast.core.ICompletableFuture
. By using these methods and the returned future objects, you can use JCache in a reactive way by registering zero or more callbacks on the future to prevent blocking the current thread.
Name of the asynchronous versions of the methods append the phrase Async
to the method name. Sample code is shown below using the method putAsync()
.
ICache<Integer, String> unwrappedCache = cache.unwrap( ICache.class );
ICompletableFuture<String> future = unwrappedCache.putAsync( 1, "value" );
future.andThen( new ExecutionCallback<String>() {
public void onResponse( String response ) {
System.out.println( "Previous value: " + response );
}
public void onFailure( Throwable t ) {
t.printStackTrace();
}
} );
Following methods are available in asynchronous versions:
get(key)
:getAsync(key)
getAsync(key, expiryPolicy)
put(key, value)
:putAsync(key, value)
putAsync(key, value, expiryPolicy)
putIfAbsent(key, value)
:putIfAbsentAsync(key, value)
putIfAbsentAsync(key, value, expiryPolicy)
getAndPut(key, value)
:getAndPutAsync(key, value)
getAndPutAsync(key, value, expiryPolicy)
remove(key)
:removeAsync(key)
remove(key, value)
:removeAsync(key, value)
getAndRemove(key)
:getAndRemoveAsync(key)
replace(key, value)
:replaceAsync(key, value)
replaceAsync(key, value, expiryPolicy)
replace(key, oldValue, newValue)
:replaceAsync(key, oldValue, newValue)
replaceAsync(key, oldValue, newValue, expiryPolicy)
getAndReplace(key, value)
:getAndReplaceAsync(key, value)
getAndReplaceAsync(key, value, expiryPolicy)
The methods with a given javax.cache.expiry.ExpiryPolicy
are further discussed in the
Custom ExpiryPolicy section.
NOTE: Asynchronous versions of the methods are not compatible with synchronous events.
The JCache specification has an option to configure a single ExpiryPolicy
per cache. Hazelcast ICache extension
offers the possibility to define a custom ExpiryPolicy
per key by providing a set of method overloads with an expirePolicy
parameter, as in the list of asynchronous methods in the Async Methods section. This means that custom expiry policies can passed to a cache operation.
Here is how an ExpirePolicy
is set on JCache configuration:
CompleteConfiguration<String, String> config =
new MutableConfiguration<String, String>()
setExpiryPolicyFactory(
AccessedExpiryPolicy.factoryOf( Duration.ONE_MINUTE )
);
To pass a custom ExpirePolicy
, a set of overloads is provided and can be used as shown in the following code snippet:
ICache<Integer, String> unwrappedCache = cache.unwrap( ICache.class );
unwrappedCache.put( 1, "value", new AccessedExpiryPolicy( Duration.ONE_DAY ) );
The ExpirePolicy
instance can be pre-created, cached, and re-used, but only for each cache instance. This is because ExpirePolicy
implementations can be marked as java.io.Closeable
. The following list shows the provided method overloads over javax.cache.Cache
by com.hazelcast.cache.ICache
featuring the ExpiryPolicy
parameter:
get(key)
:get(key, expiryPolicy)
getAll(keys)
:getAll(keys, expirePolicy)
put(key, value)
:put(key, value, expirePolicy)
getAndPut(key, value)
:getAndPut(key, value, expirePolicy)
putAll(map)
:putAll(map, expirePolicy)
putIfAbsent(key, value)
:putIfAbsent(key, value, expirePolicy)
replace(key, value)
:replace(key, value, expirePolicy)
replace(key, oldValue, newValue)
:replace(key, oldValue, newValue, expirePolicy)
getAndReplace(key, value)
:getAndReplace(key, value, expirePolicy)
Asynchronous method overloads are not listed here. Please see the Async Operations section for the list of asynchronous method overloads.
Growing to an infinite size is in general not the expected behavior of caches. Implementing an expiry policy is one way to prevent the infinite growth but sometimes it is hard to define a meaningful expiration timeout. Therefore, Hazelcast JCache provides the eviction feature. Eviction offers the possibility to remove entries based on the cache size or amount of used memory (Hazelcast Enterprise Only) and not based on timeouts.
Since a cache is designed for high throughput and fast reads, a lot of effort went into designing the eviction system as predictable as possible. All built-in implementations provide an amortized O(1) runtime. The default operation runtime is rendered as O(1) but can be faster than the normal runtime cost if the algorithm finds an expired entry while sampling.
Most importantly, in typical production system two common types of caches are found:
Hazelcast JCache eviction supports both types of caches using a slightly different approach based on the configured maximum size of the cache. For detailed information, please see the Eviction Algorithm section.
Hazelcast JCache provides two commonly known eviction policies, LRU and LFU, but loosens the rules for predictable runtime
behavior. LRU, normally recognized as Least Recently Used
, is implemented as Less Recently Used
, and LFU known as Least Frequently Used
is implemented as
Less Frequently Used
. The details about this difference is explained in the
Eviction Algorithm section.
Eviction Policies are configured by providing the corresponding abbreviation to the configuration as shown in the ICache Configuration section. As already mentioned, two built-in policies are available:
To configure the use of the LRU (Less Recently Used) policy:
<eviction size="10000" max-size-policy="ENTRY_COUNT" eviction-policy="LRU" />
And to configure the use of the LFU (Less Frequently Used) policy:
<eviction size="10000" max-size-policy="ENTRY_COUNT" eviction-policy="LFU" />
The default eviction policy is LRU. Therefore, Hazelcast JCache does not offer the possibility to perform no eviction.
Eviction strategies implement the logic of selecting one or more eviction candidates from the underlying storage implementation and passing them to the eviction policies. Hazelcast JCache provides an amortized O(1) cost implementation for this strategy to select a fixed number of samples from the current partition that it is executed against.
The default implementation is com.hazelcast.cache.impl.eviction.impl.strategy.sampling.SamplingBasedEvictionStrategy
which, as
mentioned, samples random 15 elements. A detailed description of the algorithm will be explained in the next section.
The Hazelcast JCache eviction algorithm is specially designed for the use case of high performance caches and with predictability in mind. The built-in implementations provide an amortized O(1) runtime and therefore provide a highly predictable runtime behavior which does not rely on any kind of background threads to handle the eviction. Therefore, the algorithm takes some assumptions into account to prevent network operations and concurrent accesses.
As an explanation of how the algorithm works, let's examine the following flowchart step by step.
As seen by the flowchart, the general eviction operation is easy. As long as the cache does not reach its maximum capacity or you execute updates (put/replace), no eviction is executed.
To prevent network operations and concurrent access, as mentioned earlier, the cache size is estimated based on the size of the currently handled partition. Due to the imbalanced partitions, the single partitions might start to evict earlier than the other partitions.
As mentioned in the General Information section, typically two types of caches are found in the production systems. For small caches, referred to as Reference Caches, the eviction algorithm has a special set of rules depending on the maximum configured cache size. Please see the Reference Caches section for details. The other type of cache is referred to as Active DataSet Cache, which in most cases makes heavy use of the eviction to keep the most active data set in the memory. Those kinds of caches using a very simple but efficient way to estimate the cluster-wide cache size.
All of the following calculations have a well known set of fixed variables:
GlobalCapacity
: The user defined maximum cache size (cluster-wide).PartitionCount
: The number of partitions in the cluster (defaults to 271).BalancedPartitionSize
: The number of elements in a balanced partition state, BalancedPartitionSize := GlobalCapacity / PartitionCount
.Deviation
: An approximated standard deviation (tests proofed it to be pretty near), Deviation := sqrt(BalancedPartitionSize)
.A Reference Cache is typically small and the number of elements to store in the reference caches is normally known prior to creating the cache. Typical examples of reference caches are lookup tables for abbreviations or the states of a country. They tend to have a fixed but small element number and the eviction is an unlikely event and rather undesirable behavior.
Since an imbalanced partition is the worst problem in the small and mid-sized caches than for the caches with millions of entries, the normal estimation rule (as discussed in a bit) is not applied to these kinds of caches. To prevent unwanted eviction on the small and mid-sized caches, Hazelcast implements a special set of rules to estimate the cluster size.
To adjust the imbalance of partitions as found in the typical runtime, the actual calculated maximum cache size (as known as the eviction threshold) is slightly higher than the user defined size. That means more elements can be stored into the cache than expected by the user. This needs to be taken into account especially for large objects, since those can easily exceed the expected memory consumption!
Small caches:
If a cache is configured with no more than 4.000
element, this cache is considered to be a small cache. The actual partition
size is derived from the number of elements (GlobalCapacity
) and the deviation using the following formula:
MaxPartitionSize := Deviation * 5 + BalancedPartitionSize
This formula ends up with big partition sizes which summed up exceed the expected maximum cache size (set by the user), but since the small caches typically have a well known maximum number of elements, this is not a big issue. Only if the small caches are used for a use case other than using it as a reference cache, this needs to be taken into account.
Mid-sized caches
A mid-sized cache is defined as a cache with a maximum number of elements that is bigger than 4.000
but not bigger than
1.000.000
elements. The calculation of mid-sized caches is similar to that of the small caches but with a different
multiplier. To calculate the maximum number of elements per partition, the following formula is used:
MaxPartitionSize := Deviation * 3 + BalancedPartitionSize
For large caches, where the maximum cache size is bigger than 1.000.000
elements, there is no additional calculation needed. The maximum
partition size is considered to be equal to BalancedPartitionSize
since statistically big partitions are expected to almost
balance themselves. Therefore, the formula is as easy as the following:
MaxPartitionSize := BalancedPartitionSize
As mentioned earlier, Hazelcast JCache provides an estimation algorithm to prevent cluster-wide network operations, concurrent access to other partitions and background tasks. It also offers a highly predictable operation runtime when the eviction is necessary.
The estimation algorithm is based on the previously calculated maximum partition size (please see the Reference Caches section and Active DataSet Caches section) and is calculated against the current partition only.
The algorithm to reckon the number of stored entries in the cache (cluster-wide) and if the eviction is necessary is shown in the following pseudo-code example:
RequiresEviction[Boolean] := CurrentPartitionSize >= MaxPartitionSize
Cache entries in Hazelcast are stored as partitioned across the cluster.
When you try to read a record with the key k
, if the current node is not the owner of that key (i.e. not the owner of partition that the key belongs to),
Hazelcast sends a remote operation to the owner node. Each remote operation means lots of network trips.
If your cache is used for mostly read operations, it is advised to use a near cache storage in front of the cache itself to read cache records faster and consume less network traffic.
NOTE: Near cache for JCache is only available for clients NOT servers.
However, using near cache comes with some trade-off for some cases:
Invalidation is the process of removing an entry from the near cache since the entry is not valid anymore (its value is updated or it is removed from actual cache). Near cache invalidation happens asynchronously at the cluster level, but synchronously in real-time at the current node. This means when an entry is updated (explicitly or via entry processor) or removed (deleted explicitly or via entry processor, evicted, expired), it is invalidated from all near caches asynchronously within the whole cluster but updated/removed at/from the current node synchronously. Generally, whenever the state of an entry changes in the record store by updating its value or removing it, the invalidation event is sent for that entry.
Invalidation events can be sent either individually or in batches. If there are lots of mutating operations such as put/remove on the cache, sending the events in batches is advised. This reduces the network traffic and keeps the eventing system less busy.
You can use the following system properties to configure the sending of invalidation events in batches:
hazelcast.cache.invalidation.batch.enabled
: Specifies whether the cache invalidation event batch sending is enabled or not. The default value is true
.hazelcast.cache.invalidation.batch.size
: Defines the maximum number of cache invalidation events to be drained and sent to the event listeners in a batch. The default value is 100
.hazelcast.cache.invalidation.batchfrequency.seconds
: Defines cache invalidation event batch sending frequency in seconds. When event size does not reach to hazelcast.cache.invalidation.batch.size
in the given time period, those events are gathered into a batch and sent to the target. The default value is 5
seconds.So if there are so many clients or so many mutating operations, batching should remain enabled and the batch size should be configured with the hazelcast.cache.invalidation.batch.size
system property to a suitable value.
Expiration means the eviction of expired records. A record is expired:
<max-idle-seconds>
,<time-to-live-seconds>
passed since it is put to near-cache.Expiration is performed in two cases:
null
to caller.In the scope of near cache, eviction means evicting (clearing) the entries selected according to the given eviction-policy
when the specified max-size-policy
has been reached. Eviction is handled with max-size policy
and eviction-policy
elements. Please see the JCache Near Cache Configuration section.
max-size-policy
This element defines the state when near cache is full and whether the eviction should be triggered. The following policies for maximum cache size are supported by the near cache eviction:
BINARY
and OBJECT
in-memory formats.NATIVE
in-memory format. This is supported only by Hazelcast Enterprise.NATIVE
in-memory format. This is supported only by Hazelcast Enterprise.NATIVE
in-memory format. This is supported only by Hazelcast Enterprise.NATIVE
in-memory format. This is supported only by Hazelcast Enterprise.eviction-policy
Once a near cache is full (reached to its maximum size as specified with the max-size-policy
element), an eviction policy determines which, if any, entries must be evicted. Currently, the following eviction policies are supported by near cache eviction:
The following are the example configurations.
Declarative:
<hazelcast-client>
...
<near-cache name="myCache">
<in-memory-format>BINARY</in-memory-format>
<invalidate-on-change>true</invalidate-on-change>
<cache-local-entries>false</cache-local-entries>
<time-to-live-seconds>3600000</time-to-live-seconds>
<max-idle-seconds>600000</max-idle-seconds>
<eviction size="1000" max-size-policy="ENTRY_COUNT" eviction-policy="LFU"/>
</near-cache>
...
</hazelcast-client>
Programmatic:
EvictionConfig evictionConfig = new EvictionConfig();
evictionConfig.setMaxSizePolicy(MaxSizePolicy.ENTRY_COUNT);
evictionConfig.setEvictionPolicy(EvictionPolicy.LFU);
evictionConfig.setSize(10000);
NearCacheConfig nearCacheConfig =
new NearCacheConfig()
.setName("myCache")
.setInMemoryFormat(InMemoryFormat.BINARY)
.setInvalidateOnChange(true)
.setCacheLocalEntries(false)
.setTimeToLiveSeconds(60 * 60 * 1000) // 1 hour TTL
.setMaxIdleSeconds(10 * 60 * 1000) // 10 minutes max idle seconds
.setEvictionConfig(evictionConfig);
...
clientConfig.addNearCacheConfig(nearCacheConfig);
The following are the definitions of the configuration elements and attributes.
in-memory-format
: Storage type of near cache entries. Available values are BINARY
, OBJECT
and NATIVE_MEMORY
. NATIVE_MEMORY
is available only for Hazelcast Enterprise. Default value is BINARY
.invalidate-on-change
: Specifies whether the cached entries are evicted when the entries are changed (updated or removed) on the local and global. Available values are true
and false
. Default value is true
.cache-local-entries
: Specifies whether the local cache entries are stored eagerly (immediately) to near cache when a put operation from the local is performed on the cache. Available values are true
and false
. Default value is false
.time-to-live-seconds
: Maximum number of seconds for each entry to stay in the near cache. Entries that are older than <time-to-live-seconds>
will be automatically evicted from the near cache. It can be any integer between 0
and Integer.MAX_VALUE
. 0
means infinite. Default value is 0
.max-idle-seconds
: Maximum number of seconds each entry can stay in the near cache as untouched (not-read). Entries that are not read (touched) more than <max-idle-seconds>
value will be removed from the near cache. It can be any integer between 0
and Integer.MAX_VALUE
. 0
means Integer.MAX_VALUE
. Default is 0
.eviction
: Specifies when the eviction is triggered (max-size policy
) and which eviction policy (LRU
or LFU
) is used for the entries to be evicted. The default value for max-size-policy
is ENTRY_COUNT
, default size
is 10000
and default eviction-policy
is LRU
. For High-Density Memory Store near cache, since ENTRY_COUNT
eviction policy is not supported yet, eviction must be explicitly configured with one of the supported policies:USED_NATIVE_MEMORY_SIZE
USED_NATIVE_MEMORY_PERCENTAGE
FREE_NATIVE_MEMORY_SIZE
FREE_NATIVE_MEMORY_PERCENTAGE
.Near cache can be configured only at the client side.
Near cache configuration can be defined at the client side (using hazelcast-client.xml
or ClientConfig
) as independent configuration (independent from the CacheConfig
). Near cache configuration lookup is handled as described below:
In addition to the operations explained in the Async Operations section and Custom ExpiryPolicy section, Hazelcast ICache also provides a set of convenience methods. These methods are not part of the JCache specification.
size()
: Returns the estimated size of the distributed cache.destroy()
: Destroys the cache and removes the data from memory. This is different from the method javax.cache.Cache::close
.getLocalCacheStatistics()
: Returns a com.hazelcast.cache.CacheStatistics
instance providing the same statistics data as the JMX beans. This method is not available yet on Hazelcast clients: the exception java.lang.UnsupportedOperationException
is thrown when you use this method on a Hazelcast client.Another feature, especially interesting for distributed environments like Hazelcast, is the JCache specified
javax.cache.processor.EntryProcessor
. For more general information, please see the JCache EntryProcessor section.
Since Hazelcast provides backups of cached entries on other nodes, the default way to backup an object changed by an
EntryProcessor
is to serialize the complete object and send it to the backup partition. This can be a huge network overhead for big objects.
Hazelcast offers a sub-interface for EntryProcessor
called com.hazelcast.cache.BackupAwareEntryProcessor
. This allows the user to create or pass another EntryProcessor
to run on backup
partitions and apply delta changes to the backup entries.
The backup partition EntryProcessor
can either be the currently running processor (by returning this
) or it can be
a specialized EntryProcessor
implementation (other from the currently running one) which does different operations or leaves
out operations, e.g. sending emails.
If we again take the EntryProcessor
example from the demonstration application provided in the JCache EntryProcessor section,
the changed code will look like the following snippet.
public class UserUpdateEntryProcessor
implements BackupAwareEntryProcessor<Integer, User, User> {
@Override
public User process( MutableEntry<Integer, User> entry, Object... arguments )
throws EntryProcessorException {
// Test arguments length
if ( arguments.length < 1 ) {
throw new EntryProcessorException( "One argument needed: username" );
}
// Get first argument and test for String type
Object argument = arguments[0];
if ( !( argument instanceof String ) ) {
throw new EntryProcessorException(
"First argument has wrong type, required java.lang.String" );
}
// Retrieve the value from the MutableEntry
User user = entry.getValue();
// Retrieve the new username from the first argument
String newUsername = ( String ) arguments[0];
// Set the new username
user.setUsername( newUsername );
// Set the changed user to mark the entry as dirty
entry.setValue( user );
// Return the changed user to return it to the caller
return user;
}
public EntryProcessor<K, V, T> createBackupEntryProcessor() {
return this;
}
}
You can use the additional method BackupAwareEntryProcessor::createBackupEntryProcessor
to create or return the EntryProcessor
implementation to run on the backup partition (in the example above, the same processor again).
NOTE: For the backup runs, the returned value from the backup processor is ignored and not
returned to the user.
Hazelcast JCache is fully compliant with the JSR 107 TCK (Technology Compatibility Kit), therefore it is officially a JCache implementation. This is tested by running the TCK against the Hazelcast implementation.
You can test Hazelcast JCache for compliance by executing the TCK. Just perform the instructions below:
tck-parent/pom.xml
as shown below.mvn clean install
.<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<CacheInvocationContextImpl>
javax.cache.annotation.impl.cdi.CdiCacheKeyInvocationContextImpl
</CacheInvocationContextImpl>
<domain-lib-dir>${project.build.directory}/domainlib</domain-lib-dir>
<domain-jar>domain.jar</domain-jar>
<!-- ################################################################# -->
<!-- Change the following properties on the command line
to override with the coordinates for your implementation-->
<implementation-groupId>com.hazelcast</implementation-groupId>
<implementation-artifactId>hazelcast</implementation-artifactId>
<implementation-version>3.4</implementation-version>
<!-- Change the following properties to your CacheManager and
Cache implementation. Used by the unwrap tests. -->
<CacheManagerImpl>
com.hazelcast.client.cache.impl.HazelcastClientCacheManager
</CacheManagerImpl>
<CacheImpl>com.hazelcast.cache.ICache</CacheImpl>
<CacheEntryImpl>
com.hazelcast.cache.impl.CacheEntry
</CacheEntryImpl>
<!-- Change the following to point to your MBeanServer, so that
the TCK can resolve it. -->
<javax.management.builder.initial>
com.hazelcast.cache.impl.TCKMBeanServerBuilder
</javax.management.builder.initial>
<org.jsr107.tck.management.agentId>
TCKMbeanServer
</org.jsr107.tck.management.agentId>
<jsr107.api.version>1.0.0</jsr107.api.version>
<!-- ################################################################# -->
</properties>
This will run the tests using an embedded Hazelcast Member.
In this chapter, we show you how Hazelcast is integrated with Hibernate 2nd level cache and Spring, and how Hazelcast helps with your Filter, Tomcat and Jetty based web session replications.
The Hibernate Second Level Cache section tells how you should configure Hazelcast and Hibernate to integrate them. It explains the modes of Hazelcast that can be used by Hibernate and also provides how to perform advanced settings like accessing the underlying HazelcastInstance used by Hibernate.
The Web Session Replication section provides information on how to cluster user HTTP sessions automatically. Also, you can learn how to enable session replication for JEE web applications with Tomcat and Jetty containers. Please note that Tomcat and Jetty based web session replications are Hazelcast Enterprise only modules.
The Spring Integration section tells how you can integrate Hazelcast into a Spring project by explaining the Hazelcast instance and client configurations with the hazelcast namespace. It also lists the supported Spring bean attributes.
Hazelcast provides distributed second level cache for your Hibernate entities, collections and queries.
Please see our sample application for Hibernate Second Level Cache.
To configure for Hibernate, add hazelcast-hibernate3-<
hazelcastversion>.jar
or hazelcast-
hibernate4-<
hazelcastversion>.jar
into your classpath depending on your Hibernate version.
Then add the following properties into your Hibernate configuration file (e.g. hibernate.cfg.xml
).
Enabling the use of second level cache
<property name="hibernate.cache.use_second_level_cache">true</property>
Hibernate RegionFactory
HazelcastCacheRegionFactory
HazelcastCacheRegionFactory
uses Hazelcast Distributed Map to cache the data, so all cache operations go through the wire.
<property name="hibernate.cache.region.factory_class">
com.hazelcast.hibernate.HazelcastCacheRegionFactory
</property>
HazelcastLocalCacheRegionFactory
You can use HazelcastLocalCacheRegionFactory
which stores data in a local node and sends invalidation messages when an entry is updated/deleted locally.
<property name="hibernate.cache.region.factory_class">
com.hazelcast.hibernate.HazelcastLocalCacheRegionFactory
</property>
Optional Settings
To enable use of query cache:
<property name="hibernate.cache.use_query_cache">true</property>
To force minimal puts into query cache:
<property name="hibernate.cache.use_minimal_puts">true</property>
NOTE: QueryCache is always LOCAL to the node and never distributed across Hazelcast Cluster.
hazelcast.xml
into the root of your classpath. If Hazelcast cannot find hazelcast.xml
, then it will use the default configuration from hazelcast.jar
.<property name="hibernate.cache.provider_configuration_file_resource_path">
hazelcast-custom-config.xml
</property>
<property name="hibernate.cache.hazelcast.configuration_file_path">
hazelcast-custom-config.xml
</property>
Hazelcast creates a separate distributed map for each Hibernate cache region. You can easily configure these regions via Hazelcast map configuration. You can define backup, eviction, TTL and Near Cache properties.
HazelcastCacheRegionFactory
uses standard Hazelcast Distributed Maps. All operations like get
, put
, and remove
will be performed using the Distributed Map logic. The only downside of using HazelcastCacheRegionFactory
may be the lower performance compared to HazelcastLocalCacheRegionFactory
since operations are handled as distributed calls.
NOTE: If you use HazelcastCacheRegionFactory
, you can see your maps on Management Center.
With HazelcastCacheRegionFactory
, all of the following caches are distributed across Hazelcast Cluster.
With HazelcastLocalCacheRegionFactory
, each cluster member has a local map and each of them is registered to a Hazelcast Topic (ITopic). Whenever a put
or remove
operation is performed on a member, an invalidation message is generated on the ITopic and sent to the other members. Those other members remove the related key-value pair on their local maps as soon as they get these invalidation messages. The new value is only updated on this member when a get
operation runs on that key. In the case of get
operations, invalidation messages are not generated and reads are performed on the local map.
An illustration of the above logic is shown below.
If your operations are mostly reads, then this option gives better performance.
NOTE: If you use HazelcastLocalCacheRegionFactory
, you cannot see your maps on Management Center.
With HazelcastLocalCacheRegionFactory
, all of the following caches are not distributed and are kept locally in the Hazelcast Node.
Entity and Collection are invalidated on update. When they are updated on a node, an invalidation message is sent to all other nodes in order to remove the entity from their local cache. When needed, each node reads that data from the underlying DB.
Timestamp cache is replicated. On every update, a replication message is sent to all the other nodes.
Eviction support is limited to maximum size of the map (defined by max-size
configuration element) and TTL only. When maximum size is hit, 20% of the entries will be evicted automatically.
Hibernate 2nd Level Cache can use Hazelcast in two modes: Peer-to-Peer and Client/Server.
With P2P mode, each Hibernate deployment launches its own Hazelcast Instance. You can also configure Hibernate to use an existing instance, so instead of creating a new HazelcastInstance
for each SessionFactory
, you can use an existing instance by setting the hibernate.cache.hazelcast.instance_name
Hibernate property to the HazelcastInstance
's name. For more information, please see the Named HazelcastInstance section.
Disabling shutdown during SessionFactory.close()
Shutting down HazelcastInstance
can be disabled during SessionFactory.close()
. To achieve this set the Hibernate property hibernate.cache.hazelcast.shutdown_on_session_factory_close
to false. (In this case Hazelcast property hazelcast.shutdownhook.enabled
should not be set to false.) Default value is true
.
<property name="hibernate.cache.hazelcast.use_native_client">true</property>
To set up Native Client, add the Hazelcast group-name, group-password and cluster member address properties. Native Client will connect to the defined member and will get the addresses of all members in the cluster. If the connected member dies or leaves the cluster, the client will automatically switch to another member in the cluster.
<property name="hibernate.cache.hazelcast.native_client_address">10.34.22.15</property>
<property name="hibernate.cache.hazelcast.native_client_group">dev</property>
<property name="hibernate.cache.hazelcast.native_client_password">dev-pass</property>
NOTE: To use Native Client, add hazelcast-client-<version>.jar
into your classpath. Refer to Clients for more information.
Hibernate has four cache concurrency strategies: read-only, read-write, nonstrict-read-write and transactional. Hibernate does not force cache providers to support all those strategies. Hazelcast supports the first three: read-only, read-write, and nonstrict-read-write. It has no support for transactional strategy yet.
<class name="eg.Immutable" mutable="false">
<cache usage="read-only"/>
....
</class>
<class name="eg.Cat" .... >
<cache usage="read-write"/>
....
<set name="kittens" ... >
<cache usage="read-write"/>
....
</set>
</class>
<class-cache usage="read-only" class="eg.Immutable"/>
<class-cache usage="read-write" class="eg.Cat"/>
<collection-cache collection="eg.Cat.kittens" usage="read-write"/>
@Cache(usage = CacheConcurrencyStrategy.READ_WRITE)
public class Cat implements Serializable {
...
}
Accessing underlying HazelcastInstance
Using com.hazelcast.hibernate.instance.HazelcastAccessor
, you can access the underlying HazelcastInstance
used by Hibernate SessionFactory
.
SessionFactory sessionFactory = ...;
HazelcastInstance hazelcastInstance = HazelcastAccessor
.getHazelcastInstance(sessionFactory);
Changing/setting lock timeout value of read-write strategy
You can set a lock timeout value using the hibernate.cache.hazelcast.lock_timeout_in_seconds
Hibernate property. The value should be in seconds. The default value is 300 seconds.
If you are using Tomcat as your web container, please see the Tomcat based Web Session Replication section.
Sample Code: Please see our sample application for Filter Based Web Session Replication.
Assume that you have more than one web server (A, B, C) with a load balancer in front of it. If server A goes down, your users on that server will be directed to one of the live servers (B or C), but their sessions will be lost.
We need to have all these sessions backed up somewhere if we do not want to lose the sessions upon server crashes. Hazelcast Web Manager (WM) allows you to cluster user HTTP sessions automatically. The following are required before enabling Hazelcast Session Clustering:
Target application or web server should support Java 1.6 or higher.
Target application or web server should support Servlet 3.0 or higher spec.
Session objects that need to be clustered have to be Serializable.
In the client/server architecture, session classes does not have to be present in the server classpath.
Here are the steps to setup Hazelcast Session Clustering:
Put the hazelcast
and hazelcast-wm
jars in your WEB-INF/lib
directory. Optionally, if you wish to connect to a cluster as a client, add hazelcast-client
as well.
Put the following XML into web.xml
file. Make sure Hazelcast filter is placed before all the other filters if any; for example, you can put it at the top.
<filter>
<filter-name>hazelcast-filter</filter-name>
<filter-class>com.hazelcast.web.WebFilter</filter-class>
<!--
Name of the distributed map storing
your web session objects
-->
<init-param>
<param-name>map-name</param-name>
<param-value>my-sessions</param-value>
</init-param>
<!--
TTL value of the distributed map storing
your web session objects.
Any integer between 0 and Integer.MAX_VALUE.
Default is 0 which is infinite.
-->
<init-param>
<param-name>session-ttl-seconds</param-name>
<param-value>0</param-value>
</init-param>
<!--
How is your load-balancer configured?
sticky-session means all requests of a session
is routed to the node where the session is first created.
This is excellent for performance.
If sticky-session is set to false, when a session is updated
on a node, entry for this session on all other nodes is invalidated.
You have to know how your load-balancer is configured before
setting this parameter. Default is true.
-->
<init-param>
<param-name>sticky-session</param-name>
<param-value>true</param-value>
</init-param>
<!--
Name of session id cookie
-->
<init-param>
<param-name>cookie-name</param-name>
<param-value>hazelcast.sessionId</param-value>
</init-param>
<!--
Domain of session id cookie. Default is based on incoming request.
-->
<init-param>
<param-name>cookie-domain</param-name>
<param-value>.mywebsite.com</param-value>
</init-param>
<!--
Should cookie only be sent using a secure protocol? Default is false.
-->
<init-param>
<param-name>cookie-secure</param-name>
<param-value>false</param-value>
</init-param>
<!--
Should HttpOnly attribute be set on cookie ? Default is false.
-->
<init-param>
<param-name>cookie-http-only</param-name>
<param-value>false</param-value>
</init-param>
<!--
Are you debugging? Default is false.
-->
<init-param>
<param-name>debug</param-name>
<param-value>true</param-value>
</init-param>
<!--
Configuration xml location;
* as servlet resource OR
* as classpath resource OR
* as URL
Default is one of hazelcast-default.xml
or hazelcast.xml in classpath.
-->
<init-param>
<param-name>config-location</param-name>
<param-value>/WEB-INF/hazelcast.xml</param-value>
</init-param>
<!--
Do you want to use an existing HazelcastInstance?
Default is null.
-->
<init-param>
<param-name>instance-name</param-name>
<param-value>default</param-value>
</init-param>
<!--
Do you want to connect as a client to an existing cluster?
Default is false.
-->
<init-param>
<param-name>use-client</param-name>
<param-value>false</param-value>
</init-param>
<!--
Client configuration location;
* as servlet resource OR
* as classpath resource OR
* as URL
Default is null.
-->
<init-param>
<param-name>client-config-location</param-name>
<param-value>/WEB-INF/hazelcast-client.properties</param-value>
</init-param>
<!--
Do you want to shutdown HazelcastInstance during
web application undeploy process?
Default is true.
-->
<init-param>
<param-name>shutdown-on-destroy</param-name>
<param-value>true</param-value>
</init-param>
<!--
Do you want to cache sessions locally in each instance?
Default is false.
-->
<init-param>
<param-name>deferred-write</param-name>
<param-value>false</param-value>
</init-param>
</filter>
<filter-mapping>
<filter-name>hazelcast-filter</filter-name>
<url-pattern>/*</url-pattern>
<dispatcher>FORWARD</dispatcher>
<dispatcher>INCLUDE</dispatcher>
<dispatcher>REQUEST</dispatcher>
</filter-mapping>
<listener>
<listener-class>com.hazelcast.web.SessionListener</listener-class>
</listener>
war
file as you would normally do.It is that easy. All HTTP requests will go through Hazelcast WebFilter
and it will put the session objects into Hazelcast distributed map if needed.
Sample Code: Please see our sample application for Spring Security Support.
If Spring based security is used for your application, you should use com.hazelcast.web.spring.SpringAwareWebFilter
instead of com.hazelcast.web.WebFilter
in your filter definition.
...
<filter>
<filter-name>hazelcast-filter</filter-name>
<filter-class>com.hazelcast.web.spring.SpringAwareWebFilter</filter-class>
...
</filter>
...
SpringAwareWebFilter
notifies Spring by publishing events to Spring context. These events are used by the org.springframework.security.core.session.SessionRegistry
instance.
As before, you must also define com.hazelcast.web.SessionListener
in your web.xml
. However, you do not need to define org.springframework.security.web.session.HttpSessionEventPublisher
in your web.xml
as before, since SpringAwareWebFilter
already informs Spring about session based events like create
or destroy
.
Hazelcast Session Replication works as P2P by default. To switch to Client/Server architecture, you need to set the use-client
parameter to true. P2P mode is more flexible and requires no configuration in advance; in Client/Server architecture, clients need to connect to an existing Hazelcast Cluster. In case of connection problems, clients will try to reconnect to the cluster. The default retry count is 3.
In the client/server architecture, if servers goes down, Hazelcast web manager will keep the updates in the local and after servers come back, the clients will update the distributed map.
deferred-write
If the value for deferred-write
is set as true, Hazelcast will cache the session locally and will update the local session when an attribute is set or deleted. At the end of the request, it will update the distributed map with all the updates. It will not update the distributed map upon each attribute update, but will only call it once at the end of the request. It will also cache it, i.e. whenever there is a read for the attribute, it will read it from the cache.
Important note about deferred-write=false
setting:
If deferred-write
is false, any update (i.e. setAttribute
) on the session will directly be available in the cluster. One exception to this behavior is the changes to the session attribute objects. To update an attribute cluster-wide, setAttribute
must be called after changes are made to the attribute object.
The following example explains how to update an attribute in the case of deferred-write=false
setting:
session.setAttribute("myKey", new ArrayList());
List list1 = session.getAttribute("myKey");
list1.add("myValue");
session.setAttribute("myKey", list1); // changes updated in the cluster
SessionId generation is done by the Hazelcast Web Session Module if session replication is configured in the web application. The default cookie name for the sessionId is hazelcast.sessionId
. This name is configurable with a cookie-name
parameter in the web.xml
file of the application.
hazelcast.sessionId
is just a UUID prefixed with “HZ” character and without “-“ character, e.g. HZ6F2D036789E4404893E99C05D8CA70C7
.
When called by the target application, the value of HttpSession.getId()
is the same as the value of hazelcast.sessionId
.
Hazelcast automatically removes sessions from the cluster if the sessions are expired on the Web Container. This removal is done by com.hazelcast.web.SessionListener
, which is an implementation of javax.servlet.http.HttpSessionListener
.
Default session expiration configuration depends on the Servlet Container that is being used. You can also define it in your web.xml.
<session-config>
<session-timeout>60</session-timeout>
</session-config>
If you want to override session expiry configuration with a Hazelcast specific configuration, you can use session-ttl-seconds
to specify TTL on the Hazelcast Session Replication Distributed Map.
Hazelcast holds whole session attributes in a distributed map and in a local HTTP session. Local session is required for fast access to data and distributed map is needed for fail-safety.
If sticky-session
is not used, whenever a session attribute is updated in a node (in both node local session and clustered cache), that attribute should be invalidated in all other nodes' local sessions, because now they have dirty values. Therefore, when a request arrives at one of those other nodes, that attribute value is fetched from clustered cache.
To overcome the performance penalty of sending invalidation messages during updates, you can use sticky sessions. If Hazelcast knows sessions are sticky, invalidation will not be sent because Hazelcast assumes there is no other local session at the moment. When a server is down, requests belonging to a session hold in that server will routed to other server, and that server will fetch session data from clustered cache. That means, using sticky sessions, one will not suffer the performance penalty of accessing clustered data and can benefit recover from a server failure.
If you have some attributes that you do not want them to be distributed, you can mark those attributes as transient.
Transient attributes are kept in and when the server is shutdown, you lost the attribute values.
You can set the transient attributes in your web.xml
file.
Here is an example:
...
<init-param>
<param-name>transient-attributes</param-name>
<param-value>key1,key2,key3</param-value>
</init-param>
...
NOTE: This feature is supported for Hazelcast Enterprise 3.3 or higher.
Sample Code: Please see our sample application for Tomcat Based Web Session Replication.
Session Replication with Hazelcast Enterprise is a container specific module that enables session replication for JEE Web Applications without requiring changes to the application.
Features
Supported Containers
Tomcat Web Session Replication Module has been tested against the following containers.
The latest tested versions are 6.0.39, 7.0.40 and 8.0.20.
Requirements
Tomcat Session Replication in Hazelcast Enterprise is a Hazelcast Module where each created HttpSession
Object is kept in the Hazelcast Distributed Map. If configured with Sticky Sessions, each Tomcat Instance has its own local copy of the session for performance boost.
Since the sessions are in Hazelcast Distributed Map, you can use all the available features offered by Hazelcast Distributed Map implementation, such as MapStore and WAN Replication.
Tomcat Web Sessions run in two different modes:
P2P deployment launches an embedded Hazelcast Node in each server instance.
Features
This type of deployment is simple: just configure your Tomcat and launch. There is no need for an external Hazelcast cluster.
Sample P2P Configuration to use Hazelcast Session Replication
$HAZELCAST_ENTERPRISE_ROOT
.$HAZELCAST_ENTERPRISE_ROOT/bin/hazelcast.xml
with the provided Hazelcast Enterprise License Key. Put $HAZELCAST_ENTERPRISE_ROOT/lib/hazelcast-enterprise-all-
<version>.jar
, $HAZELCAST_
ENTERPRISE_ROOT/lib/hazelcast-enterprise-
<tomcatversion>-
<version>.jar
and hazelcast.xml
in the folder $CATALINA_HOME/lib/
.
Put a <Listener>
element into the file $CATALINA_HOME$/conf/server.xml
as shown below.
<Server>
...
<Listener className="com.hazelcast.session.P2PLifecycleListener"/>
...
</Server>
<Manager>
element into the file $CATALINA_HOME$/conf/context.xml
as shown below.<Context>
...
<Manager className="com.hazelcast.session.HazelcastSessionManager"/>
...
</Context>
Optional Attributes for Listener Element
configLocation
attribute into the <Listener>
element. If not provided, hazelcast.xml
in the classpath is used by default. URL or full filesystem path as a configLocation
value is supported.In this deployment type, Tomcat instances work as clients on an existing Hazelcast Cluster.
Features
Sample Client/Server Configuration to use Hazelcast Session Replication
$HAZELCAST_ENTERPRISE_ROOT
.Put $HAZELCAST_ENTERPRISE_ROOT/lib/hazelcast-client-
<version>.jar
, $HAZELCAST_
ENTERPRISE_ROOT/lib/hazelcast-enterprise-
<version>.jar
and $HAZELCAST_ENTERPRISE_ROOT/lib/hazelcast-enterprise-
<tomcatversion>-
<version>.jar
in the folder $CATALINA_HOME/lib/
.
Put a <Listener>
element into the $CATALINA_HOME$/conf/server.xml
as shown below.
<Server>
...
<Listener className="com.hazelcast.session.ClientServerLifecycleListener"/>
...
</Server>
<Manager>
element in the $CATALINA_HOME$/conf/context.xml
as shown below.<Context>
<Manager className="com.hazelcast.session.HazelcastSessionManager"
clientOnly="true"/>
</Context>
Launch a Hazelcast Instance using $HAZELCAST_ENTERPRISE_ROOT/bin/server.sh
or $HAZELCAST_
ENTERPRISE_ROOT/bin/server.bat
.
Start Tomcat instances with a configured load balancer and deploy the web application.
Optional Attributes for Listener Element
configLocation
attribute into the <Listener>
element. If not provided, hazelcast-client-default.xml
in hazelcast-client-
<version>.jar
file is used by default. Any client XML file in the classpath, URL or full filesystem path as a configLocation
value is also supported.<Manager>
element is used both in P2P and Client/Server mode. You can use the following attributes to configure Tomcat Session Replication Module to better serve your needs.
mapName
attribute into <Manager>
element. Its default value is default Hazelcast Distributed Map. Use this attribute if you have a specially configured map for special cases like WAN Replication, Eviction, MapStore, etc.sticky
attribute into <Manager>
element. Its default value is true.processExpiresFrequency
attribute into <Manager>
element. It specifies the frequency of session validity check, in seconds. Its default value is 6 and the minimum value that you can set is 1.deferredWrite
attribute into <Manager>
elemenet. Its default value is true.Tomcat Web Session Replication Module has its own nature of caching. Attribute changes during the HTTP Request/HTTP Response cycle is cached by default. Distributing those changes to the Hazelcast Cluster is costly. Because of that, Session Replication is only done at the end of each request for updated and deleted attributes. The risk in this approach is losing data if a Tomcat crash happens in the middle of the HTTP Request operation.
You can change that behavior by setting deferredWrite=false
in your <Manager>
element. By disabling it, all updates that are done on session objects are directly distributed into Hazelcast Cluster.
Based on Tomcat configuration or sessionTimeout
setting in web.xml
, sessions are expired over time. This requires a cleanup on the Hazelcast Cluster since there is no need to keep expired sessions in the cluster.
processExpiresFrequency
, which is defined in <Manager>
, is the only setting that controls the behavior of session expiry policy in the Tomcat Web Session Replication Module. By setting this, you can set the frequency of the session expiration checks in the Tomcat Instance.
Tomcat can be configured in two ways to enable Session Replication for deployed applications.
Server Context.xml Configuration
By configuring $CATALINA_HOME$/conf/context.xml
, you can enable session replication for all applications deployed in the Tomcat Instance.
Application Context.xml Configuration
By configuring $CATALINA_HOME/conf/[enginename]/[hostname]/[applicationName].xml
, you can enable Session Replication per deployed application.
Sticky Sessions (default)
Sticky Sessions are used to improve the performance since the sessions do not move around the cluster.
Request goes always to the same instance where the session was firstly created. By using a sticky session, you eliminate session replication problems mostly, except for the failover cases. In case of failovers, Hazelcast helps you not lose existing sessions.
Non-Sticky Sessions
Non-Sticky Sessions are not good for performance because you need to move session data all over the cluster every time a new request comes in.
However, load balancing might be super easy with Non-Sticky caches. In case of heavy load, you can distribute the request to the least used Tomcat instance. Hazelcast supports Non-Sticky Sessions as well.
Each HTTP Request is redirected to the same Tomcat instance if sticky sessions are enabled. The parameter jvmRoute
is added to the end of session ID as a suffix, to make Load Balancer aware of the target Tomcat instance.
When Tomcat Failure happens and Load Balancer cannot redirect the request to the owning instance, it sends a request to one of the available Tomcat instances. Since the jvmRoute
parameter of session ID is different than that of the target Tomcat instance, Hazelcast Session Replication Module updates the session ID of the session with the new jvmRoute
parameter. That means that the Session is moved to another Tomcat instance and Load Balancer will redirect all subsequent HTTP Requests to the new Tomcat Instance.
NOTE: If stickySession is enabled, jvmRoute
parameter must be set in $CATALINA_HOME$/conf/server.xml
and unique among Tomcat instances in the cluster.
<Engine name="Catalina" defaultHost="localhost" jvmRoute="tomcat-8080">
NOTE: This feature is supported for Hazelcast Enterprise 3.4 or higher.
Sample Code: Please see our sample application for Jetty Based Web Session Replication.
Jetty Web Session Replication with Hazelcast Enterprise is a container specific module that enables session replication for JEE Web Applications without requiring changes to the application.
Features
Supported Containers
Jetty Web Session Replication Module has been tested against the following containers.
Latest tested versions are 7.6.16.v20140903, 8.1.16.v20140903 and 9.2.3.v20140905
Requirements
jetty-nosql
module. This module (jetty-nosql-<*jettyversion*>.jar
) needs to be added to $JETTY_HOME/lib/ext
.
This module can be found here.Jetty Session Replication in Hazelcast Enterprise is a Hazelcast Module where each created HttpSession
Object's state is kept in Hazelcast Distributed Map.
Since the session data are in Hazelcast Distributed Map, you can use all the available features offered by Hazelcast Distributed Map implementation, such as MapStore and WAN Replication.
Jetty Web Session Replication runs in two different modes:
P2P deployment launches embedded Hazelcast Node in each server instance.
Features
This type of deployment is simple: just configure your Jetty and launch. There is no need for an external Hazelcast cluster.
Sample P2P Configuration to use Hazelcast Session Replication
$HAZELCAST_ENTERPRISE_ROOT
.$HAZELCAST_ENTERPRISE_ROOT/bin/hazelcast.xml
with the provided Hazelcast Enterprise License Key. hazelcast.xml
in the folder $JETTY_HOME/etc
.$HAZELCAST_ENTERPRISE_ROOT/lib/hazelcast-enterprise-all-
<version>.jar
, $HAZELCAST_
ENTERPRISE_ROOT/lib/hazelcast-enterprise-
<jettyversion>-
<version>.jar
in the folder $JETTY_HOME/lib/ext
.Configuring the HazelcastSessionIdManager
You need to configure a com.hazelcast.session.HazelcastSessionIdManager
instance in jetty.xml
. Add the following lines to your jetty.xml
.
<Set name="sessionIdManager">
<New id="hazelcastIdMgr" class="com.hazelcast.session.HazelcastSessionIdManager">
<Arg><Ref id="Server"/></Arg>
<Set name="configLocation">etc/hazelcast.xml</Set>
</New>
</Set>
Configuring the HazelcastSessionManager
HazelcastSessionManager
can be configured from a context.xml
file. Each application has a context file in the $CATALINA_HOME$/contexts
folder. You need to create this context file if it does not exist.
The context filename must be the same as the application name, e.g. example.war
should have a context file named example.xml
.
The file context.xml
should have the following content.
<Ref name="Server" id="Server">
<Call id="hazelcastIdMgr" name="getSessionIdManager"/>
</Ref>
<Set name="sessionHandler">
<New class="org.eclipse.jetty.server.session.SessionHandler">
<Arg>
<New id="hazelcastMgr" class="com.hazelcast.session.HazelcastSessionManager">
<Set name="idManager">
<Ref id="hazelcastIdMgr"/>
</Set>
</New>
</Arg>
</New>
</Set>
NOTE: In Jetty 9, there is no folder with the name contexts
. You have to put the file context.xml
under the webapps
directory. And you need to add the following lines to context.xml
.
<Ref name="Server" id="Server">
<Call id="hazelcastIdMgr" name="getSessionIdManager"/>
</Ref>
<Set name="sessionHandler">
<New class="org.eclipse.jetty.server.session.SessionHandler">
<Arg>
<New id="hazelcastMgr" class="com.hazelcast.session.HazelcastSessionManager">
<Set name="sessionIdManager">
<Ref id="hazelcastIdMgr"/>
</Set>
</New>
</Arg>
</New>
</Set>
In client/server deployment type, Jetty instances work as clients to an existing Hazelcast Cluster.
Features
Sample Client/Server Configuration to use Hazelcast Session Replication
$HAZELCAST_ENTERPRISE_ROOT
.$HAZELCAST_ENTERPRISE_ROOT/bin/hazelcast.xml
with the provided Hazelcast Enterprise License Key. hazelcast.xml
in the folder $JETTY_HOME/etc
.$HAZELCAST_ENTERPRISE_ROOT/lib/hazelcast-enterprise-all-
<version>.jar
, $HAZELCAST_
ENTERPRISE_ROOT/lib/hazelcast-enterprise-
<jettyversion>-
<version>.jar
in the folder $JETTY_HOME/lib/ext
.Configuring the HazelcastSessionIdManager
You need to configure a com.hazelcast.session.HazelcastSessionIdManager
instance in jetty.xml
. Add the following lines to your jetty.xml
.
<Set name="sessionIdManager">
<New id="hazelcastIdMgr" class="com.hazelcast.session.HazelcastSessionIdManager">
<Arg><Ref id="Server"/></Arg>
<Set name="configLocation">etc/hazelcast.xml</Set>
<Set name="clientOnly">true</Set>
</New>
</Set>
Configuring the HazelcastSessionManager
HazelcastSessionManager
can be configured from a context.xml
file. Each application has a context file under the $CATALINA_HOME$/contexts
folder. You need to create this context file if it does not exist.
The context filename must be the same as the application name, e.g. example.war
should have a context file named example.xml
.
<Ref name="Server" id="Server">
<Call id="hazelcastIdMgr" name="getSessionIdManager"/>
</Ref>
<Set name="sessionHandler">
<New class="org.eclipse.jetty.server.session.SessionHandler">
<Arg>
<New id="hazelMgr" class="com.hazelcast.session.HazelcastSessionManager">
<Set name="idManager">
<Ref id="hazelcastIdMgr"/>
</Set>
</New>
</Arg>
</New>
</Set>
NOTE: In Jetty 9, there is no folder with name contexts
. You have to put the file context.xml
file under webapps
directory. And you need to add below lines to context.xml
.
<Ref name="Server" id="Server">
<Call id="hazelcastIdMgr" name="getSessionIdManager"/>
</Ref>
<Set name="sessionHandler">
<New class="org.eclipse.jetty.server.session.SessionHandler">
<Arg>
<New id="hazelMgr" class="com.hazelcast.session.HazelcastSessionManager">
<Set name="sessionIdManager">
<Ref id="hazelcastIdMgr"/>
</Set>
</New>
</Arg>
</New>
</Set>
Launch a Hazelcast Instance using $HAZELCAST_ENTERPRISE_ROOT/bin/server.sh
or $HAZELCAST_
ENTERPRISE_ROOT/bin/server.bat
.
Start Tomcat instances with a configured load balancer and deploy the web application.
HazelcastSessionIdManager
is used both in P2P and Client/Server mode. Use the following parameters to configure the Jetty Session Replication Module to better serve your needs.
workerName
: Set this attribute to a unique value for each Jetty instance to enable session affinity with a sticky-session configured load balancer.cleanUpPeriod
: Defines the working period of session clean-up task in milliseconds.configLocation
: specifies the location of hazelcast.xml
.HazelcastSessionManager
is used both in P2P and Client/Server mode. Use the following parameters to configure Jetty Session Replication Module to better serve your needs.
savePeriod
: Sets the interval of saving session data to the Hazelcast cluster. Jetty Web Session Replication Module has its own nature of caching. Attribute changes during the HTTP Request/HTTP Response cycle are cached by default. Distributing those changes to the Hazelcast Cluster is costly, so Session Replication is only done at the end of each request for updated and deleted attributes. The risk with this approach is losing data if a Jetty crash happens in the middle of the HTTP Request operation.
You can change that behavior by setting the savePeriod
attribute.Notes:
savePeriod
is set to -2, HazelcastSessionManager.save
method is called for every doPutOrRemove
operation.Based on Tomcat configuration or sessionTimeout
setting in web.xml
, the sessions are expired over time. This requires a cleanup on Hazelcast Cluster, since there is no need to keep expired sessions in it.
cleanUpPeriod
, which is defined in HazelcastSessionIdManager
, is the only setting that controls the behavior of session expiry policy in Jetty Web Session Replication Module. By setting this, you can set the frequency of the session expiration checks in the Jetty Instance.
HazelcastSessionIdManager
can work in sticky and non-sticky setups.
The clustered session mechanism works in conjunction with a load balancer that supports stickiness. Stickiness can be based on various data items, such as source IP address, or characteristics of the session ID, or a load-balancer specific mechanism.
For those load balancers that examine the session ID, HazelcastSessionIdManager
appends a node ID to the session ID, which can be used for routing.
You must configure the HazelcastSessionIdManager
with a workerName
that is unique across the cluster.
Typically the name relates to the physical node on which the instance is executed. If this name is not unique, your load balancer might fail to distribute your sessions correctly.
If sticky sessions are enabled, the workerName
parameter has to be set, as shown below.
<Set name="sessionIdManager">
<New id="hazelcastIdMgr" class="com.hazelcast.session.HazelcastSessionIdManager">
<Arg><Ref id="Server"/></Arg>
<Set name="configLocation">etc/hazelcast.xml</Set>
<Set name="workerName">unique-worker-1</Set>
</New>
</Set>
You can integrate Hazelcast with Spring and this chapter explains the configuration of Hazelcast within Spring context.
Sample Code: Please see our sample application for Spring Configuration.
Classpath Configuration
This configuration requires the following jar file in the classpath:
hazelcast-
<version>.jar
Bean Declaration
You can declare Hazelcast Objects using the default Spring beans namespace. You can find an example usage of Hazelcast Instance declaration as follows:
<bean id="instance" class="com.hazelcast.core.Hazelcast" factory-method="newHazelcastInstance">
<constructor-arg>
<bean class="com.hazelcast.config.Config">
<property name="groupConfig">
<bean class="com.hazelcast.config.GroupConfig">
<property name="name" value="dev"/>
<property name="password" value="pwd"/>
</bean>
</property>
<!-- and so on ... -->
</bean>
</constructor-arg>
</bean>
<bean id="map" factory-bean="instance" factory-method="getMap">
<constructor-arg value="map"/>
</bean>
Classpath Configuration
Hazelcast-Spring integration requires the following JAR files in the classpath:
hazelcast-spring-
<version>.jar
hazelcast-
<version>.jar
or
hazelcast-all-
<version>.jar
Bean Declaration
Hazelcast has its own namespace hazelcast for bean definitions. You can easily add the namespace declaration xmlns:hz="http://www.hazelcast.com/schema/spring" to the beans
element in the context file so that hz namespace shortcut can be used as a bean declaration.
Here is an example schema definition for Hazelcast 3.3.x:
<beans xmlns="http://www.springframework.org/schema/beans"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xmlns:hz="http://www.hazelcast.com/schema/spring"
xsi:schemaLocation="http://www.springframework.org/schema/beans
http://www.springframework.org/schema/beans/spring-beans-3.0.xsd
http://www.hazelcast.com/schema/spring
http://www.hazelcast.com/schema/spring/hazelcast-spring.xsd">
<hz:hazelcast id="instance">
<hz:config>
<hz:group name="dev" password="password"/>
<hz:network port="5701" port-auto-increment="false">
<hz:join>
<hz:multicast enabled="false"
multicast-group="224.2.2.3"
multicast-port="54327"/>
<hz:tcp-ip enabled="true">
<hz:members>10.10.1.2, 10.10.1.3</hz:members>
</hz:tcp-ip>
</hz:join>
</hz:network>
<hz:map name="map"
backup-count="2"
max-size="0"
eviction-percentage="30"
read-backup-data="true"
eviction-policy="NONE"
merge-policy="com.hazelcast.map.merge.PassThroughMergePolicy"/>
</hz:config>
</hz:hazelcast>
<hz:client id="client">
<hz:group name="${cluster.group.name}" password="${cluster.group.password}" />
<hz:network connection-attempt-limit="3"
connection-attempt-period="3000"
connection-timeout="1000"
redo-operation="true"
smart-routing="true">
<hz:member>10.10.1.2:5701</hz:member>
<hz:member>10.10.1.3:5701</hz:member>
</hz:network>
</hz:client>
Hazelcast Supported Type Configurations and Examples
map
multiMap
replicatedmap
queue
topic
set
list
executorService
idGenerator
atomicLong
atomicReference
semaphore
countDownLatch
lock
<hz:map id="map" instance-ref="client" name="map" lazy-init="true" />
<hz:multiMap id="multiMap" instance-ref="instance" name="multiMap"
lazy-init="false" />
<hz:replicatedmap id="replicatedmap" instance-ref="instance"
name="replicatedmap" lazy-init="false" />
<hz:queue id="queue" instance-ref="client" name="queue"
lazy-init="true" depends-on="instance"/>
<hz:topic id="topic" instance-ref="instance" name="topic"
depends-on="instance, client"/>
<hz:set id="set" instance-ref="instance" name="set" />
<hz:list id="list" instance-ref="instance" name="list"/>
<hz:executorService id="executorService" instance-ref="client"
name="executorService"/>
<hz:idGenerator id="idGenerator" instance-ref="instance"
name="idGenerator"/>
<hz:atomicLong id="atomicLong" instance-ref="instance" name="atomicLong"/>
<hz:atomicReference id="atomicReference" instance-ref="instance"
name="atomicReference"/>
<hz:semaphore id="semaphore" instance-ref="instance" name="semaphore"/>
<hz:countDownLatch id="countDownLatch" instance-ref="instance"
name="countDownLatch"/>
<hz:lock id="lock" instance-ref="instance" name="lock"/>
Hazelcast also supports lazy-init
, scope
and depends-on
bean attributes.
<hz:hazelcast id="instance" lazy-init="true" scope="singleton">
...
</hz:hazelcast>
<hz:client id="client" scope="prototype" depends-on="instance">
...
</hz:client>
For map-store, you should set either the class-name or the implementation attribute.
<hz:config>
<hz:map name="map1">
<hz:near-cache time-to-live-seconds="0" max-idle-seconds="60"
eviction-policy="LRU" max-size="5000" invalidate-on-change="true"/>
<hz:map-store enabled="true" class-name="com.foo.DummyStore"
write-delay-seconds="0"/>
</hz:map>
<hz:map name="map2">
<hz:map-store enabled="true" implementation="dummyMapStore"
write-delay-seconds="0"/>
</hz:map>
<bean id="dummyMapStore" class="com.foo.DummyStore" />
</hz:config>
Hazelcast Distributed Objects could be marked with @SpringAware if the object wants:
ApplicationContextAware
, BeanNameAware
,InitializingBean
, @PostConstruct
.Hazelcast Distributed ExecutorService
, or more generally any Hazelcast managed object, can benefit from these features. To enable SpringAware objects, you must first configure HazelcastInstance
using hazelcast namespace as explained in the Spring Configuration section and add <hz:spring-aware />
tag.
<hz:spring-aware />
to Hazelcast configuration to enable @SpringAware.<beans xmlns="http://www.springframework.org/schema/beans"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xmlns:context="http://www.springframework.org/schema/context"
xmlns:hz="http://www.hazelcast.com/schema/spring"
xsi:schemaLocation="http://www.springframework.org/schema/beans
http://www.springframework.org/schema/beans/spring-beans-3.0.xsd
http://www.springframework.org/schema/context
http://www.springframework.org/schema/context/spring-context-3.0.xsd
http://www.hazelcast.com/schema/spring
http://www.hazelcast.com/schema/spring/hazelcast-spring.xsd">
<context:annotation-config />
<hz:hazelcast id="instance">
<hz:config>
<hz:spring-aware />
<hz:group name="dev" password="password"/>
<hz:network port="5701" port-auto-increment="false">
<hz:join>
<hz:multicast enabled="false" />
<hz:tcp-ip enabled="true">
<hz:members>10.10.1.2, 10.10.1.3</hz:members>
</hz:tcp-ip>
</hz:join>
</hz:network>
...
</hz:config>
</hz:hazelcast>
<bean id="someBean" class="com.hazelcast.examples.spring.SomeBean"
scope="singleton" />
...
</beans>
Distributed Map Example:
SomeValue
which contains Spring Bean definitions like ApplicationContext
and SomeBean
.@SpringAware
@Component("someValue")
@Scope("prototype")
public class SomeValue implements Serializable, ApplicationContextAware {
private transient ApplicationContext context;
private transient SomeBean someBean;
private transient boolean init = false;
public void setApplicationContext( ApplicationContext applicationContext )
throws BeansException {
context = applicationContext;
}
@Autowired
public void setSomeBean( SomeBean someBean) {
this.someBean = someBean;
}
@PostConstruct
public void init() {
someBean.doSomethingUseful();
init = true;
}
...
}
SomeValue
Object from Context and put it into Hazelcast Distributed Map on Node-1.HazelcastInstance hazelcastInstance =
(HazelcastInstance) context.getBean( "hazelcast" );
SomeValue value = (SomeValue) context.getBean( "someValue" )
IMap<String, SomeValue> map = hazelcastInstance.getMap( "values" );
map.put( "key", value );
SomeValue
Object from Hazelcast Distributed Map and assert that init
method is called since it is annotated with @PostConstruct
.HazelcastInstance hazelcastInstance =
(HazelcastInstance) context.getBean( "hazelcast" );
IMap<String, SomeValue> map = hazelcastInstance.getMap( "values" );
SomeValue value = map.get( "key" );
Assert.assertTrue( value.init );
ExecutorService Example:
ApplicationContext
, SomeBean
.@SpringAware
public class SomeTask
implements Callable<Long>, ApplicationContextAware, Serializable {
private transient ApplicationContext context;
private transient SomeBean someBean;
public Long call() throws Exception {
return someBean.value;
}
public void setApplicationContext( ApplicationContext applicationContext )
throws BeansException {
context = applicationContext;
}
@Autowired
public void setSomeBean( SomeBean someBean ) {
this.someBean = someBean;
}
}
SomeTask
to two Hazelcast Members and assert that someBean
is autowired.HazelcastInstance hazelcastInstance =
(HazelcastInstance) context.getBean( "hazelcast" );
SomeBean bean = (SomeBean) context.getBean( "someBean" );
Future<Long> f = hazelcastInstance.getExecutorService().submit(new SomeTask());
Assert.assertEquals(bean.value, f.get().longValue());
// choose a member
Member member = hazelcastInstance.getCluster().getMembers().iterator().next();
Future<Long> f2 = (Future<Long>) hazelcast.getExecutorService()
.submitToMember(new SomeTask(), member);
Assert.assertEquals(bean.value, f2.get().longValue());
NOTE: Spring managed properties/fields are marked as transient
.
Sample Code: Please see our sample application for Spring Cache.
As of version 3.1, Spring Framework provides support for adding caching into an existing Spring application.
<cache:annotation-driven cache-manager="cacheManager" />
<hz:hazelcast id="hazelcast">
...
</hz:hazelcast>
<bean id="cacheManager" class="com.hazelcast.spring.cache.HazelcastCacheManager">
<constructor-arg ref="instance"/>
</bean>
Annotation Based Configuration does not require any XML definition.
CachingConfiguration
class with related Annotations.@Configuration
@EnableCaching
public class CachingConfiguration implements CachingConfigurer{
@Bean
public CacheManager cacheManager() {
ClientConfig config = new ClientConfig();
HazelcastInstance client = HazelcastClient.newHazelcastClient(config);
return new HazelcastCacheManager(client);
}
@Bean
public KeyGenerator keyGenerator() {
return null;
}
CachingConfiguration
.AnnotationConfigApplicationContext context = new AnnotationConfigApplicationContext();
context.register(CachingConfiguration.class);
context.refresh();
For more information about Spring Cache, please see Spring Cache Abstraction.
Sample Code: Please see our sample application for Hibernate 2nd Level Cache Config.
If you are using Hibernate with Hazelcast as 2nd level cache provider, you can easily create RegionFactory
instances within Spring configuration (by Spring version 3.1). That way, you can use the same HazelcastInstance
as Hibernate L2 cache instance.
<hz:hibernate-region-factory id="regionFactory" instance-ref="instance"
mode="LOCAL" />
...
<bean id="sessionFactory"
class="org.springframework.orm.hibernate3.LocalSessionFactoryBean"
scope="singleton">
<property name="dataSource" ref="dataSource"/>
<property name="cacheRegionFactory" ref="regionFactory" />
...
</bean>
Hibernate RegionFactory Modes
Please refer to the Hibernate RegionFactory Options section for more information.
Spring tries to create a new Map
/Collection
instance and fill the new instance by iterating and converting values of the original Map
/Collection
(IMap
, IQueue
, etc.) to required types when generic type parameters of the original Map
/Collection
and the target property/attribute do not match.
Since Hazelcast Map
s/Collection
s are designed to hold very large data which a single machine cannot carry, iterating through whole values can cause out of memory errors.
To avoid this issue, the target property/attribute can be declared as un-typed Map
/Collection
as shown below.
public class SomeBean {
@Autowired
IMap map; // instead of IMap<K, V> map
@Autowired
IQueue queue; // instead of IQueue<E> queue
...
}
Or, parameters of injection methods (constructor, setter) can be un-typed as shown below.
public class SomeBean {
IMap<K, V> map;
IQueue<E> queue;
// Instead of IMap<K, V> map
public SomeBean(IMap map) {
this.map = map;
}
...
// Instead of IQueue<E> queue
public void setQueue(IQueue queue) {
this.queue = queue;
}
...
}
RELATED INFORMATION
For more information please see Spring issue-3407.
This chapter describes Hazelcast's High-Density Memory Store and its configuration and provides information on the High-Density Memory First Generation, also known as Hazelcast Elastic Memory. It also gives recommendations on the storage sizing.
Hazelcast High-Density Memory Store, the successor to Hazelcast Elastic Memory, is Hazelcast's new enterprise grade backend storage solution. This solution is used with the Hazelcast JCache implementation.
By default, Hazelcast offers a production ready, low garbage collection (GC) pressure, storage backend. Serialized keys and values are still stored in the standard Java map, such as data structures on the heap. The data structures are stored in serialized form for the highest data compaction, and are still subject to Java Garbage Collection.
In Hazelcast Enterprise, the High-Density Memory Store is built around a pluggable memory manager which enables multiple memory stores. These memory stores are all accessible using a common access layer that scales up to Terabytes of main memory on a single JVM. At the same time, by further minimizing the GC pressure, High-Density Memory Store enables predictable application scaling and boosts performance and latency while minimizing pauses for Java Garbage Collection.
This foundation includes, but is not limited to, storing keys and values next to the heap in a native memory region.
RELATED INFORMATION
Please refer to the Hazelcast JCache chapter for the details of Hazelcast JCache implementation. As mentioned, High-Density Memory Store is used with Hazelcast JCache implementation.
To use the Hi-Density memory storage, the native memory usage must be enabled using the programmatic or declarative configuration. Also, you can configure its size, memory allocator type, minimum block size, page size and metadata space percentage.
allocator type: Type of the memory allocator. Available values are:
Default value is POOLED.
1 << 22
= 4194304 Bytes, about 4 MB.The following is the programmatic configuration example.
MemorySize memorySize = new MemorySize(512, MemoryUnit.MEGABYTES);
NativeMemoryConfig nativeMemoryConfig =
new NativeMemoryConfig()
.setAllocatorType(NativeMemoryConfig.MemoryAllocatorType.POOLED)
.setSize(memorySize)
.setEnabled(true)
.setMinBlockSize(16)
.setPageSize(1 << 20);
The following is the declarative configuration example.
<native-memory enabled="true" allocator-type="POOLED">
<size value="512" unit="MEGABYTES"/>
</native-memory>
By default, Hazelcast stores your distributed data (map entries, queue items) into Java heap which is subject to garbage collection (GC). As your heap gets bigger, garbage collection might cause your application to pause tens of seconds, badly effecting your application performance and response times. Elastic Memory (High-Density Memory First Generation) is Hazelcast with off-heap memory storage to avoid GC pauses. Even if you have terabytes of cache in-memory with lots of updates, GC will have almost no effect; resulting in more predictable latency and throughput.
Here are the steps to enable Elastic Memory:
java -XX:MaxDirectMemorySize=60G
.hazelcast.elastic.memory.enabled
property to true.hazelcast.elastic.memory.total.size
property. Size can be in MB or GB and abbreviation can be used, such as 60G and 500M.hazelcast.elastic.memory.chunk.size
property. Hazelcast will partition the entire off-heap memory into chunks. Default chunk size is 1K.sun.misc.Unsafe
based off-heap storage implementation instead of java.nio.DirectByteBuffer
based one, by setting the hazelcast.elastic.memory.unsafe.enabled
property to true. Default value is false.InMemoryFormat
to NATIVE. Default value is BINARY.Below is the declarative configuration.
<hazelcast>
...
<map name="default">
...
<in-memory-format>NATIVE</in-memory-format>
</map>
</hazelcast>
And, the programmatic configuration:
MapConfig mapConfig = new MapConfig();
mapConfig.setInMemoryFormat( InMemoryFormat.NATIVE );
And, the following are the High-Density Memory First Generation related system properties.
Property | Default Value | Type | Description |
---|---|---|---|
hazelcast.elastic.memory.enabled |
false | bool | Enables/disables Elastic Memory usage. |
hazelcast.elastic.memory.total.size |
128 | int | Elastic Memory storage total size in MB or GB. |
hazelcast.elastic.memory.chunk.size |
1 | int | Elastic Memory storage chunk size in KB. |
hazelcast.elastic.memory.shared.storage |
false | bool | Enables/disables Elastic Memory shared storage. |
hazelcast.elastic.memory.unsafe.enabled |
false | bool | Enables/disables usage of sun.misc.Unsafe when allocating, reading and modifying off-heap storage. |
Data in Hazelcast is both active data and backup data for high availability, so the total memory footprint is the size of active data plus the size of backup data. If you use a single backup, it means the total memory footprint is two times the active data (active data + backup data). If you use, for example, two backups, then the total memory footprint is three times the active data (active data + backup data + backup data).
If you use only heap memory, each Hazelcast node with a 4 GB heap should accommodate a maximum of 3.5 GB of total data (active and backup). If you use the High-Density Memory Store, up to 75% of your physical memory footprint may be used for active and backup data, with headroom of 25% for normal fragmentation. In both cases, however, you should also keep some memory headroom available to handle any node failure or explicit node shutdown. When a node leaves the cluster, the data previously owned by the newly offline node will be distributed among the remaining servers. For this reason, we recommend that you plan to use only 60% of available memory, with 40% headroom to handle node failure or shutdown.
There are currently three ways to connect to a running Hazelcast cluster:
Native Clients enable you to perform almost all Hazelcast operations without being a member of the cluster. It connects to one of the cluster members and delegates all cluster wide operations to it (dummy client), or it connects to all of them and delegates operations smartly (smart client). When the relied cluster member dies, the client will transparently switch to another live member.
There can be hundreds, even thousands of clients connected to the cluster. By default, there are core count 10* threads on the server side that will handle all the requests (e.g. if the server has 4 cores, it will be 40).
Imagine a trading application where all the trading data are stored and managed in a Hazelcast cluster with tens of nodes. Swing/Web applications at the traders' desktops can use Native Clients to access and modify the data in the Hazelcast cluster.
Currently, Hazelcast has Native Java, C++ and .NET Clients available. This chapter describes the Java Client.
IMPORTANT: Starting with the Hazelcast 3.5. release, a new client library is introduced in the release package: hazelcast-client-new-<version>.jar
. This new Java native client library has the support for different versions of clients in a Hazelcast cluster. This support is not valid for the releases before 3.5.
Before detailing the Java Client, this section provides the below comparison matrix to show which features are supported by the Hazelcast clients.
Feature | Java Client | .NET Client | C++ Client |
---|---|---|---|
Map | Yes | Yes | Yes |
Queue | Yes | Yes | Yes |
Set | Yes | Yes | Yes |
List | Yes | Yes | Yes |
MultiMap | Yes | Yes | Yes |
Replicated Map | Yes | No | No |
Topic | Yes | Yes | Yes |
MapReduce | Yes | No | No |
Lock | Yes | Yes | Yes |
Semaphore | Yes | Yes | Yes |
AtomicLong | Yes | Yes | Yes |
AtomicReference | Yes | Yes | Yes |
IdGenerator | Yes | Yes | Yes |
CountDownLatch | Yes | Yes | Yes |
Transactional Map | Yes | Yes | Yes |
Transactional MultiMap | Yes | Yes | Yes |
Transactional Queue | Yes | Yes | Yes |
Transactional List | Yes | Yes | Yes |
Transactional Set | Yes | Yes | Yes |
JCache | Yes | No | No |
Ringbuffer | Yes | No | No |
Reliable Topic | No | No | No |
Client Configuration Import | Yes | No | No |
Hazelcast Client Protocol | Yes | Yes | Yes |
Fail Fast on Invalid Conviguration | Yes | No | No |
Sub-Listener Interfaces for Map ListenerMap | Yes | No | No |
Continuous Query Caching | Yes | No | No |
Distributed Executor Service | Yes | No | No |
Query | Yes | Yes | Yes |
Near Cache | Yes | Yes | No |
Heartbeat | Yes | Yes | Yes |
Declarative Configuration | Yes | Yes | No |
Programmatic Configuration | Yes | Yes | Yes |
SSL Support | Yes | No | No |
XA Transactions | Yes | No | No |
Smart Client | Yes | Yes | Yes |
Dummy Client | Yes | Yes | Yes |
Lifecycle Service | Yes | Yes | Yes |
Event Listeners | Yes | Yes | Yes |
DataSerializable | Yes | Yes | Yes |
IdentifiedDataSerializable | Yes | Yes | Yes |
Portable | Yes | Yes | Yes |
The Java client is the most full featured client. It is offered both with Hazelcast and Hazelcast Enterprise. The main idea behind the Java client is to provide the same Hazelcast functionality by proxying each operation through a Hazelcast node. It can access and change distributed data, and it can listen to distributed events of an already established Hazelcast cluster from another Java application.
You should include two dependencies in your classpath to start using the Hazelcast client: hazelcast.jar
and hazelcast-client.jar
.
After adding these dependencies, you can start using the Hazelcast client as if you are using the Hazelcast API. The differences are discussed in the below sections.
If you prefer to use maven, add the following lines to your pom.xml
.
<dependency>
<groupId>com.hazelcast</groupId>
<artifactId>hazelcast-client</artifactId>
<version>$LATEST_VERSION$</version>
</dependency>
<dependency>
<groupId>com.hazelcast</groupId>
<artifactId>hazelcast</artifactId>
<version>$LATEST_VERSION$</version>
</dependency>
The first step is configuration. You can configure the Java client declaratively or programmatically. We will use the programmatic approach throughout this tutorial. Please refer to the Java Client Declarative Configuration section for details.
ClientConfig clientConfig = new ClientConfig();
clientConfig.getGroupConfig().setName("dev").setPassword("dev-pass");
clientConfig.getNetworkConfig().addAddress("10.90.0.1", "10.90.0.2:5702");
The second step is to initialize the HazelcastInstance to be connected to the cluster.
HazelcastInstance client = HazelcastClient.newHazelcastClient(clientConfig);
This client interface is your gateway to access all Hazelcast distributed objects.
Let's create a map and populate it with some data.
IMap<String, Customer> mapCustomers = client.getMap("customers"); //creates the map proxy
mapCustomers.put("1", new Customer("Joe", "Smith"));
mapCustomers.put("2", new Customer("Ali", "Selam"));
mapCustomers.put("3", new Customer("Avi", "Noyan"));
As a final step, if you are done with your client, you can shut it down as shown below. This will release all the used resources and will close connections to the cluster.
client.shutdown();
The client has two operation modes because of the distributed nature of the data and cluster.
Smart Client: In smart mode, clients connect to each cluster node. Since each data partition uses the well known and consistent hashing algorithm, each client can send an operation to the relevant cluster node, which increases the overall throughput and efficiency. Smart mode is the default mode.
Dummy Client: For some cases, the clients can be required to connect to a single node instead of to each node in the cluster. Firewalls, security, or some custom networking issues can be the reason for these cases.
In dummy client mode, the client will only connect to one of the configured addresses. This single node will behave as a gateway to the other nodes. For any operation requested from the client, it will redirect the request to the relevant node and return the response back to the client returned from this node.
There are two main failure cases you should be aware of, and configurations you can perform to achieve proper behavior.
While the client is trying to connect initially to one of the members in the ClientNetworkConfig.addressList
, all the members might be not available. Instead of giving up, throwing an exception and stopping the client, the client will retry as many as connectionAttemptLimit
times. Please see the Connection Attempt Limit section.
The client executes each operation through the already established connection to the cluster. If this connection(s) disconnects or drops, the client will try to reconnect as configured.
While sending the requests to related nodes, operation can fail due to various reasons. Read-only operations are retried by default. If you want to enable this for the other operations, set the redoOperation
to true
. Please see the Redo Operation section.
The number of retries is given with the property hazelcast.client.request.retry.count
in ClientProperties
. The client will resend the request as many as RETRY-COUNT, then it will throw an exception. Please see the Client System Properties section.
Most of the Distributed Data Structures are supported by the client. Please check for the exceptions for the clients in other languages.
As a general rule, you configure these data structures on the server side and access them through a proxy on the client side.
Map:
You can use any Distributed Map object with the client, as shown below.
Imap<Integer, String> map = client.getMap(“myMap”);
map.put(1, “Ali”);
String value= map.get(1);
map.remove(1);
Locality is ambiguous for the client, so addEntryListener
and localKeySet
are not supported. Please see the Distributed Map section for more information.
MultiMap:
A MultiMap usage example is shown below.
MultiMap<Integer, String> multiMap = client.getMultiMap("myMultiMap");
multiMap.put(1,”ali”);
multiMap.put(1,”veli”);
Collection<String> values = multiMap.get(1);
addEntryListener
, localKeySet
and getLocalMultiMapStats
are not supported because locality is ambiguous for the client. Please see the Distributed MultiMap section for more information.
Queue:
A sample usage is shown below.
IQueue<String> myQueue = client.getQueue(“theQueue”);
myQueue.offer(“ali”)
getLocalQueueStats
is not supported because locality is ambiguous for the client. Please see the Distributed Queue section for more information.
Topic:
getLocalTopicStats
is not supported because locality is ambiguous for the client.
Other Supported Distributed Structures:
The distributed data structures listed below are also supported by the client. Since their logic is the same in both the node side and client side, you can refer to their sections as listed below.
Below services are provided for some common functionalities on the client side.
Distributed Executor Service:
The distributed executor service is for distributed computing. It can be used to execute tasks on the cluster on a designated partition or on all the partitions. It can also be used to process entries. Please see the Distributed Executor Service section for more information.
IExecutorService executorService = client.getExecutorService("default");
After getting an instance of IExecutorService
, you can use the instance as the interface with the one provided on the server side. Please see the Distributed Computing chapter chapter for detailed usage.
NOTE: This service is only supported by the Java client.
Client Service:
If you need to track clients and you want to listen to their connection events, you can use the clientConnected
and clientDisconnected
methods of the ClientService
class. This class must be run on the node side. The following is an example code.
final ClientService clientService = hazelcastInstance.getClientService();
final Collection<Client> connectedClients = clientService.getConnectedClients();
clientService.addClientListener(new ClientListener() {
@Override
public void clientConnected(Client client) {
//Handle client connected event
}
@Override
public void clientDisconnected(Client client) {
//Handle client disconnected event
}
});
Partition Service:
You use partition service to find the partition of a key. It will return all partitions. See the example code below.
PartitionService partitionService = client.getPartitionService();
//partition of a key
Partition partition = partitionService.getPartition(key);
//all partitions
Set<Partition> partitions = partitionService.getPartitions();
Lifecycle Service:
Lifecycle handling performs the following:
LifecycleService lifecycleService = client.getLifecycleService();
if(lifecycleService.isRunning()){
//it is running
}
//shutdown client gracefully
lifecycleService.shutdown();
You can configure listeners to listen to various event types on the client side. You can configure global events not relating to any distributed object through Client ListenerConfig. You should configure distributed object listeners like map entry listeners or list item listeners through their proxies. You can refer to the related sections under each distributed data structure in this reference manual.
Transactional distributed objects are supported on the client side. Please see the Transactions chapter on how to use them.
Hazelcast Java Client can be configured declaratively (XML) or programmatically (API).
For declarative configuration, the Hazelcast client looks into the following places for the client configuration file
System property: The client first checks if hazelcast.client.config
system property is set to a file path, e.g. -Dhazelcast.client.config=C:/myhazelcast.xml
.
Classpath: If config file is not set as a system property, the client checks the classpath for hazelcast-client.xml
file.
If the client does not find any configuration file, it starts with the default configuration (hazelcast-client-default.xml
) located in the hazelcast-client.jar
library. Before configuring the client, please try to work with the default configuration to see if it works for you. Default should be just fine for most of the users. If not, then consider custom configuration for your environment.
If you want to specify your own configuration file to create a Config
object, the Hazelcast client supports the following.
Config cfg = new XmlClientConfigBuilder(xmlFileName).build();
Config cfg = new XmlClientConfigBuilder(inputStream).build();
For programmatic configuration of the Hazelcast Java Client, just instantiate a ClientConfig
object and configure the desired aspects, a sample of which is shown below.
ClientConfig clientConfig = new ClientConfig();
clientConfig.setGroupConfig(new GroupConfig("dev","dev-pass”);
clientConfig.setLoadBalancer(yourLoadBalancer);
...
...
All network related configuration of Hazelcast Java Client is performed via the network
element in the declarative configuration file or the class ClientNetworkConfig
when using programmatic configuration. Let's first give the examples for these two approaches. Then we will look at its sub-elements and attributes.
Declarative:
<hazelcast-client xsi:schemaLocation=
"http://www.hazelcast.com/schema/client-config hazelcast-client-config-<version>.xsd"
xmlns="http://www.hazelcast.com/schema/client-config"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
...
<network>
<cluster-members>
<address>127.0.0.1</address>
<address>127.0.0.2</address>
</cluster-members>
<smart-routing>true</smart-routing>
<redo-operation>true</redo-operation>
<socket-interceptor enabled="true">
<class-name>com.hazelcast.XYZ</class-name>
<properties>
<property name="kerberos-host">kerb-host-name</property>
<property name="kerberos-config-file">kerb.conf</property>
</properties>
</socket-interceptor>
<aws enabled="true" connection-timeout-seconds=“11">
<inside-aws>false</inside-aws>
<access-key>my-access-key</access-key>
<secret-key>my-secret-key</secret-key>
<iam-role>s3access</iam-role>
<region>us-west-1</region>
<host-header>ec2.amazonaws.com</host-header>
<security-group-name>hazelcast-sg</security-group-name>
<tag-key>type</tag-key>
<tag-value>hz-nodes</tag-value>
</aws>
</network>
Programmatic:
ClientConfig clientConfig = new ClientConfig();
ClientNetworkConfig networkConfig = clientConfig.getNetworkConfig();
Address List is the initial list of cluster addresses to which the client will connect. The client uses this list to find an alive node. Although it may be enough to give only one address of a node in the cluster (since all nodes communicate with each other), it is recommended that you give all the nodes’ addresses.
Declarative:
<hazelcast-client>
...
<network>
<cluster-members>
<address>10.1.1.21</address>
<address>10.1.1.22:5703</address>
</cluster-members>
...
</network>
...
</hazelcast-client>
Programmatic:
ClientConfig clientConfig = new ClientConfig();
ClientNetworkConfig networkConfig = clientConfig.getNetworkConfig();
networkConfig().addAddress("10.1.1.21", "10.1.1.22:5703");
If the port part is omitted, then 5701, 5702, and 5703 will be tried in random order.
You can provide multiple addresses with ports provided or not as seen above. The provided list is shuffled and tried in random order. Default value is localhost.
It defines whether the client mode is smart or dummy. The following are the example configurations.
Declarative:
...
<network>
...
<smart-routing>true</smart-routing>
...
</network>
...
Programmatic:
ClientConfig clientConfig = new ClientConfig();
ClientNetworkConfig networkConfig = clientConfig.getNetworkConfig();
networkConfig().setSmartRouting(true);
The default is smart client mode.
It enables/disables redo-able operations as described in Retry-able Operation Failure. The following are the example configurations.
Declarative:
...
<network>
...
<redo-operation>true</redo-operation>
...
</network>
Programmatic:
ClientConfig clientConfig = new ClientConfig();
ClientNetworkConfig networkConfig = clientConfig.getNetworkConfig();
networkConfig().setRedoOperation(true);
Default is disabled.
It is the timeout value in milliseconds for nodes to accept client connection requests. The following are the example configurations.
Declarative:
...
<network>
...
<connection-timeout>5000</connection-timeout>
...
</network>
Programmatic:
ClientConfig clientConfig = new ClientConfig();
clientConfig.getNetworkConfig().setConnectionTimeout(5000);
The default value is 5000 milliseconds.
While the client is trying to connect initially to one of the members in the ClientNetworkConfig.addressList
, all members might be not available. Instead of giving up, throwing an exception and stopping the client, the client will retry as many as ClientNetworkConfig.
connectionAttemptLimit
times. The following are the example configurations.
Declarative:
...
<network>
...
<connection-attempt-limit>5</connection-attempt-limit>
...
</network>
Programmatic:
ClientConfig clientConfig = new ClientConfig();
clientConfig.getNetworkConfig().setConnectionAttemptLimit(5);
Default value is 2.
It is the duration in milliseconds between the connection attempts defined by ClientNetworkConfig.connectionAttemptLimit
. The following are the example configurations.
Declarative:
...
<network>
...
<connection-attempt-period>5000</connection-attempt-period>
...
</network>
Programmatic:
ClientConfig clientConfig = new ClientConfig();
clientConfig.getNetworkConfig().setConnectionAttemptPeriod(5000);
Default value is 3000.
Following is a client configuration to set a socket intercepter. Any class implementing com.hazelcast.nio.SocketInterceptor
is a socket Interceptor.
public interface SocketInterceptor {
void init(Properties properties);
void onConnect(Socket connectedSocket) throws IOException;
}
SocketInterceptor
has two steps. First, it will be initialized by the configured properties. Second, it will be informed just after the socket is connected using onConnect
.
SocketInterceptorConfig socketInterceptorConfig = clientConfig
.getNetworkConfig().getSocketInterceptorConfig();
MyClientSocketInterceptor myClientSocketInterceptor = new MyClientSocketInterceptor();
socketInterceptorConfig.setEnabled(true);
socketInterceptorConfig.setImplementation(myClientSocketInterceptor);
If you want to configure the socket connector with a class name instead of an instance, see the example below.
SocketInterceptorConfig socketInterceptorConfig = clientConfig
.getNetworkConfig().getSocketInterceptorConfig();
MyClientSocketInterceptor myClientSocketInterceptor = new MyClientSocketInterceptor();
socketInterceptorConfig.setEnabled(true);
//These properties are provided to interceptor during init
socketInterceptorConfig.setProperty("kerberos-host","kerb-host-name");
socketInterceptorConfig.setProperty("kerberos-config-file","kerb.conf");
socketInterceptorConfig.setClassName(myClientSocketInterceptor);
RELATED INFORMATION
Please see the Socket Interceptor section for more information.
You can configure the network socket options using SocketOptions
. It has the following methods.
socketOptions.setKeepAlive(x)
: Enables/disables the SO_KEEPALIVE socket option. The default value is true
.
socketOptions.setTcpNoDelay(x)
: Enables/disables the TCP_NODELAY socket option. The default value is true
.
socketOptions.setReuseAddress(x)
: Enables/disables the SO_REUSEADDR socket option. The default value is true
.
socketOptions.setLingerSeconds(x)
: Enables/disables SO_LINGER with the specified linger time in seconds. The default value is 3
.
socketOptions.setBufferSize(x)
: Sets the SO_SNDBUF and SO_RCVBUF options to the specified value in KB for this Socket. The default value is 32
.
SocketOptions socketOptions = clientConfig.getNetworkConfig().getSocketOptions();
socketOptions.setBufferSize(32);
socketOptions.setKeepAlive(true);
socketOptions.setTcpNoDelay(true);
socketOptions.setReuseAddress(true);
socketOptions.setLingerSeconds(3);
You can use SSL to secure the connection between the client and the nodes. If you want SSL enabled for the client-cluster connection, you should set SSLConfig
. Once set, the connection (socket) is established out of an SSL factory defined either by a factory class name or factory implementation. Please see the SSLConfig
class in the com.hazelcast.config
package at the JavaDocs page of the Hazelcast Documentation web site.
The example declarative and programmatic configurations below show how to configure a Java client for connecting to a Hazelcast cluster in AWS.
Declarative:
...
<network>
<aws enabled="true">
<inside-aws>false</inside-aws>
<access-key>my-access-key</access-key>
<secret-key>my-secret-key</secret-key>
<iam-role>s3access</iam-role>
<region>us-west-1</region>
<host-header>ec2.amazonaws.com</host-header>
<security-group-name>hazelcast-sg</security-group-name>
<tag-key>type</tag-key>
<tag-value>hz-nodes</tag-value>
</aws>
...
</network>
Programmatic:
ClientConfig clientConfig = new ClientConfig();
ClientAwsConfig clientAwsConfig = new ClientAwsConfig();
clientAwsConfig.setInsideAws( false )
.setAccessKey( "my-access-key" )
.setSecretKey( "my-secret-key" )
.setRegion( "us-west-1" )
.setHostHeader( "ec2.amazonaws.com" )
.setSecurityGroupName( ">hazelcast-sg" )
.setTagKey( "type" )
.setTagValue( "hz-nodes" )
.setIamRole( "s3access" );
clientConfig.getNetworkConfig().setAwsConfig( clientAwsConfig );
HazelcastInstance client = HazelcastClient.newHazelcastClient( clientConfig );
NOTE: If the inside-aws
parameter is not set, the private addresses of nodes will always be converted to public addresses. Also, the client will use public addresses to connect to the nodes. In order to use private addresses, set the inside-aws
parameter to true
. Also note that, when connecting outside from AWS, setting the inside-aws
parameter to true
will cause the client to not be able to reach the nodes.
IAM roles are used to make secure requests from your clients. You can provide the name of your IAM role that you created previously on your AWS console using the iam-role or setIamRole() method.
LoadBalancer
allows you to send operations to one of a number of endpoints (Members). Its main purpose is to determine the next Member
if queried. It is up to your implementation to use different load balancing policies. You should implement the interface com.hazelcast.client.LoadBalancer
for that purpose.
If the client is configured in smart mode, only the operations that are not key-based will be routed to the endpoint that is returned by the LoadBalancer
. If the client is not a smart client, LoadBalancer
will be ignored.
The following are the example configurations.
Declarative:
<hazelcast-client>
...
<load-balancer type=“random”>
yourLoadBalancer
</load-balancer>
...
</hazelcast-client>
Programmatic:
ClientConfig clientConfig = new ClientConfig();
clientConfig.setLoadBalancer(yourLoadBalancer);
Hazelcast distributed map has a Near Cache feature to reduce network latencies. Since the client always requests data from the cluster nodes, it can be helpful for some of your use cases to configure a near cache on the client side. The client supports the same Near Cache that is used in Hazelcast distributed map.
You can create Near Cache on the client side by providing a configuration per map name, as shown below.
ClientConfig clientConfig = new ClientConfig();
CacheConfig nearCacheConfig = new NearCacheConfig();
nearCacheConfig.setName("mapName");
clientConfig.addNearCacheConfig(nearCacheConfig);
You can use wildcards for the map name, as shown below.
nearCacheConfig.setName("map*");
nearCacheConfig.setName("*map");
And, the following is an example declarative configuration for Near Cache.
</hazelcast-client>
...
...
<near-cache name="MENU">
<max-size>2000</max-size>
<time-to-live-seconds>0</time-to-live-seconds>
<max-idle-seconds>0</max-idle-seconds>
<eviction-policy>LFU</eviction-policy>
<invalidate-on-change>true</invalidate-on-change>
<in-memory-format>OBJECT</in-memory-format>
</near-cache>
...
</hazelcast-client>
Name of Near Cache on client side must be the same as the name of IMap on server for which this Near Cache is being created.
Near Cache can have its own in-memory-format
which is independent of the in-memory-format
of the servers.
Clients should provide a group name and password in order to connect to the cluster.
You can configure them using GroupConfig
, as shown below.
clientConfig.setGroupConfig(new GroupConfig("dev","dev-pass"));
In the cases where the security established with GroupConfig
is not enough and you want your clients connecting securely to the cluster, you can use ClientSecurityConfig
. This configuration has a credentials
parameter to set the IP address and UID. Please see ClientSecurityConfig.java
in our code.
For the client side serialization, use Hazelcast configuration. Please refer to the Serialization chapter.
You can configure global event listeners using ListenerConfig
as shown below.
ClientConfig clientConfig = new ClientConfig();
ListenerConfig listenerConfig = new ListenerConfig(LifecycleListenerImpl);
clientConfig.addListenerConfig(listenerConfig);
ClientConfig clientConfig = new ClientConfig();
ListenerConfig listenerConfig = new ListenerConfig("com.hazelcast.example.MembershipListenerImpl");
clientConfig.addListenerConfig(listenerConfig);
You can add three types of event listeners.
RELATED INFORMATION
Please refer to Hazelcast JavaDocs and see LifecycleListener, MembershipListener and DistributedObjectListener in the com.hazelcast.core
package.
Hazelcast has an internal executor service (different from the data structure Executor Service) that has threads and queues to perform internal operations such as handling responses. This parameter specifies the size of the pool of threads which perform these operations laying in the executor's queue. If not configured, this parameter has the value as 5 * core size of the client (i.e. it is 20 for a machine that has 4 cores).
You can configure a custom classLoader
. It will be used by the serialization service and to load any class configured in configuration, such as event listeners or ProxyFactories.
There are some advanced client configuration properties to tune some aspects of Hazelcast Client. You can set them as property name and value pairs through declarative configuration, programmatic configuration, or JVM system property. Please see the System Properties section to learn how to set these properties.
The table below lists the client configuration properties with their descriptions.
Property Name | Default Value | Type | Description |
---|---|---|---|
hazelcast.client.event.queue.capacity |
1000000 | string | The default value of the capacity of executor that handles incoming event packets. |
hazelcast.client.event.thread.count |
5 | string | The thread count for handling incoming event packets. |
hazelcast.client.heartbeat.interval |
10000 | string | The frequency of heartbeat messages sent by the clients to the members. |
hazelcast.client.heartbeat.timeout |
300000 | string | Timeout for the heartbeat messages sent by the client to members. If no messages pass between client and member within the given time via this property in milliseconds, the connection will be closed. |
hazelcast.client.invocation.timeout.seconds |
120 | string | Time to give up the invocation when a member in the member list is not reachable. |
hazelcast.client.shuffle.member.list |
true | string | The client shuffles the given member list to prevent all clients to connect to the same node when this property is false . When it is set to true , the client tries to connect to the nodes in the given order. |
Please refer to Client Code Samples.
This chapter describes the clients other than the Hazelcast Java Client.
You can use Native C++ Client to connect to Hazelcast nodes and perform almost all operations that a node can perform. Clients differ from nodes in that clients do not hold data. The C++ Client is by default a smart client, i.e. it knows where the data is and asks directly for the correct node. You can disable this feature (using the ClientConfig::setSmart
method) if you do not want the clients to connect to every node.
The features of C++ Clients are:
Hazelcast C++ Client is shipped with 32/64 bit, shared and static libraries. You only need to include the boost shared_ptr.hpp header in your compilation since the API makes use of the boost shared_ptr
.
The downloaded release folder consists of:
Each of the folders above contains the following:
examples/
hazelcast/
external/
The C++ Client is tested on Linux 32/64-bit, Mac 64-bit and Windows 32/64-bit machines. For each of the headers above, it is assumed that you are in the correct folder for your platform. Folders are Mac_64, Windows_32, Windows_64, Linux_32 or Linux_64.
For Linux, there are two distributions: 32 bit and 64 bit.
Here is an example script to build with static library:
g++ main.cpp -pthread -I./external/include -I./hazelcast/include
./hazelcast/lib/static/libHazelcastClientStatic_64.a
Here is an example script to build with shared library:
g++ main.cpp -lpthread -Wl,–no-as-needed -lrt -I./external/include -I./hazelcast/include -L./hazelcast/lib/shared -lHazelcastClientShared_64
For Mac, there is one distribution: 64 bit.
Here is an example script to build with static library:
g++ main.cpp -I./external/include -I./hazelcast/include ./hazelcast/lib/static/libHazelcastClientStatic_64.a
Here is an example script to build with shared library:
g++ main.cpp -I./external/include -I./hazelcast/include -L./hazelcast/lib/shared -lHazelcastClientShared_6
For Windows, there are two distributions; 32 bit and 64 bit.
A Hazelcast node should be running to make the code examples work.
NOTE: The license key should be provided in the configuration as config->getGroupConfig().setLicenseKey(PROVIDED_ENTERPRISE_KEY);
#include <hazelcast/client/HazelcastAll.h>
#include <iostream>
using namespace hazelcast::client;
int main() {
ClientConfig clientConfig;
clientConfig->getGroupConfig().setLicenseKey(PROVIDED_ENTERPRISE_KEY);
Address address( "localhost", 5701 );
clientConfig.addAddress( address );
HazelcastClient hazelcastClient( clientConfig );
IMap<int,int> myMap = hazelcastClient.getMap<int ,int>( "myIntMap" );
myMap.put( 1,3 );
boost::shared_ptr<int> value = myMap.get( 1 );
if( value.get() != NULL ) {
//process the item
}
return 0;
}
#include <hazelcast/client/HazelcastAll.h>
#include <iostream>
#include <string>
using namespace hazelcast::client;
int main() {
ClientConfig clientConfig;
clientConfig->getGroupConfig().setLicenseKey(PROVIDED_ENTERPRISE_KEY);
Address address( "localhost", 5701 );
clientConfig.addAddress( address );
HazelcastClient hazelcastClient( clientConfig );
IQueue<std::string> queue = hazelcastClient.getQueue<std::string>( "q" );
queue.offer( "sample" );
boost::shared_ptr<std::string> value = queue.poll();
if( value.get() != NULL ) {
//process the item
}
return 0;
}
#include "hazelcast/client/ClientConfig.h"
#include "hazelcast/client/EntryEvent.h"
#include "hazelcast/client/IMap.h"
#include "hazelcast/client/Address.h"
#include "hazelcast/client/HazelcastClient.h"
#include <iostream>
#include <string>
using namespace hazelcast::client;
class SampleEntryListener {
public:
void entryAdded( EntryEvent<std::string, std::string> &event ) {
std::cout << "entry added " << event.getKey() << " "
<< event.getValue() << std::endl;
};
void entryRemoved( EntryEvent<std::string, std::string> &event ) {
std::cout << "entry added " << event.getKey() << " "
<< event.getValue() << std::endl;
}
void entryUpdated( EntryEvent<std::string, std::string> &event ) {
std::cout << "entry added " << event.getKey() << " "
<< event.getValue() << std::endl;
}
void entryEvicted( EntryEvent<std::string, std::string> &event ) {
std::cout << "entry added " << event.getKey() << " "
<< event.getValue() << std::endl;
}
};
int main( int argc, char **argv ) {
ClientConfig clientConfig;
Address address( "localhost", 5701 );
clientConfig.addAddress( address );
HazelcastClient hazelcastClient( clientConfig );
IMap<std::string,std::string> myMap = hazelcastClient
.getMap<std::string ,std::string>( "myIntMap" );
SampleEntryListener * listener = new SampleEntryListener();
std::string id = myMap.addEntryListener( *listener, true );
// Prints entryAdded
myMap.put( "key1", "value1" );
// Prints updated
myMap.put( "key1", "value2" );
// Prints entryRemoved
myMap.remove( "key1" );
// Prints entryEvicted after 1 second
myMap.put( "key2", "value2", 1000 );
// WARNING: deleting listener before removing it from hazelcast leads to crashes.
myMap.removeEntryListener( id );
// Delete listener after remove it from hazelcast.
delete listener;
return 0;
};
Assume that you have the following two classes in Java and you want to use them with a C++ client.
class Foo implements Serializable {
private int age;
private String name;
}
class Bar implements Serializable {
private float x;
private float y;
}
First, let them implement Portable
or IdentifiedDataSerializable
as shown below.
class Foo implements Portable {
private int age;
private String name;
public int getFactoryId() {
// a positive id that you choose
return 123;
}
public int getClassId() {
// a positive id that you choose
return 2;
}
public void writePortable( PortableWriter writer ) throws IOException {
writer.writeUTF( "n", name );
writer.writeInt( "a", age );
}
public void readPortable( PortableReader reader ) throws IOException {
name = reader.readUTF( "n" );
age = reader.readInt( "a" );
}
}
class Bar implements IdentifiedDataSerializable {
private float x;
private float y;
public int getFactoryId() {
// a positive id that you choose
return 4;
}
public int getId() {
// a positive id that you choose
return 5;
}
public void writeData( ObjectDataOutput out ) throws IOException {
out.writeFloat( x );
out.writeFloat( y );
}
public void readData( ObjectDataInput in ) throws IOException {
x = in.readFloat();
y = in.readFloat();
}
}
Then, implement the corresponding classes in C++ with same factory and class ID as shown below.
class Foo : public Portable {
public:
int getFactoryId() const {
return 123;
};
int getClassId() const {
return 2;
};
void writePortable( serialization::PortableWriter &writer ) const {
writer.writeUTF( "n", name );
writer.writeInt( "a", age );
};
void readPortable( serialization::PortableReader &reader ) {
name = reader.readUTF( "n" );
age = reader.readInt( "a" );
};
private:
int age;
std::string name;
};
class Bar : public IdentifiedDataSerializable {
public:
int getFactoryId() const {
return 4;
};
int getClassId() const {
return 2;
};
void writeData( serialization::ObjectDataOutput& out ) const {
out.writeFloat(x);
out.writeFloat(y);
};
void readData( serialization::ObjectDataInput& in ) {
x = in.readFloat();
y = in.readFloat();
};
private:
float x;
float y;
};
Now, you can use the classes Foo
and Bar
in distributed structures. For example, use as Key or Value of IMap
or as an Item in IQueue
.
You can use the native .NET client to connect to Hazelcast nodes. All you need is to add HazelcastClient3x.dll
into your .NET project references. The API is very similar to the Java native client.
.NET Client has the following distributed objects.
IMap<K,V>
IMultiMap<K,V>
IQueue<E>
ITopic<E>
IHList<E>
IHSet<E>
IIdGenerator
ILock
ISemaphore
ICountDownLatch
IAtomicLong
ITransactionContext
ITransactionContext can be used to obtain:
ITransactionalMap<K,V>
,ITransactionalMultiMap<K,V>
,ITransactionalList<E>
, andITransactionalSet<E>
.At present the following features are not available as in the Java Client:
A code example is shown below.
using Hazelcast.Config;
using Hazelcast.Client;
using Hazelcast.Core;
using Hazelcast.IO.Serialization;
using System.Collections.Generic;
namespace Hazelcast.Client.Example
{
public class SimpleExample
{
public static void Test()
{
var clientConfig = new ClientConfig();
clientConfig.GetNetworkConfig().AddAddress( "10.0.0.1" );
clientConfig.GetNetworkConfig().AddAddress( "10.0.0.2:5702" );
// Portable Serialization setup up for Customer Class
clientConfig.GetSerializationConfig()
.AddPortableFactory( MyPortableFactory.FactoryId, new MyPortableFactory() );
IHazelcastInstance client = HazelcastClient.NewHazelcastClient( clientConfig );
// All cluster operations that you can do with ordinary HazelcastInstance
IMap<string, Customer> mapCustomers = client.GetMap<string, Customer>( "customers" );
mapCustomers.Put( "1", new Customer( "Joe", "Smith" ) );
mapCustomers.Put( "2", new Customer( "Ali", "Selam" ) );
mapCustomers.Put( "3", new Customer( "Avi", "Noyan" ) );
ICollection<Customer> customers = mapCustomers.Values();
foreach (var customer in customers)
{
//process customer
}
}
}
public class MyPortableFactory : IPortableFactory
{
public const int FactoryId = 1;
public IPortable Create( int classId ) {
if ( Customer.Id == classId )
return new Customer();
else
return null;
}
}
public class Customer : IPortable
{
private string name;
private string surname;
public const int Id = 5;
public Customer( string name, string surname )
{
this.name = name;
this.surname = surname;
}
public Customer() {}
public int GetFactoryId()
{
return MyPortableFactory.FactoryId;
}
public int GetClassId()
{
return Id;
}
public void WritePortable( IPortableWriter writer )
{
writer.WriteUTF( "n", name );
writer.WriteUTF( "s", surname );
}
public void ReadPortable( IPortableReader reader )
{
name = reader.ReadUTF( "n" );
surname = reader.ReadUTF( "s" );
}
}
}
You can configure the Hazelcast .NET client via API or XML. To start the client, you can pass a configuration or leave it empty to use default values.
NOTE: .NET and Java clients are similar in terms of configuration. Therefore, you can refer to Java Client section for configuration aspects. For information on .NET API documentation, please refer to the API document provided along with the Hazelcast Enterprise license.
After configuration, you can obtain a client using one of the static methods of Hazelcast, as shown below.
IHazelcastInstance client = HazelcastClient.NewHazelcastClient(clientConfig);
...
IHazelcastInstance defaultClient = HazelcastClient.NewHazelcastClient();
...
IHazelcastInstance xmlConfClient = Hazelcast
.NewHazelcastClient(@"..\Hazelcast.Net\Resources\hazelcast-client.xml");
The IHazelcastInstance
interface is the starting point where all distributed objects can be obtained.
var map = client.GetMap<int,string>("mapName");
...
var lock= client.GetLock("thelock");
Hazelcast provides a REST interface, i.e. it provides an HTTP service in each node so that you can access your map
and queue
using HTTP protocol. Assuming mapName
and queueName
are already configured in your Hazelcast, its structure is shown below.
http://node IP address:port/hazelcast/rest/maps/mapName/key
http://node IP address:port/hazelcast/rest/queues/queueName
For the operations to be performed, standard REST conventions for HTTP calls are used.
Assume that your cluster members are as shown below.
Members [5] {
Member [10.20.17.1:5701]
Member [10.20.17.2:5701]
Member [10.20.17.4:5701]
Member [10.20.17.3:5701]
Member [10.20.17.5:5701]
}
NOTE: All of the requests below can return one of the following responses in case of a failure.
HTTP/1.1 400 Bad Request
Content-Length: 0
< HTTP/1.1 500 Internal Server Error
< Content-Length: 0
Creating/Updating Entries in a Map
You can put a new key1/value1
entry into a map by using POST call to
http://10.20.17.1:5701/hazelcast/
rest/maps/mapName/key1
URL. This call's content body should contain the value of the key. Also, if the call contains the MIME type, Hazelcast stores this information, too.
A sample POST call is shown below.
$ curl -v -X POST -H "Content-Type: text/plain" -d "bar"
http://10.20.17.1:5701/hazelcast/rest/maps/mapName/foo
It will return the following response if successful:
< HTTP/1.1 200 OK
< Content-Type: text/plain
< Content-Length: 0
Retrieving Entries from a Map
If you want to retrieve an entry, you can use a GET call to http://10.20.17.1:5701/hazelcast/rest/maps/mapName/key1
. You can also retrieve this entry from another member of your cluster, such as
http://10.20.17.3:5701/hazelcast/rest/
maps/mapName/key1
.
An example of a GET call is shown below.
$ curl -X GET http://10.20.17.3:5701/hazelcast/rest/maps/mapName/foo
It will return the following response if there is a corresponding value:
< HTTP/1.1 200 OK
< Content-Type: text/plain
< Content-Length: 3
bar
This GET call returned a value, its length, and also the MIME type (text/plain
) since the POST call example shown above included the MIME type.
It will return the following if there is no mapping for the given key:
< HTTP/1.1 204 No Content
< Content-Length: 0
Removing Entries from a Map
You can use a DELETE call to remove an entry. A sample DELETE call is shown below with its response.
$ curl -v -X DELETE http://10.20.17.1:5701/hazelcast/rest/maps/mapName/foo
< HTTP/1.1 200 OK
< Content-Type: text/plain
< Content-Length: 0
If you leave the key empty as follows, DELETE will delete all entries from the map.
$ curl -v -X DELETE http://10.20.17.1:5701/hazelcast/rest/maps/mapName
< HTTP/1.1 200 OK
< Content-Type: text/plain
< Content-Length: 0
Offering Items on a Queue
You can use a POST call to create an item on the queue. A sample is shown below.
$ curl -v -X POST -H "Content-Type: text/plain" -d "foo"
http://10.20.17.1:5701/hazelcast/rest/queues/myEvents
The above call is equivalent to HazelcastInstance#getQueue("myEvents").offer("foo");
.
It will return the following if successful:
< HTTP/1.1 200 OK
< Content-Type: text/plain
< Content-Length: 0
It will return the following if the queue is full and the item is not able to be offered to the queue:
< HTTP/1.1 503 Service Unavailable
< Content-Length: 0
Retrieving Items from a Queue
You can use a DELETE call for retrieving items from a queue. Note that you should state the poll timeout while polling for queue events by an extra path parameter.
An example is shown below (10 being the timeout value).
$ curl -v -X DELETE \http://10.20.17.1:5701/hazelcast/rest/queues/myEvents/10
The above call is equivalent to HazelcastInstance#getQueue("myEvents").poll(10, SECONDS);
. Below is the response.
< HTTP/1.1 200 OK
< Content-Type: text/plain
< Content-Length: 3
foo
When the timeout is reached, the response will be No Content
success, i.e. there is no item on the queue to be returned.
< HTTP/1.1 204 No Content
< Content-Length: 0
Getting the size of the queue
$ curl -v -X GET \http://10.20.17.1:5701/hazelcast/rest/queues/myEvents/size
The above call is equivalent to HazelcastInstance#getQueue("myEvents").size();
. Below is a sample response.
< HTTP/1.1 200 OK
< Content-Type: text/plain
< Content-Length: 1
5
Besides the above operations, you can check the status of your cluster, a sample of which is shown below.
$ curl -v http://127.0.0.1:5701/hazelcast/rest/cluster
The return will be similar to the following:
< HTTP/1.1 200 OK
< Content-Length: 119
Members [5] {
Member [10.20.17.1:5701] this
Member [10.20.17.2:5701]
Member [10.20.17.4:5701]
Member [10.20.17.3:5701]
Member [10.20.17.5:5701]
}
ConnectionCount: 5
AllConnectionCount: 20
RESTful access is provided through any member of your cluster. You can even put an HTTP load-balancer in front of your cluster members for load balancing and fault tolerance.
NOTE: You need to handle the failures on REST polls as there is no transactional guarantee.
NOTE: Hazelcast Memcache Client only supports ASCII protocol. Binary Protocol is not supported.
A Memcache client written in any language can talk directly to a Hazelcast cluster. No additional configuration is required.
Assume that your cluster members are as shown below.
Members [5] {
Member [10.20.17.1:5701]
Member [10.20.17.2:5701]
Member [10.20.17.4:5701]
Member [10.20.17.3:5701]
Member [10.20.17.5:5701]
}
Assume that you have a PHP application that uses PHP Memcache client to cache things in Hazelcast. All you need to do is have your PHP Memcache client connect to one of these members. It does not matter which member the client connects to because the Hazelcast cluster looks like one giant machine (Single System Image). Here is a PHP client code example.
<?php
$memcache = new Memcache;
$memcache->connect( '10.20.17.1', 5701 ) or die ( "Could not connect" );
$memcache->set( 'key1', 'value1', 0, 3600 );
$get_result = $memcache->get( 'key1' ); // retrieve your data
var_dump( $get_result ); // show it
?>
Notice that Memcache client connects to 10.20.17.1
and uses port5701
. Here is a Java client code example with SpyMemcached client:
MemcachedClient client = new MemcachedClient(
AddrUtil.getAddresses( "10.20.17.1:5701 10.20.17.2:5701" ) );
client.set( "key1", 3600, "value1" );
System.out.println( client.get( "key1" ) );
If you want your data to be stored in different maps (e.g. to utilize per map configuration), you can do that with a map name prefix as in the following example code.
MemcachedClient client = new MemcachedClient(
AddrUtil.getAddresses( "10.20.17.1:5701 10.20.17.2:5701" ) );
client.set( "map1:key1", 3600, "value1" ); // store to *hz_memcache_map1
client.set( "map2:key1", 3600, "value1" ); // store to hz_memcache_map2
System.out.println( client.get( "key1" ) ); // get from hz_memcache_map1
System.out.println( client.get( "key2" ) ); // get from hz_memcache_map2
hz_memcache prefix_ separates Memcache maps from Hazelcast maps. If no map name is given, it will be stored in a default map named hz_memcache_default.
An entry written with a Memcache client can be read by another Memcache client written in another language.
CAS operations are not supported. In operations that get CAS parameters, such as append, CAS values are ignored.
Only a subset of statistics are supported. Below is the list of supported statistic values.
You need to serialize the Java objects that you put into Hazelcast because Hazelcast is a distributed system. The data and its replicas are stored in different partitions on multiple nodes. The data you need may not be present on the local machine, and in that case, Hazelcast retrieves that data from another machine. This requires serialization.
Hazelcast serializes all your objects into an instance of com.hazelcast.nio.serialization.Data
. Data
is the binary representation of an object.
Serialization is used when:
Hazelcast optimizes the serialization for the below types. You cannot override this behavior.
Hazelcast also optimizes the following types. However, you can override these types by creating a custom serializer and registering it. See Custom Serialization for more information.
Hazelcast optimizes all of the above object types. You do not need to worry about their (de)serializations.
For complex objects, the following interfaces are used for serialization and deserialization.
java.io.Serializable
java.io.Externalizable
com.hazelcast.nio.serialization.DataSerializable
com.hazelcast.nio.serialization.IdentifiedDataSerializable
com.hazelcast.nio.serialization.Portable
, and
StreamSerializer
, ByteArraySerializer
)When Hazelcast serializes an object into Data
:
(1) It first checks whether the object is an instance of com.hazelcast.nio.serialization.DataSerializable
.
(2) If the above check fails, then Hazelcast checks if it is an instance of com.hazelcast.nio.serialization.Portable
.
(3) If the above check fails, then Hazelcast checks whether the object is a well-known type like String, Long, or Integer, or if it is a user-specified type like ByteArraySerializer
or StreamSerializer
.
(4) If the above checks fail, Hazelcast will use Java serialization.
If all of the above checks do not work, then serialization will fail. When a class implements multiple interfaces, the above steps are important to determine the serialization mechanism that Hazelcast will use. When a class definition is required for any of these serializations, all the classes needed by the application should be on the classpath because Hazelcast does not download them automatically.
Below table provides a comparison between the interfaces listed in the previous section to help you in deciding which interface to use in your applications.
Serialization Interface | Advantages | Drawbacks |
---|---|---|
Serializable | - A standard and basic Java interface - Requires no implementation |
- More time and CPU usage - More space occupancy |
Externalizable | - A standard Java interface - More CPU and memory usage efficient than Serializable |
- Serialization interface must be implemented |
DataSerializable | - More CPU and memory usage efficient than Serializable | - Specific to Hazelcast |
IdentifiedDataSerializable | - More CPU and memory usage efficient than Serializable - Reflection is not used during deserialization |
- Specific to Hazelcast - Serialization interface must be implemented - A Factory and configuration must be implemented |
Portable | - More CPU and memory usage efficient than Serializable - Reflection is not used during deserialization - Versioning is supported Partial deserialization is supported during Queries |
- Specific to Hazelcast - Serialization interface must be implemented - A Factory and configuration must be implemented - Class definition is also sent with data but stored only once per class |
Custom Serialization | - Does not require class to implement an interface - Convenient and flexible - Can be based on StreamSerializer ByteArraySerializer |
- Serialization interface must be implemented - Plug in and configuration is required |
Let's dig into the details of the above serialization mechanisms in the following sections.
A class often needs to implement the java.io.Serializable
interface; native Java serialization is the easiest way to do serialization. Let's take a look at the example code below.
public class Employee implements Serializable {
private static final long serialVersionUID = 1L;
private String surname;
public Employee( String surname ) {
this.surname = surname;
}
}
Here, the fields that are non-static and non-transient are automatically serialized. To eliminate class compatibility issues, it is recommended that you add a serialVersionUID
, as shown above. Also, when you are using methods that perform byte-content comparisons (e.g. IMap.replace()
) and if byte-content of equal objects is different, you may face unexpected behaviors. Therefore, if the class relies on, for example, a hash map, replace
method may fail. The reason for this is the hash map is a serialized data structure with unreliable byte-content.
Hazelcast also supports java.io.Externalizable
. This interface offers more control on the way fields are serialized or deserialized. Compared to native Java serialization, it also can have a positive effect on performance. With java.io.Externalizable
, there is no need to add serialVersionUID
.
Let's take a look at the example code below.
public class Employee implements Externalizable {
private String surname;
public Employee(String surname) {
this.surname = surname;
}
@Override
public void readExternal( ObjectInput in )
throws IOException, ClassNotFoundException {
this.surname = in.readUTF();
}
@Override
public void writeExternal( ObjectOutput out )
throws IOException {
out.writeUTF(surname);
}
}
Writing and reading of fields are performed explicitly. Note that reading should be performed in the same order as writing.
As mentioned in the Serializable & Externalizable section, Java serialization is an easy mechanism. However, we do not have a control on how fields are serialized or deserialized. Moreover, this mechanism can lead to excessive CPU loads since it keeps track of objects to handle the cycles and streams class descriptors. These are performance decreasing factors; thus, serialized data may not have an optimal size.
The DataSerializable
interface of Hazelcast overcomes these issues. Here is an example of a class implementing the com.hazelcast.nio.serialization.DataSerializable
interface.
public class Address implements DataSerializable {
private String street;
private int zipCode;
private String city;
private String state;
public Address() {}
//getters setters..
public void writeData( ObjectDataOutput out ) throws IOException {
out.writeUTF(street);
out.writeInt(zipCode);
out.writeUTF(city);
out.writeUTF(state);
}
public void readData( ObjectDataInput in ) throws IOException {
street = in.readUTF();
zipCode = in.readInt();
city = in.readUTF();
state = in.readUTF();
}
}
Let's take a look at another example which encapsulates a DataSerializable
field.
public class Employee implements DataSerializable {
private String firstName;
private String lastName;
private int age;
private double salary;
private Address address; //address itself is DataSerializable
public Employee() {}
//getters setters..
public void writeData( ObjectDataOutput out ) throws IOException {
out.writeUTF(firstName);
out.writeUTF(lastName);
out.writeInt(age);
out.writeDouble (salary);
address.writeData (out);
}
public void readData( ObjectDataInput in ) throws IOException {
firstName = in.readUTF();
lastName = in.readUTF();
age = in.readInt();
salary = in.readDouble();
address = new Address();
// since Address is DataSerializable let it read its own internal state
address.readData(in);
}
}
As you can see, since address
field itself is DataSerializable
, it is calling address.writeData(out)
when writing and address.readData(in)
when reading. Also note that, the order of writing and reading fields should be the same. While Hazelcast serializes a DataSerializable
, it writes the className
first. When Hazelcast de-serializes it, className
is used to instantiate the object using reflection.
NOTE: Since Hazelcast needs to create an instance during deserialization,DataSerializable
class has a no-arg constructor.
NOTE: DataSerializable
is a good option if serialization is only needed for in-cluster communication.
For a faster serialization of objects, avoiding reflection and long class names, Hazelcast recommends you implement com.hazelcast.nio.serialization.IdentifiedDataSerializable
which is a slightly better version of DataSerializable
.
DataSerializable
uses reflection to create a class instance, as mentioned in the DataSerializable section. But, IdentifiedDataSerializable
uses a factory for this purpose and it is faster during deserialization which requires new instance creations.
IdentifiedDataSerializable
extends DataSerializable
and introduces two new methods.
int getId();
int getFactoryId();
IdentifiedDataSerializable
uses getId()
instead of class name, and it uses getFactoryId()
to load the class when given the Id. To complete the implementation, com.hazelcast.nio.serialization.DataSerializableFactory
should also be implemented and registered into SerializationConfig
which can be accessed from Config.getSerializationConfig()
. Factory's responsibility is to return an instance of the right IdentifiedDataSerializable
object, given the Id. So far this is the most efficient way of Serialization that Hazelcast supports off the shelf.
Let's take a look at the example code below and configuration to see IdentifiedDataSerializable
in action.
public class Employee
implements IdentifiedDataSerializable {
private String surname;
public Employee() {}
public Employee( String surname ) {
this.surname = surname;
}
@Override
public void readData( ObjectDataInput in )
throws IOException {
this.surname = in.readUTF();
}
@Override
public void writeData( ObjectDataOutput out )
throws IOException {
out.writeUTF( surname );
}
@Override
public int getFactoryId() {
return EmployeeDataSerializableFactory.FACTORY_ID;
}
@Override
public int getId() {
return EmployeeDataSerializableFactory.EMPLOYEE_TYPE;
}
@Override
public String toString() {
return String.format( "Employee(surname=%s)", surname );
}
}
The methods getId
and getFactoryId
return a unique positive number within the EmployeeDataSerializableFactory
. Now, let's create an instance of this EmployeeDataSerializableFactory
.
public class EmployeeDataSerializableFactory
implements DataSerializableFactory{
public static final int FACTORY_ID = 1;
public static final int EMPLOYEE_TYPE = 1;
@Override
public IdentifiedDataSerializable create(int typeId) {
if ( typeId == EMPLOYEE_TYPE ) {
return new Employee();
} else {
return null;
}
}
}
The only method that should be implemented is create
, as seen in the above example. It is recommended that you use a switch
-`case
statement instead of multiple if
-else
blocks if you have a lot of subclasses. Hazelcast throws an exception if null is returned for typeId
.
As the last step, you need to register EmployeeDataSerializableFactory
declaratively (declare in the configuration file hazelcast.xml
) as shown below. Note that factory-id
has the same value of FACTORY_ID
in the above code. This is crucial to enable Hazelcast to find the correct factory.
<hazelcast>
...
<serialization>
<data-serializable-factories>
<data-serializable-factory
factory-id="1">EmployeeDataSerializableFactory
</data-serializable-factory>
</data-serializable-factories>
</serialization>
...
</hazelcast>
RELATED INFORMATION
Please refer to the Serialization Configuration section for a full description of Hazelcast Serialization configuration.
As an alternative to the existing serialization methods, Hazelcast offers a language/platform independent Portable serialization that has the following advantages:
In order to support these features, a serialized Portable object contains meta information like the version and the concrete location of the each field in the binary data. This way, Hazelcast navigates in the byte[]
and de-serializes only the required field without actually de-serializing the whole object. This improves the Query performance.
With multi-version support, you can have two nodes where each of them have different versions of the same object. Hazelcast will store both meta information and use the correct one to serialize and de-serialize Portable objects depending on the node. This is very helpful when you are doing a rolling upgrade without shutting down the cluster.
Portable serialization is totally language independent and is used as the binary protocol between Hazelcast server and clients.
A sample Portable implementation of a Foo class would look like the following.
public class Foo implements Portable{
final static int ID = 5;
private String foo;
public String getFoo() {
return foo;
}
public void setFoo( String foo ) {
this.foo = foo;
}
@Override
public int getFactoryId() {
return 1;
}
@Override
public int getClassId() {
return ID;
}
@Override
public void writePortable( PortableWriter writer ) throws IOException {
writer.writeUTF( "foo", foo );
}
@Override
public void readPortable( PortableReader reader ) throws IOException {
foo = reader.readUTF( "foo" );
}
}
Similar to IdentifiedDataSerializable
, a Portable Class must provide classId
andfactoryId
. The Factory object will create the Portable object given the classId
.
An example Factory
could be implemented as following:
public class MyPortableFactory implements PortableFactory {
@Override
public Portable create( int classId ) {
if ( Foo.ID == classId )
return new Foo();
else
return null;
}
}
The last step is to register the Factory
to the SerializationConfig
. Below are the programmatic and declarative configurations for this step.
Config config = new Config();
config.getSerializationConfig().addPortableFactory( 1, new MyPortableFactory() );
<hazelcast>
<serialization>
<portable-version>0</portable-version>
<portable-factories>
<portable-factory factory-id="1">
com.hazelcast.nio.serialization.MyPortableFactory
</portable-factory>
</portable-factories>
</serialization>
</hazelcast>
Note that the id
that is passed to the SerializationConfig
is the same as the factoryId
that the Foo
class returns.
More than one version of the same class may need to be serialized and deserialized. For example, a client may have an older version of a class, and the node to which it is connected can have a newer version of the same class.
Portable serialization supports versioning. You can declare Version in the configuration file hazelcast.xml
using the portable-version
element, as shown below.
<serialization>
<portable-version>1</portable-version>
<portable-factories>
<portable-factory factory-id="1">
PortableFactoryImpl
</portable-factory>
</portable-factories>
</serialization>
You should consider the following when you perform versioning.
int
to float
).Be careful when serializing null portables. Hazelcast lazily creates a class definition of portable internally
when the user first serializes. This class definition is stored and used later for deserializing that portable class. When
the user tries to serialize a null portable when there is no class definition at the moment, Hazelcast throws an
exception saying that com.hazelcast.nio.serialization.HazelcastSerializationException: Cannot write null portable
without explicitly registering class definition!
.
There are two solutions to get rid of this exception. Either put a non-null portable class of the same type before any other operation, or manually register a class definition in serialization configuration as shown below.
Config config = new Config();
final ClassDefinition classDefinition = new ClassDefinitionBuilder(Foo.factoryId, Foo.getClassId)
.addUTFField("foo").build();
config.getSerializationConfig().addClassDefinition(classDefinition);
Hazelcast.newHazelcastInstance(config);
Putting a DistributedObject (e.g. Hazelcast Semaphore, Queue, etc.) in a machine and getting it from another one is not a straightforward operation. Passing the ID and type of the DistributedObject can be a solution. For deserialization, you can get the object from HazelcastInstance. For instance, if your distributed object is an instance of IQueue
, you can either use HazelcastInstance.getQueue(id)
or Hazelcast.getDistributedObject
.
You can use the HazelcastInstanceAware
interface in the case of a deserialization of a Portable DistributedObject if it gets an ID to be looked up. HazelcastInstance is set after deserialization, so you first need to store the ID and then retrieve the DistributedObject using the setHazelcastInstance
method.
RELATED INFORMATION
Please refer to the Serialization Configuration section for a full description of Hazelcast Serialization configuration.
Hazelcast lets you plug a custom serializer for serializing objects. You can use StreamSerializer
and ByteArraySerializer
interfaces for this purpose.
You can use a stream to serialize and deserialize data by using StreamSerializer
. This is a good option for your own implementations. It can also be adapted to external serialization libraries like Kryo, JSON, and protocol buffers.
First, let's create a simple object.
public class Employee {
private String surname;
public Employee( String surname ) {
this.surname = surname;
}
}
Now, let's implement StreamSerializer for Employee
class.
public class EmployeeStreamSerializer
implements StreamSerializer<Employee> {
@Override
public int getTypeId () {
return 1;
}
@Override
public void write( ObjectDataOutput out, Employee employee )
throws IOException {
out.writeUTF(employee.getSurname());
}
@Override
public Employee read( ObjectDataInput in )
throws IOException {
String surname = in.readUTF();
return new Employee(surname);
}
@Override
public void destroy () {
}
}
In practice, classes may have many fields. Just make sure the fields are read in the same order as they are written. The type ID must be unique and greater than or equal to 1. Uniqueness of the type ID enables Hazelcast to determine which serializer will be used during deserialization.
As the last step, let's register the EmployeeStreamSerializer
in the configuration file hazelcast.xml
, as shown below.
<serialization>
<serializers>
<serializer type-class="Employee" class-name="EmployeeStreamSerializer" />
</serializers>
</serialization>
NOTE: StreamSerializer
cannot be created for well-known types (e.g. Long, String) and primitive arrays. Hazelcast already registers these types.
Let's take a look at another example implementing StreamSerializer
.
public class Foo {
private String foo;
public String getFoo() {
return foo;
}
public void setFoo( String foo ) {
this.foo = foo;
}
}
Assume that our custom serialization will serialize
Foo into XML. First we need to implement a
com.hazelcast.nio.serialization.StreamSerializer
. A very simple one that uses XMLEncoder and XMLDecoder could look like the following:
public static class FooXmlSerializer implements StreamSerializer<Foo> {
@Override
public int getTypeId() {
return 10;
}
@Override
public void write( ObjectDataOutput out, Foo object ) throws IOException {
ByteArrayOutputStream bos = new ByteArrayOutputStream();
XMLEncoder encoder = new XMLEncoder( bos );
encoder.writeObject( object );
encoder.close();
out.write( bos.toByteArray() );
}
@Override
public Foo read( ObjectDataInput in ) throws IOException {
InputStream inputStream = (InputStream) in;
XMLDecoder decoder = new XMLDecoder( inputStream );
return (Foo) decoder.readObject();
}
@Override
public void destroy() {
}
}
Note that typeId
must be unique because Hazelcast will use it to look up the StreamSerializer
while it de-serializes the object. The last required step is to register the StreamSerializer
to the Configuration. Below are the programmatic and declarative configurations for this step.
SerializerConfig sc = new SerializerConfig()
.setImplementation(new FooXmlSerializer())
.setTypeClass(Foo.class);
Config config = new Config();
config.getSerializationConfig().addSerializerConfig(sc);
<hazelcast>
<serialization>
<serializers>
<serializer type-class="com.www.Foo">com.www.FooXmlSerializer</serializer>
</serializers>
</serialization>
</hazelcast>
From now on, Hazelcast will use FooXmlSerializer
to serialize Foo objects. This way one can write an adapter (StreamSerializer) for any Serialization framework and plug it into Hazelcast.
RELATED INFORMATION
Please refer to the Serialization Configuration section for a full description of Hazelcast Serialization configuration.
ByteArraySerializer
exposes the raw ByteArray used internally by Hazelcast. It is a good option if the serialization library you are using deals with ByteArrays instead of streams.
Let's implement ByteArraySerializer
for the Employee
class mentioned in the StreamSerializer section.
public class EmployeeByteArraySerializer
implements ByteArraySerializer<Employee> {
@Override
public void destroy () {
}
@Override
public int getTypeId () {
return 1;
}
@Override
public byte[] write( Employee object )
throws IOException {
return object.getName().getBytes();
}
@Override
public Employee read( byte[] buffer )
throws IOException {
String surname = new String( buffer );
return new Employee( surname );
}
}
As usual, let's register the EmployeeByteArraySerializer
in the configuration file hazelcast.xml
, as shown below.
<serialization>
<serializers>
<serializer type-class="Employee">EmployeeByteArraySerializer</serializer>
</serializers>
</serialization>
RELATED INFORMATION
Please refer to the Serialization Configuration section for a full description of Hazelcast Serialization configuration.
You can implement the HazelcastInstanceAware
interface to access distributed objects for cases where an object is deserialized and needs access to HazelcastInstance.
Let's implement it for the Employee
class mentioned in the Custom Serialization section.
public class Employee
implements Serializable, HazelcastInstanceAware {
private static final long serialVersionUID = 1L;
private String surname;
private transient HazelcastInstance hazelcastInstance;
public Person( String surname ) {
this.surname = surname;
}
@Override
public void setHazelcastInstance( HazelcastInstance hazelcastInstance ) {
this.hazelcastInstance = hazelcastInstance;
System.out.println( "HazelcastInstance set" );
}
@Override
public String toString() {
return String.format( "Person(surname=%s)", surname );
}
}
After deserialization, the object is checked if it implements HazelcastInstanceAware
and the method setHazelcastInstance
is called. Notice the hazelcastInstance
is transient
. This is because this field should not be serialized.
It may be a good practice to inject a HazelcastInstance into a domain object (e.g. Employee
in the above sample) when used together with Runnable
/Callable
implementations. These runnables/callables are executed by IExecutorService
which sends them to another machine. And after a task is deserialized, run/call method implementations need to access HazelcastInstance.
We recommend you only to set the HazelcastInstance field while using setHazelcastInstance
method and not to execute operations on the HazelcastInstance. Because, when HazelcastInstance is injected for a HazelcastInstanceAware
implementation, it may not be up and running at the injection time.
This chapter provides information on managing and monitoring your Hazelcast cluster. It gives detailed instructions related to gathering statistics, monitoring via JMX protocol and managing the cluster with useful utilities. It also includes the usage explanations of Hazelcast Management Center.
You can gather various statistics from your distributed data structures via Statistics API. Since the data structures are distributed in the cluster, the Statistics API provides statistics for the local portion (1/Number of Nodes) of data on each node.
The IMap
interface has a getLocalMapStats()
method which returns a
LocalMapStats
object that holds local map statistics.
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
IMap<String, Customer> customers = hazelcastInstance.getMap( "customers" );
LocalMapStats mapStatistics = customers.getLocalMapStats();
System.out.println( "number of entries owned on this node = "
+ mapStatistics.getOwnedEntryCount() );
Below is the list of metrics that you can access via the LocalMapStats
object.
/**
* Returns the number of entries owned by this member.
*/
long getOwnedEntryCount();
/**
* Returns the number of backup entries hold by this member.
*/
long getBackupEntryCount();
/**
* Returns the number of backups per entry.
*/
int getBackupCount();
/**
* Returns memory cost (number of bytes) of owned entries in this member.
*/
long getOwnedEntryMemoryCost();
/**
* Returns memory cost (number of bytes) of backup entries in this member.
*/
long getBackupEntryMemoryCost();
/**
* Returns the creation time of this map on this member.
*/
long getCreationTime();
/**
* Returns the last access (read) time of the locally owned entries.
*/
long getLastAccessTime();
/**
* Returns the last update time of the locally owned entries.
*/
long getLastUpdateTime();
/**
* Returns the number of hits (reads) of the locally owned entries.
*/
long getHits();
/**
* Returns the number of currently locked locally owned keys.
*/
long getLockedEntryCount();
/**
* Returns the number of entries that the member owns and are dirty (updated
* but not persisted yet).
* dirty entry count is meaningful when there is a persistence defined.
*/
long getDirtyEntryCount();
/**
* Returns the number of put operations
*/
long getPutOperationCount();
/**
* Returns the number of get operations
*/
long getGetOperationCount();
/**
* Returns the number of Remove operations
*/
long getRemoveOperationCount();
/**
* Returns the total latency of put operations. To get the average latency,
* divide by number of puts
*/
long getTotalPutLatency();
/**
* Returns the total latency of get operations. To get the average latency,
* divide by number of gets
*/
long getTotalGetLatency();
/**
* Returns the total latency of remove operations. To get the average latency,
* divide by number of gets
*/
long getTotalRemoveLatency();
/**
* Returns the maximum latency of put operations. To get the average latency,
* divide by number of puts
*/
long getMaxPutLatency();
/**
* Returns the maximum latency of get operations. To get the average latency,
* divide by number of gets
*/
long getMaxGetLatency();
/**
* Returns the maximum latency of remove operations. To get the average latency,
* divide by number of gets
*/
long getMaxRemoveLatency();
/**
* Returns the number of Events Received
*/
long getEventOperationCount();
/**
* Returns the total number of Other Operations
*/
long getOtherOperationCount();
/**
* Returns the total number of total operations
*/
long total();
/**
* Cost of map & near cache & backup in bytes
* todo in object mode object size is zero.
*/
long getHeapCost();
/**
* Returns statistics related to the Near Cache.
*/
NearCacheStats getNearCacheStats();
You can access Near Cache statistics from the LocalMapStats
object via the
getNearCacheStats()
method, which returns a NearCacheStats
object.
HazelcastInstance node = Hazelcast.newHazelcastInstance();
IMap<String, Customer> customers = node.getMap( "customers" );
LocalMapStats mapStatistics = customers.getLocalMapStats();
NearCacheStats nearCacheStatistics = mapStatistics.getNearCacheStats();
System.out.println( "near cache hit/miss ratio= "
+ nearCacheStatistics.getRatio() );
Below is the list of metrics that you can access via the NearCacheStats
object.
This behavior applies to both client and node near caches.
/**
* Returns the creation time of this NearCache on this member
*/
long getCreationTime();
/**
* Returns the number of entries owned by this member.
*/
long getOwnedEntryCount();
/**
* Returns memory cost (number of bytes) of entries in this cache.
*/
long getOwnedEntryMemoryCost();
/**
* Returns the number of hits (reads) of the locally owned entries.
*/
long getHits();
/**
* Returns the number of misses of the locally owned entries.
*/
long getMisses();
/**
* Returns the hit/miss ratio of the locally owned entries.
*/
double getRatio();
The MultiMap
interface has a getLocalMultiMapStats()
method which returns a
LocalMultiMapStats
object that holds local MultiMap statistics.
HazelcastInstance node = Hazelcast.newHazelcastInstance();
MultiMap<String, Customer> customers = node.getMultiMap( "customers" );
LocalMultiMapStats multiMapStatistics = customers.getLocalMultiMapStats();
System.out.println( "last update time = "
+ multiMapStatistics.getLastUpdateTime() );
Below is the list of metrics that you can access via the LocalMultiMapStats
object.
/**
* Returns the number of entries owned by this member.
*/
long getOwnedEntryCount();
/**
* Returns the number of backup entries hold by this member.
*/
long getBackupEntryCount();
/**
* Returns the number of backups per entry.
*/
int getBackupCount();
/**
* Returns memory cost (number of bytes) of owned entries in this member.
*/
long getOwnedEntryMemoryCost();
/**
* Returns memory cost (number of bytes) of backup entries in this member.
*/
long getBackupEntryMemoryCost();
/**
* Returns the creation time of this map on this member.
*/
long getCreationTime();
/**
* Returns the last access (read) time of the locally owned entries.
*/
long getLastAccessTime();
/**
* Returns the last update time of the locally owned entries.
*/
long getLastUpdateTime();
/**
* Returns the number of hits (reads) of the locally owned entries.
*/
long getHits();
/**
* Returns the number of currently locked locally owned keys.
*/
long getLockedEntryCount();
/**
* Returns the number of entries that the member owns and are dirty (updated
* but not persisted yet).
* dirty entry count is meaningful when a persistence is defined.
*/
long getDirtyEntryCount();
/**
* Returns the number of put operations
*/
long getPutOperationCount();
/**
* Returns the number of get operations
*/
long getGetOperationCount();
/**
* Returns the number of Remove operations
*/
long getRemoveOperationCount();
/**
* Returns the total latency of put operations. To get the average latency,
* divide by number of puts
*/
long getTotalPutLatency();
/**
* Returns the total latency of get operations. To get the average latency,
* divide by number of gets
*/
long getTotalGetLatency();
/**
* Returns the total latency of remove operations. To get the average latency,
* divide by number of gets
*/
long getTotalRemoveLatency();
/**
* Returns the maximum latency of put operations. To get the average latency,
* divide by number of puts
*/
long getMaxPutLatency();
/**
* Returns the maximum latency of get operations. To get the average latency,
* divide by number of gets
*/
long getMaxGetLatency();
/**
* Returns the maximum latency of remove operations. To get the average latency,
* divide by number of gets
*/
long getMaxRemoveLatency();
/**
* Returns the number of Events Received
*/
long getEventOperationCount();
/**
* Returns the total number of Other Operations
*/
long getOtherOperationCount();
/**
* Returns the total number of total operations
*/
long total();
/**
* Cost of map & near cache & backup in bytes
* todo in object mode object size is zero.
*/
long getHeapCost();
The IQueue
interface has a getLocalQueueStats()
method which returns a
LocalQueueStats
object that holds local queue statistics.
HazelcastInstance node = Hazelcast.newHazelcastInstance();
IQueue<Order> orders = node.getQueue( "orders" );
LocalQueueStats queueStatistics = orders.getLocalQueueStats();
System.out.println( "average age of items = "
+ queueStatistics.getAvgAge() );
Below is the list of metrics that you can access via the LocalQueueStats
object.
/**
* Returns the number of owned items in this member.
*/
long getOwnedItemCount();
/**
* Returns the number of backup items in this member.
*/
long getBackupItemCount();
/**
* Returns the min age of the items in this member.
*/
long getMinAge();
/**
* Returns the max age of the items in this member.
*/
long getMaxAge();
/**
* Returns the average age of the items in this member.
*/
long getAvgAge();
/**
* Returns the number of offer/put/add operations.
* Offers returning false will be included.
* #getRejectedOfferOperationCount can be used
* to get the rejected offers.
*/
long getOfferOperationCount();
/**
* Returns the number of rejected offers. Offer
* can be rejected because of max-size limit
* on the queue.
*/
long getRejectedOfferOperationCount();
/**
* Returns the number of poll/take/remove operations.
* Polls returning null (empty) will be included.
* #getEmptyPollOperationCount can be used to get the
* number of polls returned null.
*/
long getPollOperationCount();
/**
* Returns number of null returning poll operations.
* Poll operation might return null, if the queue is empty.
*/
long getEmptyPollOperationCount();
/**
* Returns number of other operations
*/
long getOtherOperationsCount();
/**
* Returns number of event operations
*/
long getEventOperationCount();
The ITopic
interface has a getLocalTopicStats()
method which returns a
LocalTopicStats
object that holds local topic statistics.
HazelcastInstance node = Hazelcast.newHazelcastInstance();
ITopic<Object> news = node.getTopic( "news" );
LocalTopicStats topicStatistics = news.getLocalTopicStats();
System.out.println( "number of publish operations = "
+ topicStatistics.getPublishOperationCount() );
Below is the list of metrics that you can access via the LocalTopicStats
object.
/**
* Returns the creation time of this topic on this member
*/
long getCreationTime();
/**
* Returns the total number of published messages of this topic on this member
*/
long getPublishOperationCount();
/**
* Returns the total number of received messages of this topic on this member
*/
long getReceiveOperationCount();
The IExecutorService
interface has a getLocalExecutorStats()
method which returns a
LocalExecutorStats
object that holds local executor statistics.
HazelcastInstance node = Hazelcast.newHazelcastInstance();
IExecutorService orderProcessor = node.getExecutorService( "orderProcessor" );
LocalExecutorStats executorStatistics = orderProcessor.getLocalExecutorStats();
System.out.println( "completed task count = "
+ executorStatistics.getCompletedTaskCount() );
Below is the list of metrics that you can access via the LocalExecutorStats
object.
/**
* Returns the number of pending operations of the executor service
*/
long getPendingTaskCount();
/**
* Returns the number of started operations of the executor service
*/
long getStartedTaskCount();
/**
* Returns the number of completed operations of the executor service
*/
long getCompletedTaskCount();
/**
* Returns the number of cancelled operations of the executor service
*/
long getCancelledTaskCount();
/**
* Returns the total start latency of operations started
*/
long getTotalStartLatency();
/**
* Returns the total execution time of operations finished
*/
long getTotalExecutionLatency();
Hazelcast members expose various management beans which include statistics about distributed data structures and the states of Hazelcast node internals.
The metrics are local to the nodes, i.e. they do not reflect cluster wide values.
You can find the JMX API definition below with descriptions and the API methods in parenthesis.
Atomic Long (IAtomicLong
)
name
)currentValue
)set(v)
)addAndGet(v)
)compareAndSet(e,v)
)decrementAndGet()
)getAndAdd(v)
)getAndIncrement()
)getAndSet(v)
)incrementAndGet()
)partitionKey
)Atomic Reference ( IAtomicReference
)
name
)partitionKey
)Countdown Latch ( ICountDownLatch
)
name
)count
)countDown()
)partitionKey
)Executor Service ( IExecutorService
)
localPendingTaskCount
)localStartedTaskCount
)localCompletedTaskCount
)localCancelledTaskCount
)localTotalStartLatency
)localTotalExecutionLatency
)List ( IList
)
name
)clear
)totalAddedItemCount
)totalRemovedItemCount
)Lock ( ILock
)
name
)lockObject
)partitionKey
)Map ( IMap
)
name
)size
)config
)localOwnedEntryCount
)localOwnedEntryMemoryCost
)localBackupEntryCount
)localBackupEntryMemoryCost
)localBackupCount
)localCreationTime
)localLastAccessTime
)localLastUpdateTime
)localHits
)localLockedEntryCount
)localDirtyEntryCount
)localPutOperationCount
)localGetOperationCount
)localRemoveOperationCount
)localTotalPutLatency
)localTotalGetLatency
)localTotalRemoveLatency
)localMaxPutLatency
)localMaxGetLatency
)localMaxRemoveLatency
)localEventOperationCount
)localOtherOperationCount
)localTotal
)localHeapCost
)totalAddedEntryCount
)totalRemovedEntryCount
)totalUpdatedEntryCount
)totalEvictedEntryCount
)clear()
)values(p)
)entrySet(p)
)MultiMap ( MultiMap
)
name
)size
)localOwnedEntryCount
)localOwnedEntryMemoryCost
)localBackupEntryCount
)localBackupEntryMemoryCost
)localBackupCount
)localCreationTime
)localLastAccessTime
)localLastUpdateTime
)localHits
)localLockedEntryCount
)localPutOperationCount
)localGetOperationCount
)localRemoveOperationCount
)localTotalPutLatency
)localTotalGetLatency
)localTotalRemoveLatency
)localMaxPutLatency
)localMaxGetLatency
)localMaxRemoveLatency
)localEventOperationCount
)localOtherOperationCount
)localTotal
)clear()
)Replicated Map ( ReplicatedMap
)
name
)size
)config
)localOwnedEntryCount
)localCreationTime
)localLastAccessTime
)localLastUpdateTime
)localHits
)localPutOperationCount
)localGetOperationCount
)localRemoveOperationCount
)localTotalPutLatency
)localTotalGetLatency
)localTotalRemoveLatency
)localMaxPutLatency
)localMaxGetLatency
)localMaxRemoveLatency
)localEventOperationCount
)localReplicationEventCount
)localOtherOperationCount
)localTotal
)totalAddedEntryCount
)totalRemovedEntryCount
)totalUpdatedEntryCount
)clear()
)values()
)entrySet()
)Queue ( IQueue
)
name
)QueueConfig
)partitionKey
)localOwnedItemCount
)localBackupItemCount
)localMinAge
)localMaxAge
)localAveAge
)localOfferOperationCount
)localRejectedOfferOperationCount
)localPollOperationCount
)localEmptyPollOperationCount
)localOtherOperationsCount
)localEventOperationCount
)totalAddedItemCount
)totalRemovedItemCount
)clear()
)Semaphore ( ISemaphore
)
name
)available
)partitionKey
)drain()
)reduce(v)
)release(v)
)Set ( ISet
)
name
)partitionKey
)totalAddedItemCount
)totalRemovedItemCount
)clear()
)Topic ( ITopic
)
name
)config
)localCreationTime
)localPublishOperationCount
)localReceiveOperationCount
)totalMessageCount
)Hazelcast Instance ( HazelcastInstance
)
name
)version
)build
)config
)configSource
)groupName
)port
)clusterTime
)memberCount
)Members
)running
)shutdown()
)HazelcastInstance.Node
)address
)masterAddress
)HazelcastInstance.EventService
)eventThreadCount
)eventQueueSize
)eventQueueCapacity
)Operation Service ( HazelcastInstance.OperationService
)
responseQueueSize
)operationExecutorQueueSize
)runningOperationsCount
)remoteOperationCount
)executedOperationCount
)operationThreadCount
)Proxy Service ( HazelcastInstance.ProxyService
)
proxyCount
)Partition Service ( HazelcastInstance.PartitionService
)
partitionCount
)activePartitionCount
)isClusterSafe
)isLocalMemberSafe
)Connection Manager ( HazelcastInstance.ConnectionManager
)
clientConnectionCount
)activeConnectionCount
)connectionCount
)Client Engine ( HazelcastInstance.ClientEngine
)
clientEndpointCount
)System Executor ( HazelcastInstance.ManagedExecutorService
)
name
)queueSize
)poolSize
)maximumPoolSize
)remainingQueueCapacity
)isShutdown
)isTerminated
)completedTaskCount
) Operation Executor ( HazelcastInstance.ManagedExecutorService
)
name
)queueSize
)poolSize
)maximumPoolSize
)remainingQueueCapacity
)isShutdown
)isTerminated
)completedTaskCount
)Async Executor (HazelcastInstance.ManagedExecutorService
)
name
)queueSize
)poolSize
)maximumPoolSize
)remainingQueueCapacity
)isShutdown
)isTerminated
)completedTaskCount
)Scheduled Executor ( HazelcastInstance.ManagedExecutorService
)
name
)queueSize
)poolSize
)maximumPoolSize
)remainingQueueCapacity
)isShutdown
)isTerminated
)completedTaskCount
)Client Executor ( HazelcastInstance.ManagedExecutorService
)
name
)queueSize
)poolSize
)maximumPoolSize
)remainingQueueCapacity
)isShutdown
)isTerminated
)completedTaskCount
)Query Executor ( HazelcastInstance.ManagedExecutorService
)
name
)queueSize
)poolSize
)maximumPoolSize
)remainingQueueCapacity
)isShutdown
)isTerminated
)completedTaskCount
)IO Executor ( HazelcastInstance.ManagedExecutorService
)
name
)queueSize
)poolSize
)maximumPoolSize
)remainingQueueCapacity
)isShutdown
)isTerminated
)completedTaskCount
)You can monitor your Hazelcast members via the JMX protocol.
Add the following system properties to enable JMX agent:
-Dcom.sun.management.jmxremote
-Dcom.sun.management.jmxremote.port=\_portNo\_
(to specify JMX port) (optional)-Dcom.sun.management.jmxremote.authenticate=false
(to disable JMX auth) (optional)Enable the Hazelcast property hazelcast.jmx
(please refer to the System Properties section):
-Dhazelcast.jmx=true
Use jconsole, jvisualvm (with mbean plugin) or another JMX compliant monitoring tool.
This section provides information on programmatic utilities you can use to listen to the cluster events, to check whether the cluster and/or members are safe before shutting down a member, and to define the minimum number of cluster members required for the cluster to remain up and running.
Hazelcast allows you to register for membership events so you will be notified when members are added or removed. You can also get the set of cluster members.
import com.hazelcast.core.*;
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
Cluster cluster = hazelcastInstance.getCluster();
cluster.addMembershipListener( new MembershipListener() {
public void memberAdded( MembershipEvent membershipEvent ) {
System.out.println( "MemberAdded " + membershipEvent );
}
public void memberRemoved( MembershipEvent membershipEvent ) {
System.out.println( "MemberRemoved " + membershipEvent );
}
} );
Member localMember = cluster.getLocalMember();
System.out.println ( "my inetAddress= " + localMember.getInetAddress() );
Set setMembers = cluster.getMembers();
for ( Member member : setMembers ) {
System.out.println( "isLocalMember " + member.localMember() );
System.out.println( "member.inetaddress " + member.getInetAddress() );
System.out.println( "member.port " + member.getPort() );
}
RELATED INFORMATION
Please refer to the Membership Listener section for more information on membership events.
You can define various member attributes on your Hazelcast members. You can use these member attributes to tag your members as your business logic requirements.
In order to define member attribute on a member you can either:
provide MemberAttributeConfig
to your Config
object,
or provide member attributes at runtime via attribute setter methods on the Member
interface.
For example, you can tag your members with their CPU characteristics and you can route CPU intensive tasks to those CPU-rich members.
MemberAttributeConfig fourCore = new MemberAttributeConfig();
memberAttributeConfig.setIntAttribute( "CPU_CORE_COUNT", 4 );
MemberAttributeConfig twelveCore = new MemberAttributeConfig();
memberAttributeConfig.setIntAttribute( "CPU_CORE_COUNT", 12 );
MemberAttributeConfig twentyFourCore = new MemberAttributeConfig();
memberAttributeConfig.setIntAttribute( "CPU_CORE_COUNT", 24 );
Config member1Config = new Config();
config.setMemberAttributeConfig( fourCore );
Config member2Config = new Config();
config.setMemberAttributeConfig( twelveCore );
Config member3Config = new Config();
config.setMemberAttributeConfig( twentyFourCore );
HazelcastInstance member1 = Hazelcast.newHazelcastInstance( member1Config );
HazelcastInstance member2 = Hazelcast.newHazelcastInstance( member2Config );
HazelcastInstance member3 = Hazelcast.newHazelcastInstance( member3Config );
IExecutorService executorService = member1.getExecutorService( "processor" );
executorService.execute( new CPUIntensiveTask(), new MemberSelector() {
@Override
public boolean select(Member member) {
int coreCount = (int) member.getIntAttribute( "CPU_CORE_COUNT" );
// Task will be executed at either member2 or member3
if ( coreCount > 8 ) {
return true;
}
return false;
}
} );
HazelcastInstance member4 = Hazelcast.newHazelcastInstance();
// We can also set member attributes at runtime.
member4.setIntAttribute( "CPU_CORE_COUNT", 2 );
To prevent data loss when shutting down a node, Hazelcast provides a graceful shutdown feature. You perform this by calling the method HazelcastInstance.shutdown()
. Once this method is called, it checks the following conditions to ensure the node is safe to shutdown.
Even if the above conditions are not met, HazelcastInstance.shutdown()
will force them to be completed. Eventually, when this method returns, it means the node has been brought to a safe state and it can be shut down without any data loss.
What if you want to be sure that your cluster is in a safe state? What does it mean that cluster is safe to shutdown without any data loss?
There may be some use cases like rolling upgrades, development/testing or any logic that require a cluster/member to be safe. To provide this, Hazelcast offers the PartitionService
interface with the methods isClusterSafe
, isMemberSafe
, isLocalMemberSafe
and forceLocalMemberToBeSafe
. These methods can be deemed as decoupled pieces from the method Hazelcast.shutdown
.
public interface PartitionService {
...
...
boolean isClusterSafe();
boolean isMemberSafe(Member member);
boolean isLocalMemberSafe();
boolean forceLocalMemberToBeSafe(long timeout, TimeUnit unit);
}
The method isClusterSafe
checks whether the cluster is in a safe state. It returns true
if there are no active partition migrations and there are sufficient backups for each partition. Once it returns true
, the cluster is safe and a node can be shut down without data loss.
The method isMemberSafe
checks whether a specific node is in a safe state. This check controls if the first backups of partitions of the given node are synced with the primary ones. Once it returns true
, the given node is safe and it can be shut down without data loss. Similarly, the method isLocalMemberSafe
does the same check for the local member. The method forceLocalMemberToBeSafe
forces the owned and backup partitions to be synchronized, making the local member safe.
NOTE: These methods are available from Hazelcast 3.3.
PartitionService partitionService = hazelcastInstance.getPartitionService().isClusterSafe()
if (partitionService().isClusterSafe()) {
hazelcastInstance.shutdown(); // or terminate
}
OR
PartitionService partitionService = hazelcastInstance.getPartitionService().isClusterSafe()
if (partitionService().isLocalMemberSafe()) {
hazelcastInstance.shutdown(); // or terminate
}
RELATED INFORMATION
For more code samples please refer to PartitionService Code Samples.
Hazelcast Cluster Quorum enables you to define the minimum number of machines required in a cluster for the cluster to remain in an operational state. If the number of machines is below the defined minimum at any time, the operations are rejected and the rejected operations return a QuorumException
to their callers.
When a network partitioning happens, by default Hazelcast chooses to be available. With Cluster Quorum, you can tune your Hazelcast instance towards to achieve better consistency, by rejecting updates with a minimum threshold, this reduces the chance that number of concurrent updates to an entry from two partitioned clusters . Note that the consistency defined here is best effort not full or strong consistency.
Hazelcast initiates a quorum when a change happens on the member list.
NOTE: Currently cluster quorum only applies to the Map and Transactional Map, support for other data structures will be added soon. Also lock methods in the IMap interface do not participate in a quorum.
You can set up Cluster Quorum using either declarative or programmatic configuration.
Assume that you have a 5-node Hazelcast Cluster and you want to set the minimum number of 3 nodes for cluster to continue operating. The following are example configurations for this scenario.
<hazelcast>
....
<quorum name="quorumRuleWithThreeNodes" enabled=true>
<quorum-size>3</quorum-size>
</quorum>
<map name="default">
<quorum-name>quorumRuleWithThreeNodes</quorum-name>
</map>
....
</hazelcast>
QuorumConfig quorumConfig = new QuorumConfig();
quorumConfig.setName("quorumRuleWithThreeNodes")
quorumConfig.setEnabled(true);
quorumConfig.setSize(3);
MapConfig mapConfig = new MapConfig();
mapConfig.setQuorumName("quorumRuleWithThreeNodes");
Config config = new Config();
config.addQuorumConfig(quorumConfig);
config.addMapConfig(mapConfig);
You can register quorum listeners to be notified about quorum results. Quorum listeners are local to the node that they are registered, so they receive only events occurred on that local node.
Quorum listeners can be configured via declarative or programmatic configuration. The following are the example configurations.
<hazelcast>
....
<quorum name="quorumRuleWithThreeNodes" enabled=true>
<quorum-size>3</quorum-size>
<quorum-listeners>
<quorum-listener>com.company.quorum.ThreeNodeQuorumListener </quorum-listener>
</quorum-listeners>
</quorum>
<map name="default">
<quorum-name>quorumRuleWithThreeNodes</quorum-name>
</map>
....
</hazelcast>
QuorumListenerConfig listenerConfig = new QuorumListenerConfig();
// You can either directly set quorum listener implementation of your own
listenerConfig.setImplementation(new QuorumListener() {
@Override
public void onChange(QuorumEvent quorumEvent) {
if (QuorumResult.PRESENT.equals(quorumEvent.getType())) {
// handle quorum presence
} else if (QuorumResult.ABSENT.equals(quorumEvent.getType())) {
// handle quorum absence
}
}
});
// Or you can give the name of the class that implements QuorumListener interface.
listenerConfig.setClassName("com.company.quorum.ThreeNodeQuorumListener");
QuorumConfig quorumConfig = new QuorumConfig();
quorumConfig.setName("quorumRuleWithThreeNodes")
quorumConfig.setEnabled(true);
quorumConfig.setSize(3);
quorumConfig.addListenerConfig(listenerConfig);
MapConfig mapConfig = new MapConfig();
mapConfig.setQuorumName("quorumRuleWithThreeNodes");
Config config = new Config();
config.addQuorumConfig(quorumConfig);
config.addMapConfig(mapConfig);
Quorum service gives an ability to query quorum results over the Quorum
instances.
Quorum instances let you to query quorum result of a particular quorum.
Here is the Quorum interface that you can interact with.
/**
* {@link Quorum} provides access to the current status of a quorum.
*/
public interface Quorum {
/**
* Returns true if quorum is present, false if absent.
*
* @return boolean presence of the quorum
*/
boolean isPresent();
}
You can retrieve quorum instance for a particular quorum over the quorum service. An example can be seen below
String quorumName = "at-least-one-storage-member";
QuorumConfig quorumConfig = new QuorumConfig();
quorumConfig.setName(quorumName)
quorumConfig.setEnabled(true);
MapConfig mapConfig = new MapConfig();
mapConfig.setQuorumName(quorumName);
Config config = new Config();
config.addQuorumConfig(quorumConfig);
config.addMapConfig(mapConfig);
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance(config);
QuorumService quorumService = hazelcastInstance.getQuorumService();
Quorum quorum = quorumService.getQuorum(quorumName);
boolean quorumPresence = quorum.isPresent();
Hazelcast Management Center enables you to monitor and manage your nodes running Hazelcast. In addition to monitoring overall state of your clusters, you can also analyze and browse your data structures in detail, update map configurations and take thread dump from nodes. With its scripting and console module, you can run scripts (JavaScript, Groovy, etc.) and commands on your nodes.
You have two options for installing Hazelcast Management Center. You can either deploy the mancenter
-version.war
application into your Java application server/container or you can start Hazelcast Management Center from the command line and then have the Hazelcast nodes communicate with that web application. This means that your Hazelcast nodes should know the URL of the mancenter
application before they start.
Here are the steps:
mancenter
-version.war
file. mancenter
-version.war
file from the command line. The following command will start Hazelcast Management Center on port 8080 with context root 'mancenter' (http://localhost:8080/mancenter
).java -jar mancenter-*version*.war 8080 mancenter
http://localhost:8080/mancenter
.http://localhost:8080/mancenter
is up.hazelcast.xml
. Hazelcast nodes will send their states to this URL.<management-center enabled="true">
http://localhost:8080/mancenter
</management-center>
http://localhost:8080/mancenter
and login. Initial login username/password is admin/admin
The Management Center creates a folder with the name "mancenter" under your "user/home" folder to save data files. You can change the data folder by setting the hazelcast.mancenter.home
system property.
RELATED INFORMATION
Please refer to the Management Center Configuration section for a full description of Hazelcast Management Center configuration.
Once the page is loaded after selecting a cluster, the tool's home page appears as shown below.
This page provides the fundamental properties of the selected cluster which are explained in the Home Page section. The page has a toolbar on the top and a menu on the left.
The toolbar has the following buttons:
Cluster Selector: Switches between clusters. When the mouse is moved onto this item, a drop down list of clusters appears.
The user can select any cluster and once selected, the page immediately loads with the selected cluster's information.
NOTE: Some of the above listed toolbar items are not visible to users who are not admin or who have read-only permission. Also, some of the operations explained in the later sections cannot be performed by users with read-only permission. Please see the Administration section for details.
The Home page includes a menu on the left which lists the distributed data structures in the cluster and all the cluster members (nodes), as shown below.
NOTE: Distributed data structures will be shown there when the proxies are created for them.
You can expand and collapse menu items by clicking on them. Below is the list of menu items with links to their explanations.
Each time you select an item from the toolbar or menu, the item is added to the main view as a tab, as shown below.
In the above example, Home, Scripting, Console, queue1 and map1 windows can be seen as tabs. Windows can be closed using the icon on each tab (except the Home Page; it cannot be closed).
This is the first page appearing after logging in. It gives an overview of the connected cluster. The following subsections describe each portion of the page.
This part of the page provides load and utilization information for the CPUs for each node, as shown below.
The first column lists the nodes with their IPs and ports. The next columns list the loads on each CPU for the last 1, 5 and 15 minutes. The last column (Chart) graphically shows the utilization of CPUs. When you move the mouse cursor on a desired graph, you can see the CPU utilization at the time where the cursor is placed. Graphs under this column shows the CPU utilizations approximately for the last 2 minutes.
This part of the page provides information related to memory usages for each node, as shown below.
The first column lists the nodes with their IPs and ports. The next columns show the used and free memories out of the total memory reserved for Hazelcast usage, in real-time. The Max column lists the maximum memory capacity of each node and the Percent column lists the percentage value of used memory out of the maximum memory. The last column (Chart) shows the memory usage of nodes graphically. When you move the mouse cursor on a desired graph, you can see the memory usage at the time where the cursor is placed. Graphs under this column shows the memory usages approximately for the last 2 minutes.
This part of the page graphically provides the cluster wise breakdown of memory, as shown below. The blue area is the memory used by maps, the dark yellow area is the memory used by non-Hazelcast entities, and the green area is the free memory out of the whole cluster`s memory capacity.
In the above example, you can see 0.32% of the total memory is used by Hazelcast maps (it can be seen by placing the mouse cursor on it), 58.75% is used by non-Hazelcast entities and 40.85% of the total memory is free.
This part is the breakdown of the blue area shown in the Memory Distribution graph explained above. It provides the percentage values of the memories used by each map, out of the total cluster memory reserved for all Hazelcast maps.
In the above example, you can see 49.55% of the total map memory is used by map1 and 49.55% is used by map2.
This pie chart shows what percentage of partitions each node has, as shown below.
You can see each node's partition percentages by placing the mouse cursor on the chart. In the above example, you can see the node "127.0.0.1:5708" has 5.64% of the total partition count (which is 271 by default and configurable, please see the hazelcast.partition.count
property explained in the System Properties section).
You can monitor your caches' metrics by clicking the cache name listed on the left panel under Caches menu item. A new tab for monitoring that cache instance is opened on the right, as shown below.
On top of the page, four charts monitor the Gets, Puts, Removals and Evictions in real-time. The X-axis of all the charts show the current system time. To open a chart as a separate dialog, click on the button placed at the top right of each chart.
Under these charts is the Cache Statistics Data Table. From left to right, this table lists the IP addresses and ports of each node, get, put, removal, eviction, hit and miss count per second in real-time.
You can navigate through the pages using the buttons at the bottom right of the table (First, Previous, Next, Last). You can ascend or descend the order of the listings in each column by clicking on column headings.
Map instances are listed under the Maps menu item on the left. When you click on a map, a new tab for monitoring that map instance opens on the right, as shown below. In this tab, you can monitor metrics and also re-configure the selected map.
The below subsections explain the portions of this window.
Map Browser is a tool you can use to retrieve properties of the entries stored in the selected map. To open it, click on the Map Browser button, located at the top right of the window. Once opened, the tool appears as a dialog, as shown below.
Once the key and key's type is specified and the Browse button is clicked, the key's properties along with its value are listed.
By using the Map Config tool, you can set selected map's attributes like the backup count, TTL, and eviction policy. To open it, click on the Map Config button, located at the top right of the window. Once opened, the tool appears as a dialog, as shown below.
Change any attribute as required and click the Update button to save changes.
Besides Map Browser and Map Config tools, this page has monitoring options explained below. All of these options perform real-time monitoring.
On top of the page, small charts monitor the size, throughput, memory usage, backup size, etc. of the selected map in real-time. The X-axis of all the charts show the current system time. You can select other small monitoring charts using the button at the top right of each chart. When you click the button, the monitoring options are listed, as shown below.
When you click on a desired monitoring, the chart is loaded with the selected option. To open a chart as a separate dialog, click on the button placed at the top right of each chart. The monitoring charts below are available:
Under these charts are Map Memory and Map Throughput data tables. The Map Memory data table provides memory metrics distributed over nodes, as shown below.
From left to right, this table lists the IP address and port, entry counts, memory used by entries, backup entry counts, memory used by backup entries, events, hits, locks and dirty entries (in the cases where MapStore is enabled, these are the entries that are put to/removed from the map but not written to/removed from a database yet) of each node in the map. You can navigate through the pages using the buttons at the bottom right of the table (First, Previous, Next, Last). You can ascend or descend the order of the listings by clicking on the column headings.
Map Throughput data table provides information about the operations (get, put, remove) performed on each node in the map, as shown below.
From left to right, this table lists the IP address and port of each node, the put, get and remove operations on each node, the average put, get, remove latencies, and the maximum put, get, remove latencies on each node.
You can select the period in the combo box placed at the top right corner of the window, for which the table data will be shown. Available values are Since Beginning, Last Minute, Last 10 Minutes and Last 1 Hour.
You can navigate through the pages using the buttons placed at the bottom right of the table (First, Previous, Next, Last). To ascend or descent the order of the listings, click on the column headings.
Using the menu item Queues, you can monitor your queues data structure. When you expand this menu item and click on a queue, a new tab for monitoring that queue instance is opened on the right, as shown below.
On top of the page, small charts monitor the size, offers and polls of the selected queue in real-time. The X-axis of all the charts shows the current system time. To open a chart as a separate dialog, click on the button placed at the top right of each chart. The monitoring charts below are available:
Under these charts are Queue Statistics and Queue Operation Statistics tables. The Queue Statistics table provides item and backup item counts in the queue and age statistics of items and backup items at each node, as shown below.
From left to right, this table lists the IP address and port, items and backup items on the queue of each node, and maximum, minimum and average age of items in the queue. You can navigate through the pages using the buttons placed at the bottom right of the table (First, Previous, Next, Last). The order of the listings in each column can be ascended or descended by clicking on column headings.
Queue Operations Statistics table provides information about the operations (offers, polls, events) performed on the queues, as shown below.
From left to right, this table lists the IP address and port of each node, and counts of offers, rejected offers, polls, poll misses and events.
You can select the period in the combo box placed at the top right corner of the window to show the table data. Available values are Since Beginning, Last Minute, Last 10 Minutes and Last 1 Hour.
You can navigate through the pages using the buttons placed at the bottom right of the table (First, Previous, Next, Last). Click on the column headings to ascend or descend the order of the listings.
To monitor your topics' metrics, click the topic name listed on the left panel under the Topics menu item. A new tab for monitoring that topic instance opens on the right, as shown below.
On top of the page, two charts monitor the Publishes and Receives in real-time. They show the published and received message counts of the cluster, nodes of which are subscribed to the selected topic. The X-axis of both charts show the current system time. To open a chart as a separate dialog, click on the button placed at the top right of each chart.
Under these charts is the Topic Operation Statistics table. From left to right, this table lists the IP addresses and ports of each node, and counts of message published and receives per second in real-time. You can select the period in the combo box placed at top right corner of the table to show the table data. The available values are Since Beginning, Last Minute, Last 10 Minutes and Last 1 Hour.
You can navigate through the pages using the buttons placed at the bottom right of the table (First, Previous, Next, Last). Click on the column heading to ascend or descend the order of the listings.
MultiMap is a specialized map where you can associate a key with multiple values. This monitoring option is similar to the Maps option: the same monitoring charts and data tables monitor MultiMaps. The differences are that you cannot browse the MultiMaps and re-configure it. Please see the Maps section.
Executor instances are listed under the Executors menu item on the left. When you click on a executor, a new tab for monitoring that executor instance opens on the right, as shown below.
On top of the page, small charts monitor the pending, started, completed, etc. executors in real-time. The X-axis of all the charts shows the current system time. You can select other small monitoring charts using the button placed at the top right of each chart. When it is clicked, the monitoring options are listed, as shown below.
When you click on a desired monitoring, the chart loads with the selected option. To open a chart as a separate dialog, click on the button placed at top right of each chart. The below monitoring charts are available:
Under these charts is the Executor Operation Statistics table, as shown below.
From left to right, this table lists the IP address and port of nodes, the counts of pending, started and completed executors per second, and the execution time and average start latency of executors on each node. You can navigate through the pages using the buttons placed at the bottom right of the table (First, Previous, Next, Last). Click on the column heading to ascend or descend the order of the listings.
Use this menu item to monitor each cluster member (node) and perform operations like running garbage collection (GC) and taking a thread dump. Once you select a member from the menu, a new tab for monitoring that member opens on the right, as shown below.
The CPU Utilization chart shows the percentage of CPU usage on the selected member. The Memory Utilization chart shows the memory usage on the selected member with three different metrics (maximum, used and total memory). You can open both of these charts as separate windows using the button placed at top right of each chart; this gives you a clearer view of the chart.
The window titled Partitions shows which partitions are assigned to the selected member. Runtime is a dynamically updated window tab showing the processor number, the start and up times, and the maximum, total and free memory sizes of the selected member. Next to this, the Properties tab shows the system properties. The Member Configuration window shows the connected Hazelcast cluster's XML configuration.
The List of Slow Operations gives an overview of detected slow operations which occurred on that member. The data is collected by the SlowOperationDetector.
By clicking on an entry you can open a dialog which shows the stacktrace and detailed information about each slow invocation of this operation.
Besides the aforementioned monitoring charts and windows, you can also perform operations on the selected member through this page. The operation buttons are located at the top right of the page, as explained below:
You can use the scripting feature of this tool to execute codes on the cluster. To open this feature as a tab, select Scripting located at the toolbar on top. Once selected, the scripting feature opens as shown below.
In this window, the Scripting part is the actual coding editor. You can select the members on which the code will execute from the Members list shown at the right side of the window. Below the members list, a combo box enables you to select a scripting language: currently, JavaScript, Ruby, Groovy and Python languages are supported. After you write your script and press the Execute button, you can see the execution result in the Result part of the window.
NOTE: To use the scripting languages other than JavaScript on a member, the libraries for those languages should be placed in the classpath of that member.
There are Save and Delete buttons on the top right of the scripting editor. To save your scripts, press the Save button after you type a name for your script into the field next to this button. The scripts you saved are listed in the Saved Scripts part of the window, located at the bottom right of the page. Click on a saved script from this list to execute or edit it. If you want to remove a script that you wrote and saved before, select it from this list and press the Delete button.
In the scripting engine you have a HazelcastInstance
bonded to a variable named hazelcast
. You can invoke any method that HazelcastInstance
has via the hazelcast
variable. You can see example usage for JavaScript below.
var name = hazelcast.getName();
var node = hazelcast.getCluster().getLocalMember();
var employees = hazelcast.getMap("employees");
employees.put("1","John Doe");
employees.get("1"); // will return "John Doe"
The Management Center has a console feature that enables you to execute commands on the cluster. For example, you can perform put
s and get
s on a map, after you set the namespace with the command ns <name of your map>
. The same is valid for queues, topics, etc. To execute your command, type it into the field below the console and press Enter. Type help
to see all the commands that you can use.
Open a console window by clicking on the Console button located on the toolbar. Below is a sample view with some executed commands.
You can use the alerts feature of this tool to receive alerts by creating filters. In these filters, you can specify criteria for cluster, nodes or data structures. When the specified criteria are met for a filter, the related alert is shown as a pop-up message on the top right of the page.
Once you click the Alerts button located on the toolbar, the page shown below appears.
Creating Filters for Cluster
Select the Cluster Alerts check box to create a cluster wise filter. Once selected, the next screen asks for the items for which alerts will be created, as shown below.
Select the desired items and click the Next button. On the next page (shown below), specify the frequency of checks in hour and min fields, give a name for the filter, select whether notification e-mails will be sent (to no one, only admin or to all users) and select whether the alert data will be written to the disk (if checked, you can see the alert log at the folder /users/
Click on the Save button; your filter will be saved and put into the Filters part of the page, as shown below.
To edit the filter, click on the icon. To delete the filter, click on the icon.
Creating Filters for Cluster Members
Select Member Alerts check box to create filters for some or all members in the cluster. Once selected, the next screen asks for which members the alert will be created. Select the desired members and click on the Next button. On the next page (shown below), specify the criteria.
Alerts can be created when:
When two or more criteria is specified they will be bound with the logical operator AND.
On the next page, give a name for the filter, select whether notification e-mails will be sent (to no one, only admin, or to all users) and select whether the alert data will be written to the disk (if checked, you can see the alert log at the folder /users/
Click on the Save button; your filter will be saved and put into the Filters part of the page. To edit the filter, click on the icon. To delete it, click on the icon.
Creating Filters for Data Types
Select the Data Type Alerts check box to create filters for data structures. The next screen asks for which data structure (maps, queues, multimaps, executors) the alert will be created. Once a structure is selected, the next screen immediately loads and you then select the data structure instances (i.e. if you selected Maps, it will list all the maps defined in the cluster, you can select one map or more). Select as desired, click on the Next button, and select the members on which the selected data structure instances will run.
The next screen, as shown below, is the one where you specify the criteria for the selected data structure.
As the screen shown above shows, you will select an item from the left combo box, select the operator in the middle one, specify a value in the input field, and click on the Add button. You can create more than one criteria in this page; those will be bound by the logical operator AND.
After you specify the criteria and click the Next button, give a name for the filter, select whether notification e-mails will be sent (to no one, only admin or to all users) and select whether the alert data will be written to the disk (if checked, you can see the alert log at the folder /users/
Click on the Save button; your filter will be saved and put into the Filters part of the page. To edit the filter, click on the icon. To delete it, click on the icon.
NOTE: This toolbar item is available only to admin users, i.e. the users who initially have **admin** as their both usernames and passwords.
The Admin user can add, edit, and remove users and specify the permissions for the users of Management Center. To perform these operations, click on the Administration button located on the toolbar. The page below appears.
To add a user to the system, specify the username, e-mail and password in the Add/Edit User part of the page. If the user to be added will have administrator privileges, select isAdmin checkbox. Permissions checkboxes have two values:
After you enter/select all fields, click Save button to create the user. You will see the newly created user's username on the left side, in the Users part of the page.
To edit or delete a user, select a username listed in the Users. Selected user information appears on the right side of the page. To update the user information, change the fields as desired and click the Save button. To delete the user from the system, click the Delete button.
Time Travel is used to check the status of the cluster at a time in the past. When this item is selected on the toolbar, a small window appears on top of the page, as shown below.
To see the cluster status in a past time, Time Travel should be enabled first. Click on the area where it says OFF (on the right of Time Travel window). It will turn to ON after it asks whether to enable the Time Travel with a dialog: click on Enable in the dialog to enable Time Travel.
Once it is ON, the status of your cluster will be stored on your disk as long as your web server is alive.
You can go back in time using the slider and/or calendar and check your cluster's situation at the selected time. All data structures and members can be monitored as if you are using the management center normally (charts and data tables for each data structure and members). Using the arrow buttons placed at both sides of the slider, you can go back or further with steps of 5 seconds. It will show status if Time Travel has been ON at the selected time in past; otherwise, all the charts and tables will be shown as empty.
The historical data collected with Time Travel feature are stored in a file database on the disk. These files can be found in the folder <User's Home Directory>/mancenter<Hazelcast version>
, e.g. /home/mancenter3.5
. This folder can be changed using the hazelcast.mancenter.home
property on the server where Management Center is running.
Time travel data files are created monthly. Their file name format is [group-name]-[year][month].db
and
[group-name]-[year][month].lg
. Time travel data is kept in the *.db
files. The files with the extension lg
are temporary files created internally and you do not have to worry about them.
Management Center has no automatic way of removing or archiving old time travel data files. They remain in the aforementioned folder until you delete or archive them.
To see the documentation, click on the Documentation button located at the toolbar. Management Center manual will appear as a tab.
For 2 Nodes
Mancenter Heap Size | # of Maps | # of Queues | # of Topics |
---|---|---|---|
256m | 3k | 1k | 1k |
1024m | 10k | 1k | 1k |
For 10 Nodes
Mancenter Heap Size | # of Maps | # of Queues | # of Topics |
---|---|---|---|
256m | 50 | 30 | 30 |
1024m | 2k | 1k | 1k |
For 20 Nodes
Mancenter Heap Size | # of Maps | # of Queues | # of Topics |
---|---|---|---|
256m* | N/A | N/A | N/A |
1024m | 1k | 1k | 1k |
* With 256m heap, management center is unable to collect statistics.
Clustered JMX via Management Center allows you to monitor clustered statistics of distributed objects from a JMX interface.
In order to configure Clustered JMX, use two command line parameters for your Management Center deployment.
-Dhazelcast.mc.jmx.enabled=true
(default is false)-Dhazelcast.mc.jmx.port=9000
(optional, default is 9999)With embedded Jetty, you do not need to deploy your Management Center application to any container or application server.
You can start Management Center application with Clustered JMX enabled as shown below.
java -Dhazelcast.mc.jmx.enabled=true -Dhazelcast.mc.jmx.port=9999 -jar mancenter-3.3.jar
Once Management Center starts, you should see a log similar to below.
INFO: Management Center 3.3
Jun 05, 2014 11:55:32 AM com.hazelcast.webmonitor.service.jmx.impl.JMXService
INFO: Starting Management Center JMX Service on port :9999
You should be able to connect to Clustered JMX interface from the address localhost:9999
.
You can use jconsole
or any other JMX client to monitor your Hazelcast Cluster. As a sample, below is the jconsole
screenshot of the Clustered JMX hierarchy.
The management beans are exposed with the following object name format.
ManagementCenter[
cluster name]:type=<
object type>,name=<
object name>,member="<
cluster member IP address>"
Object name starts with ManagementCenter
prefix. Then it has the cluster name in brackets followed by a colon. After that, type
,name
and member
attributes follows, each separated with a comma.
type
is the type of object. Values are Clients
, Executors
, Maps
, Members
, MultiMaps
, Queues
, Services
, and Topics
.
name
is the name of object.
member
is the node address of object (only required if the statistics are local to the node).
A sample bean is shown below.
ManagementCenter[dev]:type=Services,name=OperationService,member="192.168.2.79:5701"
Here is the list of attributes that are exposed from the Clustered JMX interface.
Use the Clustered JMX interface to integrate Hazelcast Management Center with New Relic. To perform this integration, attach New Relic Java agent and provide an extension file that describes which metrics will be sent to New Relic.
Please see Custom JMX instrumentation by YAML on the New Relic webpage.
Below is an example Map monitoring .yml
file for New Relic.
name: Clustered JMX
version: 1.0
enabled: true
jmx:
- object_name: ManagementCenter[clustername]:type=Maps,name=mapname
metrics:
- attributes: PutOperationCount, GetOperationCount, RemoveOperationCount, Hits,\
BackupEntryCount, OwnedEntryCount, LastAccessTime, LastUpdateTime
type: simple
- object_name: ManagementCenter[clustername]:type=Members,name="node address in\
double quotes"
metrics:
- attributes: OwnedPartitionCount
type: simple
Put the .yml
file in the extensions
folder in your New Relic installation. If an extensions
folder does not exist there, create one.
After you set your extension, attach the New Relic Java agent and start Management Center as shown below.
java -javaagent:/path/to/newrelic.jar -Dhazelcast.mc.jmx.enabled=true\
-Dhazelcast.mc.jmx.port=9999 -jar mancenter-3.3.jar
If your logging level is set as FINER, you should see the log listing in the file newrelic_agent.log
, which is located in the logs
folder in your New Relic installation. Below is an example log listing.
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINE:
JMX Service : querying MBeans (1)
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINER:
JMX Service : MBeans query ManagementCenter[dev]:type=Members,
name="192.168.2.79:5701", matches 1
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINER:
Recording JMX metric OwnedPartitionCount : 68
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINER:
JMX Service : MBeans query ManagementCenter[dev]:type=Maps,name=orders,
matches 1
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINER:
Recording JMX metric Hits : 46,593
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINER:
Recording JMX metric BackupEntryCount : 1,100
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINER:
Recording JMX metric OwnedEntryCount : 1,100
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINER:
Recording JMX metric RemoveOperationCount : 0
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINER:
Recording JMX metric PutOperationCount : 118,962
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINER:
Recording JMX metric GetOperationCount : 0
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINER:
Recording JMX metric LastUpdateTime : 1,401,962,426,811
Jun 5, 2014 14:18:43 +0300 [72696 62] com.newrelic.agent.jmx.JmxService FINER:
Recording JMX metric LastAccessTime : 1,401,962,426,811
Then you can navigate to your New Relic account and create Custom Dashboards. Please see Creating custom dashboards.
While you are creating the dashboard, you should see the metrics that you are sending to New Relic from Management Center in the Metrics section under the JMX folder.
Use the Clustered JMX interface to integrate Hazelcast Management Center with AppDynamics. To perform this integration, attach AppDynamics Java agent to the Management Center.
For agent installation, refer to Install the App Agent for Java page.
For monitoring on AppDynamics, refer to Using AppDynamics for JMX Monitoring page.
After installing AppDynamics agent, you can start Management Center as shown below.
java -javaagent:/path/to/javaagent.jar -Dhazelcast.mc.jmx.enabled=true\
-Dhazelcast.mc.jmx.port=9999 -jar mancenter-3.3.jar
When Management Center starts, you should see the logs below.
Started AppDynamics Java Agent Successfully.
Hazelcast Management Center starting on port 8080 at path : /mancenter
The Clustered REST API is exposed from Management Center to allow you to monitor clustered statistics of distributed objects.
To enable Clustered REST on your Management Center, pass the following system property at startup. This property is disabled by default.
-Dhazelcast.mc.rest.enabled=true
The entry point for Clustered REST API is /rest/
.
This resource does not have any attributes.
This resource returns a list of clusters that are connected to the Management Center.
/rest/clusters
Request:
curl http://localhost:8083/mancenter/rest/clusters
200
(application/json)Body:
["dev","qa"]
This resource returns information related to the provided cluster name.
/rest/clusters/{clustername}
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/
200
(application/json)Body:
{"masterAddress":"192.168.2.78:5701"}
This resource returns a list of members belonging to the provided clusters.
/rest/clusters/{clustername}/members
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/members
200
(application/json)Body:
["192.168.2.78:5701","192.168.2.78:5702","192.168.2.78:5703","192.168.2.78:5704"]
This resource returns information related to the provided member.
/rest/clusters/{clustername}/members/{member}
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/members/192.168.2.78:5701
200
(application/json)Body:
{
"cluster":"dev",
"name":"192.168.2.78:5701",
"maxMemory":129957888,
"ownedPartitionCount":68,
"usedMemory":60688784,
"freeMemory":24311408,
"totalMemory":85000192,
"connectedClientCount":1,
"master":true
}
/rest/clusters/{clustername}/members/{member}/connectionManager
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/members/192.168.2.78:5701/connectionManager
200
(application/json)Body:
{
"clientConnectionCount":2,
"activeConnectionCount":5,
"connectionCount":5
}
/rest/clusters/{clustername}/members/{member}/operationService
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/members/192.168.2.78:5701/operationService
200
(application/json)Body:
{
"responseQueueSize":0,
"operationExecutorQueueSize":0,
"runningOperationsCount":0,
"remoteOperationCount":1,
"executedOperationCount":461139,
"operationThreadCount":8
}
/rest/clusters/{clustername}/members/{member}/eventService
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/members/192.168.2.78:5701/eventService
200
(application/json)Body:
{
"eventThreadCount":5,
"eventQueueCapacity":1000000,
"eventQueueSize":0
}
/rest/clusters/{clustername}/members/{member}/partitionService
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/members/192.168.2.78:5701/partitionService
200
(application/json)Body:
{
"partitionCount":271,
"activePartitionCount":68
}
/rest/clusters/{clustername}/members/{member}/proxyService
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/members/192.168.2.78:5701/proxyService
200
(application/json)Body:
{
"proxyCount":8
}
/rest/clusters/{clustername}/members/{member}/managedExecutors
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/members/192.168.2.78:5701/managedExecutors
200
(application/json)Body:
["hz:system","hz:scheduled","hz:client","hz:query","hz:io","hz:async"]
/rest/clusters/{clustername}/members/{member}/managedExecutors/{managedExecutor}
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/members/192.168.2.78:5701
/managedExecutors/hz:system
200
(application/json)Body:
{
"name":"hz:system",
"queueSize":0,
"poolSize":0,
"remainingQueueCapacity":2147483647,
"maximumPoolSize":4,
"completedTaskCount":12,
"terminated":false
}
This resource returns a list of clients belonging to the provided cluster.
/rest/clusters/{clustername}/clients
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/clients
200
(application/json)Body:
["192.168.2.78:61708"]
/rest/clusters/{clustername}/clients/{client}
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/clients/192.168.2.78:61708
200
(application/json)Body:
{
"uuid":"6fae7af6-7a7c-4fa5-b165-cde24cf070f5",
"address":"192.168.2.78:61708",
"clientType":"JAVA"
}
This resource returns a list of maps belonging to the provided cluster.
/rest/clusters/{clustername}/maps
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/maps
200
(application/json)Body:
["customers","orders"]
/rest/clusters/{clustername}/maps/{mapName}
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/maps/customers
200
(application/json)Body:
{
"cluster":"dev",
"name":"customers",
"ownedEntryCount":1000,
"backupEntryCount":1000,
"ownedEntryMemoryCost":157890,
"backupEntryMemoryCost":113683,
"heapCost":297005,
"lockedEntryCount":0,
"dirtyEntryCount":0,
"hits":3001,
"lastAccessTime":1403608925777,
"lastUpdateTime":1403608925777,
"creationTime":1403602693388,
"putOperationCount":110630,
"getOperationCount":165945,
"removeOperationCount":55315,
"otherOperationCount":0,
"events":0,
"maxPutLatency":52,
"maxGetLatency":30,
"maxRemoveLatency":21
}
This resource returns a list of multimaps belonging to the provided cluster.
/rest/clusters/{clustername}/multimaps
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/multimaps
200
(application/json)Body:
["customerAddresses"]
/rest/clusters/{clustername}/multimaps/{multimapname}
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/multimaps/customerAddresses
200
(application/json)Body:
{
"cluster":"dev",
"name":"customerAddresses",
"ownedEntryCount":996,
"backupEntryCount":996,
"ownedEntryMemoryCost":0,
"backupEntryMemoryCost":0,
"heapCost":0,
"lockedEntryCount":0,
"dirtyEntryCount":0,
"hits":0,
"lastAccessTime":1403603095521,
"lastUpdateTime":1403603095521,
"creationTime":1403602694158,
"putOperationCount":166041,
"getOperationCount":110694,
"removeOperationCount":55347,
"otherOperationCount":0,
"events":0,
"maxPutLatency":77,
"maxGetLatency":69,
"maxRemoveLatency":42
}
This resource returns a list of queues belonging to the provided cluster.
/rest/clusters/{clustername}/queues
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/queues
200
(application/json)Body:
["messages"]
/rest/clusters/{clustername}/queues/{queueName}
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/queues/messages
200
(application/json)Body:
{
"cluster":"dev",
"name":"messages",
"ownedItemCount":55408,
"backupItemCount":55408,
"minAge":0,
"maxAge":0,
"aveAge":0,
"numberOfOffers":55408,
"numberOfRejectedOffers":0,
"numberOfPolls":0,
"numberOfEmptyPolls":0,
"numberOfOtherOperations":0,
"numberOfEvents":0,
"creationTime":1403602694196
}
This resource returns a list of topics belonging to the provided cluster.
/rest/clusters/{clustername}/topics
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/topics
200
(application/json)Body:
["news"]
/rest/clusters/{clustername}/topics/{topicName}
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/topics/news
200
(application/json)Body:
{
"cluster":"dev",
"name":"news",
"numberOfPublishes":56370,
"totalReceivedMessages":56370,
"creationTime":1403602693411
}
This resource returns a list of executors belonging to the provided cluster.
/rest/clusters/{clustername}/executors
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/executors
200
(application/json)Body:
["order-executor"]
/rest/clusters/{clustername}/executors/{executorName}
Request:
curl http://localhost:8083/mancenter/rest/clusters/dev/executors/order-executor
200
(application/json)Body:
{
"cluster":"dev",
"name":"order-executor",
"creationTime":1403602694196,
"pendingTaskCount":0,
"startedTaskCount":1241,
"completedTaskCount":1241,
"cancelledTaskCount":0
}
This chapter provides information on the security features of Hazelcast. These features allow you to perform security activities including intercepting socket connections and remote operations executed by the clients, encrypting the communications between the members at socket level and using SSL socket communication.
Hazelcast has an extensible, JAAS based security feature you can use to authenticate both cluster members and clients, and to perform access control checks on client operations. Access control can be done according to endpoint principal and/or endpoint address.
You can enable security declaratively or programmatically, as shown below.
<hazelcast xsi:schemaLocation="http://www.hazelcast.com/schema/config
http://www.hazelcast.com/schema/config/hazelcast-config-3.3.xsd"
xmlns="http://www.hazelcast.com/schema/config"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
...
<security enabled="true">
...
</security>
</hazelcast>
Config cfg = new Config();
SecurityConfig securityCfg = cfg.getSecurityConfig();
securityCfg.setEnabled( true );
Also, see Setting License Key.
Hazelcast allows you to intercept socket connections before a node joins to cluster or a client connects to a node. This provides the ability to add custom hooks to join and perform connection procedures (like identity checking using Kerberos, etc.). Implement com.hazelcast.nio.MemberSocketInterceptor
for members and com.hazelcast.nio.SocketInterceptor
for clients.
public class MySocketInterceptor implements MemberSocketInterceptor {
public void init( SocketInterceptorConfig socketInterceptorConfig ) {
// initialize interceptor
}
void onConnect( Socket connectedSocket ) throws IOException {
// do something meaningful when connected
}
public void onAccept( Socket acceptedSocket ) throws IOException {
// do something meaningful when accepted a connection
}
}
<hazelcast>
...
<network>
...
<socket-interceptor enabled="true">
<class-name>com.hazelcast.examples.MySocketInterceptor</class-name>
<properties>
<property name="kerberos-host">kerb-host-name</property>
<property name="kerberos-config-file">kerb.conf</property>
</properties>
</socket-interceptor>
</network>
...
</hazelcast>
public class MyClientSocketInterceptor implements SocketInterceptor {
void onConnect( Socket connectedSocket ) throws IOException {
// do something meaningful when connected
}
}
ClientConfig clientConfig = new ClientConfig();
clientConfig.setGroupConfig( new GroupConfig( "dev", "dev-pass" ) )
.addAddress( "10.10.3.4" );
MyClientSocketInterceptor clientSocketInterceptor = new MyClientSocketInterceptor();
clientConfig.setSocketInterceptor( clientSocketInterceptor );
HazelcastInstance client = HazelcastClient.newHazelcastClient( clientConfig );
Hazelcast allows you to intercept every remote operation executed by the client. This lets you add a very flexible custom security logic. To do this, implement com.hazelcast.security.SecurityInterceptor
.
public class MySecurityInterceptor implements SecurityInterceptor {
public void before( Credentials credentials, String serviceName,
String methodName, Parameters parameters )
throws AccessControlException {
// credentials: client credentials
// serviceName: MapService.SERVICE_NAME, QueueService.SERVICE_NAME, ... etc
// methodName: put, get, offer, poll, ... etc
// parameters: holds parameters of the executed method, iterable.
}
public void after( Credentials credentials, String serviceName,
String methodName, Parameters parameters ) {
// can be used for logging etc.
}
}
The before
method will be called before processing the request on the remote server. The after
method will be called after the processing. Exceptions thrown while executing the before
method will propagate to the client, but exceptions thrown while executing the after
method will be suppressed.
Hazelcast allows you to encrypt the entire socket level communication among all Hazelcast members. Encryption is based on Java Cryptography Architecture. In symmetric encryption, each node uses the same key, so the key is shared. Here is an example configuration for symmetric encryption.
<hazelcast>
...
<network>
...
<!--
Make sure to set enabled=true
Make sure this configuration is exactly the same on
all members
-->
<symmetric-encryption enabled="true">
<!--
encryption algorithm such as
DES/ECB/PKCS5Padding,
PBEWithMD5AndDES,
Blowfish,
DESede
-->
<algorithm>PBEWithMD5AndDES</algorithm>
<!-- salt value to use when generating the secret key -->
<salt>thesalt</salt>
<!-- pass phrase to use when generating the secret key -->
<password>thepass</password>
<!-- iteration count to use when generating the secret key -->
<iteration-count>19</iteration-count>
</symmetric-encryption>
</network>
...
</hazelcast>
RELATED INFORMATION
Please see the SSL section.
Hazelcast allows you to use SSL socket communication among all Hazelcast members. To use it, you need to implement com.hazelcast.nio.ssl.SSLContextFactory
and configure the SSL section in network configuration.
public class MySSLContextFactory implements SSLContextFactory {
public void init( Properties properties ) throws Exception {
}
public SSLContext getSSLContext() {
...
SSLContext sslCtx = SSLContext.getInstance( protocol );
return sslCtx;
}
}
<hazelcast>
...
<network>
...
<ssl enabled="true">
<factory-class-name>
com.hazelcast.examples.MySSLContextFactory
</factory-class-name>
<properties>
<property name="foo">bar</property>
</properties>
</ssl>
</network>
...
</hazelcast>
Hazelcast provides a default SSLContextFactory, com.hazelcast.nio.ssl.BasicSSLContextFactory
, which uses configured keystore to initialize SSLContext
. You define keyStore
and keyStorePassword
, and you can set keyManagerAlgorithm
(default SunX509
), trustManagerAlgorithm
(default SunX509
) and protocol
(default TLS
).
<hazelcast>
...
<network>
...
<ssl enabled="true">
<factory-class-name>
com.hazelcast.nio.ssl.BasicSSLContextFactory
</factory-class-name>
<properties>
<property name="keyStore">keyStore</property>
<property name="keyStorePassword">keyStorePassword</property>
<property name="keyManagerAlgorithm">SunX509</property>
<property name="trustManagerAlgorithm">SunX509</property>
<property name="protocol">TLS</property>
</properties>
</ssl>
</network>
...
</hazelcast>
Hazelcast client also has SSL support. You can configure Client SSL programmatically as shown below.
Properties props = new Properties();
...
ClientConfig config = new ClientConfig();
config.getSocketOptions().setSocketFactory( new SSLSocketFactory( props ) );
You can also set keyStore
and keyStorePassword
with the following system properties.
javax.net.ssl.keyStore
javax.net.ssl.keyStorePassword
NOTE: You cannot use SSL when Hazelcast Encryption is enabled.
One of the key elements in Hazelcast security is the Credentials
object, which is used to carry all credentials of an endpoint (member or client). Credentials is an interface which extends Serializable
and has three methods to implement. You can either implement the Credentials
interface or extend the AbstractCredentials
class, which is an abstract implementation of Credentials
.
package com.hazelcast.security;
public interface Credentials extends Serializable {
String getEndpoint();
void setEndpoint( String endpoint ) ;
String getPrincipal() ;
}
Hazelcast calls the Credentials.setEndpoint()
method when an authentication request arrives at the node before authentication takes place.
package com.hazelcast.security;
...
public abstract class AbstractCredentials implements Credentials, DataSerializable {
private transient String endpoint;
private String principal;
...
}
UsernamePasswordCredentials
, a custom implementation of Credentials, is in the Hazelcast com.hazelcast.security
package. UsernamePasswordCredentials
is used for default configuration during the authentication process of both members and clients.
package com.hazelcast.security;
...
public class UsernamePasswordCredentials extends Credentials {
private byte[] password;
...
}
All security attributes are carried in the Credentials
object. Credentials
is used by LoginModules during the authentication process. User supplied attributes from LoginModule
s are accessed by CallbackHandlers. To access the Credentials
object, Hazelcast uses its own specialized CallbackHandler
. During initialization of LoginModules
, Hazelcast passes this special CallbackHandler
into the LoginModule.initialize()
method.
LoginModule
implementations should create an instance of com.hazelcast.security.CredentialsCallback
and call the handle(Callback[] callbacks)
method of CallbackHandler
during the login process.
CredentialsCallback.getCredentials()
returns the supplied Credentials
object.
public class CustomLoginModule implements LoginModule {
CallbackHandler callbackHandler;
Subject subject;
public void initialize( Subject subject, CallbackHandler callbackHandler,
Map<String, ?> sharedState, Map<String, ?> options ) {
this.subject = subject;
this.callbackHandler = callbackHandler;
}
public final boolean login() throws LoginException {
CredentialsCallback callback = new CredentialsCallback();
try {
callbackHandler.handle( new Callback[] { callback } );
credentials = cb.getCredentials();
} catch ( Exception e ) {
throw new LoginException( e.getMessage() );
}
...
}
...
}
To use the default Hazelcast permission policy, you must create an instance of com.hazelcast.security.ClusterPrincipal
that holds the Credentials
object, and you must add it to Subject.principals onLoginModule.commit()
as shown below.
public class MyCustomLoginModule implements LoginModule {
...
public boolean commit() throws LoginException {
...
Principal principal = new ClusterPrincipal( credentials );
subject.getPrincipals().add( principal );
return true;
}
...
}
Hazelcast has an abstract implementation of LoginModule
that does callback and cleanup operations and holds the resulting Credentials
instance. LoginModule
s extending ClusterLoginModule
can access Credentials
, Subject
, LoginModule
instances and options, and sharedState
maps. Extending the ClusterLoginModule
is recommended instead of implementing all required stuff.
package com.hazelcast.security;
...
public abstract class ClusterLoginModule implements LoginModule {
protected abstract boolean onLogin() throws LoginException;
protected abstract boolean onCommit() throws LoginException;
protected abstract boolean onAbort() throws LoginException;
protected abstract boolean onLogout() throws LoginException;
}
Using the above API, it should be possible to implement a LoginModule
that performs authentication against the Security System of your choice, possibly an LDAP store like Apache Directory or some other corporate standard you have. For example, you may wish to have your clients send an identification token in the Credentials
object. This token can then be sent to your back-end security system via the LoginModule
that runs on the cluster side.
Additionally, the same system may authenticate the user and also then return the roles that are attributed to the user. These roles can then be used for data structure authorization.
RELATED INFORMATION
Please refer to JAAS Reference Guide for further information.
Hazelcast supports standard Java Security (JAAS) based authentication between cluster members. To implement it, you configure one or more LoginModules and an instance of com.hazelcast.security.ICredentialsFactory
. Although Hazelcast has default implementations using cluster group and group-password and UsernamePasswordCredentials on authentication, it is recommended that you implement the LoginModules and an instance of com.hazelcast.security.ICredentialsFactory
according to your specific needs and environment.
<security enabled="true">
<member-credentials-factory
class-name="com.hazelcast.examples.MyCredentialsFactory">
<properties>
<property name="property1">value1</property>
<property name="property2">value2</property>
</properties>
</member-credentials-factory>
<member-login-modules>
<login-module usage="required"
class-name="com.hazelcast.examples.MyRequiredLoginModule">
<properties>
<property name="property3">value3</property>
</properties>
</login-module>
<login-module usage="sufficient"
class-name="com.hazelcast.examples.MySufficientLoginModule">
<properties>
<property name="property4">value4</property>
</properties>
</login-module>
<login-module usage="optional"
class-name="com.hazelcast.examples.MyOptionalLoginModule">
<properties>
<property name="property5">value5</property>
</properties>
</login-module>
</member-login-modules>
...
</security>
You can define as many as LoginModules you wanted in configuration. They are executed in the given order. The usage
attribute has 4 values: 'required', 'requisite', 'sufficient' and 'optional' as defined in javax.security.auth.login.AppConfigurationEntry.LoginModuleControlFlag
.
package com.hazelcast.security;
/**
* ICredentialsFactory is used to create Credentials objects to be used
* during node authentication before connection accepted by master node.
*/
public interface ICredentialsFactory {
void configure( GroupConfig groupConfig, Properties properties );
Credentials newCredentials();
void destroy();
}
Properties defined in configuration are passed to the ICredentialsFactory.configure()
method as java.util.Properties and to the LoginModule.initialize()
method as java.util.Map.
Hazelcast's Client security includes both authentication and authorization.
The authentication mechanism works the same as cluster member authentication. To implement client authentication, configure a Credential and one or more LoginModules. The client side does not have and does not need a factory object to create Credentials objects like ICredentialsFactory
. Credentials must be created at the client side and sent to the connected node during the connection process.
<security enabled="true">
<client-login-modules>
<login-module usage="required"
class-name="com.hazelcast.examples.MyRequiredClientLoginModule">
<properties>
<property name="property3">value3</property>
</properties>
</login-module>
<login-module usage="sufficient"
class-name="com.hazelcast.examples.MySufficientClientLoginModule">
<properties>
<property name="property4">value4</property>
</properties>
</login-module>
<login-module usage="optional"
class-name="com.hazelcast.examples.MyOptionalClientLoginModule">
<properties>
<property name="property5">value5</property>
</properties>
</login-module>
</client-login-modules>
...
</security>
You can define as many as LoginModules
as you want in configuration. Those are executed in the given order. The usage
attribute has 4 values: 'required', 'requisite', 'sufficient' and 'optional' as defined in javax.security.auth.login.AppConfigurationEntry.LoginModuleControlFlag
.
ClientConfig clientConfig = new ClientConfig();
clientConfig.setCredentials( new UsernamePasswordCredentials( "dev", "dev-pass" ) );
HazelcastInstance client = HazelcastClient.newHazelcastClient( clientConfig );
Hazelcast client authorization is configured by a client permission policy. Hazelcast has a default permission policy implementation that uses permission configurations defined in the Hazelcast security configuration. Default policy permission checks are done against instance types (map, queue, etc.), instance names (map, queue, name, etc.), instance actions (put, read, remove, add, etc.), client endpoint addresses, and client principal defined by the Credentials object. Instance and principal names and endpoint addresses can be defined as wildcards(*). Please see the Network Configuration section and Using Wildcard section.
<security enabled="true">
<client-permissions>
<!-- Principal 'admin' from endpoint '127.0.0.1' has all permissions. -->
<all-permissions principal="admin">
<endpoints>
<endpoint>127.0.0.1</endpoint>
</endpoints>
</all-permissions>
<!-- Principals named 'dev' from all endpoints have 'create', 'destroy',
'put', 'read' permissions for map named 'default'. -->
<map-permission name="default" principal="dev">
<actions>
<action>create</action>
<action>destroy</action>
<action>put</action>
<action>read</action>
</actions>
</map-permission>
<!-- All principals from endpoints '127.0.0.1' or matching to '10.10.*.*'
have 'put', 'read', 'remove' permissions for map
whose name matches to 'com.foo.entity.*'. -->
<map-permission name="com.foo.entity.*">
<endpoints>
<endpoint>10.10.*.*</endpoint>
<endpoint>127.0.0.1</endpoint>
</endpoints>
<actions>
<action>put</action>
<action>read</action>
<action>remove</action>
</actions>
</map-permission>
<!-- Principals named 'dev' from endpoints matching to either
'192.168.1.1-100' or '192.168.2.*'
have 'create', 'add', 'remove' permissions for all queues. -->
<queue-permission name="*" principal="dev">
<endpoints>
<endpoint>192.168.1.1-100</endpoint>
<endpoint>192.168.2.*</endpoint>
</endpoints>
<actions>
<action>create</action>
<action>add</action>
<action>remove</action>
</actions>
</queue-permission>
<!-- All principals from all endpoints have transaction permission.-->
<transaction-permission />
</client-permissions>
</security>
Users can also define their own policy by implementing com.hazelcast.security.IPermissionPolicy
.
package com.hazelcast.security;
/**
* IPermissionPolicy is used to determine any Subject's
* permissions to perform a security sensitive Hazelcast operation.
*
*/
public interface IPermissionPolicy {
void configure( SecurityConfig securityConfig, Properties properties );
PermissionCollection getPermissions( Subject subject,
Class<? extends Permission> type );
void destroy();
}
Permission policy implementations can access client-permissions in configuration by using
SecurityConfig.
getClientPermissionConfigs()
during configure(SecurityConfig securityConfig, Properties properties)
method is called by Hazelcast.
The IPermissionPolicy.getPermissions(Subject subject, Class<? extends Permission> type)
method is used to determine a client request that has been granted permission to perform a security-sensitive operation.
Permission policy should return a PermissionCollection
containing permissions of the given type for the given Subject
. The Hazelcast access controller will call PermissionCollection.implies(Permission)
on returning PermissionCollection
and will decide if the current Subject
has permission to access the requested resources or not.
<all-permissions principal="principal">
<endpoints>
...
</endpoints>
</all-permissions>
<map-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</map-permission>
Actions: all, create, destroy, put, read, remove, lock, intercept, index, listen
<queue-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</queue-permission>
Actions: all, create, destroy, add, remove, read, listen
<multimap-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</multimap-permission>
Actions: all, create, destroy, put, read, remove, listen, lock
<topic-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</topic-permission>
Actions: create, destroy, publish, listen
<list-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</list-permission>
Actions: all, create, destroy, add, read, remove, listen
<set-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</set-permission>
Actions: all, create, destroy, add, read, remove, listen
<lock-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</lock-permission>
Actions: all, create, destroy, lock, read
<atomic-long-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</atomic-long-permission>
Actions: all, create, destroy, read, modify
<countdown-latch-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</countdown-latch-permission>
Actions: all, create, destroy, modify, read
<semaphore-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</semaphore-permission>
Actions: all, create, destroy, acquire, release, read
<executor-service-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</executor-service-permission>
Actions: all, create, destroy
<transaction-permission principal="principal">
<endpoints>
...
</endpoints>
</transaction-permission>
This chapter provides information on the performance features of Hazelcast including slow operations detector, back pressure and data affinity. Moreover, the chapter describes the best performance practices for Hazelcast deployed on Amazon EC2. It also describes the threading models for I/O, events, executors and operations.
Data affinity ensures that related entries exist on the same node. If related data is on the same node, operations can be executed without the cost of extra network calls and extra wire data. This feature is provided by using the same partition keys for related data.
Co-location of related data and computation
Hazelcast has a standard way of finding out which member owns/manages each key object. The following operations will be routed to the same member, since all of them are operating based on the same key "key1"
.
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
Map mapA = hazelcastInstance.getMap( "mapA" );
Map mapB = hazelcastInstance.getMap( "mapB" );
Map mapC = hazelcastInstance.getMap( "mapC" );
// since map names are different, operation will be manipulating
// different entries, but the operation will take place on the
// same member since the keys ("key1") are the same
mapA.put( "key1", value );
mapB.get( "key1" );
mapC.remove( "key1" );
// lock operation will still execute on the same member
// of the cluster since the key ("key1") is same
hazelcastInstance.getLock( "key1" ).lock();
// distributed execution will execute the 'runnable' on the
// same member since "key1" is passed as the key.
hazelcastInstance.getExecutorService().executeOnKeyOwner( runnable, "key1" );
When the keys are the same, entries are stored on the same node. But we sometimes want to have related entries stored on the same node, such as a customer and his/her order entries. We would have a customers map with customerId as the key and an orders map with orderId as the key. Since customerId and orderId are different keys, a customer and his/her orders may fall into different members/nodes in your cluster. So how can we have them stored on the same node? We create an affinity between customer and orders. If we make them part of the same partition then these entries will be co-located. We achieve this by making orderIds PartitionAware
.
public class OrderKey implements Serializable, PartitionAware {
private final long customerId;
private final long orderId;
public OrderKey( long orderId, long customerId ) {
this.customerId = customerId;
this.orderId = orderId;
}
public long getCustomerId() {
return customerId;
}
public long getOrderId() {
return orderId;
}
public Object getPartitionKey() {
return customerId;
}
@Override
public String toString() {
return "OrderKey{"
+ "customerId=" + customerId
+ ", orderId=" + orderId
+ '}';
}
}
Notice that OrderKey implements PartitionAware
and that getPartitionKey()
returns the customerId
. This will make sure that the Customer
entry and its Order
s will be stored on the same node.
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
Map mapCustomers = hazelcastInstance.getMap( "customers" );
Map mapOrders = hazelcastInstance.getMap( "orders" );
// create the customer entry with customer id = 1
mapCustomers.put( 1, customer );
// now create the orders for this customer
mapOrders.put( new OrderKey( 21, 1 ), order );
mapOrders.put( new OrderKey( 22, 1 ), order );
mapOrders.put( new OrderKey( 23, 1 ), order );
Assume that you have a customers map where customerId
is the key and the customer object is the value. You want to remove one of the customer orders and return the number of remaining orders. Here is how you would normally do it.
public static int removeOrder( long customerId, long orderId ) throws Exception {
IMap<Long, Customer> mapCustomers = instance.getMap( "customers" );
IMap mapOrders = hazelcastInstance.getMap( "orders" );
mapCustomers.lock( customerId );
mapOrders.remove( orderId );
Set orders = orderMap.keySet(Predicates.equal( "customerId", customerId ));
mapCustomers.unlock( customerId );
return orders.size();
}
There are couple of things you should consider.
Instead, why not move the computation over to the member (JVM) where your customer data resides. Here is how you can do this with distributed executor service.
PartitionAware
Callable
task.Callable
does the deletion of the order right there and returns with the remaining
order count.Callable
task, return the result (remaining order count). You
do not have to wait until the task is completed; since distributed executions are asynchronous, you can do other things in the meantime.Here is some example code.
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
public int removeOrder( long customerId, long orderId ) throws Exception {
IExecutorService executorService
= hazelcastInstance.getExecutorService( "ExecutorService" );
OrderDeletionTask task = new OrderDeletionTask( customerId, orderId );
Future<Integer> future = executorService.submit( task );
int remainingOrders = future.get();
return remainingOrders;
}
public static class OrderDeletionTask
implements Callable<Integer>, PartitionAware, Serializable {
private long customerId;
private long orderId;
public OrderDeletionTask() {
}
public OrderDeletionTask(long customerId, long orderId) {
super();
this.customerId = customerId;
this.orderId = orderId;
}
@Override
public Integer call() {
Map<Long, Customer> customerMap = hazelcastInstance.getMap( "customers" );
IMap<OrderKey, Order> orderMap = hazelcastInstance.getMap( "orders" );
mapCustomers.lock( customerId );
Customer customer = mapCustomers.get( customerId );
Predicate predicate = Predicates.equal( "customerId", customerId );
Set<OrderKey> orderKeys = orderMap.localKeySet( predicate );
int orderCount = orderKeys.size();
for (OrderKey key : orderKeys) {
if (key.orderId == orderId) {
orderCount--;
orderMap.delete( key );
}
}
mapCustomers.unlock( customerId );
return orderCount;
}
@Override
public Object getPartitionKey() {
return customerId;
}
}
The benefits of doing the same operation with distributed ExecutorService
based on the key are:
executorService.submit(task)
), instead of four.Customer
entry is much less, thus enabling higher concurrency.Hazelcast uses operations to make remote calls. For example, a map.get
is an operation and a map.put
is one operation for the primary
and one operation for each of the backups, i.e. map.put
is executed for the primary and also for each backup. In most cases, there will be a natural balance between the number of threads performing operations
and the number of operations being executed. However, there are two situations where this balance and operations
can pile up and eventually lead to Out of Memory Exception (OOME):
Asynchronous calls: With async calls, the system may be flooded with the requests.
Asynchronous backups: The asynchronous backups may be piling up.
To prevent the system from crashing, Hazelcast provides back pressure. Back pressure works by:
limiting the number of concurrent operation invocations,
periodically making an async backup sync.
Back pressure is disabled by default and you can enable it using the following system property:
hazelcast.backpressure.enabled
To control the number of concurrent invocations, you can configure the number of invocations allowed per partition using the following system property:
hazelcast.backpressure.max.concurrent.invocations.per.partition
The default value of this system property is 100. Using a default configuration a system is allowed to have (271 + 1) * 100 = 27200 concurrent invocations (271 partitions + 1 for generic operations).
Back pressure is only applied to normal operations. System operations like heart beats and partition migration operations
are not influenced by back pressure. 27200 invocations might seem like a lot, but keep in mind that executing a task on IExecutor
or acquiring a lock also requires an operation.
If the maximum number of invocations has been reached, Hazelcast will automatically apply an exponential back off policy. This
gives the system some time to deal with the load. Using the following system property, you can configure the maximum time to wait before a HazelcastOverloadException
is thrown:
hazelcast.backpressure.backoff.timeout.millis
This system property's default value is 60000 ms.
The Health Monitor keeps an eye on the usage of the invocations. If it sees a member has consumed 70% or more of the invocations, it starts to log health messages.
Apart from controlling the number of invocations, you also need to control the number of pending async backups. This is done by periodically making these backups sync instead of async. This forces all pending backups to get drained. For this, Hazelcast tracks the number of asynchronous backups for each partition. At every Nth call, one synchronization is forced. This N is controlled through the following property:
hazelcast.backpressure.syncwindow
This system property's default value is 100. It means, out of 100 asynchronous backups, Hazelcast makes 1 of them a synchronous one. A randomization is added, so the sync window with default configuration will be between 75 and 125 invocations.
RELATED INFORMATION
Please refer to the System Properties section to learn how to configure the system properties.
Your application server has its own threads. Hazelcast does not use these - it manages its own threads.
Hazelcast uses a pool of threads for I/O. A single thread does not do all the IO: instead, multiple threads do the IO. On each cluster member, the IO-threading is split up in 3 types of IO-threads:
You can configure the number of IO-threads using the hazelcast.io.thread.count
system property. Its default value is 3 per member.
This means that if 3 is used, in total there are 7 IO-threads; 1 accept-IO-thread, 3 read-IO-threads, and 3 write-IO-threads. Each IO-thread has its own Selector instance and waits on Selector.select
if there is nothing to do.
In case of the read-IO-thread, when sufficient bytes for a packet have been received, the Packet object is created. This Packet is then sent to the System where it is de-multiplexed. If the Packet header signals that it is an operation/response, the Packet is handed over to the operation service (please see the Operation Threading section). If the Packet is an event, it is handed over to the event service (please see the Event Threading section).
Hazelcast uses a shared event system to deal with components that rely on events, such as topic, collections, listeners, and Near Cache.
Each cluster member has an array of event threads and each thread has its own work queue. When an event is produced, either locally or remotely, an event thread is selected (depending on if there is a message ordering) and the event is placed in the work queue for that event thread.
The following properties can be set to alter the behavior of the system.
hazelcast.event.thread.count
: Number of event-threads in this array. Its default value is 5.hazelcast.event.queue.capacity
: Capacity of the work queue. Its default value is 1000000.hazelcast.event.queue.timeout.millis
: Timeout for placing an item on the work queue. Its default value is 250.If you process a lot of events and have many cores, changing the value of hazelcast.event.thread.count
property to
a higher value is a good idea. This way, more events can be processed in parallel.
Multiple components share the same event queues. If there are 2 topics, say A and B, for certain messages they may share the same queue(s) and hence the same event thread. If there are a lot of pending messages produced by A, then B needs to wait. Also, when processing a message from A takes a lot of time and the event thread is used for that, B will suffer from this. That is why it is better to offload processing to a dedicated thread (pool) so that systems are better isolated.
If events are produced at a higher rate than they are consumed, the queue will grow in size. To prevent overloading the system
and running into an OutOfMemoryException
, the queue is given a capacity of 1 million items. When the maximum capacity is reached, the items are
dropped. This means that the event system is a 'best effort' system. There is no guarantee that you are going to get an
event. Topic A might have a lot of pending messages, and therefore B cannot receive messages because the queue
has no capacity and messages for B are dropped.
Executor threading is straight forward. When a task is received to be executed on Executor E, then E will have its
own ThreadPoolExecutor
instance and the work is put on the work queue of this executor. Thus, Executors are fully isolated, but still share the same underlying hardware; most importantly the CPUs.
You can configure the IExecutor using the ExecutorConfig
(programmatic configuration) or using <executor>
(declarative configuration).
There are 2 types of operations:
IMap.get(key)
.IExecutorService.executeOnMember(command, member)
operation.Each of these operation types has a different threading model, explained below.
To execute partition-aware operations, an array of operation threads is created. The size of this array has a default value of two times the number of cores and a minimum value of 2. This value can be changed using the hazelcast.operation.thread.count
property.
Each operation-thread has its own work queue and it will consume messages from this work queue. If a partition-aware operation needs to be scheduled, the right thread is found using the formula below.
threadIndex = partitionId % partition-thread-count
After the threadIndex
is determined, the operation is put in the work queue of that operation-thread. This means that:
a single operation thread executes operations for multiple partitions; if there are 271 partitions and 10 partition-threads, then roughly every operation-thread will execute operations for 27 partitions.
each partition belongs to only 1 operation thread. All operations for a partition will always be handled by exactly the same operation-thread.
no concurrency control is needed to deal with partition-aware operations because once a partition-aware operation is put on the work queue of a partition-aware operation thread, only 1 thread is able to touch that partition.
Because of this threading strategy, there are two forms of false sharing you need to be aware of:
false sharing of the partition: two completely independent data structures share the same partitions; e.g. if there
is a map employees
and a map orders
, the method employees.get("peter")
running on partition 25 may be blocked
by a map.get()
of orders.get(1234)
also running on partition 25. If independent data structure share the same partition,
a slow operation on one data structure can slow down the other data structures.
false sharing of the partition-aware operation-thread: each operation-thread is responsible for executing operations of a number of partitions. For example, thread-1 could be responsible for partitions 0,10,20,... thread-2 for partitions 1,11,21,... etc. If an operation for partition 1 takes a lot of time, it will block the execution of an operation of partition 11 because both of them are mapped to exactly the same operation-thread.
You need to be careful with long running operations because you could starve operations of a thread.
As a general rule, the partition thread should be released as soon as possible because operations are not designed
to execute long running operations. That is why, for example, it is very dangerous to execute a long running operation
using AtomicReference.alter()
or an IMap.executeOnKey()
, because these operations will block other operations to be executed.
Currently, there is no support for work stealing. Different partitions that map to the same thread may need to wait till one of the partitions is finished, even though there are other free partition-operation threads available.
Example:
Take a 3 node cluster. Two members will have 90 primary partitions and one member will have 91 primary partitions. Let's say you have one CPU and 4 cores per CPU. By default, 8 operation threads will be allocated to serve 90 or 91 partitions.
To execute non partition-aware operations, e.g. IExecutorService.executeOnMember(command, member)
, generic operation
threads are used. When the Hazelcast instance is started, an array of operation threads is created. The size of this array
has a default value of the number of cores divided by two with a minimum value of 2. It can be changed using the
hazelcast.operation.generic.thread.count
property. This means that:
Unlike the partition-aware operation threads, all the generic operation threads share the same work queue: genericWorkQueue
.
If a non partition-aware operation needs to be executed, it is placed in that work queue and any generic operation thread can execute it. The big advantage is that you automatically have work balancing since any generic operation thread is allowed to pick up work from this queue.
The disadvantage is that this shared queue can be a point of contention. We do not practically see this in production because performance is dominated by I/O and the system is not executing very many non partition-aware operations.
In some cases, the system needs to execute operations with a higher priority, e.g. an important system operation. To support priority operations, we do the following:
For partition-aware operations: each partition thread has its own work queue. But apart from that, it also has a priority work queue. It will always check this priority queue before it processes work from its normal work queue.
For non partition-aware operations: next to the genericWorkQueue
, there also is a genericPriorityWorkQueue
. So when a priority operation
needs to be executed, it is put in this genericPriorityWorkQueue
. And just like the partition-aware operation threads, a generic
operation thread will first check the genericPriorityWorkQueue
for work.
Because a worker thread will block on the normal work queue (either partition specific or generic), a priority operation may not be picked up because it will not be put on the queue where it is blocking. We always send a 'kick the worker' operation that does nothing else than trigger the worker to wake up and check the priority queue.
When an Operation is invoked, a Future
is returned. Let's take the example code below.
GetOperation operation = new GetOperation( mapName, key );
Future future = operationService.invoke( operation );
future.get();
The calling side blocks for a reply. In this case, GetOperation
is set in the work queue for the partition of key
, where
it eventually is executed. On execution, a response is returned and placed on the genericWorkQueue
where it is executed by a
"generic operation thread". This thread will signal the future
and notifies the blocked thread that a response is available.
In the future
, we will expose this Future to the outside world, and we will provide the ability to register a completion listener so you can do asynchronous calls.
When a local partition-aware call is done, an operation is made and handed over to the work queue of the correct partition operation thread, and a future is returned. When the calling thread calls get on that future, it will acquire a lock and wait for the result to become available. When a response is calculated, the future is looked up, and the waiting thread is notified.
In the future, this will be optimized to reduce the amount of expensive systems calls, such as lock.acquire()
/notify()
and the expensive
interaction with the operation-queue. Probably, we will add support for a caller-runs mode, so that an operation is directly executed on
the calling thread.
The SlowOperationDetector
monitors the operation threads and collects information about all slow operations. An Operation
is a task executed by a generic or partition thread (see Operation Threading). An operation is considered as slow when it takes more computation time than the configured threshold.
The SlowOperationDetector
stores the fully qualified classname of the operation and its stacktrace as well as operation details, start time and duration of each slow invocation. All collected data is available in the Management Center.
The SlowOperationDetector
is configured via the following system properties.
hazelcast.slow.operation.detector.enabled
hazelcast.slow.operation.detector.log.purge.interval.seconds
hazelcast.slow.operation.detector.log.retention.seconds
hazelcast.slow.operation.detector.stacktrace.logging.enabled
hazelcast.slow.operation.detector.threshold.millis
Please refer to the System Properties section for explanations of these properties.
The detected slow operations are logged as warnings in the Hazelcast log files:
WARN 2015-05-07 11:05:30,890 SlowOperationDetector: [127.0.0.1]:5701
Slow operation detected: com.hazelcast.map.impl.operation.PutOperation
Hint: You can enable the logging of stacktraces with the following config
property: hazelcast.slow.operation.detector.stacktrace.logging.enabled
WARN 2015-05-07 11:05:30,891 SlowOperationDetector: [127.0.0.1]:5701
Slow operation detected: com.hazelcast.map.impl.operation.PutOperation
(2 invocations)
WARN 2015-05-07 11:05:30,892 SlowOperationDetector: [127.0.0.1]:5701
Slow operation detected: com.hazelcast.map.impl.operation.PutOperation
(3 invocations)
Stacktraces are always reported to the Management Center, but by default they are not printed to keep the log size small. If logging of stacktraces is enabled, the full stacktrace is printed every 100 invocations. All other invocations print a shortened version.
Since a Hazelcast cluster can run for a very long time, Hazelcast purges the slow operation logs periodically to prevent an OOME. You can configure the purge interval and the retention time for each invocation.
The purging removes each invocation whose retention time is exceeded. When all invocations are purged from a slow operation log, the log is deleted.
Amazon Web Services (AWS) platform can be an unpredictable environment compared to traditional in-house data centers. This is because the machines, databases or CPUs are shared with other unknown applications in the cloud, causing fluctuations. When you gear up your Hazelcast application from a physical environment to Amazon EC2, you should configure it so that any network outage or fluctuation is minimized and its performance is maximized. This section provides notes on improving the performance of Hazelcast on AWS.
Hazelcast is an in-memory data grid that distributes the data and computation to the nodes that are connected with a network, making Hazelcast very sensitive to the network. Not all EC2 Instance types are the same in terms of the network performance. It is recommended that you choose instances that have 10 Gigabit or High network performance for Hazelcast deployments. Please see the below table for the recommended instances.
Instance Type | Network Performance |
---|---|
m3.2xlarge | High |
m1.xlarge | High |
c3.2xlarge | High |
c3.4xlarge | High |
c3.8xlarge | 10 Gigabit |
c1.xlarge | High |
cc2.8xlarge | 10 Gigabit |
m2.4xlarge | High |
cr1.8xlarge | 10 Gigabit |
Since data is sent and received very frequently in Hazelcast applications, latency in the network becomes a crucial issue. In terms of the latency, AWS cloud performance is not the same for each region. There are vast differences in the speed and optimization from region to region.
When you do not pay attention to AWS regions, Hazelcast applications may run tens or even hundreds of times slower than necessary. The following notes are potential workarounds.
AWS uses two virtualization types to launch the EC2 instances: Para-Virtualization (PV) and Hardware-assisted Virtual Machine (HVM). According to the tests we performed, HVM provided up to three times higher throughput than PV. Therefore, we recommend you use HVM when you run Hazelcast on EC2.
Hazelcast Simulator is a production simulator used to test Hazelcast and Hazelcast-based applications in clustered environments. It also allows you to create your own tests and perform them on your Hazelcast clusters and applications that are deployed to cloud computing environments. In your tests, you can provide any property that can be specified on these environments (Amazon EC2, Google Compute Engine(GCE), or your own environment): properties such as hardware specifications, operating system, Java version, etc.
Hazelcast Simulator allows you to add potential production problems, such as real-life failures, network problems, overloaded CPU, and failing nodes to your tests. It also provides a benchmarking and performance testing platform by supporting performance tracking and also supporting various out-of-the-box profilers.
Hazelcast Simulator makes use of Apache jclouds®, an open source multi-cloud toolkit that is primarily designed for testing on the clouds like Amazon EC2 and GCE.
You can use Hazelcast Simulator for the following use cases:
Hazelcast Simulator is available as a downloadable package on the Hazelcast web site. Please refer to the Installing Simulator section for more information.
The following are the key concepts mentioned with Hazelcast Simulator.
Test - A test class for the functionality you want to test, such as a Hazelcast map. This test class may seem like a JUnit test, but it uses custom annotations to define methods for different test phases (e.g. setup, warmup, run, verify).
TestSuite - A property file that contains the name of the test class and the properties you want to set on that test class instance. In most cases, a TestSuite
contains a single test class, but you can configure multiple tests within a single TestSuite
.
Failure - An indication that something has gone wrong. Failures are picked up by the Agent
and sent back to the Coordinator
. Please see the descriptions below for the Agent
and Coordinator
.
Worker - A Java Virtual Machine (JVM) responsible for running a TestSuite
. It can be configured to spawn a Hazelcast client or member instance.
Agent - A JVM installed on a piece of hardware. Its main responsibility is spawning, monitoring and terminating Workers
.
Coordinator - A JVM that can run anywhere, such as on your local machine. Coordinator is actually responsible for running the test using the Agents
. You configure it with a list of Agent
IP addresses, and you run it by sending a command like "run this testsuite with 10 worker JVMs for 2 hours".
Provisioner - Spawns and terminates cloud instances, and installs Agents
on the remote machines. It can be used in combination with EC2 (or any other cloud), but it can also be used in a static setup, such as a local machine or a cluster of machines in your data center.
Communicator - A JVM that enables the communication between the Agents
and Workers
.
simulator.properties
- The configuration file you use to adapt the Hazelcast Simulator to your business needs (e.g. cloud selection and configuration).
Hazelcast Simulator needs a Unix shell to run. Ensure that your local and remote machines are running under Unix, Linux or Mac OS. Hazelcast Simulator may work with Windows using a Unix-like environment such as Cygwin, but that is not officially supported at the moment.
Please ensure that all remote machines are reachable via TCP ports 22, 9000 and 5701 to 5751 on their external network interface (for example, eth0
). The first two ports are used by Hazelcast Simulator. The other ports are used by Hazelcast itself. Port 9001 is used on the loopback device on all remote machines for local communication.
Hazelcast Simulator is provided as a separate downloadable package, in zip
or tar.gz
format. You can download either one here.
After the download is completed, follow the below steps.
Unpack the tar.gz
or zip
file to a folder that you prefer to be the home folder for Hazelcast Simulator. The file extracts with the name hazelcast-simulator-<
version>
. (If you are updating Hazelcast Simulator, perform this same unpacking, but skip the following steps.)
Add the following lines to the file ~/.bashrc
(for Unix/Linux) or to the file ~/.profile
(for Mac OS).
export SIMULATOR_HOME=<extracted folder path>/hazelcast-simulator-<version>
PATH=$SIMULATOR_HOME/bin:$PATH
TestSuite
(tests
is an example name in the following commands).mkdir ~/tests
simulator.properties
file to your working folder.cp $SIMULATOR_HOME/conf/simulator.properties ~/tests
After you have installed Hazelcast Simulator as described in the previous section, make sure you create a user on the remote machines upon which you want to run Agents
and Workers
. The default username used by Hazelcast Simulator is simulator
. You can change this in the simulator.properties
file in your working folder.
Please ensure that you can connect to the remote machines with the configured username and without password authentication (see the next section). The Provisioner terminates when it needs to access the remote machines and cannot connect automatically.
The preferred method for password free authentication is using an RSA (Rivest,Shamir and Adleman cryptosystem) public/private key pair. The RSA key should not require you to enter the pass-phrase manually. A key with a pass-phrase and ssh-agent-forwarding is strongly recommended, but a key without a pass-phrase also works.
Make sure you have the files id_rsa.pub
and id_rsa
in your local ~/.ssh
folder.
If you do not have the RSA keys, you can generate a public/private key pair using the following command.
ssh-keygen -t rsa -C "your_email@example.com"
Press [Enter]
for all questions. The value for the e-mail address is not relevant in this case. After you execute this command, you should have the files id_rsa.pub
and id_rsa
in your ~/.ssh
folder.
Please ensure you have appended the public key (id_rsa.pub
) to the ~/.ssh/authorized_keys
file on all remote machines (Agents
and Workers
). You can
copy the public key to all your remote machines using the following command.
ssh-copy-id -i ~/.ssh/id_rsa.pub simulator@remote-ip-address
You can check if the connection works as expected using the following command from the Coordinator
machine (it will print ok
if everything is fine).
ssh -o BatchMode=yes simulator@remote-ip-address "echo ok" 2>&1
Having installed the Simulator, this section describes how to prepare the Simulator for testing a Hazelcast cluster deployed at Amazon EC2.
To do this, copy the file SIMULATOR_HOME/conf/simulator.properties
to your working folder and edit this file. You should set the values for the following parameters that are included in this file.
aws-ec2
for Amazon EC2. Please refer to the Simulator.Properties File Description section for a full list of cloud providers.The following is an example of a simulator.properties
file with the parameters explained above. For this example, you should have created the files ~/ec2.identity
and ~/ec2.credential
that contain your EC2 access key and secret key, respectively.
CLOUD_PROVIDER=aws-ec2
CLOUD_IDENTITY=~/ec2.identity
CLOUD_CREDENTIAL=~/ec2.credential
MACHINE_SPEC=hardwareId=c3.xlarge,imageId=us-east-1/ami-1b3b2472
NOTE: Creating these files in your working folder instead of just setting the access and secret keys in the simulator.properties
file is for security reasons. It is too easy to share your credentials with the outside world; now you can safely add the simulator.properties
file in your source repository or share it with other people.
NOTE: For the full description of the simulator.properties
file, please refer to the Simulator.Properties File Description section.
To prepare the Simulator for testing a Hazelcast cluster deployed at Google Compute Engine (GCE), first you need an e-mail address to be used as a GCE service account. You can obtain this e-mail address in the Admin GUI console of GCE. In this console, select Credentials in the menu API & Auth. Then, click the Create New Client ID button and select Service Account. Usually, this e-mail address is in this form: <your account ID>@developer.gserviceaccount.com
.
Save the p12 keystore file that you obtained while creating your Service Account (you will refer to that path). In the bin
folder of the Hazelcast Simulator package that you downloaded, edit the setupGce.sh
script to specify the following parameters:
After you run the edited setupGce.sh
script, the simulator.properties
file that you need for a proper testing of your instances on GCE is created in the conf
folder of Hazelcast Simulator.
You may want to set up Hazelcast Simulator on the environments different than your clusters placed on a cloud: for example, your local machines, a test laboratory, etc. In this case, perform the following steps.
Copy the SIMULATOR_HOME/conf/simulator.properties
to your working directory.
Edit the USER
in the simulator.properties
file if you want to use a different user name than simulator
.
Create an RSA key pair or use an existing one. Using the key should not require entering the pass-phrase manually. A key with pass-phrase and ssh-agent-forwarding is strongly recommended, but a key without a pass-phrase will also work.
You can check whether a key pair exists with this command:
ls -al ~/.ssh
If it does not exist, you can create a key pair on the client machine with this command:
ssh-keygen -t rsa
You will get a few more questions:
Copy the public key into the ~/.ssh/authorized_keys
file on the remote machines with this command:
ssh-copy-id user@123.45.56.78
Create the agents.txt
file and add the IP addresses of the machines. The content of the agents.txt
file with the IP addresses added looks like the following:
98.76.65.54
10.28.37.46
Run the command provisioner --restart
to verify.
NOTE: For the full description of the simulator.properties
file, please refer to the Simulator.Properties File Description section.
After you install and prepare the Hazelcast Simulator for your environment, it is time to perform a test. In the following sections, you are going to verify the setup by running a simple map test with strings as keys and values.
You can start with creating the working folder.
mkdir simulator-example
A path of working folder needs to be visible in the output of the provisioner/coordinator.
You need to create the file test.properties
in the working folder. Execute the following command to create and edit this file.
cat > test.properties
Copy the following lines into the file test.properties
.
class=com.hazelcast.simulator.tests.map.StringStringMapTest
threadCount=10
keyLocality=Random
keyLength=300
valueLength=300
keyCount=100000
putProb=0.2
basename=map
The property class
defines the actual test case and the rest are the properties you want to bind to your test. If a
property is not defined in this file, the default value of the property given in your test code is used. Please see the properties
comment in the StringStringMapTest
. You will see the following.
// properties
public int keyLength = 10;
public int valueLength = 10;
public int keyCount = 10000;
public int valueCount = 10000;
public String basename = "stringStringMap";
public KeyLocality keyLocality = KeyLocality.RANDOM;
public int minNumberOfMembers = 0;
After you created the file test.properties
and set your properties successfully, you need to configure the simulator using the file simulator.properties
.
Execute the following command to create and edit this file.
cat > simulator.properties
Copy the following lines into this file and set the properties.
CLOUD_PROVIDER=aws-ec2
CLOUD_IDENTITY=~/ec2.identity
CLOUD_CREDENTIAL=~/ec2.credential
MACHINE_SPEC=hardwareId=m3.medium,locationId=us-east-1,imageId=us-east-1/ami-fb8e9292
JDK_FLAVOR=oracle
JDK_VERSION=7
Please refer to here for information on CLOUD_IDENTITY
and CLOUD_CREDENTIAL
.
NOTE: For a full description of the file simulator.properties
, please see the Simulator.Properties File Description section. You can find the sample simulator properties in the dist/simulator-tests/simulator.properties
. You can also copy this file to the working folder and then edit according to your needs.
When in the working folder, execute the following commands step by step to run the test.
1. Starting Instances
First of all, you need agents to run the test on them. Execute the following command to start 4 EC2 instances and install Java and the agents to these instances.
provisioner --scale 4
The output of the command looks like the following.
INFO 09:05:06 Hazelcast Simulator Provisioner
INFO 09:05:06 Version: 0.5, Commit: c6e82c5, Build Time: 18.06.2015 @ 11:58:06 UTC
INFO 09:05:06 SIMULATOR_HOME: /disk1/hazelcast-simulator-0.5
INFO 09:05:07 Loading simulator.properties: /disk1/exampleSandbox/simulator.properties
INFO 09:05:07 ==============================================================
INFO 09:05:07 Provisioning 4 aws-ec2 machines
INFO 09:05:07 ==============================================================
INFO 09:05:07 Current number of machines: 0
INFO 09:05:07 Desired number of machines: 4
INFO 09:05:07 Using init script:/disk1/hazelcast-simulator-0.5/conf/init.sh
INFO 09:05:07 JDK spec: oracle 7
INFO 09:05:07 Hazelcast version-spec: outofthebox
INFO 09:05:11 Created compute
INFO 09:05:11 Machine spec: hardwareId=m3.medium,locationId=us-east-1,imageId=us-east-1/ami-fb8e9292
INFO 09:05:18 Created template
INFO 09:05:18 Login name to the remote machines: simulator
INFO 09:05:18 Security group: 'simulator' is found in region 'us-east-1'
INFO 09:05:18 Creating machines... (can take a few minutes)
INFO 09:06:18 54.211.146.186 LAUNCHED
INFO 09:06:18 54.166.1.79 LAUNCHED
INFO 09:06:18 54.147.196.63 LAUNCHED
INFO 09:06:18 54.144.235.111 LAUNCHED
INFO 09:06:30 54.211.146.186 JAVA INSTALLED
INFO 09:06:32 54.166.1.79 JAVA INSTALLED
INFO 09:06:32 54.144.235.111 JAVA INSTALLED
INFO 09:06:34 54.147.196.63 JAVA INSTALLED
INFO 09:06:40 54.166.1.79 SIMULATOR AGENT INSTALLED
INFO 09:06:40 Killing Agent on: 54.166.1.79
INFO 09:06:40 Starting Agent on: 54.166.1.79
INFO 09:06:40 54.211.146.186 SIMULATOR AGENT INSTALLED
INFO 09:06:40 Killing Agent on: 54.211.146.186
INFO 09:06:40 54.166.1.79 SIMULATOR AGENT STARTED
INFO 09:06:40 Starting Agent on: 54.211.146.186
INFO 09:06:40 54.211.146.186 SIMULATOR AGENT STARTED
INFO 09:06:42 54.144.235.111 SIMULATOR AGENT INSTALLED
INFO 09:06:42 Killing Agent on: 54.144.235.111
INFO 09:06:42 Starting Agent on: 54.144.235.111
INFO 09:06:43 54.144.235.111 SIMULATOR AGENT STARTED
INFO 09:06:47 54.147.196.63 SIMULATOR AGENT INSTALLED
INFO 09:06:47 Killing Agent on: 54.147.196.63
INFO 09:06:47 Starting Agent on: 54.147.196.63
INFO 09:06:47 54.147.196.63 SIMULATOR AGENT STARTED
INFO 09:06:47 Pausing for machine warmup... (10 sec)
INFO 09:06:57 Duration: 00d 00h 01m 49s
INFO 09:06:57 ==============================================================
INFO 09:06:57 Successfully provisioned 4 aws-ec2 machines
INFO 09:06:57 ==============================================================
INFO 09:06:57 Shutting down Provisioner...
INFO 09:06:57 Done!
You can also see the file agents.txt
that was created automatically by the provisioner in the working folder. The file agents.txt
includes IP addresses of the started EC2 instances. You can see this file's content using the following command.
less agents.txt
First column lists the public IP addresses and the second one lists the private IP addresses. A public IP address is used for the communication between the coordinator and agent. A private IP address is used for the communications between client and member and also between member and member. A private IP address cannot be connected to from the outside of EC2 environment.
2. Running the Test Suite
After you created the instances and agents are installed to them, execute the following command to run your test suite.
coordinator test.properties
Please refer to the Coordinator section for detailed information about the arguments of coordinator
.
The output looks like the following.
INFO 09:57:17 Hazelcast Simulator Coordinator
INFO 09:57:17 Version: 0.5, Commit: c6e82c5, Build Time: 02.07.2015 @ 09:50:21 UTC
INFO 09:57:17 SIMULATOR_HOME: /disk1/hazelcast-simulator-0.5
INFO 09:57:17 Loading simulator.properties: /disk1/exampleSandbox/simulator.properties
INFO 09:57:17 Loading testsuite file: /disk1/exampleSandbox/test.properties
INFO 09:57:17 Loading Hazelcast configuration: /disk1/hazelcast-simulator-0.5/conf/hazelcast.xml
INFO 09:57:17 Loading Hazelcast client configuration: /disk1/hazelcast-simulator-0.5/conf/client-hazelcast.xml
INFO 09:57:17 Loading Log4j configuration for worker: /disk1/hazelcast-simulator-0.5/conf/worker-log4j.xml
INFO 09:57:17 Loading agents file: /disk1/exampleSandbox/agents.txt
INFO 09:57:17 HAZELCAST_VERSION_SPEC: maven=3.5
INFO 09:57:17 --------------------------------------------------------------
INFO 09:57:17 Waiting for agents to start
INFO 09:57:17 --------------------------------------------------------------
INFO 09:57:17 Connect to agent 54.211.146.186 OK
INFO 09:57:17 Connect to agent 54.166.1.79 OK
INFO 09:57:17 Connect to agent 54.147.196.63 OK
INFO 09:57:17 Connect to agent 54.144.235.111 OK
INFO 09:57:17 --------------------------------------------------------------
INFO 09:57:17 All agents are reachable!
INFO 09:57:17 --------------------------------------------------------------
INFO 09:57:21 Performance monitor enabled: false
INFO 09:57:21 Total number of agents: 4
INFO 09:57:21 Total number of Hazelcast member workers: 4
INFO 09:57:21 Total number of Hazelcast client workers: 0
INFO 09:57:21 Agent 54.211.146.186 members: 1 clients: 0 mode: MIXED
INFO 09:57:21 Agent 54.166.1.79 members: 1 clients: 0 mode: MIXED
INFO 09:57:21 Agent 54.147.196.63 members: 1 clients: 0 mode: MIXED
INFO 09:57:21 Agent 54.144.235.111 members: 1 clients: 0 mode: MIXED
INFO 09:57:21 Killing all remaining workers
INFO 09:57:21 Successfully killed all remaining workers
INFO 09:57:21 Starting 4 member workers
INFO 09:57:41 Successfully started member workers
INFO 09:57:41 Skipping client startup, since no clients are configured
INFO 09:57:41 Successfully started a grand total of 4 Workers JVMs after 20120 ms
INFO 09:57:41 Starting testsuite: 2015-07-02__09_57_17
INFO 09:57:41 Tests in testsuite: 1
INFO 09:57:41 Running time per test: 00d 00h 01m 00s
INFO 09:57:41 Expected total testsuite time: 00d 00h 01m 00s
INFO 09:57:41 Running 1 tests sequentially
INFO 09:57:41 --------------------------------------------------------------
Running Test:
TestCase{
id=
, class=com.hazelcast.simulator.tests.map.StringStringMapTest
, keyCount=100000
, keyLength=300
, keyLocality=Random
, putProb=0.2
, threadCount=10
, valueLength=300
}
--------------------------------------------------------------
INFO 09:57:41 Starting Test initialization
INFO 09:57:42 Completed Test initialization
INFO 09:57:42 Starting Test setup
INFO 09:57:44 Completed Test setup
INFO 09:57:44 Starting Test local warmup
INFO 09:57:46 Waiting for localWarmup completion: 00d 00h 00m 00s
INFO 09:57:52 Waiting for localWarmup completion: 00d 00h 00m 06s
INFO 09:57:57 Waiting for localWarmup completion: 00d 00h 00m 12s
INFO 09:58:03 Waiting for localWarmup completion: 00d 00h 00m 18s
INFO 09:58:09 Waiting for localWarmup completion: 00d 00h 00m 24s
INFO 09:58:15 Waiting for localWarmup completion: 00d 00h 00m 30s
INFO 09:58:20 Waiting for localWarmup completion: 00d 00h 00m 35s
INFO 09:58:26 Waiting for localWarmup completion: 00d 00h 00m 41s
INFO 09:58:32 Completed Test local warmup
INFO 09:58:32 Starting Test global warmup
INFO 09:58:33 Completed Test global warmup
INFO 09:58:33 Starting Test start
INFO 09:58:34 Completed Test start
INFO 09:58:34 Test will run for 00d 00h 01m 00s
INFO 09:59:04 Running 00d 00h 00m 30s 50.00% complete
INFO 09:59:34 Running 00d 00h 01m 00s 100.00% complete
INFO 09:59:34 Test finished running
INFO 09:59:34 Starting Test stop
INFO 09:59:36 Completed Test stop
INFO 09:59:37 Starting Test global verify
INFO 09:59:39 Completed Test global verify
INFO 09:59:39 Starting Test local verify
INFO 09:59:41 Completed Test local verify
INFO 09:59:41 Starting Test global tear down
INFO 09:59:43 Finished Test global tear down
INFO 09:59:43 Starting Test local tear down
INFO 09:59:45 Completed Test local tear down
INFO 09:59:45 Terminating workers
INFO 09:59:45 All workers have been terminated
INFO 09:59:45 Starting cool down (10 sec)
INFO 09:59:55 Finished cool down
INFO 09:59:55 Total running time: 133 seconds
INFO 09:59:55 -----------------------------------------------------------------------------
INFO 09:59:55 No failures have been detected!
INFO 09:59:55 -----------------------------------------------------------------------------
3. Downloading the Results
Now you need the logs and results that the workers generated. You can get these requirements from agents via provisioner
.
provisioner --download
The output looks like the following.
INFO 10:05:41 Hazelcast Simulator Provisioner
INFO 10:05:41 Version: 0.5, Commit: c6e82c5, Build Time: 02.07.2015 @ 09:50:21 UTC
INFO 10:05:41 SIMULATOR_HOME: /disk1/hazelcast-simulator-0.5
INFO 10:05:41 Loading simulator.properties: /disk1/exampleSandbox/simulator.properties
INFO 10:05:42 ==============================================================
INFO 10:05:42 Download artifacts of 4 machines
INFO 10:05:42 ==============================================================
INFO 10:05:42 Downloading from 54.211.146.186
INFO 10:05:42 Downloading from 54.166.1.79
INFO 10:05:42 Downloading from 54.147.196.63
INFO 10:05:42 Downloading from 54.144.235.111
INFO 10:05:43 ==============================================================
INFO 10:05:43 Finished Downloading Artifacts of 4 machines
INFO 10:05:43 ==============================================================
INFO 10:05:43 Shutting down Provisioner...
INFO 10:05:43 Done!
The artifacts (log files) are downloaded into the workers
subfolder of the working folder.
4. Terminating the Instances
If want to terminate the instances, execute the following command.
provisioner --terminate
If an EC2 machine with an agent running is idle for 2 hours, that machine will automatically terminate itself to prevent running into a big bill.
The output looks like the following.
INFO 10:26:46 Hazelcast Simulator Provisioner
INFO 10:26:46 Version: 0.5, Commit: c6e82c5, Build Time: 02.07.2015 @ 09:50:21 UTC
INFO 10:26:46 SIMULATOR_HOME: /disk1/hazelcast-simulator-0.5
INFO 10:26:46 Loading simulator.properties: /disk1/exampleSandbox/simulator.properties
INFO 10:26:46 ==============================================================
INFO 10:26:46 Terminating 4 aws-ec2 machines (can take some time)
INFO 10:26:46 ==============================================================
INFO 10:26:46 Current number of machines: 4
INFO 10:26:46 Desired number of machines: 0
INFO 10:27:10 54.211.146.186 Terminating
INFO 10:27:10 54.147.196.63 Terminating
INFO 10:27:10 54.144.235.111 Terminating
INFO 10:27:10 54.166.1.79 Terminating
INFO 10:28:13 Updating /disk1/exampleSandbox/agents.txt
INFO 10:28:13 Duration: 00d 00h 01m 27s
INFO 10:28:13 ==============================================================
INFO 10:28:13 Terminated 4 of 4, remaining=0
INFO 10:28:13 ==============================================================
INFO 10:28:13 Shutting down Provisioner...
INFO 10:28:13 Done!
Another option to run the test is using a script. Execute the following command to create a script called, for example, run.sh
.
cat > run.sh
This option is for your convenience. It gathers all the commands used to perform a test into one script. The following is the content of this example run.sh
script.
#!/bin/bash
set -e
provisioner --scale 4
coordinator test.properties
provisioner --download
Note that you should make the script run.sh
executable executing the following command.
chmod +x run.sh
RELATED INFORMATION
Please see the Provisioner section and the Coordinator section for more provisioner
and coordinator
commands.
Alternatively, you can execute tests using the Simulator archetype. Please see the following.
mvn archetype:generate \
-DarchetypeGroupId=com.hazelcast.simulator \
-DarchetypeArtifactId=archetype \
-DarchetypeVersion=0.5 \
-DgroupId=yourgroupid \
-DartifactId=yourproject
This creates a fully working Simulator project, including the test having yourgroupid
.
After this project is generated, go to the created folder and execute the following command.
mvn clean install
Then, go to your working folder.
cd <working folder>
Edit the simulator.properties
file as explained in the Editing the Simulator.Properties File section.
Run the test from your working folder using the following command.
./run.sh
The output is the same as shown in the Running the Test section.
The provisioner is responsible for provisioning (starting/stopping) instances in a cloud. It will start an Operating System instance, install Java, open firewall ports and install Simulator Agents.
You can configure the behavior of the cluster—such as cloud, operating system, hardware, JVM version, Hazelcast version or region—through
the file simulator.properties
. Please see the Simulator.Properties File Description section for more information.
You can use the following arguments with the provisioner
.
To start a cluster:
provisioner --scale 1
To scale to a 2 member cluster:
provisioner --scale 2
To scale back to a 1 member cluster:
provisioner --scale 1
To terminate all members in the cluster:
provisioner --terminate
or
provisioner --scale 0
If you want to restart all agents and also upload the newest JARs to the machines:
provisioner --restart
To download all the worker home folders (containing logs and whatever has been put inside):
provisioner --download
This command is also useful if you added a profiling because the profiling information will also be downloaded. The command is also useful when an out of memory exception is thrown because you can download the heap dump.
To remove all the worker home directories:
provisioner --clean
When a machine is provisioned, a user with the name simulator
is created on the remote machine by default, and that user is added
to the sudousers list. Also, the public key of your local user is copied to the remote machine and added to the file
~/.ssh/authorized_keys
. You can login to that machine using the following command.
ssh simulator@ip
You can change the name of the created user to something else by setting the USER=<somename>
property in the file simulator.properties
. Be careful not to pick a name that is used on the target image: for example, if you use ec2-user/ubuntu
, and the
default user of that image is ec2-user/ubuntu
, then you can run into authentication problems.
The Coordinator is responsible for actually running the test using the agents.
You can deploy your test on the workers using the following command.
coordinator yourtest.properties.
This command creates a single worker per agent and runs the test for 60 seconds (the default duration for a Hazelcast Simulator test).
If your test properties file is called test.properties
, then you can use the following command to have the coordinator pick up your test.properties
file automatically.
coordinator
By default, the coordinator uses the files SIMULATOR_HOME/conf/hazelcast.xml
and SIMULATOR_HOME/conf/
client-hazelcast.xml
to generate the correct Hazelcast configuration. To use your own configuration files instead, use the following arguments:
coordinator --clientHzFile=your-client-hazelcast.xml --hzFile your-hazelcast.xml ....
You can control the duration of a single test using the --duration
argument. The default duration is 60 seconds. You can specify your own durations using m for minutes, d for days or s for seconds with this argument.
You can see the usage of the --duration
argument in the following example commands.
coordinator --duration 90s map.properties
coordinator --duration 3m map.properties
coordinator --duration 12h map.properties
coordinator --duration 2d map.properties
By default, the provisioner starts the cluster members. You can also use the --memberWorkerCount
and --clientWorkerCount
arguments to control how many members and clients you want to have.
The following command creates a 4 node Hazelcast cluster and 8 clients, and all load will be generated through the clients. It also runs the map.properties
test for a duration of 12 hours.
coordinator --memberWorkerCount 4 --clientWorkerCount 8 --duration 12h map.properties
Profiles are usually configured with some clients and some members. If you want to have members and no clients:
coordinator --memberWorkerCount 12 --duration 12h map.properties
If you want to have a JVM with embedded client plus member and all communication goes through the client:
coordinator --mixedWorkerCount 12 --duration 12h map.properties
If you want to run 2 member JVMs per machine:
coordinator --memberWorkerCount 24 --duration 12h map.properties
As you notice, you can play with the actual deployment.
Communicator enables you to pass messages to Agents, Workers and Tests. You can use messages to simulate various conditions: for example, Hazelcast discomforts like network partitioning and high CPU utilization.
$ communicator --message-address Agent=*,Worker=* spinCore
This will send the message spinCore
to all Workers.
Each interaction with Communicator has to specify:
kill
- Kills a JVM running a message recipient. In practice, you probably want to send this message to Worker(s) only. The reason for this is you rarely want to kill an Agent and it does not make sense to send this to just a single test; it would kill other tests sharing the same JVM as well.blockHzTraffic
- Blocks the incoming traffic to TCP port range 5700:5800.newMember
- Starts a new member. You can send this message to Agents only.softKill
- Instructs a JVM that is running a message recipient to exit.spinCore
- Starts a new busy-spinning thread. You can use it to simulate increased CPU consumption.unblockTraffic
- Open ports blocked by the blockHzTraffic
message.oom
- Forces a message recipient to use all memory and cause an OutOfMemoryError.terminateWorker
- Terminates a random Worker. This message type can be targeted to an Agent only.You can send a message to Agent, Worker or Test. These resources create a naturally hierarchy, making the messaging address hierarchical as well.
Syntax: Agent=<mode>[,Worker=<mode>[,Test=<mode>]]
.
Mode can be either '*' for broadcast or 'R' for a single random destination.
Addressing Example 1:
Agent=*,Worker=R
: A message will be routed to all agents, then each agent will pass it to a single random worker, and each worker will pass the message for processing.
Addressing Example 2:
Agent=*,Worker=R,Test=*
: A message will be routed to all agents, then each agent will pass the message to a single random worker and workers will pass the message to all tests for processing.
Hierarchical addressing is powerful, but it can be quite verbose. You can use convenient shortcuts, as shown below.
--oldest-member
: Sends a message to a worker with the oldest cluster member.--random-agent
: Sends a message to a random agent.--random-worker
: Sends a message to a random worker.Example: The following command starts a busy-spinning thread in a JVM running a random Worker.
communicator --random-worker spinCore
The file simulator.properties
is placed at the conf
folder of your Hazelcast Simulator. This file is used to prepare the Simulator tests for their proper executions according to your business needs.
NOTE: Currently, the main focuses are on the Simulator tests of Hazelcast on Amazon EC2 and Google Compute Engine (GCE). For the preparation of simulator.properties
for GCE, please refer to the Setting Up For GCE section. The following simulator.properties
file description is mainly for Amazon EC2.
This file includes the following parameters.
CLOUD_PROVIDER
: The Maven artifact ID of your cloud provider. For example, it is aws-ec2
if you are going to test your Hazelcast on Amazon EC2. For the full list of supported clouds, please refer to http://jclouds.apache.org/reference/providers/.CLOUD_IDENTITY
: The full path of the file containing your AWS access key.CLOUD_CREDENTIAL
: The full path of the file containing your AWS secret key. CLOUD_POLL_INITIAL_PERIOD
: The time in milliseconds between the requests (polls) from jclouds® to your cloud. Its default value is 50
.CLOUD_POLL_MAX_PERIOD
: The maximum time in milliseconds between the polls to your cloud. Its default value is 1000
.CLOUD_BATCH_SIZE
: The number of machines to be started/terminated in one go. For Amazon EC2, its acceptable value is 20
.GROUP_NAME
: The prefix for the agent name. You may want to give different names for different test clusters. For GCE, you need to be very careful using multiple group names, since for every port and every group name, a firewall rule is made and you can only have 100 firewall rules. If the name contains ${username}
, this section will be replaced by the actual user that runs the test. This makes it very easy to identify which user owns a certain machine.USER
: The name of the user on your local machine. jclouds® automatically creates a new user on the remote machine with this name as the login name. It also copies the public key of your system to the remote machine and adds it to the file ~/.ssh/authorized_keys
. Therefore, once the instance is created, you can login with the command ssh <USER>@<IP address>
. Its default value is simulator
.SSH_OPTIONS
: The options added to SSH. You do not need to change these options.SECURITY_GROUP
: The name of the security group that includes the instances created for the Simulator test. For Amazon EC2, this group will be created automatically if it does not exist. If you do not specify a region for the parameter MACHINE_SPEC
(using the locationId
attribute), the region will be us-east-1
. If a security group already exists, please make sure the ports 22, 9000, 9001 and the ports between 5701 and 5751 are open. For GCE, this parameter is not used.SUBNET_ID
: The VPC Subnet ID for Amazon EC2. If this value is different from default
, then the instances will be created in EC2 VPC and the parameter SECURITY_GROUP
will be ignored. For GCE, this parameter is not used.MACHINE_SPEC
: Specifications of the instance to be created. You can specify attributes such as the operating system, Amazon Machine Image (AMI), hardware properties, EC2 instance type and EC2 region. Please see the Setting Up For EC2 section for an example MACHINE-SPEC
value and please refer to the TemplateBuilderSpec
class of the org.jclouds.compute.domain
package at jclouds® JavaDoc for a full list of machine specifications. Please refer to Amazon EC2 for more information, such as for Amazon EC2 instance types.HAZELCAST_VERSION_SPEC
: The workers can be configured to use a specific version of Hazelcast. By this way, you do not need to depend on the Hazelcast version provided by the simulator. You can configure the Hazelcast version in one of the following ways:outofthebox
: This is the default value provided by the Simulator itself.maven=<version>
: Used to give a specific version from the maven repository (for examples, maven=3.2
, maven=3.3-SNAPSHOT
). Local Hazelcast artifacts will be preferred, so you can checkout, for example, an experimental branch and build the artifacts locally. This will all be done on the local machine, not on the agent machine.bringmyown
: Used to specify your own dependencies. For more information on the values, please see the --workerClassPath
setting of the Controller.git=<version>
: If you want the Simulator to use a specific version of Hazelcast from GIT, you can use this parameter (for example, git=f0288f713
to build a specific revision, or git=v3.2.3
to build a version from a GIT tag, or git=<your repository>/<your branch>
to build a version from a branch in a specific repository). Use the parameter GIT_CUSTOM_REPOSITORIES
to specify custom repositories, explained below. The main Hazelcast repository is always named as origin
.GIT_BUILD_DIR
: When you set the parameter HAZELCAST_VERSION_SPEC
to git=<version>
, the Hazelcast sources will be downloaded to this directory. Its default value is $HOME/.hazelcast-build/
GIT_CUSTOM_REPOSITORIES
: Comma separated list of additional GIT repositories to be fetched. Use this parameter when you set the parameter HAZELCAST_VERSION_SPEC
to git=<version>
and specify additional repositories. Hazelcast Simulator will always fetch the repository at https://github.com/hazelcast/hazelcast. This parameter specifies additional repositories. You can use both remote and local repositories. Remote repositories must be accessible for anonymous and local repositories must be accessible for the current user. Its default value is empty. Only the main Hazelcast repository is used by default.MVN_EXECUTABLE
: This parameter specifies the path to a local Maven installation when you set the parameter HAZELCAST_VERSION_SPEC
to git=<version>
. Its default value is /usr/bin/mvn
.JDK_FLAVOR
: Available flavors are oracle
, openjdk
, ibm
and outofthebox
. outofthebox
is the one provided by the image so no software is installed by the Simulator. If you select a flavor different than outofthebox
, the currect behavior is that only 64-bit JVMs are going to be installed. Therefore, make sure that your operating system is 64-bit. JDK_64_BITS
: Specifies whether a 64-bit JVM should be installed or not. For now, only true
is allowed.JDK_VERSION
: The version of Java to be installed. Oracle and IBM support 6, 7, and 8. OpenJDK supports 6 and 7.PROFILER
: The worker can be configured with a profiler. Available options are none
, yourkit
, hprof
, perf
, vtune
and flightrecorder
. The yourkit
profiles currently only work on 64-bit Linux (there is no support for Windows or Mac).FLIGHTRECORDER_SETTINGS
: Includes the settings for the flightrecorder
profiler. For options, please refer to http://docs.oracle.com/cd/E15289_01/doc.40/e15062/optionxx.htm#BABIECII.YOURKIT_SETTINGS
: Includes the settings for the yourkit
profiler. When yourkit
is enabled, a snapshot is created and put in the worker home directory. Therefore, when the artifacts are downloaded, the snapshots are included and can be loaded with your Yourkit GUI. Make sure that the path matches the JVM 32/64 bits. The files libypagent.so
, which are included in the Simulator, are for YourKit Java Profiler 2013. For more information on the Yourkit setting, please see http://www.yourkit.com/docs/java/help/agent.jsp and
http://www.yourkit.com/docs/java/help/startup_options.jsp.HPROF_SETTINGS
: Includes the settings for the hprof
profiler, which is a part of the JDK. By default, the file java.hprof.txt
is created in the worker directory. This file can be downloaded using the command provisioner --download
after a test has run. For configuration options, please see http://docs.oracle.com/javase/7/docs/technotes/samples/hprof.html.PERF_SETTINGS
: Includes the settings for the perf
profiler, available only for Linux. For more information, please see https://perf.wiki.kernel.org/index.php/Tutorial#Sampling_with_perf_record.VTUNE_SETTINGS
: Includes the settings for the vtune
profiler. It requires Intel VTune to be installed on the system. For more information, please see https://software.intel.com/sites/products/documentation/doclib/iss/2013/amplifier/lin/ug_docs/GUID-09766DB6-3FA8-445B-8E70-5BC9A1BE7C55.htm#GUID-09766DB6-3FA8-445B-8E70-5BC9A1BE7C55.Hazelcast Simulator can use probes to record throughput and latency while running a test. Hazelcast Simulator can inject a probe into a test, and then it is the responsibility of the test to notify the probe about the start/end of each action.
There are two classes of probes:
SimpleProbe
: Counts the number of events. It does not have a notion of start/end.IntervalProbe
: Differentiates between start/end of an action. Used to measure latency.How to use probes is explained below.
Define a probe as a test property. Hazelcast Simulator will inject the appropriate probe implementation.
public class IntIntMapTest {
private static final ILogger log = Logger.getLogger(IntIntMapTest.class);
private enum Operation {
PUT,
GET
}
[...]
// Probes will be injected by Hazelcast Simulator
public IntervalProbe intervalProbe;
public IntervalProbe anotherIntervalProbe;
public SimpleProbe simpleProbe;
Use the probe in your test code.
getLatency.started();
map.get(key);
getLatency.done();
Configure the probe in your test.properties
file.
probe-intervalProbe=throughput
probe-simpleProbe=throughput
The configuration format is probe-<nameOfField>=<type>
, where nameOfField
is the name you choose for the probe, and type
is the type of probe. Please keep in mind that this format is likely to change in future versions of Hazelcast Simulator.
A probe of class IntervalProbe
can have the following types.
latency
: Measures the latency distribution.maxLatency
: Records the highest latency. Unlike the previous probe, it records only the single highest latency measured, not a full distribution.hdr
: Same as latency, but it uses HdrHistogram under the hood. This will replace the latency probe in future versions of Simulator.disabled
: Dummy probe. It does not record anything.A probe of class SimpleProbe
can have the following implementations.
throughput
: Measures throughput.disabled
: Dummy probe. It does not record anything.It is important to understand that the class of a probe does not mandate what the probe is actually measuring. Therefore, the tests just know a class of probe, but they do not know if the probe generates, for example, a full latency histogram or just a maximum recorded latency. This detail must be implemented from a point of view of a test.
This chapter explains how you can replicate the state of your clusters over Wide Area Network (WAN) environmments.
There are cases where you need to synchronize multiple clusters to the same state. Synchronization of clusters, also known as WAN Replication, is mainly used for replicating state of different clusters over WAN environments like the Internet.
Imagine you have different data centers in New York, London and Tokyo each running an independent Hazelcast cluster. Every cluster would be operating at native speed in their own LAN (Local Area Network), but you also want some or all recordsets in these clusters to be replicated to each other: updates in the Tokyo cluster should also replicate to London and New York, in the meantime updates in the New York cluster are synchronized to the Tokyo and London clusters.
The current WAN Replication implementation supports two different operation modes.
Active-Passive: This mode is mostly used for failover scenarios where you want to replicate an active cluster to one or more passive clusters, for the purpose of maintaining a backup.
Active-Active: Every cluster is equal, each cluster replicate to all other clusters. This is normally used to connect different clients to different clusters for the sake of the shortest path between client and server.
Let's see how we can configure WAN Replication from the New York cluster to target the London and Tokyo clusters:
<hazelcast>
<wan-replication name="my-wan-cluster">
<target-cluster group-name="tokyo" group-password="tokyo-pass">
<replication-impl>com.hazelcast.wan.impl.WanNoDelayReplication</replication-impl>
<end-points>
<address>10.2.1.1:5701</address>
<address>10.2.1.2:5701</address>
</end-points>
</target-cluster>
<target-cluster group-name="london" group-password="london-pass">
<replication-impl>com.hazelcast.wan.impl.WanNoDelayReplication</replication-impl>
<end-points>
<address>10.3.5.1:5701</address>
<address>10.3.5.2:5701</address>
</end-points>
</target-cluster>
</wan-replication>
...
</hazelcast>
Using this configuration, the cluster running in New York is replicating to Tokyo and London. The Tokyo and London clusters should have a similar configurations if you want to run in Active-Active mode.
If the New York and London cluster configurations contain the wan-replication
element and the Tokyo cluster does not, it means
New York and London are active endpoints and Tokyo is a passive endpoint.
When using Active-Active Replication, multiple clusters can simultaneously update the same entry in a distributed data structure. You can configure a merge-policy to resolve these potential conflicts.
<hazelcast>
<wan-replication name="my-wan-cluster">
<merge-policy>com.hazelcast.map.merge.PassThroughMergePolicy</merge-policy>
...
</wan-replication>
...
</hazelcast>
Hazelcast can configure WAN replication on a per Map basis. Imagine you have different distributed maps, however only one map should be replicated to a target cluster. To achieve this, configure map to be
replicated by adding the wan-replication-ref
element in the map configuration as shown below.
<hazelcast>
<wan-replication name="my-wan-cluster">
...
</wan-replication>
<map name="my-shared-map">
<wan-replication-ref name="my-wan-cluster">
<merge-policy>com.hazelcast.map.merge.PassThroughMergePolicy</merge-policy>
...
</wan-replication-ref>
</map>
...
</hazelcast>
You see that we have my-shared-map
configured to replicate itself to the cluster targets defined in the earlier
wan-replication
element.
You will also have to define a merge policy
for merging replica entries and resolving conflicts during the merge
as mentioned before.
RELATED INFORMATION
You can download the white paper Hazelcast on AWS: Best Practices for Deployment from Hazelcast.com.
RELATED INFORMATION
Please refer to the WAN Replication Configuration section for a full description of Hazelcast WAN Replication configuration.
Enterprise WAN replication has two different replication implementations. These are WanNoDelayReplication
and WanBatchReplication
implementations.
You can configure them using the configuration element replication-impl
, as shown below.
<hazelcast>
<wan-replication name="my-wan-cluster">
<target-cluster group-name="tokyo" group-password="tokyo-pass">
<replication-impl>com.hazelcast.enterprise.wan.replication.WanNoDelayReplication</replication-impl>
...
</target-cluster>
</wan-replication>
</hazelcast>
<hazelcast>
<wan-replication name="my-wan-cluster">
<target-cluster group-name="tokyo" group-password="tokyo-pass">
<replication-impl>com.hazelcast.enterprise.wan.replication.WanBatchReplication</replication-impl>
...
</target-cluster>
</wan-replication>
</hazelcast>
WanNoDelayReplication
sends replication events to the target cluster as soon as they are generated.
WanBatchReplication
waits until:
When WanBatchReplication
is preferred as the replication implementation, the maximum size of events that are sent in a single batch can be changed
depending on your needs. Default value for batch size is 50
.
To change the WanBatchReplication
batch size, use the hazelcast.enterprise.wanrep.batch.size
property in Hazelcast Enterprise.
You can do this by setting the property on the command line (where xxx is the batch size),
-Dhazelcast.enterprise.wanrep.batch.size=xxx
or by setting the property inside the hazelcast.xml
(where xxx is the requested batch size):
<hazelcast>
<properties>
<property name="hazelcast.enterprise.wanrep.batch.size">xxx</property>
</properties>
</hazelcast>
When using WanBatchReplication
if the number of WAN replication events generated does not reach Wan Replication Batch Size,
they are sent to the target cluster after a certain amount of time is passed.
Default value of for this duration is 5
seconds.
To change the WanBatchReplication
batch sending frequency, set hazelcast.enterprise.wanrep.
batchfrequency.seconds
property.
You can set the property on the command line (where xxx is the batch sending frequency in seconds),
-Dhazelcast.enterprise.wanrep.batchfrequency.seconds=xxx
or by setting the properties inside the hazelcast.xml
(where xxx is the requested batch sending frequency):
<hazelcast>
<properties>
<property name="hazelcast.enterprise.wanrep.batchfrequency.seconds">xxx</property>
</properties>
</hazelcast>
After a replication event is sent to the target cluster, the source member waits for an acknowledge that event has reached the target. If confirmation is not received inside a timeout duration window, the event is resent to the target cluster.
Default value of for this duration is 5000
milliseconds.
You can change this duration depending on your network latency. The Hazelcast Enterprise user can set the hazelcast.enterprise.wanrep.optimeout.millis
property to change the timeout duration.
You can do this by setting the property on the command line (where xxx is the timeout duration in milliseconds),
-Dhazelcast.enterprise.wanrep.optimeout.millis=xxx
or by setting the property inside the hazelcast.xml
(where xxx is the requested timeout duration):
<hazelcast>
<properties>
<property name="hazelcast.enterprise.wanrep.optimeout.millis">xxx</property>
</properties>
</hazelcast>
For huge clusters or high data mutation rates, you might need to increase the replication queue size. The default queue
size for replication queues is 100000
. This means, if you have heavy put/update/remove rates, you might exceed the queue size
so that the oldest, not yet replicated, updates might get lost.
To increase the replication queue capacity, the Hazelcast Enterprise user can use the hazelcast.enterprise.
wanrep.queue.capacity
property.
You can do this by setting the property on the command line (where xxx is the queue size),
-Dhazelcast.enterprise.wanrep.queue.capacity=xxx
or by setting the properties inside the hazelcast.xml
(where xxx is the requested queue size):
<hazelcast>
<properties>
<property name="hazelcast.enterprise.wanrep.queue.capacity">xxx</property>
</properties>
</hazelcast>
Each cluster in WAN topology has to have a unique group-name
property for a proper handling of forwarded events.
Please refer to the Enterprise WAN Replication Configuration section for a full description of Hazelcast WAN Replication configuration.
This chapter covers all the elements and attributes used to configure Hazelcast. It includes the following sections:
*
) while configuring Hazelcast.The rest of the chapter explains the configuration items listed below.
The chapter ends with the configuration via System Properties.
Hazelcast can be configured declaratively (XML) or programmatically (API) or even by a mix of both.
1- Declarative Configuration
If you are creating new Hazelcast instance by passing the null
parameter to Hazelcast.newHazelcastInstance(null)
or just using an empty factory method (Hazelcast.newHazelcastInstance()
), Hazelcast will look in two places for the configuration file.
hazelcast.config
" system property is set to a file path. Example: -Dhazelcast.config=C:/myhazelcast.xml
.hazelcast.xml
file.If Hazelcast does not find any configuration file, it will start with the default configuration (hazelcast-default.xml
) located in hazelcast.jar
. (Before configuring Hazelcast, please try to work with the default configuration to see if it works for you. Default should be just fine for most users. If not, then consider custom configuration for your environment.)
If you want to specify your own configuration file to create Config
, Hazelcast supports several ways including filesystem, classpath, InputStream, URL, etc.:
Config cfg = new XmlConfigBuilder(xmlFileName).build();
Config cfg = new XmlConfigBuilder(inputStream).build();
Config cfg = new ClasspathXmlConfig(xmlFileName);
Config cfg = new FileSystemXmlConfig(configFilename);
Config cfg = new UrlXmlConfig(url);
Config cfg = new InMemoryXmlConfig(xml);
2- Programmatic Configuration
To configure Hazelcast programmatically, just instantiate a Config
object and set/change its properties/attributes to your needs. Below is a code sample in which some network, map, map store, and near cache attributes are configured for a Hazelcast instance.
Config config = new Config();
config.getNetworkConfig().setPort( 5900 );
config.getNetworkConfig().setPortAutoIncrement( false );
NetworkConfig network = config.getNetworkConfig();
JoinConfig join = network.getJoin();
join.getMulticastConfig().setEnabled( false );
join.getTcpIpConfig().addMember( "10.45.67.32" ).addMember( "10.45.67.100" )
.setRequiredMember( "192.168.10.100" ).setEnabled( true );
network.getInterfaces().setEnabled( true ).addInterface( "10.45.67.*" );
MapConfig mapConfig = new MapConfig();
mapConfig.setName( "testMap" );
mapConfig.setBackupCount( 2 );
mapConfig.getMaxSizeConfig().setSize( 10000 );
mapConfig.setTimeToLiveSeconds( 300 );
MapStoreConfig mapStoreConfig = new MapStoreConfig();
mapStoreConfig.setClassName( "com.hazelcast.examples.DummyStore" )
.setEnabled( true );
mapConfig.setMapStoreConfig( mapStoreConfig );
NearCacheConfig nearCacheConfig = new NearCacheConfig();
nearCacheConfig.setMaxSize( 1000 ).setMaxIdleSeconds( 120 )
.setTimeToLiveSeconds( 300 );
mapConfig.setNearCacheConfig( nearCacheConfig );
config.addMapConfig( mapConfig );
After creating a Config
object, you can use it to create a new Hazelcast instance.
HazelcastInstance hazelcast = Hazelcast.newHazelcastInstance( config );
HazelcastInstance
you should set instanceName
of Config
object. Config config = new Config();
config.setInstanceName( "my-instance" );
Hazelcast.newHazelcastInstance( config );
To retrieve an existing HazelcastInstance
using its name, use;
Hazelcast.getHazelcastInstanceByName( "my-instance" );
To retrieve all existing HazelcastInstance
s, use;
Hazelcast.getAllHazelcastInstances();
NOTE: Hazelcast performs the schema validation through the hazelcast-config-<version>.xsd
file. It throws a meaningful exception if there is an error in the declarative or programmatic configuration.
Hazelcast supports wildcard configuration for all distributed data structures that can be configured using Config
(i.e. for all except IAtomicLong
, IAtomicReference
). Using an asterisk (*) character in the name, different instances of maps, queues, topics, semaphores, etc. can be configured by a single configuration.
A single (only one) asterisk (*) can be placed anywhere inside the configuration name.
For instance, a map named com.hazelcast.test.mymap
can be configured using one of the following configurations.
<map name="com.hazelcast.test.*">
...
</map>
<map name="com.hazel*">
...
</map>
<map name="*.test.mymap">
...
</map>
<map name="com.*test.mymap">
...
</map>
Or a queue 'com.hazelcast.test.myqueue
':
<queue name="*hazelcast.test.myqueue">
...
</queue>
<queue name="com.hazelcast.*.myqueue">
...
</queue>
In your Hazelcast and/or Hazelcast Client declarative configuration, you can use variables to set the values of the elements. This is valid when you set a system property programmatically or you use the command line interface. You can use a variable in the declarative configuration to access the values of the system properties you set.
For example, see the following command that sets two system properties.
-Dgroup.name=dev -Dgroup.password=somepassword
Let's get the values of these system properties in the declarative configuration of Hazelcast, as shown below.
<hazelcast>
<group>
<name>${group.name}</name>
<password>${group.password}</password>
</group>
</hazelcast>
This also applies to the declarative configuration of Hazelcast Client, as shown below.
<hazelcast-client>
<group>
<name>${group.name}</name>
<password>${group.password}</password>
</group>
</hazelcast-client>
If you do not want to rely on the system properties, you can use the XmlConfigBuilder
and explicitly set a Properties
instance, as shown below.
Properties properties = new Properties();
// fill the properties, e.g. from database/LDAP, etc.
XmlConfigBuilder builder = new XmlConfigBuilder();
builder.setProperties(properties)
Config config = builder.build();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(config);
You can compose the declarative configuration of your Hazelcast or Hazelcast Client from multiple declarative configuration snippets. In order to compose a declarative configuration, you can use the <import/>
element to load different declarative configuration files. Please see the following examples.
Let's say you want to compose the declarative configuration for Hazelcast out of two configurations: development-group-config.xml
and development-network-config.xml
. These two configurations are shown below.
development-group-config.xml
:
<hazelcast>
<group>
<name>dev</name>
<password>dev-pass</password>
</group>
</hazelcast>
development-network-config.xml
:
<hazelcast>
<network>
<port auto-increment="true" port-count="100">5701</port>
<join>
<multicast enabled="true">
<multicast-group>224.2.2.3</multicast-group>
<multicast-port>54327</multicast-port>
</multicast>
</join>
</network>
</hazelcast>
To get your example Hazelcast declarative configuration out of the above two, use the <import/>
element as shown below.
<hazelcast>
<import resource="development-group-config.xml"/>
<import resource="development-network-config.xml"/>
</hazelcast>
This feature also applies to the declarative configuration of Hazelcast Client. Please see the following examples.
client-group-config.xml
:
<hazelcast-client>
<group>
<name>dev</name>
<password>dev-pass</password>
</group>
</hazelcast-client>
client-network-config.xml
:
<hazelcast-client>
<network>
<cluster-members>
<address>127.0.0.1:7000</address>
</cluster-members>
</network>
</hazelcast-client>
To get a Hazelcast Client declarative configuration from the above two examples, use the <import/>
element as shown below.
<hazelcast-client>
<import resource="client-group-config.xml"/>
<import resource="client-network-config.xml"/>
</hazelcast>
NOTE: You can only use <import/>
element on top level of the XML hierarchy.
<hazelcast>
<import resource="file:///etc/hazelcast/development-group-config.xml"/> <!-- loaded from filesystem -->
<import resource="classpath:development-network-config.xml"/> <!-- loaded from classpath -->
</hazelcast>
<import/>
elements. Please see the following example.<hazelcast>
<import resource="${environment}-group-config.xml"/>
<import resource="${environment}-network-config.xml"/>
</hazelcast>
NOTE: When explaining configuration elements and attributes, most of the sections below use the tags used in the declarative configurations. We assume that the reader is familiar with these tags' programmatic equivalents, since declarative and programmatic approaches have the similar tag/method names. For example, the port-count
tag in declarative configuration is equivalent to setPortCount
in programmatic configuration.
All network related configuration is performed via the network
element in the Hazelcast XML configuration file or the class NetworkConfig
when using programmatic configuration. Let's first give the examples for these two approaches. Then we will look at its sub-elements and attributes.
Declarative:
<network>
<public-address></public-address>
<port auto-increment="true" port-count="100">5701</port>
<outbound-ports>
<ports>0</ports>
</outbound-ports>
<reuse-address>false</reuse-address>
<join>
<multicast enabled="true">
<multicast-group>224.2.2.3</multicast-group>
<multicast-port>54327</multicast-port>
</multicast>
<tcp-ip enabled="false">
<interface>127.0.0.1</interface>
</tcp-ip>
<aws enabled="false">
<access-key>my-access-key</access-key>
<secret-key>my-secret-key</secret-key>
<region>us-west-1</region>
<host-header>ec2.amazonaws.com</host-header>
<security-group-name>hazelcast-sg</security-group-name>
<tag-key>type</tag-key>
<tag-value>hz-nodes</tag-value>
</aws>
</join>
<interfaces enabled="false">
<interface>10.10.1.*</interface>
</interfaces>
<ssl enabled="false" />
<socket-interceptor enabled="false" />
<symmetric-encryption enabled="false">
<algorithm>PBEWithMD5AndDES</algorithm>
<salt>thesalt</salt>
<password>thepass</password>
<iteration-count>19</iteration-count>
</symmetric-encryption>
</network>
Programmatic:
Network netConfig = new NetworkConfig();
netConfig.setReuseAddress( false );
AwsConfig awsConfig = new AwsConfig();
awsConfig.setTagKey( "5551234" ).setTagValue( "Node1234" );
It has the following sub-elements which are described in the following sections.
public-address
overrides the public address of a node. By default, a node selects its socket address as its public address. But behind a network address translation (NAT), two endpoints (nodes) may not be able to see/access each other. If both nodes set their public addresses to their defined addresses on NAT, then that way they can communicate with each other. In this case, their public addresses are not an address of a local network interface but a virtual address defined by NAT. It is optional to set and useful when you have a private cloud.
You can specify the ports that Hazelcast will use to communicate between cluster members. Its default value is 5701
. The following are example configurations.
Declarative:
<network>
<port port-count="20" auto-increment="false">5701</port>
</network>
Programmatic:
Config config = new Config();
config.getNetworkConfig().setPort( "5701" );
.setPortCount( "20" ).setPortAutoIncrement( false );
port
has the following attributes.
port-count
: By default, Hazelcast will try 100 ports to bind. Meaning that, if you set the value of port as 5701, as members are joining to the cluster, Hazelcast tries to find ports between 5701 and 5801. You can choose to change the port count in the cases like having large instances on a single machine or willing to have only a few ports to be assigned. The parameter port-count
is used for this purpose, whose default value is 100.auto-increment
: According to the above example, Hazelcast will try to find free ports between 5701 and 5801. Normally, you will not need to change this value, but it will come very handy when needed. You may also want to choose to use only one port. In that case, you can disable the auto-increment feature of port
by setting auto-increment
to false
.The parameter port-count
is ignored when the above configuration is made.
By default, Hazelcast lets the system pick up an ephemeral port during socket bind operation. But security policies/firewalls may require you to restrict outbound ports to be used by Hazelcast-enabled applications. To fulfill this requirement, you can configure Hazelcast to use only defined outbound ports. The following are example configurations.
Declarative:
<network>
<outbound-ports>
<!-- ports between 33000 and 35000 -->
<ports>33000-35000</ports>
<!-- comma separated ports -->
<ports>37000,37001,37002,37003</ports>
<ports>38000,38500-38600</ports>
</outbound-ports>
</network>
Programmatic:
...
NetworkConfig networkConfig = config.getNetworkConfig();
// ports between 35000 and 35100
networkConfig.addOutboundPortDefinition("35000-35100");
// comma separated ports
networkConfig.addOutboundPortDefinition("36001, 36002, 36003");
networkConfig.addOutboundPort(37000);
networkConfig.addOutboundPort(37001);
...
Note: You can use port ranges and/or comma separated ports.
As shown in the programmatic configuration, you use the method addOutboundPort
to add only one port. If you need to add a group of ports, then use the method addOutboundPortDefinition
.
In the declarative configuration, the element ports
can be used for both single and multiple port definitions.
When you shutdown a cluster member, the server socket port will be in the TIME_WAIT
state for the next couple of minutes. If you start the member right after shutting it down, you may not be able to bind it to the same port because it is in the TIME_WAIT
state. If you set the reuse-address
element to true
, the TIME_WAIT
state is ignored and you can bind the member to the same port again.
The following are example configurations.
Declarative:
<network>
<reuse-address>true</reuse-address>
</network>
Programmatic:
...
NetworkConfig networkConfig = config.getNetworkConfig();
networkConfig.setReuseAddress( true );
...
The join
configuration element is used to enable the Hazelcast instances to form a cluster, i.e. to join the members. Three ways can be used to join the members: discovery by TCP/IP, by multicast, and by discovery on AWS (EC2 auto-discovery). The following are example configurations.
Declarative:
<network>
<join>
<multicast enabled="true">
<multicast-group>224.2.2.3</multicast-group>
<multicast-port>54327</multicast-port>
<multicast-time-to-live>32</multicast-time-to-live>
<multicast-timeout-seconds>2</multicast-timeout-seconds>
<trusted-interfaces>
<interface>192.168.1.102</interface>
</trusted-interfaces>
</multicast>
<tcp-ip enabled="false">
<required-member>192.168.1.104</required-member>
<member>192.168.1.104</member>
<members>192.168.1.105,192.168.1.106</members>
</tcp-ip>
<aws enabled="false">
<access-key>my-access-key</access-key>
<secret-key>my-secret-key</secret-key>
<region>us-west-1</region>
<host-header>ec2.amazonaws.com</host-header>
<security-group-name>hazelcast-sg</security-group-name>
<tag-key>type</tag-key>
<tag-value>hz-nodes</tag-value>
</aws>
</join>
<network>
Programmatic:
Config config = new Config();
NetworkConfig network = config.getNetworkConfig();
JoinConfig join = network.getJoin();
join.getMulticastConfig().setEnabled( false )
.addTrustedInterface( "192.168.1.102" );
join.getTcpIpConfig().addMember( "10.45.67.32" ).addMember( "10.45.67.100" )
.setRequiredMember( "192.168.10.100" ).setEnabled( true );
The join
element has the following sub-elements and attributes.
The multicast
element includes parameters to fine tune the multicast join mechanism.
enabled
: Specifies whether the multicast discovery is enabled or not, true
or false
.multicast-group
: The multicast group IP address. Specify it when you want to create clusters within the same network. Values can be between 224.0.0.0 and 239.255.255.255. Default value is 224.2.2.3.multicast-port
: The multicast socket port that the Hazelcast member listens to and sends discovery messages through. Default value is 54327.multicast-time-to-live
: Time-to-live value for multicast packets sent out to control the scope of multicasts. See more information here.multicast-timeout-seconds
: Only when the nodes are starting up, this timeout (in seconds) specifies the period during which a node waits for a multicast response from another node. For example, if you set it as 60 seconds, each node will wait for 60 seconds until a leader node is selected. Its default value is 2 seconds. trusted-interfaces
: Includes IP addresses of trusted members. When a node wants to join to the cluster, its join request will be rejected if it is not a trusted member. You can give an IP addresses range using the wildcard (*) on the last digit of IP address (e.g. 192.168.1.* or 192.168.1.100-110).The tcp-ip
element includes parameters to fine tune the TCP/IP join mechanism.
enabled
: Specifies whether the TCP/IP discovery is enabled or not. Values can be true
or false
.required-member
: IP address of the required member. Cluster will only formed if the member with this IP address is found.member
: IP address(es) of one or more well known members. Once members are connected to these well known ones, all member addresses will be communicated with each other. You can also give comma separated IP addresses using the members
element.NOTE: tcp-ip
element also accepts the interface
parameter. Please refer to the Interfaces element description.
connection-timeout-seconds
: Defines the connection timeout. This is the maximum amount of time Hazelcast is going to try to connect to a well known member before giving up. Setting it to a too low value could mean that a member is not able to connect to a cluster. Setting it to a too high value means that member startup could slow down because of longer timeouts (e.g. when a well known member is not up). Increasing this value is recommended if you have many IPs listed and the members cannot properly build up the cluster. Its default value is 5.The aws
element includes parameters to allow the nodes to form a cluster on the Amazon EC2 environment.
enabled
: Specifies whether the EC2 discovery is enabled or not, true
or false
.access-key
, secret-key
: Access and secret keys of your account on EC2.region
: The region where your nodes are running. Default value is us-east-1
. You need to specify this if the region is other than the default one.host-header
: The URL that is the entry point for a web service. It is optional.security-group-name
: Name of the security group you specified at the EC2 management console. It is used to narrow the Hazelcast nodes to be within this group. It is optional.tag-key
, tag-value
: To narrow the members in the cloud down to only Hazelcast nodes, you can set these parameters as the ones you specified in the EC2 console. They are optional.connection-timeout-seconds
: The maximum amount of time Hazelcast will try to connect to a well known member before giving up. Setting this value too low could mean that a member is not able to connect to a cluster. Setting the value too high means that member startup could slow down because of longer timeouts (for example, when a well known member is not up). Increasing this value is recommended if you have many IPs listed and the members cannot properly build up the cluster. Its default value is 5.NOTE: If you are using a cloud provider other than AWS, you can use the programmatic configuration to specify a TCP/IP cluster. The members will need to be retrieved from that provider (e.g. JClouds).
To make sure EC2 instances are found correctly, you can use the AWSClient
class. It determines the private IP addresses of EC2 instances to be connected. Give the AWSClient
class the values for the parameters that you specified in the aws
element, as shown below. You will see whether your EC2 instances are found.
public static void main( String[] args )throws Exception{
AwsConfig config = new AwsConfig();
config.setSecretKey( ... ) ;
config.setSecretKey( ... );
config.setRegion( ... );
config.setSecurityGroupName( ... );
config.setTagKey( ... );
config.setTagValue( ... );
config.setEnabled( true );
AWSClient client = new AWSClient( config );
List<String> ipAddresses = client.getPrivateIpAddresses();
System.out.println( "addresses found:" + ipAddresses );
for ( String ip: ipAddresses ) {
System.out.println( ip );
}
}
You can specify which network interfaces that Hazelcast should use. Servers mostly have more than one network interface, so you may want to list the valid IPs. Range characters ('*' and '-') can be used for simplicity. For instance, 10.3.10.* refers to IPs between 10.3.10.0 and 10.3.10.255. Interface 10.3.10.4-18 refers to IPs between 10.3.10.4 and 10.3.10.18 (4 and 18 included). If network interface configuration is enabled (it is disabled by default) and if Hazelcast cannot find an matching interface, then it will print a message on the console and will not start on that node.
The following are example configurations.
Declarative:
<hazelcast>
...
<network>
...
<interfaces enabled="true">
<interface>10.3.16.*</interface>
<interface>10.3.10.4-18</interface>
<interface>192.168.1.3</interface>
</interfaces>
</network>
...
</hazelcast>
Programmatic:
Config config = new Config();
NetworkConfig network = config.getNetworkConfig();
InterfacesConfig interface = network.getInterfaces();
interface.setEnabled( true )
.addInterface( "192.168.1.3" );
This is a Hazelcast Enterprise feature, please see the Security chapter.
This is a Hazelcast Enterprise feature, please see the Security chapter.
This is a Hazelcast Enterprise feature, please see the Security chapter.
Hazelcast supports IPv6 addresses seamlessly (This support is switched off by default, please see the note at the end of this section).
All you need is to define IPv6 addresses or interfaces in network configuration. The only current limitation is that you cannot define wildcard IPv6 addresses in the TCP/IP join configuration (tcp-ip
element). Interfaces configuration does not have this limitation, you can configure wildcard IPv6 interfaces in the same way as IPv4 interfaces.
<hazelcast>
...
<network>
<port auto-increment="true">5701</port>
<join>
<multicast enabled="false">
<multicast-group>FF02:0:0:0:0:0:0:1</multicast-group>
<multicast-port>54327</multicast-port>
</multicast>
<tcp-ip enabled="true">
<member>[fe80::223:6cff:fe93:7c7e]:5701</member>
<interface>192.168.1.0-7</interface>
<interface>192.168.1.*</interface>
<interface>fe80:0:0:0:45c5:47ee:fe15:493a</interface>
</tcp-ip>
</join>
<interfaces enabled="true">
<interface>10.3.16.*</interface>
<interface>10.3.10.4-18</interface>
<interface>fe80:0:0:0:45c5:47ee:fe15:*</interface>
<interface>fe80::223:6cff:fe93:0-5555</interface>
</interfaces>
...
</network>
...
</hazelcast>
JVM has two system properties for setting the preferred protocol stack (IPv4 or IPv6) as well as the preferred address family types (inet4 or inet6). On a dual stack machine, IPv6 stack is preferred by default, you can change this through the java.net.preferIPv4Stack=<true|false>
system property. When querying name services, JVM prefers IPv4 addresses over IPv6 addresses and will return an IPv4 address if possible. You can change this through java.net.preferIPv6Addresses=<true|false>
system property.
Also see additional details on IPv6 support in Java.
NOTE: IPv6 support has been switched off by default, since some platforms have issues using the IPv6 stack. Some other platforms such as Amazon AWS have no support at all. To enable IPv6 support, just set configuration property hazelcast.prefer.ipv4.stack
to false. Please refer to the System Properties section for details.
This configuration is used to create multiple Hazelcast clusters. The cluster members (nodes) and clients having the same group configuration (i.e., the same group name and password) forms a private cluster.
Each cluster will have its own group and it will not interfere with other clusters. The name of the element to configure cluster groups is group
.
The following are example configurations.
Declarative:
<group>
<name>testCluster</name>
<password>1q2w3e</password>
<group>
Programmatic:
GroupConfig groupConfig = new GroupConfig();
groupConfig.setName( "testCluster" ).setPassword( "1q2w3e" );
It has the following elements.
name
: Name of the group to be created.password
: Password of the group to be created.The following are example map configurations.
Declarative:
<hazelcast>
<map name="default">
<in-memory-format>BINARY</in-memory-format>
<backup-count>0</backup-count>
<async-backup-count>1</async-backup-count>
<read-backup-data>true</read-backup-data>
<time-to-live-seconds>0</time-to-live-seconds>
<max-idle-seconds>0</max-idle-seconds>
<eviction-policy>LRU</eviction-policy>
<max-size policy="PER_NODE">5000</max-size>
<eviction-percentage>25</eviction-percentage>
<near-cache>
<invalidate-on-change>true</invalidate-on-change>
<cache-local-entries>false</cache-local-entries>
<near-cache>
<map-store enabled="true">
<write-delay-seconds>60</write-delay-seconds>
<write-batch-size>1000</write-batch-size>
<write-coalescing>true</write-coalescing>
</map-store>
<indexes>
<index ordered="true">id</index>
<index ordered="false">name</index>
</indexes>
<entry-listeners>
<entry-listener include-value="true" local="false">
com.hazelcast.examples.EntryListener
</entry-listener>
</entry-listeners>
</map>
</hazelcast>
Programmatic:
MapConfig mapConfig = new MapConfig();
mapConfig.setName( "default" ).setInMemoryFormat( "BINARY" );
mapConfig.setBackupCount( "0" ).setAsyncBackupCount( "1" )
.setReadBackupData( "true" );
MapStoreConfig mapStoreConfig = mapConfig.getMapStoreConfig();
mapStoreConfig.setWriteDelaySeconds( "60" )
.setWriteBatchSize( "1000" );
MapIndexConfig mapIndexConfig = mapConfig.getMapIndexConfig();
mapIndexConfig.setAttribute( "id" ).setOrdered( "true" );
mapIndexConfig.setAttribute( "name" ).setOrdered( "false" );
Map configuration has the following elements.
in-memory-format
: Determines how the data will be stored in memory. It has two values: BINARY and OBJECT. BINARY is the default option, storing the data in serialized binary format. If set to OBJECT, data will be stored in deserialized form.backup-count
: Defines the count of synchronous backups. If it is set as 1, for example, backup of a partition will be placed on 1 other node. If it is 2, it will be placed on 2 other nodes.async-backup-count
: The number of synchronous backups. Count behavior is the same as that of backup-count
element.read-backup-data
: When set to true
, enables reading of local backup entries.time-to-live-seconds
: Maximum time in seconds for each entry to stay in the map.max-idle-seconds
: Maximum time in seconds for each entry to stay idle in the map.eviction-policy
: Policy for evicting entries. It has three values: NONE, LRU (Least Recently Used) and LFU (Least Frequently Used). If set to NONE, no items will be evicted.max-size
: Maximum size of the map (i.e. maximum entry count of the map). When the maximum size is reached, the map is evicted based on the eviction policy defined. It has four attributes: PER_NODE
(Maximum number of map entries in each JVM), PER_PARTITION
(Maximum number of map entries within each partition), USED_HEAP_SIZE
(Maximum used heap size in megabytes for each JVM) and USED_HEAP_PERCENTAGE
(Maximum used heap size percentage for each JVM). eviction-percentage
: When the map reaches max-size
, this specified percentage of the map will be evicted.merge-policy
: Policy for merging maps after a split-brain syndrome was detected and the different network partitions need to be merged. The available merge policy classes are explained below.HigherHitsMapMergePolicy
causes the merging entry to be merged from the source map to the destination map if the source entry has more hits than the destination one.LatestUpdateMapMergePolicy
causes the merging entry to be merged from the source map to the destination map if the source entry has updated more recently than the destination entry. This policy can only be used if the machine clocks are in sync.PassThroughMergePolicy
causes the merging entry to be merged from source to destination map unless merging entry is null.PutIfAbsentMapMergePolicy
causes the merging entry to be merged from source to destination map if it does not exist in the destination map.statistics-enabled
: You can retrieve statistics information like owned entry count, backup entry count, last update time, and locked entry count by setting statistics-enabled
to true
. The method for retrieving the statistics is getLocalMapStats()
.wan-replication-ref
: Hazelcast can replicate some or all of the cluster data. For example, suppose you can have 5 different maps but you want only one of these maps to replicate across clusters. To achieve this, you mark the maps to be replicated by adding this element in the map configuration.optimize-queries
: This element is used to increase the speed of query processes in the map. It only works when in-memory-format
is set as BINARY
and performs a pre-caching on the entries queried.class-name
: Name of the class implementing MapLoader and/or MapStore.write-delay-seconds
: Number of seconds to delay to call the MapStore.store(key, value). If the value is zero then it is write-through so MapStore.store(key, value) will be called as soon as the entry is updated. Otherwise it is write-behind so updates will be stored after write-delay-seconds value by calling Hazelcast.storeAll(map). Default value is 0.write-batch-size
: Used to create batch chunks when writing map store. In default mode, all map entries will be tried to be written in one go. To create batch chunks, the minimum meaningful value for write-batch-size is 2. For values smaller than 2, it works as in default mode.write-coalescing
: In write-behind mode, by default Hazelcast coalesces updates on a specific key, i.e. applies only the last update on it. You can set this element to false
to store all updates performed on a key to the data store.Most of the map near cache properties have the same names and tasks explained in the map properties above. Below are the ones specific to near cache.
invalidate-on-change
: Determines whether the cached entries get evicted if the entries are updated or removed.cache-local-entries
: If you want the local entries to be cached, set this element's value to true
.This configuration lets you index the attributes of an object in the map and also order these attributes. See the above declarative and programmatic configuration examples.
This configuration lets you add listeners (listener classes) for the map entries. You can also set the attribute include-value
to true
if you want the entry event to contain the entry values, and you can set local
to true
if you want to listen to the entries on the local node.
The following are the example MultiMap configurations.
Declarative:
<hazelcast>
<multimap name="default">
<backup-count>0</backup-count>
<async-backup-count>1</async-backup-count>
<value-collection-type>SET</value-collection-type>
<entry-listeners>
<entry-listener include-value="false" local="false">
com.hazelcast.examples.EntryListener
</entry-listener>
</entry-listeners>
</map>
</hazelcast>
Programmatic:
MultiMapConfig mmConfig = new MultiMapConfig();
mmConfig.setName( "default" );
mmConfig.setBackupCount( "0" ).setAsyncBackupCount( "1" );
mmConfig.setValueCollectionType( "SET" );
Most of the MultiMap configuration elements and attributes have the same names and functionalities explained in the Map Configuration section. Below are the ones specific to MultiMap.
statistics-enabled
: You can retrieve some statistics like owned entry count, backup entry count, last update time, locked entry count by setting this parameter's value as "true". The method for retrieving the statistics is getLocalMultiMapStats()
.value-collection-type
: Type of the value collection. It can be Set
or List
.The following are example queue configurations.
Declarative:
<queue name="default">
<max-size>0</max-size>
<backup-count>1</backup-count>
<async-backup-count>0</async-backup-count>
<empty-queue-ttl>-1</empty-queue-ttl>
<item-listeners>
<item-listener>
com.hazelcast.examples.ItemListener
</item-listener>
<item-listeners>
</queue>
<queue-store>
<class-name>com.hazelcast.QueueStoreImpl</class-name>
<properties>
<property name="binary">false</property>
<property name="memory-limit">10000</property>
<property name="bulk-load">500</property>
</properties>
</queue-store>
Programmatic:
Config config = new Config();
QueueConfig queueConfig = config.getQueueConfig();
queueConfig.setName( "MyQueue" ).setBackupCount( "1" )
.setMaxSize( "0" ).setStatisticsEnabled( "true" );
queueConfig.getQueueStoreConfig()
.setEnabled ( "true" )
.setClassName( "com.hazelcast.QueueStoreImpl" )
.setProperty( "binary", "false" );
Queue configuration has the following elements.
max-size
: Maximum number of items in the Queue.backup-count
: Number of synchronous backups. Queue is a non-partitioned data structure, so all entries of a Set resides in one partition. When this parameter is '1', it means there will be 1 backup of that Set in another node in the cluster. When it is '2', 2 nodes will have the backup.async-backup-count
: Number of asynchronous backups.empty-queue-ttl
: Used to purge unused or empty queues. If you define a value (time in seconds) for this element, then your queue will be destroyed if it stays empty or unused for that time.item-listeners
: Lets you add listeners (listener classes) for the queue items. You can also set the attribute include-value
to true
if you want the item event to contain the item values.queue-store
: Includes the queue store factory class name and the properties binary, memory limit and bulk load. Please refer to Queue Persistence.statistics-enabled
: If set to true
, you can retrieve statistics for this Queue using the method getLocalQueueStats()
.The following are example topic configurations.
Declarative Configuration:
<hazelcast>
...
<topic name="yourTopicName">
<global-ordering-enabled>true</global-ordering-enabled>
<statistics-enabled>true</statistics-enabled>
<message-listeners>
<message-listener>MessageListenerImpl</message-listener>
</message-listeners>
</topic>
...
</hazelcast>
Programmatic Configuration:
TopicConfig topicConfig = new TopicConfig();
topicConfig.setGlobalOrderingEnabled( true );
topicConfig.setStatisticsEnabled( true );
topicConfig.setName( "yourTopicName" );
MessageListener<String> implementation = new MessageListener<String>() {
@Override
public void onMessage( Message<String> message ) {
// process the message
}
};
topicConfig.addMessageListenerConfig( new ListenerConfig( implementation ) );
HazelcastInstance instance = Hazelcast.newHazelcastInstance()
Topic configuration has the following elements.
statistics-enabled
: Default is true
, meaning statistics are calculated.global-ordering-enabled
: Default is false
, meaning there is no global order guarantee.message-listeners
: Lets you add listeners (listener classes) for the topic messages.Topic related but not topic specific configuration parameters
hazelcast.event.queue.capacity
: default value is 1,000,000.hazelcast.event.queue.timeout.millis
: default value is 250.hazelcast.event.thread.count
: default value is 5.
RELATED INFORMATION
For description of these parameters, please see Global Event Configuration.
The following are example Reliable Topic configurations.
Declarative:
<reliable-topic name="default">
<statistics-enabled>true</statistics-enabled>
<message-listeners>
<message-listener>
...
</message-listener>
</message-listeners>
<read-batch-size>10</read-batch-size>
<topic-overload-policy>BLOCK</topic-overload-policy>
</reliable-topic>
Programmatic:
Config config = new Config();
ReliableTopicConfig rtConfig = config.getReliableTopicConfig();
rtConfig.setTopicOverloadPolicy( "BLOCK" )
.setReadBatchSize( 10 )
.setStatisticsEnabled( "true" );
Reliable Topic configuration has the following elements.
statistics-enabled
: Enables or disables the statistics collection for the Reliable Topic. The default value is true
.message-listener
: Message listener class that listens to the messages when they are added or removed.read-batch-size
: Minimum number of messages that Reliable Topic will try to read in batches. The default value is 10.topic-overload-policy
: Policy to handle an overloaded topic. Available values are DISCARD_OLDEST
, DISCARD_NEWEST
, BLOCK
and ERROR
. The default value is `BLOCK.The following are example list configurations.
Declarative:
<list name="default">
<backup-count>1</backup-count>
<async-backup-count>0</async-backup-count>
<max-size>10</max-size>
<item-listeners>
<item-listener>
com.hazelcast.examples.ItemListener
</item-listener>
</item-listeners>
</list>
Programmatic:
Config config = new Config();
CollectionConfig collectionList = config.getCollectionConfig();
collectionList.setName( "MyList" ).setBackupCount( "1" )
.setMaxSize( "10" );
List configuration has the following elements.
backup-count
: Number of synchronous backups. List is a non-partitioned data structure, so all entries of a List reside in one partition. When this parameter is '1', there will be 1 backup of that List in another node in the cluster. When it is '2', 2 nodes will have the backup.async-backup-count
: Number of asynchronous backups.max-size
: The maximum number of entries for this List.item-listeners
: Lets you add listeners (listener classes) for the list items. You can also set the attribute include-value
to true
if you want the item event to contain the item values, and you can set the attribute local
to true
if you want to listen the items on the local node.The following are the example set configurations.
Declarative:
<set name="default">
<backup-count>1</backup-count>
<async-backup-count>0</async-backup-count>
<max-size>10</max-size>
<item-listeners>
<item-listener>
com.hazelcast.examples.ItemListener
</item-listener>
<item-listeners>
</set>
Programmatic:
Config config = new Config();
CollectionConfig collectionSet = config.getCollectionConfig();
collectionSet.setName( "MySet" ).setBackupCount( "1" )
.setMaxSize( "10" );
Set configuration has the following elements.
backup-count
: Count of synchronous backups. Set is a non-partitioned data structure, so all entries of a Set reside in one partition. When this parameter is '1', it means there will be 1 backup of that Set in another node in the cluster. When it is '2', 2 nodes will have the backup.async-backup-count
: Count of asynchronous backups.max-size
: The maximum number of entries for this Set.item-listeners
: Lets you add listeners (listener classes) for the list items. You can also set the attributes include-value
to true
if you want the item event to contain the item values, and you can set local
to true
if you want to listen to the items on the local node.The following are example Ringbuffer configurations.
Declarative:
<ringbuffer name="default">
<capacity>1000</capacity>
<time-to-live-seconds>0</time-to-live-seconds>
<backup-count>1</backup-count>
<async-backup-count>0</async-backup-count>
<in-memory-format>BINARY</in-memory-format>
</ringbuffer>
Programmatic:
Config config = new Config();
RingbufferConfig rbConfig = config.getRingbufferConfig();
rbConfig.setCapacity( 1000 )
.setTimeToLiveSeconds( 0 )
.setBackupCount( 1 )
.setAsyncBackupCount( 0 )
.setInMemoryFormat( "BINARY" );
Ringbuffer configuration has the following elements.
capacity
: Total number of items in the Ringbuffer. The default value is 10000.time-to-live-seconds
: Duration that the Ringbuffer retains the items before deleting them. When it is set to 0, it will be disabled. The default value is 0.backup-count
: Number of synchronous backups. The default value is 1.async-backup-count
: Number of asynchronous backups. The default value is 0.in-memory-format
: In-memory format of an item when stored in the Ringbuffer. Available values are OBJECT
and BINARY
. The default value is BINARY
. The following are example semaphore configurations.
Declarative:
<semaphore name="semaphore">
<backup-count>1</backup-count>
<async-backup-count>0</async-backup-count>
<initial-permits>3</initial-permits>
</semaphore>
Programmatic:
Config config = new Config();
SemaphoreConfig semaphoreConfig = config.getSemaphoreConfig();
semaphoreConfig.setName( "semaphore" ).setBackupCount( "1" )
.setInitialPermits( "3" );
It has below elements.
initial-permits
: the thread count to which the concurrent access is limited. For example, if you set it to "3", concurrent access to the object is limited to 3 threads.backup-count
: Number of synchronous backups.async-backup-count
: Number of asynchronous backups.Following are the example configurations for executor service.
Declarative:
<executor-service name="exec">
<pool-size>1</pool-size>
<queue-capacity>10</queue-capacity>
<statistics-enabled>true</statistics-enabled>
</executor-service>
Programmatic:
Config config = new Config();
ExecutorConfig executorConfig = config.getExecutorConfig();
executorConfig.setPoolSize( "1" ).setQueueCapacity( "10" )
.setStatisticsEnabled( true );
Executor service configuration has the following elements.
pool-size
: The number of executor threads per Member for the Executor.queue-capacity
: Executor's task queue capacity.statistics-enabled
: Some statistics like pending operations count, started operations count, completed operations count, cancelled operations count can be retrieved by setting this parameter's value as true
. The method for retrieving the statistics is getLocalExecutorStats()
.Following are examples of cache configurations.
Declarative:
<cache name="default">
<key-type class-name="exampleClass"></key-type>
<value-type class-name="exampleClass"></value-type>
<statistics-enabled>true</statistics-enabled>
<management-enabled>true</management-enabled>
<read-through>false</read-through>
<write-through>true</write-through>
<cache-loader-factory class-name="exampleClass"></cache-loader-factory>
<cache-writer-factory class-name="exampleClass"></cache-writer-factory>
<expiry-policy-factory class-name="exampleClass"></expiry-policy-factory>
<cache-entry-listeners>
<cache-entry-listener old-value-required="true">
<cache-entry-listener-factory class-name="exampleClass"</cache-entry-listener-factory>
<cache-entry-event-filter-factory class-name="exampleClass"</cache-entry-event-filter-factory>
</cache-entry-listener>
<cache-entry-listeners>
<in-memory-format>BINARY</in-memory-format>
<backup-count>1</backup-count>
<async-backup-count>0</async-backup-count>
<eviction size="10000" max-size-policy="ENTRY_COUNT"></eviction>
</cache>
Programmatic:
ICache<Object, Object> cache = cacheManager.getCache().unwrap(ICache.class);
CacheConfig cacheConfig = cache.getConfiguration(CacheConfig.class);
cacheConfig.setStatisticsEnabled( true ).setManagementEnabled( true );
cacheConfig.setReadThrough( true ).setWriteThrough( true );
.setBackupCount( "1" )
.setAsyncBackupCount( "0" );
CacheEvictionConfig cacheEvictionConfig = cacheConfig.getEvictionConfig();
cacheEvictionConfig.setSize( "10000" ).setMaxSizePolicy( CacheMaxSizePolicy.ENTRY_COUNT );
Cache configuration has the following elements.
name
: Name of the cache.key-type
: Type of the keys provided as a full class name.value-type
: Type of the values provided as a full class name. statistics-enabled
: If set as true
, you can retrieve statistics for this cache using the getLocalCacheStatistics()
method.management-enabled
: Defines whether the management is enabled on this cache. If you set it to true
, you can monitor this cache on the Hazelcast Management Center.read-through
: If you want to use the read-through caching mode, set this value to true
.write-through
: If you want to use the write-through caching mode, set this value to true
. cache-loader-factory
: Defines the cache loader factory class name.cache-writer-factory
: Defines the cache writer factory class name.expiry-policy-factory
: Defines the expiry policy factory class name.cache-entry-listeners
: Includes the list of cache entry listeners.in-memory-format
: Data type that will be used for storing the records. Possible values are BINARY
(default), OBJECT
and NATIVE
. If you set it to BINARY
, the keys and values will be stored as binary data. If you set it to OBJECT
, the values will be stored in their object forms. If it is NATIVE
, the keys and values will be stored in the native memory.backup-count
: Count of synchronous backups.async-backup-count
: Count of asynchronous backups.eviction
: When the maximum size is reached, the cache is evicted based on the eviction policy. It has the following attributes.size
: The cache size can be any integer between 0 and Integer.MAX_VALUE. Default value is 0. max-size-policy
: Available policies are listed below.eviction-policy
: Available policies are LRU (Least Recently Used) and LFU (Least Frequently Used). Default value is LRU.The following are example serialization configurations.
Declarative:
<serialization>
<portable-version>2</portable-version>
<use-native-byte-order>true</use-native-byte-order>
<byte-order>BIG_ENDIAN</byte-order>
<enable-compression>true</enable-compression>
<enable-shared-object>false</enable-shared-object>
<allow-unsafe>true</allow-unsafe>
<data-serializable-factories>
<data-serializable-factory factory-id="1001">
abc.xyz.Class
</data-serializable-factory>
</data-serializable-factories>
<portable-factories>
<portable-factory factory-id="9001">
xyz.abc.Class
</portable-factory>
</portable-factories>
<serializers>
<global-serializer>abc.Class</global-serializer>
<serializer type-class="Employee" class-name="com.EmployeeSerializer">
</serializer>
</serializers>
<check-class-def-errors>true</check-class-def-errors>
</serialization>
Programmatic:
Config config = new Config();
SerializationConfig srzConfig = config.getSerializationConfig();
srzConfig.setPortableVersion( "2" ).setUseNativeByteOrder( true );
srzConfig.setAllowUnsafe( true ).setEnableCompression( true );
srzConfig.setCheckClassDefErrors( true );
GlobalSerializerConfig globSrzConfig = srzConfig.getGlobalSerializerConfig();
globSrzConfig.setClassName( "abc.Class" );
SerializerConfig serializerConfig = srzConfig.getSerializerConfig();
serializerConfig.setTypeClass( "Employee" )
.setClassName( "com.EmployeeSerializer" );
Serialization configuration has the following elements.
portable-version
: Defines versioning of the portable serialization. Portable version differentiates two of the same classes that have changes, such as adding/removing field or changing a type of a field.use-native-byte-order
: Set to true
to use native byte order for the underlying platform. byte-order
: Defines the byte order that the serialization will use: BIG_ENDIAN
or LITTLE_ENDIAN
. The default value is BIG_ENDIAN
.enable-compression
: Enables compression if default Java serialization is used. enable-shared-object
: Enables shared object if default Java serialization is used. allow-unsafe
: Set to true
to allow unsafe
to be used. data-serializable-factory
: The DataSerializableFactory class to be registered.portable-factory
: The PortableFactory class to be registered.global-serializer
: The global serializer class to be registered if no other serializer is applicable.serializer
: The class name of the serializer implementation.check-class-def-errors
: When set to true
, the serialization system will check for class definitions error at start and will throw a Serialization Exception with an error definition.The MapReduce JobTracker configuration sets the behavior of the Hazelcast MapReduce framework. Every JobTracker is capable of running multiple MapReduce jobs at the same time. Therefore, one configuration is a shared resource for all jobs created by the same JobTracker. The configuration gives full control over the expected load behavior and thread counts to be used.
Declarative:
<job-tracker name="default">
<max-thread-size>0</max-thread-size>
<queue-size>0</queue-size>
<retry-count>0</retry-count>
<chunk-size>1000</chunk-size>
<communicate-stats>true</communicate-stats>
<topology-changed-strategy>CANCEL_RUNNING_OPERATION</topology-changed-strategy>
</job-tracker>
Programmatic:
Config config = new Config();
JobTrackerConfig JTcfg = config.getJobTrackerConfig()
JTcfg.setName( "default" ).setQueueSize( "0" )
.setChunkSize( "1000" );
MapReduce JobTracker configuration has the following elements.
max-thread-size
: The maximum thread pool size of the JobTracker.queue-size
: The maximum number of tasks that can wait to be processed. A value of 0 means an unbounded queue. Very low numbers can prevent successful execution since the job might not be correctly scheduled or intermediate chunks are lost.retry-count
: Currently not used but reserved for later use where the framework will automatically try to restart / retry operations from an available save point.chunk-size
: Defines the number of emitted values before a chunk is sent to the reducers. If your emitted values are big or you want to better balance your work, you might want to change this to a lower or higher value. A value of 0 means immediate transmission, but remember that low values mean higher traffic costs. A very high value might cause an OutOfMemoryError if the emitted values do not fit into heap memory before
being sent to reducers. To prevent this, you might want to use a combiner to pre-reduce values on mapping nodes.communicate-stats
: Defines if statistics (for example, about processed entries) are transmitted to the job emitter. This might be used to show some kind of progress to a user inside of an UI system, but it produces additional traffic. If not needed, you might want to deactivate this.topology-changed-strategy
: Defines how the MapReduce framework will react on topology changes while executing a job. Currently, only CANCEL_RUNNING_OPERATION is fully supported, which throws an exception to the job emitter (will throw a com.hazelcast.mapreduce.TopologyChangedException
).This configuration is used for Hazelcast Service Provider Interface (SPI). The following are example configurations.
Declarative:
<services enable-defaults="true">
<service enabled="true">
<name>MyService</name>
<class-name>MyServiceClass</class-name>
<properties>
<property>
<property name="com.property.foo">value</property>
</properties>
<configuration>
abcConfig
</configuration>
</service>
</services>
Programmatic:
Config config = new Config();
ServicesConfig servicesConfig = config.getServicesConfig();
servicesConfig.setEnableDefaults( true );
ServiceConfig svcConfig = servicesConfig.getServiceConfig();
svcConfig.setEnabled( true ).setName( "MyService" )
.setClassName( "MyServiceClass" );
svcConfig.setProperty( "com.property.foo", "value" );
SPI configuration has the following elements.
name
: Name of the service to be registered.class-name
: Name of the class that you develop for your service.properties
: The custom properties that you can add to your service. You enable/disable
these properties and set their values using this element.configuration
: You can include configuration items which you develop using the Config
object in your code.Management Center configuration is used to enable/disable Hazelcast Management Center and specify a time frequency for which the tool is updated with the cluster information.
The example configurations are shown below.
Declarative:
<management-center enabled="true" update-interval="3">http://localhost:8080/
mancenter</management-center>
Programmatic:
Config config = new Config();
config.getManagementCenterConfig().setEnabled( true )
.setUrl( "http://localhost:8080/mancenter" )
.setUpdateInterval( 3 );
Management Center configuration has the following attributes.
enabled
: Set to true
to enable Management Center.url
: The URL where Management Center will work.updateInterval
: The time frequency (in seconds) for which Management Center will take information from Hazelcast cluster.The following are example WAN replication configurations.
Declarative Configuration:
<wan-replication name="my-wan-cluster">
<target-cluster group-name="tokyo" group-password="tokyo-pass">
<replication-impl>com.hazelcast.wan.impl.WanNoDelayReplication</replication-impl>
<end-points>
<address>10.2.1.1:5701</address>
<address>10.2.1.2:5701</address>
</end-points>
</target-cluster>
<target-cluster group-name="london" group-password="london-pass">
<replication-impl>com.hazelcast.wan.impl.WanNoDelayReplication</replication-impl>
<end-points>
<address>10.3.5.1:5701</address>
<address>10.3.5.2:5701</address>
</end-points>
</target-cluster>
</wan-replication>
Programmatic Configuration:
Config config = new Config();
WanReplicationConfig wrConfig = config.getWanReplicationConfig();
WanTargetClusterConfig wtcConfig = wrConfig.getWanTargetClusterConfig();
wrConfig.setName("my-wan-cluster");
wtcConfig.setGroupName("tokyo").setGroupPassword("tokyo-pass");
wtcConfig.setReplicationImplObject("com.hazelcast.wan.impl.WanNoDelayReplication");
WAN replication configuration has the following elements.
name
: Name for your WAN replication configuration.target-cluster
: Creates a group and its password.replication-impl
: Name of the class implementation for the WAN replication.end-points
: IP addresses of the cluster members for which the WAN replication is implemented.The following are example Enterprise WAN replication configurations.
Declarative Configuration:
<wan-replication name="my-wan-cluster">
<target-cluster group-name="tokyo" group-password="tokyo-pass">
<replication-impl>com.hazelcast.enterprise.wan.replication.
WanNoDelayReplication</replication-impl>
<end-points>
<address>10.2.1.1:5701</address>
<address>10.2.1.2:5701</address>
</end-points>
</target-cluster>
</wan-replication>
<wan-replication name="my-wan-cluster-batch" snapshot-enabled="false">
<target-cluster group-name="london" group-password="london-pass">
<replication-impl>com.hazelcast.enterprise.wan.replication.
WanBatchReplication</replication-impl>
<end-points>
<address>10.3.5.1:5701</address>
<address>10.3.5.2:5701</address>
</end-points>
</target-cluster>
</wan-replication>
Programmatic Configuration:
Config config = new Config();
//No delay replication config
WanReplicationConfig wrConfig = new WanReplicationConfig();
WanTargetClusterConfig wtcConfig = wrConfig.getWanTargetClusterConfig();
wrConfig.setName("my-wan-cluster");
wtcConfig.setGroupName("tokyo").setGroupPassword("tokyo-pass");
wtcConfig.setReplicationImpl("com.hazelcast.enterprise.wan.replication.WanNoDelayReplication");
List<String> endpoints = new ArrayList<String>();
endpoints.add("10.2.1.1:5701");
endpoints.add("10.2.1.1:5701");
wtcConfig.setEndpoints(endpoints);
config.addWanReplicationConfig(wrConfig);
//Batch Replication Config
WanReplicationConfig wrConfig = new WanReplicationConfig();
WanTargetClusterConfig wtcConfig = wrConfig.getWanTargetClusterConfig();
wrConfig.setName("my-wan-cluster-batch");
wrConfig.setSnapshotEnabled(false);
wtcConfig.setGroupName("london").setGroupPassword("london");
wtcConfig.setReplicationImpl("com.hazelcast.enterprise.wan.replication.WanBatchReplication");
List<String> batchEndpoints = new ArrayList<String>();
batchEndpoints.add("10.3.5.1:5701");
batchEndpoints.add("10.3.5.2:5701");
wtcConfig.setEndpoints(batchEndpoints);
config.addWanReplicationConfig(wrConfig);
Enterprise WAN replication configuration has the following elements.
name
: Name for your WAN replication configuration.snapshot-enabled
: Only valid when used with WanBatchReplication
. When set to true
, only the latest events (based on key) are selected and sent in a batch. target-cluster
: Configures target cluster's group name and password.replication-impl
: Name of the class implementation for the Enterprise WAN replication.end-points
: IP addresses of the cluster members for which the Enterprise WAN replication is implemented.To enable WAN replication for an IMap or ICache instance, you can use wan-replication-ref
element.
Each IMap and ICache instance can have different WAN replication configurations.
Declarative Configuration:
<wan-replication name="my-wan-cluster">
...
</wan-replication>
<map name="testMap">
<wan-replication-ref name="testWanRef">
<merge-policy>TestMergePolicy</merge-policy>
<republishing-enabled>false</republishing-enabled>
</wan-replication-ref>
</map>
<cache name="testCache">
<wan-replication-ref name="testWanRef">
<merge-policy>com.hazelcast.cache.merge.PassThroughCacheMergePolicy</merge-policy>
</wan-replication-ref>
</cache>
Programmatic Configuration:
Config config = new Config();
WanReplicationConfig wrConfig = new WanReplicationConfig();
WanTargetClusterConfig wtcConfig = wrConfig.getWanTargetClusterConfig();
wrConfig.setName("my-wan-cluster");
...
config.addWanReplicationConfig(wrConfig);
WanReplicationRef wanRef = new WanReplicationRef();
wanRef.setName("my-wan-cluster");
wanRef.setMergePolicy(PassThroughMergePolicy.class.getName());
wanRef.setRepublishingEnabled(true);
config.getMapConfig("testMap").setWanReplicationRef(wanRef);
WanReplicationRef cacheWanRef = new WanReplicationRef();
cacheWanRef.setName("my-wan-cluster");
cacheWanRef.setMergePolicy("com.hazelcast.cache.merge.PassThroughCacheMergePolicy");
cacheWanRef.setRepublishingEnabled(true);
config.getCacheConfig("testCache").setWanReplicationRef(cacheWanRef);
NOTE: Caches that are created dynamically do not support WAN replication functionality. Cache configurations should be defined either declaratively (by XML) or programmatically on both source and target clusters.
wan-replication-ref
has the following elements;
name
: Name of wan-replication
configuration. IMap or ICache instance uses this wan-replication
config. Please refer to the Enterprise WAN Replication Configuration section for details about wan-replication
configuration.merge-policy
: Resolve conflicts that are occurred when target cluster already has the replicated entry key.republishing-enabled
: When enabled, an incoming event to a member is forwarded to target cluster of that member.Merge policies:
4 merge policy implementations for IMap and 2 merge policy implementations for ICache are provided out of the box.
IMap has the following merge policies:
com.hazelcast.map.merge.PutIfAbsentMapMergePolicy
: Incoming entry merges from the source map to the target map if it does not exist in the target map.com.hazelcast.map.merge.HigherHitsMapMergePolicy
: Incoming entry merges from the source map to the target map if the source entry has more hits than the target one.com.hazelcast.map.merge.PassThroughMergePolicy
: Incoming entry merges from the source map to the target map unless the incoming entry is not null.com.hazelcast.map.merge.LatestUpdateMapMergePolicy
: Incoming entry merges from the source map to the target map if the source entry has been updated more recently than the target entry. Please note that this merge policy can only be used when the clusters' clocks are in sync.ICache has the following merge policies:
com.hazelcast.cache.merge.HigherHitsCacheMergePolicy
: Incoming entry merges from the source cache to the target cache if the source entry has more hits than the target one.com.hazelcast.cache.merge.PassThroughCacheMergePolicy
: Incoming entry merges from the source cache to the target cache unless the incoming entry is not null.Hazelcast distributes key objects into partitions using a consistent hashing algorithm. Those partitions are assigned to nodes. An entry is stored in the node that owns the partition to which the entry's key is assigned. The total partition count is 271 by default; you can change it with the configuration property hazelcast.map.partition.count
. Please see the System Properties section.
Along with those partitions, there are also copies of the partitions as backups. Backup partitions can have multiple copies due to the backup count defined in configuration, such as first backup partition, second backup partition, etc. A node cannot hold more than one copy of a partition (ownership or backup). By default, Hazelcast distributes partitions and their backup copies randomly and equally among cluster nodes, assuming all nodes in the cluster are identical.
But what if some nodes share the same JVM or physical machine or chassis and you want backups of these nodes to be assigned to nodes in another machine or chassis? What if processing or memory capacities of some nodes are different and you do not want an equal number of partitions to be assigned to all nodes?
You can group nodes in the same JVM (or physical machine) or nodes located in the same chassis. Or you can group nodes to create identical capacity. We call these groups partition groups. Partitions are assigned to those partition groups instead of to single nodes. Backups of these partitions are located in another partition group.
When you enable partition grouping, Hazelcast presents three choices for you to configure partition groups.
<partition-group enabled="true" group-type="HOST_AWARE" />
Config config = ...;
PartitionGroupConfig partitionGroupConfig = config.getPartitionGroupConfig();
partitionGroupConfig.setEnabled( true )
.setGroupType( MemberGroupType.HOST_AWARE );
<partition-group enabled="true" group-type="CUSTOM">
<member-group>
<interface>10.10.0.*</interface>
<interface>10.10.3.*</interface>
<interface>10.10.5.*</interface>
</member-group>
<member-group>
<interface>10.10.10.10-100</interface>
<interface>10.10.1.*</interface>
<interface>10.10.2.*</interface>
</member-group
</partition-group>
Config config = ...;
PartitionGroupConfig partitionGroupConfig = config.getPartitionGroupConfig();
partitionGroupConfig.setEnabled( true )
.setGroupType( MemberGroupType.CUSTOM );
MemberGroupConfig memberGroupConfig = new MemberGroupConfig();
memberGroupConfig.addInterface( "10.10.0.*" )
.addInterface( "10.10.3.*" ).addInterface("10.10.5.*" );
MemberGroupConfig memberGroupConfig2 = new MemberGroupConfig();
memberGroupConfig2.addInterface( "10.10.10.10-100" )
.addInterface( "10.10.1.*").addInterface( "10.10.2.*" );
partitionGroupConfig.addMemberGroupConfig( memberGroupConfig );
partitionGroupConfig.addMemberGroupConfig( memberGroupConfig2 );
<partition-group enabled="true" group-type="PER_MEMBER" />
Config config = ...;
PartitionGroupConfig partitionGroupConfig = config.getPartitionGroupConfig();
partitionGroupConfig.setEnabled( true )
.setGroupType( MemberGroupType.PER_MEMBER );
You can add or remove event listeners to/from the related object using the Hazelcast API.
The downside of attaching listeners using this API is the possibility of missing events between the creation of an object and registering the listener. To overcome this race condition, Hazelcast allows you to register listeners in configuration. You can register listeners using declarative, programmatic, or Spring configuration.
<listeners>
<listener>com.hazelcast.examples.MembershipListener</listener>
</listeners>
config.addListenerConfig(
new ListenerConfig( "com.hazelcast.examples.MembershipListener" ) );
<hz:listeners>
<hz:listener class-name="com.hazelcast.spring.DummyMembershipListener"/>
<hz:listener implementation="dummyMembershipListener"/>
</hz:listeners>
<listeners>
<listener>com.hazelcast.examples.DistributedObjectListener</listener>
</listeners>
config.addListenerConfig(
new ListenerConfig( "com.hazelcast.examples.DistributedObjectListener" ) );
<hz:listeners>
<hz:listener class-name="com.hazelcast.spring.DummyDistributedObjectListener"/>
<hz:listener implementation="dummyDistributedObjectListener"/>
</hz:listeners>
<listeners>
<listener>com.hazelcast.examples.MigrationListener</listener>
</listeners>
config.addListenerConfig(
new ListenerConfig( "com.hazelcast.examples.MigrationListener" ) );
<hz:listeners>
<hz:listener class-name="com.hazelcast.spring.DummyMigrationListener"/>
<hz:listener implementation="dummyMigrationListener"/>
</hz:listeners>
<listeners>
<listener>com.hazelcast.examples.LifecycleListener</listener>
</listeners>
config.addListenerConfig(
new ListenerConfig( "com.hazelcast.examples.LifecycleListener" ) );
<hz:listeners>
<hz:listener class-name="com.hazelcast.spring.DummyLifecycleListener"/>
<hz:listener implementation="dummyLifecycleListener"/>
</hz:listeners>
<map name="default">
...
<entry-listeners>
<entry-listener include-value="true" local="false">
com.hazelcast.examples.EntryListener
</entry-listener>
</entry-listeners>
</map>
mapConfig.addEntryListenerConfig(
new EntryListenerConfig( "com.hazelcast.examples.EntryListener", false, false ) );
<hz:map name="default">
<hz:entry-listeners>
<hz:entry-listener include-value="true"
class-name="com.hazelcast.spring.DummyEntryListener"/>
<hz:entry-listener implementation="dummyEntryListener" local="true"/>
</hz:entry-listeners>
</hz:map>
<multimap name="default">
<value-collection-type>SET</value-collection-type>
<entry-listeners>
<entry-listener include-value="true" local="false">
com.hazelcast.examples.EntryListener
</entry-listener>
</entry-listeners>
</multimap>
multiMapConfig.addEntryListenerConfig(
new EntryListenerConfig( "com.hazelcast.examples.EntryListener", false, false ) );
<hz:multimap name="default" value-collection-type="LIST">
<hz:entry-listeners>
<hz:entry-listener include-value="true"
class-name="com.hazelcast.spring.DummyEntryListener"/>
<hz:entry-listener implementation="dummyEntryListener" local="true"/>
</hz:entry-listeners>
</hz:multimap>
<queue name="default">
...
<item-listeners>
<item-listener include-value="true">
com.hazelcast.examples.ItemListener
</item-listener>
</item-listeners>
</queue>
queueConfig.addItemListenerConfig(
new ItemListenerConfig( "com.hazelcast.examples.ItemListener", true ) );
<hz:queue name="default" >
<hz:item-listeners>
<hz:item-listener include-value="true"
class-name="com.hazelcast.spring.DummyItemListener"/>
</hz:item-listeners>
</hz:queue>
<topic name="default">
<message-listeners>
<message-listener>
com.hazelcast.examples.MessageListener
</message-listener>
</message-listeners>
</topic>
topicConfig.addMessageListenerConfig(
new ListenerConfig( "com.hazelcast.examples.MessageListener" ) );
<hz:topic name="default">
<hz:message-listeners>
<hz:message-listener
class-name="com.hazelcast.spring.DummyMessageListener"/>
</hz:message-listeners>
</hz:topic>
<listeners>
<listener>com.hazelcast.examples.ClientListener</listener>
</listeners>
topicConfig.addMessageListenerConfig(
new ListenerConfig( "com.hazelcast.examples.ClientListener" ) );
<hz:listeners>
<hz:listener class-name="com.hazelcast.spring.DummyClientListener"/>
<hz:listener implementation="dummyClientListener"/>
</hz:listeners>
Hazelcast has a flexible logging configuration and does not depend on any logging framework except JDK logging. It has built-in adaptors for a number of logging frameworks and it also supports custom loggers by providing logging interfaces.
To use built-in adaptors, set the hazelcast.logging.type
property to one of the predefined types below.
jdk: JDK logging (default)
log4j: Log4j
slf4j: Slf4j
none: disable logging
You can set hazelcast.logging.type
through declarative configuration, programmatic configuration, or JVM system property.
NOTE: If you choose to use log4j
or slf4j
, you should include the proper dependencies in the classpath.
Declarative Configuration
<hazelcast xsi:schemaLocation="http://www.hazelcast.com/schema/config
http://www.hazelcast.com/schema/config/hazelcast-config-3.0.xsd"
xmlns="http://www.hazelcast.com/schema/config"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
....
<properties>
<property name="hazelcast.logging.type">jdk</property>
....
</properties>
</hazelcast>
Programmatic Configuration
Config config = new Config() ;
config.setProperty( "hazelcast.logging.type", "log4j" );
System Property
- Using JVM parameter: `java -Dhazelcast.logging.type=slf4j`
- Using System class: `System.setProperty( "hazelcast.logging.type", "none" );`
If the provided logging mechanisms are not satisfactory, you can implement your own using the custom logging feature. To use it, implement the com.hazelcast.logging.LoggerFactory
and com.hazelcast.logging.ILogger
interfaces and set the system property hazelcast.logging.class
as your custom LoggerFactory
class name.
-Dhazelcast.logging.class=foo.bar.MyLoggingFactory
You can also listen to logging events generated by Hazelcast runtime by registering LogListener
s to LoggingService
.
LogListener listener = new LogListener() {
public void log( LogEvent logEvent ) {
// do something
}
}
HazelcastInstance instance = Hazelcast.newHazelcastInstance();
LoggingService loggingService = instance.getLoggingService();
loggingService.addLogListener( Level.INFO, listener );
Through the LoggingService
, you can get the currently used ILogger implementation and log your own messages too.
NOTE: If you are not using command line for configuring logging, you should be careful about Hazelcast classes. They may be defaulted to jdk
logging before newly configured logging is read. When logging mechanism is selected, it will not change.
Hazelcast has system properties to tune some aspects of Hazelcast. You can set them as property name and value pairs through declarative configuration, programmatic configuration or JVM system property.
Declarative Configuration
<hazelcast xsi:schemaLocation="http://www.hazelcast.com/schema/config
http://www.hazelcast.com/schema/config/hazelcast-config-3.0.xsd"
xmlns="http://www.hazelcast.com/schema/config"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
....
<properties>
<property name="hazelcast.property.foo">value</property>
....
</properties>
</hazelcast>
Programmatic Configuration
Config config = new Config() ;
config.setProperty( "hazelcast.property.foo", "value" );
System Property
Using JVM parameter: java -Dhazelcast.property.foo=value
Using System class: System.setProperty( "hazelcast.property.foo", "value" );
The table below lists the system properties with their descriptions in alphabetical order.
Property Name | Default Value | Type | Description |
---|---|---|---|
hazelcast.application.validation.token |
string | This property can be used to verify that Hazelcast nodes only join when their application level configuration is the same. | |
hazelcast.backpressure.backoff.timeout.millis |
60000 | int | Controls the maximum timeout in milliseconds to wait for an invocation space to be available. The value needs to be equal to or larger than 0. |
hazelcast.backpressure.enabled |
false | bool | Enable back pressure. |
hazelcast.backpressure.max.concurrent.invocations.per.partition |
100 | int | The maximum number of concurrent invocations per partition. |
hazelcast.backpressure.syncwindow |
1000 | string | Used when back pressure is enabled. The larger the sync window value, the less frequent a asynchronous backup is converted to a sync backup. |
hazelcast.cache.invalidation.batch.enabled |
true | bool | Specifies whether the cache invalidation event batch sending is enabled or not. |
hazelcast.cache.invalidation.batch.size |
100 | int | Defines the maximum number of cache invalidation events to be drained and sent to the event listeners in a batch. |
hazelcast.cache.invalidation.batchfrequency.seconds |
5 | int | Defines cache invalidation event batch sending frequency in seconds. |
hazelcast.clientengine.thread.count |
int | Maximum number of threads to process non-partition-aware client requests, like map.size() , query, executor tasks, etc. Default count is 20 times number of cores. |
|
hazelcast.client.event.queue.capacity |
1000000 | string | Default value of the capacity of executor that handles incoming event packets. |
hazelcast.client.event.thread.count |
5 | string | Thread count for handling incoming event packets. |
hazelcast.client.heartbeat.interval |
10000 | string | The frequency of heartbeat messages sent by the clients to members. |
hazelcast.client.heartbeat.timeout |
300000 | string | Timeout for the heartbeat messages sent by the client to members. If no messages pass between client and member within the given time via this property in milliseconds, the connection will be closed. |
hazelcast.client.invocation.timeout.seconds |
120 | string | Time to give up the invocation when a member in the member list is not reachable. |
hazelcast.client.max.no.heartbeat.seconds |
int | 300 | Time after which the node assumes the client is dead and closes its connections to the client. |
hazelcast.client.shuffle.member.list |
true | string | The client shuffles the given member list to prevent all clients to connect to the same node when this property is false . When it is set to true , the client tries to connect to the nodes in the given order. |
hazelcast.connect.all.wait.seconds |
120 | int | Timeout to connect all other cluster members when a member is joining to a cluster. |
hazelcast.connection.monitor.interval |
100 | int | Minimum interval in milliseconds to consider a connection error as critical. |
hazelcast.connection.monitor.max.faults |
3 | int | Maximum IO error count before disconnecting from a node. |
hazelcast.enterprise.license.key |
null | string | Hazelcast Enterprise license key. |
hazelcast.enterprise.wanrep.batch.size |
50 | int | Maximum number of WAN replication events to be drained and sent to the target cluster in a batch. |
hazelcast.enterprise.wanrep.batchfrequency.seconds |
5 | int | Batch sending frequency in seconds. |
hazelcast.enterprise.wanrep.optimeout.millis |
60000 | int | Timeout duration (in milliseconds) for a WAN replication event before retry. |
hazelcast.enterprise.wanrep.queue.capacity |
100000 | int | Event queue capacity for WAN replication. |
hazelcast.event.queue.capacity |
1000000 | int | Capacity of internal event queue. |
hazelcast.event.queue.timeout.millis |
250 | int | Timeout to enqueue events to event queue. |
hazelcast.event.thread.count |
5 | int | Number of event handler threads. |
hazelcast.graceful.shutdown.max.wait |
600 | int | Maximum wait in seconds during graceful shutdown. |
hazelcast.health.monitoring.delay.seconds |
30 | int | Health monitoring logging interval in seconds. |
hazelcast.health.monitoring.level |
SILENT | string | Health monitoring log level. When SILENT, logs are printed only when values exceed some predefined threshold. When NOISY, logs are always printed periodically. Set OFF to turn off completely. |
hazelcast.heartbeat.interval.seconds |
1 | int | Heartbeat send interval in seconds. |
hazelcast.icmp.enabled |
false | bool | Enable ICMP ping. |
hazelcast.icmp.timeout |
1000 | int | ICMP timeout in milliseconds. |
hazelcast.icmp.ttl |
0 | int | ICMP TTL (maximum numbers of hops to try). |
hazelcast.initial.min.cluster.size |
0 | int | Initial expected cluster size to wait before node to start completely. |
hazelcast.initial.wait.seconds |
0 | int | Initial time in seconds to wait before node to start completely. |
hazelcast.io.balancer.interval.seconds |
20 | int | Interval in seconds between IOBalancer executions. |
hazelcast.io.input.thread.count |
3 | int | Number of socket input threads. |
hazelcast.io.output.thread.count |
3 | int | Number of socket output threads. |
hazelcast.io.thread.count |
3 | int | Number of threads performing socket input and socket output. If, for example, the default value (3) is used, it means there are 3 threads performing input and 3 threads performing output (6 threads in total). |
hazelcast.jcache.provider.type |
string | Type of the JCache provider. Values can be client or server . |
|
hazelcast.jmx |
false | bool | Enable JMX agent. |
hazelcast.logging.type |
jdk | enum | Name of logging framework type to send logging events. |
hazelcast.map.expiry.delay.seconds |
10 | int | Useful to deal with some possible edge cases. For example, when using EntryProcessor, without this delay, you may see an EntryProcessor running on owner partition found a key but EntryBackupProcessor did not find it on backup. As a result of this, when backup promotes to owner, you will end up an unprocessed key. |
hazelcast.map.load.chunk.size |
1000 | int | Chunk size for MapLoader's map initialization process (MapLoader.loadAllKeys()). |
hazelcast.map.replica.wait.seconds.for.scheduled.tasks |
10 | int | Scheduler delay for map tasks those will be executed on backup members. |
hazelcast.map.write.behind.queue.capacity |
50000 | string | Maximum write-behind queue capacity per node. It is the total of all write-behind queue sizes in a node including backups. Its maximum value is Integer.MAX_VALUE . The value of this property is taken into account only if the write-coalescing element of the Map Store configuration is false . Please refer to the Map Store section for the description of the write-coalescing element. |
hazelcast.master.confirmation.interval.seconds |
30 | int | Interval at which nodes send master confirmation. |
hazelcast.max.join.merge.target.seconds |
20 | int | Split-brain merge timeout for a specific target. |
hazelcast.max.join.seconds |
300 | int | Join timeout, maximum time to try to join before giving. |
hazelcast.max.no.heartbeat.seconds |
300 | int | Maximum timeout of heartbeat in seconds for a node to assume it is dead. |
hazelcast.max.no.master.confirmation.seconds |
450 | int | Max timeout of master confirmation from other nodes. |
hazelcast.max.wait.seconds.before.join |
20 | int | Maximum wait time before join operation. |
hazelcast.mc.max.visible.instance.count |
100 | int | Management Center maximum visible instance count. |
hazelcast.mc.max.visible.slow.operations.count |
10 | int | Management Center maximum visible slow operations count. |
hazelcast.mc.url.change.enabled |
true | bool | Management Center changing server url is enabled. |
hazelcast.member.list.publish.interval.seconds |
600 | int | Interval at which master node publishes a member list. |
hazelcast.memcache.enabled |
true | bool | Enable Memcache client request listener service. |
hazelcast.merge.first.run.delay.seconds |
300 | int | Initial run delay of split brain/merge process in seconds. |
hazelcast.merge.next.run.delay.seconds |
120 | int | Run interval of split brain/merge process in seconds. |
hazelcast.migration.min.delay.on.member.removed.seconds |
5 | int | Minimum delay (in seconds) between detection of a member that has left and start of the rebalancing process. |
hazelcast.operation.backup.timeout.millis |
5 | int | Maximum time a caller to wait for backup responses of an operation. After this timeout, operation response will be returned to the caller even no backup response is received. |
hazelcast.operation.call.timeout.millis |
60000 | int | Timeout to wait for a response when a remote call is sent, in milliseconds. |
hazelcast.operation.generic.thread.count |
-1 | int | Number of generic operation handler threads. -1 means CPU core count x 2. |
hazelcast.operation.thread.count |
-1 | int | Number of partition based operation handler threads. -1 means CPU core count x 2. |
hazelcast.partition.backup.sync.interval |
30 | int | Interval for syncing backup replicas. |
hazelcast.partition.count |
271 | int | Total partition count. |
hazelcast.partition.max.parallel.replications |
5 | int | Maximum number of parallel partition backup replication operations per node. When a partition backup ownership changes or a backup inconsistency is detected, the nodes start to sync their backup partitions. This parameter limits the maximum running replication operations in parallel. |
hazelcast.partition.migration.interval |
0 | int | Interval to run partition migration tasks in seconds. |
hazelcast.partition.migration.timeout |
300 | int | Timeout for partition migration tasks in seconds. |
hazelcast.partition.table.send.interval |
15 | int | Interval for publishing partition table periodically to all cluster members. |
hazelcast.partitioning.strategy.class |
null | string | Class name implementing com.hazelcast.core.PartitioningStrategy , which defines key to partition mapping. |
hazelcast.performance.monitor.max.rolled.file.count |
10 | int | The PerformanceMonitor uses a rolling file approach to prevent eating too much disk space. This property sets the maximum number of rolling files to keep on disk. |
hazelcast.performance.monitor.max.rolled.file.size.mb |
10 | int | The performance monitor uses a rolling file approach to prevent eating too much disk space. This property sets the maximum size in MB for a single file. Every HazelcastInstance gets its own history of log files. |
hazelcast.performance.monitoring.enabled |
bool | Enable the performance monitor which is a tool enabling you to see internal performance metrics. These metrics are written to the log file. | |
hazelcast.performance.monitoring.delay.seconds |
int | The delay in seconds between monitoring of the performance. | |
hazelcast.prefer.ipv4.stack |
true | bool | Prefer Ipv4 network interface when picking a local address. |
hazelcast.query.max.local.partition.limit.for.precheck |
3 | int | Maximum value of local partitions to trigger local pre-check for TruePredicate query operations on maps. |
hazelcast.query.result.size.limit |
-1 | int | Result size limit for query operations on maps. This value defines the maximum number of returned elements for a single query result. If a query exceeds this number of elements, a QueryResultSizeExceededException will be thrown. Its default value is -1, meaning it is disabled. |
hazelcast.rest.enabled |
true | bool | Enable REST client request listener service. |
hazelcast.shutdownhook.enabled |
true | bool | Enable Hazelcast shutdownhook thread. When this is enabled, this thread terminates the Hazelcast instance without waiting to shutdown gracefully. |
hazelcast.slow.operation.detector.enabled |
true | bool | Enables/disables the SlowOperationDetector. |
hazelcast.slow.operation.detector.log.purge.interval.seconds |
300 | int | Purge interval for slow operation logs. |
hazelcast.slow.operation.detector.log.retention.seconds |
3600 | int | Defines the retention time of invocations in slow operation logs. If an invocation is older than this value, it will be purged from the log to prevent unlimited memory usage. When all invocations are purged from a log, the log itself will be deleted. |
hazelcast.slow.operation.detector.stacktrace.logging.enabled |
false | bool | Defines if the stacktraces of slow operations are logged in the log file. Stack traces are always reported to the Management Center, but by default, they are not printed to keep the log size small. |
hazelcast.slow.operation.detector.threshold.millis |
10000 | int | Defines a threshold above which a running operation in OperationService is considered to be slow. These operations log a warning and are shown in the Management Center with detailed information, e.g. stacktrace. |
hazelcast.socket.bind.any |
true | bool | Bind both server-socket and client-sockets to any local interface. |
hazelcast.socket.client.bind |
true | bool | Bind client socket to an interface when connecting to a remote server socket. When set to false , client socket is not bound to any interface. |
hazelcast.socket.client.bind.any |
true | bool | Bind client-sockets to any local interface. If not set, hazelcast.socket.bind.any will be used as default. |
hazelcast.socket.client.receive.buffer.size |
-1 | int | Hazelcast creates all connections with receive buffer size set according to the hazelcast.socket.receive.buffer.size . When it detects a connection opened by a client, then it adjusts the receive buffer size according to this property. It is in kilobytes and the default value is -1. |
hazelcast.socket.client.send.buffer.size |
-1 | int | Hazelcast creates all connections with send buffer size set according to the hazelcast.socket.send.buffer.size . When it detects a connection opened by a client, then it adjusts the send buffer size according to this property. It is in kilobytes and the default value is -1. |
hazelcast.socket.connect.timeout.seconds |
0 | int | Socket connection timeout in seconds. Socket.connect() will be blocked until either connection is established or connection is refused or this timeout passes. Default is 0, means infinite. |
hazelcast.socket.keep.alive |
true | bool | Socket set keep alive (SO_KEEPALIVE ). |
hazelcast.socket.linger.seconds |
0 | int | Set socket SO_LINGER option. |
hazelcast.socket.no.delay |
true | bool | Socket set TCP no delay. |
hazelcast.socket.receive.buffer.size |
32 | int | Socket receive buffer (SO_RCVBUF ) size in KB. If you have a very fast network (e.g. 10gbit) and/or you have large entries, then you may benefit from increasing sender/receiver buffer sizes. Use this property and the next one below tune the size. For example, a send/receive buffer size of 1024 kB is a safe starting point for a 10gbit network. |
hazelcast.socket.send.buffer.size |
32 | int | Socket send buffer (SO_SNDBUF ) size in KB. |
hazelcast.socket.server.bind.any |
true | bool | Bind server-socket to any local interface. If not set, hazelcast.socket.bind.any will be used as default. |
hazelcast.tcp.join.port.try.count |
3 | int | The number of incremental ports, starting with the port number defined in the network configuration, that will be used to connect to a host (which is defined without a port in TCP/IP member list while a node is searching for a cluster). |
hazelcast.unsafe.mode |
auto | string | Other than the default "auto", it has two other values. One of them is "disabled" to explicitly disable the Unsafe usage in your platform. The other one is "enforced" to enforce the usage of Unsafe even if your platform does not support it. The default value "auto" automatically detects whether the usage of Unsafe is suitable for a given platform. This property can only be set by passing a JVM-wide system property. |
hazelcast.version.check.enabled |
true | bool | Enable Hazelcast new version check on startup. |
hazelcast.wait.seconds.before.join |
5 | int | Wait time before join operation. |
Imagine that you have 10-node cluster and that the network is divided into two in a way that 4 servers cannot see the other 6. As a result, you end up having two separate clusters: 4-node cluster and 6-node cluster. Members in each sub-cluster think that the other nodes are dead even though they are not. This situation is called Network Partitioning (a.k.a. Split-Brain Syndrome).
However, these two clusters have a combination of the 271 (using default) primary and backup partitions. It’s very likely that not all of the 271 partitions, including both primaries and backups, exist in both mini-clusters. Therefore, from each mini-cluster’s perspective, data has been lost as some partitions no longer exist (they exist on the other segment).
If a MapStore was in use, those lost partitions would be reloaded from some database, making each mini-cluster complete. Each mini-cluster will then recreate the missing primary partitions and continue to store data in them, including backups on the other nodes.
When primary partitions exist without a backup, a backup version problem will be detected and a backup partition will be created. When backups exist without a primary, the backups will be promoted to primary partitions and new backups will be created with proper versioning. At this time, both mini-clusters have repaired themselves with all 271 partitions with backups, and continue to handle traffic without any knowledge of each other. Given that they have enough remaining memory (assumption), they are just smaller and can handle less throughput.
If a MapStore is in use and the network to the database is available, one or both of the mini-clusters will write updates to the same database. There is a potential for the mini-clusters to overwrite the same cache entry records if modified in both mini-clusters. This overwrite scenario represents a potential data loss, and thus the database design should consider an insert and aggregate on read or version strategy rather than update records in place.
If the network to the database is not available, then based on the configured or coded consistency level or transaction, entry updates are held in cache or updates are rejected (fully synchronous and consistent). When held in cache, the updates will be considered dirty and will be written to the database when it becomes available. You can view the dirty entry counts per cluster member in the Management Center web console (please see the Map Monitoring section).
Since it is a network failure, there is no way to programmatically avoid your application running as two separate independent clusters. But what will happen after the network failure is fixed and connectivity is restored between these two clusters? Will these two clusters merge into one again? If they do, how are the data conflicts resolved, because you might end up having two different values for the same key in the same map?
When the network is restored, all 271 partitions should exist in both mini-clusters and they should all undergo the merge. Once all primaries are merged,
all backups are rewritten so their versions are correct. You may want to write a merge policy using the MapMergePolicy
interface that rebuilds the entry from the database rather than from memory.
The only metadata available for merge decisions are from the EntryView
interface that includes object size (cost), hits count, last updated/stored dates, and a version number that starts at zero and is incremented for each entry update.
You could also create your own versioning scheme or capture a time series of deltas to reconstruct an entry.
Here is, step by step, how Hazelcast split brain merge happens:
Each member of the merging cluster will do the following.
Pause.
Each member of the merging cluster rejoins the new cluster and sends a merge request for each of its locally owned map entries. Two important points:
MergePolicy
set for that map. There are built-in merge policies such as PassThroughMergePolicy
, PutIfAbsentMapMergePolicy
, HigherHitsMapMergePolicy
and LatestUpdateMapMergePolicy
. You can develop your own merge policy by implementing com.hazelcast.map.merge.MapMergePolicy
. You should set the full class name of your implementation to the merge-policy
configuration.public interface MergePolicy {
/**
* Returns the value of the entry after the merge
* of entries with the same key. Returning value can be
* You should consider the case where existingEntry is null.
*
* @param mapName name of the map
* @param mergingEntry entry merging into the destination cluster
* @param existingEntry existing entry in the destination cluster
* @return final value of the entry. If returns null then entry will be removed.
*/
Object merge( String mapName, EntryView mergingEntry, EntryView existingEntry );
}
Here is how merge policies are specified per map:
<hazelcast>
...
<map name="default">
<backup-count>1</backup-count>
<eviction-policy>NONE</eviction-policy>
<max-size>0</max-size>
<eviction-percentage>25</eviction-percentage>
<!--
While recovering from split-brain (network partitioning),
map entries in the small cluster will merge into the bigger cluster
based on the policy set here. When an entry merge into the
cluster, there might an existing entry with the same key already.
Values of these entries might be different for that same key.
Which value should be set for the key? Conflict is resolved by
the policy set here. Default policy is hz.ADD_NEW_ENTRY
There are built-in merge policies such as
There are built-in merge policies such as
com.hazelcast.map.merge.PassThroughMergePolicy; entry will be added if
there is no existing entry for the key.
com.hazelcast.map.merge.PutIfAbsentMapMergePolicy ; entry will be
added if the merging entry doesn't exist in the cluster.
com.hazelcast.map.merge.HigherHitsMapMergePolicy ; entry with the
higher hits wins.
com.hazelcast.map.merge.LatestUpdateMapMergePolicy ; entry with the
latest update wins.
-->
<merge-policy>MY_MERGE_POLICY_CLASS</merge-policy>
</map>
...
</hazelcast>
NOTE: Map is the only Hazelcast distributed data structure that merges after a split brain syndrome. For the other data structures (e.g. Queue, Topic, IdGenerator, etc. ), one instance of that data structure is chosen after split brain syndrome.
Hazelcast is distributed using the Apache License 2, therefore permissions are granted to use, reproduce and distribute it along with any kind of open source and closed source applications.
Hazelcast Enterprise is a commercial product of Hazelcast, Inc. and is distributed under a commercial license that must be acquired before using it in any type of released software. Feel free to contact Hazelcast sales department for more information on commercial offers.
Depending on the used feature-set, Hazelcast has certain runtime dependencies which might have different licenses. Following are dependencies and their respective licenses.
Embedded dependencies are merged (shaded) with the Hazelcast codebase at compile-time. These dependencies become an integral part of the Hazelcast distribution.
For license files of embedded dependencies, please see the license
directory of the Hazelcast distribution, available at our
download page.
minimal-json:
minimal-json is a JSON parsing and generation library which is a part of the Hazelcast distribution. It is used for communication between the Hazelcast cluster and the Management Center.
minimal-json is distributed under the MIT license and offers the same rights to add, use, modify, and distribute the source code as the Apache License 2.0 that Hazelcast uses. However, some other restrictions might apply.
Depending on the used features, additional dependencies might be added to the dependency set. Those runtime dependencies might have other licenses. See the following list of additional runtime dependencies.
Spring Framework:
Hazelcast offers a tight integration into the Spring Framework. Hazelcast can be configured and controlled using Spring.
The Spring Framework is distributed under the terms of the Apache License 2 and therefore it is fully compatible with Hazelcast.
Hibernate:
Hazelcast integrates itself into Hibernate as a second-level cache provider.
Hibernate is distributed under the terms of the Lesser General Public License 2.1, also known as LGPL. Please read carefully the terms of the LGPL since restrictions might apply.
Apache Tomcat:
Hazelcast Enterprise offers native integration into Apache Tomcat for web session clustering.
Apache Tomcat is distributed under the terms of the Apache License 2 and therefore fully compatible with Hazelcast.
Eclipse Jetty:
Hazelcast Enterprise offers native integration into Jetty for web session clustering.
Jetty is distributed with a dual licensing strategy. It is licensed under the terms of the Apache License 2 and under the Eclipse Public License v1.0, also known as EPL. Due to the Apache License, it is fully compatible with Hazelcast.
JCache API (JSR 107):
Hazelcast offers a native implementation for JCache (JSR 107), which has a runtime dependency to the JCache API.
The JCache API is distributed under the terms of the so called Specification License. Please read carefully the terms of this license since restrictions might apply.
Boost C++ Libraries:
Hazelcast Enterprise offers a native C++ client, which has a link-time dependency to the Boost C++ Libraries.
The Boost Libraries are distributed under the terms of the Boost Software License, which is very similar to the MIT or BSD license. Please read carefully the terms of this license since restrictions might apply.
You may see the following exceptions in any Hazelcast operation when the following situations occur:
HazelcastInstanceNotActiveException
: Thrown when HazelcastInstance
is not active (already shutdown or being shutdown) during an invocation.
HazelcastOverloadException
: Thrown when the system will not handle more load due to an overload. This exception is thrown when back pressure is enabled.
DistributedObjectDestroyedException
: Thrown when an already destroyed DistributedObject
(IMap, IQueue, etc.) is accessed and when a method call is done over a destroyed object.
MemberLeftException
: Thrown when a member leaves during an invocation or execution.
Hazelcast also throws the following exceptions in the cases of overall system problems such as networking issues and long pauses:
PartitionMigratingException
: Thrown when an operation is executed on a partition, but that partition is currently being moved around.
TargetNotMemberException
: Thrown when an operation is sent to a machine that is not a member of the cluster.
CallerNotMemberException
: Thrown when an operation was sent by a machine which is not a member in the cluster when the operation is executed.
WrongTargetException
: Thrown when an operation is executed on the wrong machine, mostly because the partition that operation belongs to is already moved to some other member.
The partition count of 271, being a prime number, is a good choice because it will be distributed to the nodes almost evenly. For a small to medium sized cluster, the count of 271 gives an almost even partition distribution and optimal-sized partitions. As your cluster becomes bigger, you should make this count bigger to have evenly distributed partitions.
Yes. All Hazelcast data structures are thread safe.
When a node is started in a cluster, it will dynamically and automatically be discovered. There are three types of discovery.
Once nodes are discovered, all the communication between them will be via TCP/IP.
RELATED INFORMATION
Please refer to the Hazelcast Cluster Discovery section for detailed information.
Once a node is gone (crashes), the following happens since data in each node has a backup in other nodes.
So eventually, no data is lost.
If you notice that there is a problem with a node joining a cluster, you may want to perform a connectivity test between the node to be joined and a node from the cluster. You can use the iperf
tool for this purpose. For example, you can execute the below command on one node (i.e. listening on port 5701).
iperf -s -p 5701
And you can execute the below command on the other node.
iperf -c
<IP address>
-d -p 5701
The output should include connection information, such as the IP addresses, transfer speed, and bandwidth. Otherwise, if the output says No route to host
, it means a network connection problem exists.
When you store a key and value in a distributed Map, Hazelcast serializes the key and value, and stores the byte array version of them in local ConcurrentHashMaps. These ConcurrentHashMaps use equals
and hashCode
methods of byte array version of your key. It does not take into account the actual equals
and hashCode
implementations of your objects. So it is important that you choose your keys in a proper way.
Implementing equals
and hashCode
is not enough, it is also important that the object is always serialized into the same byte array. All primitive types like String, Long, Integer, etc. are good candidates for keys to be used in Hazelcast. An unsorted Set is an example of a very bad candidate because Java Serialization may serialize the same unsorted set in two different byte arrays.
Hazelcast always return a clone copy of a value. Modifying the returned value does not change the actual value in the map (or multimap, list, set). You should put the modified value back to make changes visible to all nodes.
V value = map.get( key );
value.updateSomeProperty();
map.put( key, value );
Collections which return values of methods (such as IMap.keySet
, IMap.values
, IMap.entrySet
, MultiMap.get
, MultiMap.remove
, IMap.keySet
, IMap.values
) contain cloned values. These collections are NOT backed up by related Hazelcast objects. Therefore, changes to them are NOT reflected in the originals, and vice-versa.
Hazelcast allows you to create more than one instance on the same JVM. Each member is called HazelcastInstance
and each will have its own configuration, socket and threads, so you can treat them as totally separate instances.
This enables you to write and to run cluster unit tests on a single JVM. Because you can use this feature for creating separate members different applications running on the same JVM (imagine running multiple web applications on the same JVM), you can also use this feature for testing your Hazelcast cluster.
Let's say you want to test if two members have the same size of a map.
@Test
public void testTwoMemberMapSizes() {
// start the first member
HazelcastInstance h1 = Hazelcast.newHazelcastInstance();
// get the map and put 1000 entries
Map map1 = h1.getMap( "testmap" );
for ( int i = 0; i < 1000; i++ ) {
map1.put( i, "value" + i );
}
// check the map size
assertEquals( 1000, map1.size() );
// start the second member
HazelcastInstance h2 = Hazelcast.newHazelcastInstance();
// get the same map from the second member
Map map2 = h2.getMap( "testmap" );
// check the size of map2
assertEquals( 1000, map2.size() );
// check the size of map1 again
assertEquals( 1000, map1.size() );
}
In the test above, everything happens in the same thread. When developing a multi-threaded test, you need to carefully handle coordination of the thread executions. it is highly recommended that you use CountDownLatch
for thread coordination (you can certainly use other ways). Here is an example where we need to listen for messages and make sure that we got these messages.
@Test
public void testTopic() {
// start two member cluster
HazelcastInstance h1 = Hazelcast.newHazelcastInstance();
HazelcastInstance h2 = Hazelcast.newHazelcastInstance();
String topicName = "TestMessages";
// get a topic from the first member and add a messageListener
ITopic<String> topic1 = h1.getTopic( topicName );
final CountDownLatch latch1 = new CountDownLatch( 1 );
topic1.addMessageListener( new MessageListener() {
public void onMessage( Object msg ) {
assertEquals( "Test1", msg );
latch1.countDown();
}
});
// get a topic from the second member and add a messageListener
ITopic<String> topic2 = h2.getTopic(topicName);
final CountDownLatch latch2 = new CountDownLatch( 2 );
topic2.addMessageListener( new MessageListener() {
public void onMessage( Object msg ) {
assertEquals( "Test1", msg );
latch2.countDown();
}
} );
// publish the first message, both should receive this
topic1.publish( "Test1" );
// shutdown the first member
h1.shutdown();
// publish the second message, second member's topic should receive this
topic2.publish( "Test1" );
try {
// assert that the first member's topic got the message
assertTrue( latch1.await( 5, TimeUnit.SECONDS ) );
// assert that the second members' topic got two messages
assertTrue( latch2.await( 5, TimeUnit.SECONDS ) );
} catch ( InterruptedException ignored ) {
}
}
You can start Hazelcast members with different configurations. Remember to call Hazelcast.shutdownAll()
after each test case to make sure that there is no other running member left from the previous tests.
@After
public void cleanup() throws Exception {
Hazelcast.shutdownAll();
}
For more information please check our existing tests.
Yes. Hazelcast performed a successful test on Amazon EC2 with 200 nodes.
Yes. However, there are some points you should consider. The environment should be LAN with a high stability and the network speed should be 10 Gbps or higher. If the number of nodes is high, the client type should be selected as Dummy, not Smart Client. In the case of Smart Clients, since each client will open a connection to the nodes, these nodes should be powerful enough (for example, more cores) to handle hundreds or thousands of connections and client requests. Also, you should consider using near caches in clients to lower the network traffic. And you should use the Hazelcast releases with the NIO implementation (which starts with Hazelcast 3.2).
Also, you should configure the clients attentively. Please refer to the Java Client section section for configuration notes.
LiteMember supports task execution (distributed executor service), smart client does not. Also, LiteMember is highly coupled with cluster, smart client is not.
We have two support services: community and commercial support. Community support is provided through our Mail Group and StackOverflow web site. For information on support subscriptions, please see Hazelcast.com.
No. However, Hazelcast provides MapStore
and MapLoader
interfaces. For example, when you implement the MapStore
interface, Hazelcast calls your store and load methods whenever needed.
Yes. But please note that Hazelcast's main design focus is multi-node clusters to be used as a distribution platform.
Hazelcast Management Center is what you use to monitor and manage the nodes running Hazelcast. In addition to monitoring the overall state of a cluster, you can analyze and browse data structures in detail, you can update map configurations, and you can take thread dumps from nodes.
Moreover, JMX monitoring is also provided. Please see the Monitoring with JMX section for details.
By changing the log level to "Debug". Below are sample lines for log4j logging framework. Please see the Logging Configuration section to learn how to set logging types.
First, set the logging type as follows.
String location = "log4j.configuration";
String logging = "hazelcast.logging.type";
System.setProperty( logging, "log4j" );
/**if you want to give a new location. **/
System.setProperty( location, "file:/path/mylog4j.properties" );
Then set the log level to "Debug" in the properties file. Below is example content.
# direct log messages to stdout #
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.Target=System.out
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d{ABSOLUTE} %5p [%c{1}] - %m%n
log4j.logger.com.hazelcast=debug
#log4j.logger.com.hazelcast.cluster=debug
#log4j.logger.com.hazelcast.partition=debug
#log4j.logger.com.hazelcast.partition.InternalPartitionService=debug
#log4j.logger.com.hazelcast.nio=debug
#log4j.logger.com.hazelcast.hibernate=debug
The line log4j.logger.com.hazelcast=debug
is used to see debug logs for all Hazelcast operations. Below this line, you can select to see specific logs (cluster, partition, hibernate, etc.).
In the embedded topology, nodes include both the data and application. This type of topology is the most useful if your application focuses on high performance computing and many task executions. Since application is close to data, this topology supports data locality.
In the client-server topology, you create a cluster of nodes and scale the cluster independently. Your applications are hosted on the clients, and the clients communicate with the nodes in the cluster to reach data.
Client-server topology fits better if there are multiple applications sharing the same data or if application deployment is significantly greater than the cluster size (for example, 500 application servers vs. 10 node cluster).
Programmatically:
PartitionService partitionService = hazelcastInstance.getPartitionService().isClusterSafe()
if (partitionService().isClusterSafe()) {
hazelcastInstance.shutdown(); // or terminate
}
Or declaratively:
PartitionService partitionService = hazelcastInstance.getPartitionService().isClusterSafe()
if (partitionService().isLocalMemberSafe()) {
hazelcastInstance.shutdown(); // or terminate
}
RELATED INFORMATION
Please refer to the Cluster-Member Safety Check section for more information.
Native Memory solutions can be preferred:
The only disadvantage when using Near Cache is that it may cause stale reads.
Hazelcast supports symmetric encryption, secure sockets layer (SSL), and Java Authentication and Authorization Service (JAAS). Please see the Security chapter for more information.
Hazelcast allows you to set some socket options such as SO_KEEPALIVE
, SO_SNDBUF
, and SO_RCVBUF
using Hazelcast configuration properties. Please see hazelcast.socket.*
properties explained in the System Properties section.
In Hazelcast, socket connections are created with the SO_KEEPALIVE
option enabled by default. In most operating systems, default keep-alive time is 2 hours. If you have a firewall between clients and servers which is configured to reset idle connections/sessions, make sure that the firewall's idle timeout is greater than the TCP keep-alive defined in the OS.
For additional information please see:
If you encounter an error of java.lang.OutOfMemoryError: unable to create new native thread
, it may be caused by exceeding the available file descriptors on your operating system, especially if it is Linux. This exception is usually thrown on a running node, after a period of time when the thread count exhausts the file descriptor availability.
The JVM on Linux consumes a file descriptor for each thread created. The default number of file descriptors available in Linux is usually 1024. If you have many JVMs running on a single machine, it is possible to exceed this default number.
You can view the limit using the following command.
# ulimit -a
At the operating system level, Linux users can control the amount of resources (and in particular, file descriptors) used via one of the following options.
1 - Editing the limits.conf
file:
# vi /etc/security/limits.conf
testuser soft nofile 4096<br>
testuser hard nofile 10240<br>
2 - Or using the ulimit
command:
# ulimit -Hn
10240
The default number of process per users is 1024. Adding the following to your $HOME/.profile
could solve the issue:
# ulimit -u 4096
Repartitioning is the process of redistributing the partition ownerships. Hazelcast performs the repartitioning in the cases where a node leaves the cluster or joins the cluster. If a repartitioning will happen while an entry processor is active in a node processing on an entry object, the repartitioning waits for the entry processor to complete its job.
Assume you have two instances on two different machines and you develop a configuration as shown below.
Config config = new Config();
NetworkConfig network = config.getNetworkConfig();
JoinConfig join = network.getJoin();
join.getMulticastConfig().setEnabled(false);
join.getTcpIpConfig().addMember("IP1")
.addMember("IP2").setEnabled(true);
network.getInterfaces().setEnabled(true)
.addInterface("IP1").addInterface("IP2");
When you create the Hazelcast instance, you have to pass the configuration to the instance. If you create the instances without passing the configuration, each instance starts but cannot see each other. Therefore, a correct way to create the instance is the following:
HazelcastInstance instance = Hazelcast.newHazelcastInstance(config);
The following is an incorrect way:
HazelcastInstance instance = Hazelcast.newHazelcastInstance();
When you start more nodes after the first one is started, you will see replica: 1 has no owner
entry in the newly started node's log. There is no need to worry about it since it refers to a transitory state. It only means the replica partition is not ready/assigned yet and eventually it will be.
Term | Definition |
---|---|
2-phase Commit | 2-phase commit protocol is an atomic commitment protocol for distributed systems. It consists of two phases: commit-request and commit. In commit-request phase, transaction manager coordinates all of the transaction resources to commit or abort. In commit-phase, transaction manager decides to finalize operation by committing or aborting according to the votes of the each transaction resource. |
ACID | A set of properties (Atomicity, Consistency, Isolation, Durability) guaranteeing that transactions are processed reliably. Atomicity requires that each transaction be all or nothing (i.e. if one part of the transaction fails, the entire transaction will fail). Consistency ensures that only valid data following all rules and constraints is written. Isolation ensures that transactions are securely and independently processed at the same time without interference (and without transaction ordering). Durability means that once a transaction has been committed, it will remain so, no matter if there is a power loss, crash, or error. |
Cache | A high-speed access area that can be either a reserved section of main memory or storage device. |
Garbage Collection | Garbage collection is the recovery of storage that is being used by an application when that application no longer needs the storage. This frees the storage for use by other applications (or processes within an application). It also ensures that an application using increasing amounts of storage does not reach its quota. Programming languages that use garbage collection are often interpreted within virtual machines like the JVM. The environment that runs the code is also responsible for garbage collection. |
Hazelcast Cluster | It is a virtual environment formed by Hazelcast nodes communicating with each other. |
Hazelcast Partitions | Memory segments containing the data. Hazelcast is built-on the partition concept, it uses partitions to store and process data. Each partition can have hundreds or thousands of data entries depending on your memory capacity. You can think of a partition as a block of data. In general and optimally, a partition should have a maximum size of 50-100 Megabytes. |
IMDG | An in-memory data grid (IMDG) is a data structure that resides entirely in memory, and is distributed among many nodes in a single location or across multiple locations. IMDGs can support thousands of in-memory data updates per second, and they can be clustered and scaled in ways that support large quantities of data. |
Invalidation | The process of marking an object as being invalid across the distributed cache. |
Java heap | Java heap is the space that Java can reserve and use in memory for dynamic memory allocation. All runtime objects created by a Java application are stored in heap. By default, the heap size is 128 MB, but this limit is reached easily for business applications. Once the heap is full, new objects cannot be created and the Java application shows errors. |
LRU, LFU | LRU and LFU are two of eviction algorithms. LRU is the abbreviation for Least Recently Used. It refers to entries eligible for eviction due to lack of interest by applications. LFU is the abbreviation for Least Frequently Used. It refers to the entries eligible for eviction due to having the lowest usage frequency. |
+Member | A Hazelcast instance. Depending on your Hazelcast usage, it can refer to a server or a Java virtual machine (JVM). Members are also referred as nodes . |
Multicast | It is a type of communication where data is addressed to a group of destination nodes simultaneously. |
Near Cache | It is a caching model. When it is enabled, an object retrieved from a remote node is put into the local cache and the future requests made to this object will be handled by this local node. For example, if you have a map with data that is mostly read, then using near cache is a good idea. |
NoSQL | "Not Only SQL". It is a database model that provides a mechanism for storage and retrieval of data that is tailored in means other than the tabular relations used in relational databases. It is a type of database which does not adhering to the traditional relational database management system (RDMS) structure. It is not built on tables and does not employ SQL to manipulate data. It also may not provide full ACID guarantees, but still has a distributed and fault tolerant architecture. |
Race Condition | This condition occurs when two or more threads can access shared data and they try to change it at the same time. |
RSA | An algorithm developed by Rivest, Shamir and Adleman to generate, encrypt and decrypt keys for secure data transmissions. |
Serialization | Process of converting an object into a stream of bytes in order to store the object or transmit it to memory, a database, or a file. Its main purpose is to save the state of an object in order to be able to recreate it when needed. The reverse process is called deserialization. |
Split Brain | Split brain syndrome, in a clustering context, is a state in which a cluster of nodes gets divided (or partitioned) into smaller clusters of nodes, each of which believes it is the only active cluster. |
Transaction | Means a sequence of information exchange and related work (such as data store updating) that is treated as a unit for the purposes of satisfying a request and for ensuring data store integrity. |