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.
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 3.6 release.
The following are the the enhancements performed for Hazelcast 3.6 release.
The following are the other improvements performed to solve the enhancement issues opened by the Hazelcast customers/team.
BasicCompletableFuture
. [6080]Map.Entry
supplied to Entry Processor is not Serializable any more. [5611]minimal-json
with the provided scope is not picked up by the shade plugin. [5543]<hz:hazelcast:....</hz:hazelcast
)
a SAXParse
exception is thrown. [5528]socket-options
should be of type xs:string
. [4700]ClientMembershipEvent
does not need to have the member
field. [4282]lock
with lease time feature but does not support tryLock
with lease time. [1564]3.6 Fixes
The following are the issues solved for Hazelcast 3.6 release.
EntryEvictedListener
that is notified both for expiration and eviction events. There should be a separate listener for expired entries: eviction happens due to size constraints, and expiry is once the entry has expired. [6311]InvocationFuture
s async calls do not detect the lost operations. [6250]setBooleanAttribute
of the class Member
is run, Null Pointer Exception is occurred on STDOUT
. The problem is in the method sendMemberAttributeEvent
of the class ClusterServiceImpl
. [6223]IOBalancer
keeps references of all the socket reader/writers but when destroying the connection, they release the references for only the ones which has endpoints. This causes a memory leak. [6199]ILIKE
and Regex
examples should be added to the Reference Manual under the "Supported SQL Syntax" section. [6190]GroupProperty
defaulting does not work properly when programmatic configuration is used. [6174]HazelcastInstance
is created using the default newHazelcastInstance
static method, then an HazelcastInstance
whose Config
has a valid configurationUrl
property is created. However, XmlBuilder
does not set this URL in the configuration it parses. [6061]ClientExceptionFactory
which has been developed for exception processing and working well in that sense. [6010]HazelcastXATest
has only fast and slow modes (nothing in between) and possibly due to this, sometimes a transaction is waiting for a timeout. Either the transaction recovery or the test class itself is racy. [5923]ClientEndpointImpl
is the leftover. This runnable collection is used to cleanup the
listeners when client is disconnected, it should be removed too after the listener is removed. [5893] CacheRemoveAllOperation
does not send the "completed" event in some cases, e.g. if CacheRecordStore
for that partition is not created yet or if the filtered keys are empty. [5865]MapProxyImpl
, the methods executeOnKey
and submitToKey
create an EntryOperation
with the thread ID set. This does not happen with the class ClientMapProxy
. Therefore, the class MapExecuteOnKeyRequest
should take a thread ID and set this on the generated EntryOperation
. [5857]IndexImpl.getRecords()
fails with Null Pointer Exception due to the inconsistency between the not(...equals())
and notEquals()
. [5807]HazelcastHttpSession.getAttribute()
for WebFilter does not work when deferredWrite
is set to true
. [5798]hazelcast.nio.faststring
is enabled, UTFEncoderDecoder
tries to create a FastStringCreator
. However, if the reflection is not available due to the security manager, buildFastStringCreator
returns null and consequently StringCreator
becomes null. [5777]hazelcast-jca-rar/pom.xml
references to src/main/rar/ra.xml
which does not exist. [5760]mvn clean compile -Pqa
does not exist but it is documented in the README of Hazelcast. [5746]PerformanceLogFile
only compiles if JDK 1.7 or above is used. [5729]BufferObjectDataInput
is created. This generates waste since it is created with an array of data for every deserialization. The BufferObjectDataOutput
is already cached; the input should use a similar approach. [5562]UnsupportedOperationException
. [5562]keySet()
relies on QueryOperation
. The QueryOperation
does not accept IterationType
- it always returns both keys and values. This can lead to unnecessary load and potentially even an OOM exception. [4642]NullPointerException
is thrown after the cluster is reset. [175]putAll()
method is used [159]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 6 - Distributed Data Structures | Map | Map Eviction section updated by adding the definition for the min-eviction-check-millis property. The section Understanding Map Eviction added for a more clearer explanation of the eviction mechanism. |
Chapter 9 - Distributed Query | Filtering with Paging Predicates | The note stating that the random page accessing is not supported removed, since it is now supported with this release. |
Chapter 11 - Transactions | ONE_PHASE vs. TWO_PHASE | Added as a new section explaining the trade offs between these two transaction types. |
Creating a Transaction Interface | Replaced the transaction type name LOCAL with ONE_PHASE. Updated the definitions of transaction types. | |
Chapter 12 - Hazelcast JCache | ICache Partition Lost Listener | Added as a new section explaining how to listen when a partition is lost in a Hazelcast JCache implementation. |
Chapter 13 - Integrated Clustering | Web Session Replication | Marking Transient Attributes added as a new section. |
Hibernate Second Level Cache | Added additional information related to Hibernate Native Client mode to the introduction paragraph of the Setting Client/Server for Hibernate section. | |
Chapter 15 - Hazelcast Java Client | Hazelcast Clients Feature Comparison | Added as a new section. |
Client Network Configuration | Updated by adding the definition of the new IAM role configuration element. | |
Chapter 18 - Management | Defining a Cluster Quorum | Added information on quorum support for caches. Added the definition of the new configuration element quorum-ref to ICache Configuration section. |
Management Center | A note on how to see the cache statistics on the Management Center added under the Caches section. Replicated Maps added as a new section. Added the information explaining the new e-mail notification mechanism to Creating Alerts. |
|
Monitoring with JMX | MBean Naming for Hazelcast Data Structures and Connecting to JMX Agent added as new sections. | |
Chapter 23 - Hazelcast Configuration | Improved by adding missing configuration elements and attributes. Added Quorum Configuration section. | |
System Properties | Added the new system properties hazelcast.unsafe.mode , hazelcast.io.input.thread.count , hazelcast.io.output.thread.count and hazelcast.query.optimizer.type . Updated the definition of hazelcast.io.thread.count . |
|
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>3.5.1</version>
</dependency>
</dependencies>
As an alternative, you can download and install Hazelcast yourself. You only need to:
Download hazelcast-<
version>.zip
file from 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 uses phone home data to learn about usage of Hazelcast.
Hazelcast member instances call our phone home server initially when they are started and then for every 24 hours. This applies to all the instances joined to the cluster.
What is sent in?
Phone Home Code
The phone home code itself is open source. Please see here.
Disabling Phone Homes
Set the hazelcast.version.check.enabled
system property to false either in the config or on the Java command line. Please see the System Properties section for information on how to set a property.
Phone Home URLs
For older versions( versions 1.x - 2.x): http://www.hazelcast.com:80/version.jsp.
For new versions (versions 3.x): http://version.checkhazelcast.com:80/version.jsp.
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 which 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.
You can deploy a Hazelcast cluster in two ways: Embedded or Client/Server.
If you have an application whose main focal point is asynchronous or high performance computing and lots of task executions, then Embedded deployment is useful. In this type, members include both the application and Hazelcast data and services. The advantage of the Embedded deployment is having a low-latency data access.
See the below illustration.
In the Client/Server deployment, Hazelcast data and services are centralized in one or more server members and they are accessed by the application through clients. You can have a cluster of server members that can be independently created and scaled. Your clients communicate with these members to reach to Hazelcast data and services on them. Hazelcast provides native clients (Java, .NET and C++), Memcache clients and REST clients. See the below illustration.
Client/Server deployment has advantages including more predictable and reliable Hazelcast performance, easier identification of problem causes, and most importantly, better scalability. When you need to scale in this deployment type, just add more Hazelcast server members. You can address client and server scalability concerns separately.
If you want low-latency data access, as it is in the Embedded deployment, and you also want the scalability advantages of the Client/Server deployment, you can consider to define near caches for your clients. This enables the frequently used data to be kept in the client's local memory. Please refer to Configuring Client Near Cache.
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 cluster member, it starts with these 271 partitions. The following illustration shows the partitions in a Hazelcast cluster with single member.
When you start a second node on that cluster (creating a Hazelcast cluster with 2 members), 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 member has 135 primary partitions (black), and each of these partitions are backed up in the second member (blue). At the same time, the first member also has the replica partitions of the second member's primary partitions.
As you add more members, Hazelcast one-by-one moves some of the primary and replica partitions to the new members, making all members 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 Hazelcast cluster with 4 members.
Hazelcast distributes the partitions equally among the members of the cluster. Hazelcast creates the backups of partitions and distributes them among the members for redundancy.
NOTE: Partition distributions in the above illustrations are for your convenience and for a more clearer description. Normally, the partitions are not distributed in an order (as they are shown in these illustrations), they are distributed randomly. The important point here is that Hazelcast equally distributes the partitions and their backups among the members.
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, i.e. the partition ID. For ALL the members you have in your cluster, the partition ID for a given key will always be the same.
When you start a member, a partition table is created within it. This table stores the partition IDs and the cluster members they belong. The purpose of this table is to make all members in the cluster aware of this information, making sure that each member knows where the data is.
The oldest member in the cluster (the one that started first) periodically sends the partition table to all members. In this way, each member in the cluster is informed about any changes to the partition ownership. The ownerships may be changed when, for example, a new member joins the cluster, or when a member leaves the cluster.
NOTE: If the oldest member goes down, the next oldest member sends the partition table information to the other ones.
You can configure the frequency (how often) that the member 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 member 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>
<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 perform 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 them onto all Hazelcast members. Each member carries approximately "(1/n *
total-data) + backups", n being the number of members in the cluster. For example, if you have a member with 1000 objects to be stored in the cluster, and then you start a second member, each member will both store 500 objects and back up the 500 objects in the other member.
Let's create a Hazelcast instance and fill a map named Capitals
with key-value pairs using the following code. Use the HazelcastInstance getMap
method to get the map, then use the map put
method to put an entry into the map.
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 cluster member is created with a map whose entries are distributed across the members's partitions. See the below illustration. For now, this is a single member 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 member by running the above code again. This will create a cluster with 2 members. This is also where backups of entries are created; remember the backup partitions mentioned in the Hazelcast Overview section. The following illustration shows two members 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 cluster members (JVMs). Each member 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 map 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 cluster member will be copied onto other member(s).
To create synchronous backups, 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.
To create asynchronous backups, select the number of async backups with 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.
To enable backup reads (read local backup entries), set 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. The eviction process starts according to this calculated per-partition maximum size when you try to put an entry. The section below 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: The 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
, EntryProcessor.process
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 cluster member. 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 cluster member. 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>
USED_NATIVE_MEMORY_SIZE
: Maximum used native memory size in megabytes for each JVM.
<max-size policy="USED_NATIVE_MEMORY_SIZE">1024</max-size>
USED_NATIVE_MEMORY_PERCENTAGE
: Maximum used native memory size percentage for each JVM.
<max-size policy="USED_NATIVE_MEMORY_PERCENTAGE">65</max-size>
FREE_NATIVE_MEMORY_SIZE
: Maximum free native memory size in megabytes for each JVM.
<max-size policy="FREE_NATIVE_MEMORY_SIZE">256</max-size>
FREE_NATIVE_MEMORY_PERCENTAGE
: Maximum free native memory size percentage for each JVM.
<max-size policy="FREE_NATIVE_MEMORY_PERCENTAGE">5</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
: 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 example, 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()
. An example 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
.
To evict all keys from the map except the locked ones, use the method evictAll()
. If a MapStore is defined for the map, deleteAll
is not called by evictAll
. If you want to call the method deleteAll
, use clear()
.
An example 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 (and a few other Hazelcast data structures, such as ICache) 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, such as entry processor and queries.
To set how the data will be stored in memory, set in-memory-format
in configuration. 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 first includes a serialization on the member owning the instance, and then a deserialization on the member calling the instance. When the BINARY
format is used, only a deserialization is required; BINARY
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 be a serialization and then a 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 members. 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 the subsections below.
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. However, 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 using the system property hazelcast.map.write.behind.queue.capacity
. Please refer to the System Properties section for information on this property and how to set the system properties.
In write-behind 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 store entries when there is one configuration applied to multiple maps, use Hazelcast's MapStoreFactory
interface. Using the MapStoreFactory
interface, MapStore
s for each map can be created when a wildcard configuration is used. Example 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;
}
});
To initialize the MapLoader
implementation with the given map name, configuration properties, and the Hazelcast instance, implement the MapLoaderLifecycleSupport
interface. 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 implementing
* MapLoader such as reading a configuration file and/or creating
* a 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 MapLoader implementation, such as closing the
* database connections, etc.
*/
void destroy();
}
To pre-populate the in-memory map when the map is first touched/used, use the MapLoader.loadAllKeys
API.
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 cluster member load 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 member, 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 members.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, the MapLoader.loadAllKeys()
method returns an Iterable
which can be lazily populated with the 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 Iterator
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 example 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 synchronization between database and data grid.
To post-process an object in the map store, implement the PostProcessingMapStore
interface to put the modified object into the distributed map. That causes an extra step of Serialization
, so use it only when needed. (This 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() );
}
}
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 this period is 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 more than this period is 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 are evicted if the entries are updated or removed.
Values can be true of false. Default is true.
-->
<invalidate-on-change>true</invalidate-on-change>
<!--
You may also want local entries to be cached.
This is useful when in memory format for near cache is different from
the map's near cache.
By default it is disabled (false).
-->
<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 cluster members 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 Configuring Client Near Cache. Please note that near cache configuration is specific to the member or client itself, a map in a member may not have near cache configured while the same map in a client may have near cache configured.
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 members; this has an impact on IMap's maximum idle seconds or time-to-live seconds expiration. Therefore, even thought there is a hit on a key in near cache, your original key on the remote member 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 with pessimistic locking: lock the map entry until you are finished with it.
To perform pessimistic locking, use the lock mechanism provided by Hazelcast distributed map, i.e. the map.lock
and map.unlock
methods. See the below example 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.
In Hazelcast, you can apply the 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.
See the below example 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: The above example code is intentionally broken.
Depending on your locking requirements, you can pick one locking strategy.
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, rather than using 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 that nothing has changed between the reads. Another thread may change the value, do work, and change the value back, while the first thread thinks 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 the optimistic locking strategy with the map replace
method.
Hazelcast keeps statistics about each map entry, such as creation time, last update time, last access time, number of hits, and version. To access the map entry statistics, use an 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() );
To listen to map-wide or entry-based events, implement 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
.
To catch an event, you should explicitly
implement a corresponding sub-interface of a MapListener
,
such as EntryAddedListener
or MapClearedListener
.
Let's take a look at the following code example.
NOTE: EntryListener
interface still can be implemented,
we kept that for 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 member 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 member and its first backup member crash simultaneously, the
given MapPartitionLostListener
receives a
corresponding MapPartitionLostEvent
. If only a single member crashes in the cluster,
there will be no MapPartitionLostEvent
fired for this map since backups for the partitions
owned by the crashed member are kept on other members.
Please refer to Listening for Partition Lost Events 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 you add the interceptor in this way, be sure to implement the hashCode()
method 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.
The IMap API has two methods for adding and removing an interceptor to the map: addInterceptor
and removeInterceptor
.
/**
* Adds an interceptor for the map. Added interceptor intercepts operations
* and executes user defined methods and cancels operations if
* user defined methods throw exceptions.
*
* @param interceptor map interceptor.
* @return id of registered interceptor.
*/
String addInterceptor( MapInterceptor interceptor );
/**
* Removes the given interceptor for this map. So it does not
* intercept operations anymore.
*
* @param id registration ID of the 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 causes 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 is 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 causes the put() operation to operate as expected,
* namely no interception. Throwing an exception cancels the put operation.
*
*
* @param oldValue the value currently existing in the 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 cancels 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
: 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 is thrown.hazelcast.query.max.local.partition.limit.for.precheck
: Maximum value of local partitions to trigger local pre-check for TruePredicate query operations on maps.Please refer to the System Properties section to see the full descriptions of these properties and how to set them.
Hazelcast distributed queue is an implementation of java.util.concurrent.BlockingQueue
. Being distributed, Hazelcast distributed queue enables all cluster members to interact with it. Using Hazelcast distributed queue, you can add an item in one cluster member and remove it from another one.
Use the HazelcastInstance getQueue
method to get the queue, then use the queue put
method to put items into the queue.
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 example code illustrates a distributed queue that connects a producer and consumer.
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 to 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 example 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 example 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 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.
To turn a Hazelcast distributed queue into a bounded queue, set the capacity limit with the max-size
property. You can set 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
, which happens when a consumer removes items from the queue.
Let's set 10 as the maximum size of our example queue in Creating an Example Queue.
<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 example 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 example 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.
The QueueStore
interface enables you to store, load, and delete queue 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 queue store properties.
Binary: By default, Hazelcast stores the queue items in serialized form, and 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. Do this by setting the binary
property to true: then you can get rid of the de-serialization step, which is a performance optimization. The binary
property is false 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. If you want to always use memory, you can set it to Integer.MAX_VALUE
. The default number for memory-limit
is 1000.
Bulk Load: When the queue is initialized, items are loaded from QueueStore
in bulks. Bulk load is the size of these bulks. The default value of 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 backup of that queue will continue. Therefore, no items are lost. You can define the number of synchronous backups for a queue using the backup-count
element in the declarative configuration. A queue can also have asynchronous backups: you can define the number of asynchronous backups using the async-backup-count
element.
To set the maximum size of the queue, use the max-size
element. To purge unused or empty queues after a period of time, use the empty-queue-ttl
element. If you define a value (time in seconds) for the empty-queue-ttl
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.
The following example creates a MultiMap and puts items into it. Use the HazelcastInstance getMultiMap
method to get the MultiMap, then use the MultiMap put
method to put an entry into the 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
.Use the HazelcastInstance getSet
method to get the Set, then use the set put
method to put items into the Set.
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
}
RELATED INFORMATION
Please refer to the Set Configuration section for a full description of Hazelcast Distributed Set configuration.
#
Hazelcast Set uses ItemListener
to listen to events which occur when items are added and removed from the Set.
Use the set addItemListener
method to create an ItemListener
.
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.
Hazelcast List is similar to Hazelcast Set, but Hazelcast List also allows duplicate elements.
Use the HazelcastInstance getList
method to get the list, then use the list put
method to put items into the List.
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 from the List.
Use the list addItemListener
method to create an ItemListener
.
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 that stores its data in a ring-like structure. You can think of it as a circular array with a given 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 simple: get the Ringbuffer with the HazelcastInstance getRingbuffer
method, get its current head with
the headSequence
method, and start reading. Use the method readOne
to return the item at the
given sequence; readOne
blocks if no item is available. To read the next item, increment the sequence 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 the Ringbuffer as long as the item is still available. If the item is not available
any longer, StaleSequenceException
is thrown.
Adding an item to a Ringbuffer is also easy with the Ringbuffer add
method:
Ringbuffer<String> ringbuffer = hz.getRingbuffer("rb");
ringbuffer.add("someitem")
Use the method add
to returns the sequence of the inserted item; the sequence value will always be unique. You can use this 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 to this approach:
queue.take()
is more expensive than a ringBuffer.read(...)
.By default, a Ringbuffer is configured with a capacity
of 10000 items. This creates an array with a size of 10000. 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 capacity
number to something that better fits your needs.
Below is a declarative configuration example of a Ringbuffer with a capacity
of 2000 items.
<ringbuffer name="rb">
<capacity>2000</capacity>
</ringbuffer>
Currently, Hazelcast Ringbuffer is not a partitioned data structure; 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 1 single synchronous backup by default. You can control the Ringbuffer backup just like most of the other Hazelcast
distributed data structures by setting the synchronous and asynchronous backups: backup-count
and async-backup-count
. In the example below, a Ringbuffer is configured with 0
synchronous backups and 1 asynchronous backup:
<ringbuffer name="rb">
<backup-count>0</backup-count>
<async-backup-count>1</async-backup-count>
</ringbuffer>
An asynchronous backup will probably give you better performance. However, there is a chance that the item added will be lost when the member owning the primary crashes before the backup could complete. You may want to consider batching methods if you need high performance but do not want to give up on consistency.
You can configure Hazelcast Ringbuffer with a time to live in 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 you set a time to live 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. The below options are currently 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 you fine control on what to do if the Ringbuffer is full. You can also use the overflow policy 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);
}
You can configure Hazelcast Ringbuffer with an in-memory format which controls the format of the Ringbuffer's stored items. By default, BINARY
in-memory format 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 the 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. You can use the method addAllAsync
to support batching.
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
method read items from the Ringbuffer. readOne
is simple but not very efficient for the following reasons:
readOne
does not use batching.readOne
cannot filter items at the source; the items need to be retrieved before being filtered.The method readManyAsync
can read a batch of items and can filter items at the source.
Please see the following example code.
ICompletableFuture<ReadResultSet<E>> readManyAsync(
long startSequence,
int minCount,
int maxCount,
IFunction<E, Boolean> filter);
The meanings of the readManyAsync
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 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 your 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 writing or batched reading with filtering.
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, you can also use ICompletableFuture
to get notified when the operation has completed. The advantage of ICompletableFuture
is that the thread used for the call is not blocked till the response is returned.
Please see the below code as an example of 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 following shows the declarative configuration of a Ringbuffer called rb
. The configuration is modeled after the 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 a 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.
Use the HazelcastInstance getTopic
method to get the Topic, then use the topic publish
method to publish your messages (messageObject
).
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 Monitoring Topics.
Each cluster member has a list of all registrations in the cluster. When a new member is registered for a topic, it sends a registration message to all members in the cluster. Also, when a new member 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
.
If globalOrderEnabled
is disabled, messages are ordered: 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 members (member1, member2 and member3) and that member1 and member2 are registered to a topic named news
. Note that all three members know that member1 and member2 are registered to news
.
In this example, member1 publishes two messages: a1
and a2
, and member3 publishes two messages: c1
and c2
. When member1 and member3 publish a message, they will check their local list for registered members, and they will discover that member1 and member2 are in their lists, then they will fire messages to those members. One possible order of the messages received can be the following.
member1 -> c1
, b1
, a2
, c2
member2 -> c1
, c2
, a1
, a2
If globalOrderEnabled
is enabled, all members 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 members (member1, member2 and member3) and that member1 and member2 are registered to a topic named news
. Note that all three members know that member1 and member2 are registered to news
.
In this example, member1 publishes two messages: a1
and a2
, and member3 publishes two messages: c1
and c2
. When a member 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 member to publish messages. Let's assume that news
corresponds to a partition that member2 owns. member1 and member3 first sends all messages to member2. Assume that the messages are published in the following order:
member1 -> a1
, c1
, a2
, c2
member2 then publishes these messages by looking at registrations in its local list. It sends these messages to member1 and member2 (it makes a local dispatch for itself).
member1 -> a1
, c1
, a2
, c2
member2 -> a1
, c1
, a2
, c2
This way, we guarantee that all members 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 Getting a Topic and Publishing Messages section.
To configure a topic, set the topic name, decide on statistics and global ordering, and set message listeners. 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.Example configuration code follows. For a full description of Hazelcast Distributed Topic configuration, refer to the Topic Configuration section.
<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>
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 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 5For a description of these parameters, please see the Global Event Configuration section.
The Reliable Topic data structure has been introduced with the release of Hazelcast 3.5. The Reliable Topic uses the same ITopic
interface
as a regular topic. The main difference is that Reliable Topic 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() );
}
}
You can configure the Reliable ITopic
using its RingBuffer. If there is a Reliable Topic with the name Foo
, then you can configure this topic
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 that configuration.
By default, the Reliable ITopic
uses a shared thread pool. If you need 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. You can control the size of the Ringbuffer by 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.RELATED INFORMATION
Please refer to the Reliable Topic Configuration section for more information on configuring the Reliable Topic.
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.
Always use locks with try-catch blocks. It will ensure that locks will be released if an exception is thrown from
the code in a critical section. Also note that the lock
method is outside the try-catch block, because we do not want to unlock
if the lock operation itself fails.
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();
}
If a lock is not released in the cluster, another thread that is trying to get the
lock can wait forever. To avoid this, use 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 the 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 fail-safe. If a member holds a lock and some other members go down, the cluster will keep your locks safe and available. Moreover, when a member leaves the cluster, all the locks acquired by that dead member will be removed so that those locks are immediately available for live members.
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 examples for a 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 example 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 example 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).
An example 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:
Below are some issues you need to know when you use IAtomicReference.
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 about 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
.
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 the 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 distributed key-value data structure where the data is replicated to all members in the cluster.
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 members.
This leads to higher memory consumption. However, a replicated map has faster read and write access since the data are available on all members.
Writes could take place on local/remote members in order to provide write-order, eventually being replicated to all other members.
Replicated map is suitable for 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.
NOTE: If Replicated Map is used from a dummy client and this dummy client is connected to a lite member, the entry listeners cannot be registered/de-registered.
Here is an example of replicated map code. The HazelcastInstance getReplicatedMap
methods gets the replicated map, and the replicated map put
creates map entries.
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.
Replicated map has an anti-entropy system, which will converge values to a common one if some of the members are missing replication updates.
Replicated map does not guarantee eventual consistency because there are some edge cases which fails to provide consistency.
Replicated map uses internal partition system of Hazelcast in order to serialize updates happening on the same key at the same time. This happens by sending updates of the same key to the same Hazelcast member in the cluster.
Due to asynchronous nature of replication, a Hazelcast member could die before successfully replicating a "write" operation to other members, after sending the "write completed" response to it's caller during the write process. In this scenario, Hazelcast's internal partition system will promote one of the replicas of the partition as the primary one. The new primary partition will not have the latest "write" since the died member could not successfully replicate the update. This will leave the system in a state that the caller is the only one that has the update and the rest of the cluster have not. In this case even the anti-entropy system simply could not converge the value since the source of true information is lost for the update. This leads to a break in the eventual consistency because different values can be read from the system for the same key.
Other than the aforementioned scenario, it will behave like an eventually consistent system with read-your-writes consistency.
There are several technical design decisions you should consider when you configure a replicated map.
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. Each involves how you configure for async fill up.
First, you can configure async fill up to true, 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).
Second, you can configure for a synchronous initial fill up (by configuring the async fill up to false), which blocks every read or write access to the map until the fill up operation is finished. Use this with caution since it might block your application from operating.
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
. See the following example declarative configuration.
<replicatedmap name="default">
<in-memory-format>BINARY</in-memory-format>
<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
: Internal storage format. Please see the In-Memory Format section. The default value is BINARY
.async-fillup
: Specifies 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 null
values can be seen until the initial replication is completed.statistics-enabled
: If set to true
, the statistics such as cache hits and misses are collected. The default value is false
.entry-listener
: Full canonical classname of the EntryListener
implementation.entry-listener#include-value
: Specifies whether the event includes 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
: 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 );
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.
Here is a code example for using EntryListener on a replicated map.
The HazelcastInstance method getReplicated
map gets a replicated map (customers), and the ReplicatedMap method
addEntryListener
adds an entry listener to the replicated map. Then the ReplicatedMap put
method adds a replicated map
entry, then updates it, and then the remove
method removes the entry.
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 (member/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 Listening to Map Events 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 interface has methods that are invoked for the following events.
memberAdded
: A new member is added to the cluster.memberRemoved
: An existing member leaves the cluster.memberAttributeChanged
: An attribute of a member is changed. Please refer to Defining Member Attributes to learn about member attributes.To write a Membership Listener class, you implement the MembershipListener interface and its methods.
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 methods distributedObjectCreated
and distributedObjectDestroyed
are invoked when a distributed object is created and destroyed throughout the cluster. To write a Distributed Object Listener class, you implement the DistributedObjectListener interface and its methods.
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 interface has methods that are invoked for the following events:
migrationStarted
: A partition migration is started.migrationCompleted
: A partition migration is completed.migrationFailed
: A partition migration failed.To write a Migration Listener class, you implement the DistributedObjectListener interface and its methods.
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 cluster members become owner and some of the other members become replica members based on your configuration. Nevertheless, data loss may occur if a few members crash simultaneously.
Let`s consider the following example with three members: 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 member crash is detected by the other members and the crashed member is removed from the cluster. Please note that false-positive PartitionLostEvent
instances may be fired on partial network split errors.
To write a Partial Lost Listener, you implement the PartitionLostListener interface and its partitionLost
method, which is invoked when a partition loses its owner and all backups.
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 member 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:
STARTING
: A member is starting.STARTED
: A member started.SHUTTING_DOWN
: A member is shutting down.SHUTDOWN
: A member's shutdown has completed.MERGING
: A member is merging with the cluster.MERGED
: A member's merge operation has completed.CLIENT_CONNECTED
: A Hazelcast Client connected to the cluster.CLINET_DISCONNECTED
: A Hazelcast Client disconnected from the cluster.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 member (node). It notifies the application that uses Hazelcast about the events mentioned above for a particular member.
The Item Listener is used by the Hazelcast IQueue
, ISet
and IList
interfaces.
To write an Item Listener class, you implement the ItemListener interface and its methods itemAdded
and itemRemoved
. These methods
are invoked 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.
To write a Message Listener class, you implement the MessageListener interface and its method onMessage
, which is invoked
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 cluster members. It notifies the cluster members when a client is connected to or disconnected from the cluster.
To write a Client Listener class, you implement the ClientListener interface and its methods clientConnected
and clientDisconnected
,
which are invoked 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 Members 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 cluster member 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.
To achieve the order guarantee, you make 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
(within com.hazelcast.util.executor
).
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 task. SumTask prints out map keys and returns the summed map values.
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 callable 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 runnable 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 Configuring Executor Threads (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
):
echoOnTheMember
: On a specific cluster member you choose with the IExecutorService
submitToMember
method.echoOnTheMemberOwningTheKey
: On the member owning the key you choose with the IExecutorService
submitToKeyOwner
method.echoOnSomewhere
: On the member Hazelcast picks with the IExecutorService
submit
method.echoOnMembers
: On all or a subset of the cluster members with the IExecutorService
submitToMembers
method.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 that 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.
onResponse
method.onFailure
method.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 you will just allow Hazelcast to select a member.
If you want more control over where your code runs, 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. Implement this method 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 process described 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. You can apply it 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 completes 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 the 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 returning null, 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: It is possible 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.
You can join predicates 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 )
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 )
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')
ILIKE: <attribute> [NOT] ILIKE ‘expression’
Similar to LIKE predicate but in a case-insensitive manner.
name ILIKE 'Jo%'
(true for 'Joe', 'joe', 'jOe','Josh','joSH', etc.)name ILIKE 'Jo_'
(true for 'Joe' or 'jOE'; false for 'Josh')
REGEX: <attribute> [NOT] REGEX ‘expression’
name REGEX 'abc-.*’
(true for 'abc-123'; false for 'abx-123')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:
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. 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. 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.
RELATED INFORMATION
Please see the Predicates class for all predicates provided.
Hazelcast distributed queries will run on each member in parallel and will return only the results to the caller. Then, on the caller side, the results will be merged.
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 you do an active and age < 30
query, make sure you add an index for the active
and
age
fields. The following example code does that by:
addIndex
method.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 Retrieving a JobTracker Instance, you create a Job using the retrieved JobTracker
instance. A Job defines exactly one configuration of a MapReduce task. Mapper, combiner and reducers will be defined per job. However, since the Job instance is only a configuration, it can be submitted multiple times, no matter if executions happen 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 to retrieve 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. KeyValueSource
is used to wrap any kind of data or data structure into a well defined set of key-value pairs.
The example code below is a direct follow up of the example in Retrieving a JobTracker Instance. The example reuses the already created HazelcastInstance and JobTracker
instances.
The example starts by retrieving an instance of our data map, and then it creates the Job instance. Implementations used to configure the Job will be discussed while walking further through the API documentation.
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. Then we submit the request to the cluster. The submit
method returns an ICompletableFuture that can be used to attach our callbacks or 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 Hazelcast source code for Job.java.
KeyValueSource
can 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 member. 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, and aggregate data from data sources. In Hazelcast, you can also integrate data from more than the KeyValueSource data source by implementing com.hazelcast.core.HazelcastInstanceAware
and requesting additional maps, multimaps, list, and/or 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 members, on the members' 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 members 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 the finalizeChunk
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.
Different from 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 are 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 member 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.
You can use KeyPredicate
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 members.
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
). You can use it 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 members (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.
The example performs the following steps to get the job instance.
getMap
method.fromMap
method.newJob
method.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 member and client.
You configure JobTracker
configuration to set up 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. The configuration properties are discussed below the example.
<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 (a cluster with three members) 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 Configuring JobTracker. 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.
com.hazelcast.query.Predicate
if you want to filter on values and / or keys, orcom.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 we 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, we 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) )
)
);
You might prefer or need to implement your Supplier
based on special
requirements. This is a very basic task. The Supplier
abstract class has just one method: the apply
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;
}
}
The Supplier
apply
methods are expected to return null 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.
You can use PropertyExtractor
s 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 Retrieving a JobTracker Instance) using the naming spec we just learned. For more information on configuration of the
JobTracker
, please see Configuring Jobtracker.
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 the quick joke of the previous two code lines, 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 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.
An Aggregation
implementation is defining a MapReduce task, but 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.
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();
}
The getMapper
and getReducerFactory
methods should return non-null values. getCombinerFactory
and getCollator
are
optional operations and you do not need to implement them. 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 Listening to Map Events 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.
A continuous query cache is used to cache the result of a continuous query. After the construction of a continuous query cache, all changes on the underlying IMap
are immediately reflected to this cache as a stream of events.
Therefore, this cache will be an always up-to-date view of the IMap
.
A continuous query cache is beneficial when you need to query the distributed IMap
data in a very frequent and fast way. By using a continuous query cache, the result of the query will always be ready and local to the application.
The following code snippet shows how you can access a continuous query cache from the server side.
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");
The following code snippet shows how you can access a continuous query cache from the client side. The difference in this code from the server side code above is that you configure and instantiate a client instance instead of a member (node) instance.
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 the continuous query cache construction, according to the supplied predicate via QueryCacheConfig#setPopulate
.QueryCacheConfig#setEvictionConfig
.QueryCache#addEntryListener
.The events on the IMap
are reflected in a continuous query cache, keeping the same order of those events. Note that the order of the events implies the order in a partition. Therefore, you can only expect ordered events from the same partition. You can listen to the event losses using EventLostListener
and events can be recoverable with the method QueryCache#tryRecover
. If your buffer size on the node side is large enough, you can recover from a possible event loss scenario.
Currently, setting the size of QueryCacheConfig#setBufferSize
is the only option for recovery since the events which feed a continuous query cache have no backups.
You can use the following example code for a 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);
You can perform event batching and coalescing on a continuous query cache.
QueryCache#get
from the underlying IMap
. This helps to decrease the initial population time when the values are very large.
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 are done with the steps below. Each step adds a new functionality to this counter.
To have the counter as a functioning distributed object, we need a class. This class (named CounterService in the following example code) is the gateway between Hazelcast internals and the counter, allowing us to add features to the counter. The following example code creates the class CounterService
. Its lifecycle is managed by Hazelcast.
CounterService
should implement the interface com.hazelcast.spi.ManagedService
as shown below. The com.hazelcast.spi.ManagedService
source code is here.
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 face 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 reset
as a no-op.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() {
}
}
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>
The CounterService
is declared within the services
configuration element.
enabled
attribute to true
to enable the service.name
attribute to 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
attribute to 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 init
method; see Creating the Service Class. You can add properties declaratively as shown below. (You likely want to name your properties something other than someproperty.)
<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 starts, 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. You can read the source code for RemoteService here.
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. CounterProxy
extends AbstractDistributedObject, source code here.
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 propagate the exception or just use the ExceptionUtil.rethrow
for all exceptions. Please see the example code below.
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 the PartitionAwareOperation
interface, meaning that it will be executed on the partition that hosts the counter. See the PartitionAwareOperation source code here.
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
; retrieve it with the method getResponse
.
There are two more methods in this code: writeInternal
and readInternal
. Since IncOperation
needs to be serialized, these two methods are overridden, and hence, objectId
and amount
are serialized and available when those operations are executed.
For the deserialization, note that the operation must have a no-arg constructor.
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();
}
}
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.
The code performs the tasks below.
init
.createDistributedObject
.destroyDistributedObject
, otherwise we may get an OutOfMemory exception.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);
}
}
}
As the last step in creating a Container, we connect the method IncOperation.run
to the Container, as shown below.
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.
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();
}
}
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
.
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.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);
}
}
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.
An example 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.
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. You can read the MigrationAwareService source code here.
The method commitMigration
commits the data, meaning that in this case, it clears the partition container of the old owner.
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() {
}
}
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 counter data is available on another member when a member goes down. To do this, 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
. It implements BackupOperation
, you can see the
source code for BackupOperation here.
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. You can see the WaitNotifyService source code here.
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. A continuous task 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).
This chapter explains the usage of Hazelcast in transactional context. It describes the Hazelcast transaction types and how they work, how to provide XA (eXtended Architeture) transactions, and how to integrate Hazelcast with J2EE containers.
You create a TransactionContext
object 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. You can see the TransactionContext source code here.
Hazelcast supports two types of transactions: 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.
NOTE: Starting with Hazelcast 3.6, the transaction type LOCAL
has been deprecated. Please use ONE_PHASE
for the Hazelcast releases 3.6 and higher.
ONE_PHASE: By selecting this transaction type, you execute the transactions with a single phase, that is committing the changes. Since a preparing phase does not exist, the conflicts are not detected. When a conflict happens during committing the changes (e.g. due to a member crash), it means not all the changes are written and this leaves the system in an inconsistent state.
TWO_PHASE: When you select this transaction type, it first tries to execute the prepare phase. This phase fails if there are any conflicts. Once the prepare phase is successful, then it executes the commit phase (writing the changes). Before TWO_PHASE commits, it copies the commit log to other members, so in case of a 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.ONE_PHASE );
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 in the map but the key will be locked against 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
.
Hazelcast implements queue/set/list operations differently than map/multimap operations. 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
do 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 discussed in Creating a Transaction Interface, when you choose ONE_PHASE as the transaction type, Hazelcast tracks all changes you make locally in a commit log, i.e. a list of changes. In this case, all the other members are asked to agree that the commit can succeed and once they agree, 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 ONE_PHASE as the transaction type if you want a better performance, and that you choose 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).
When you implement the XAResource
interface, Hazelcast provides XA transactions. You can obtain the HazelcastXAResource
instance via the HazelcastInstance getXAResource
method. You can see the
HazelcastXAResource source code here.
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();
}
You can integrate Hazelcast 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
. In addition to 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.To configure an example Glassfish v3 web application:
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.
To configure a JBoss AS 5 web application:
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.
Deploying on JBoss AS 7 or JBoss EAP 6 is a 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. 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, Hazelcast offers a specification compliant JCache implementation. To show our commitment to this important specification that the Java world was waiting for over a decade, we do not just provide a simple wrapper around our existing APIs; we implemented a caching structure from the ground up to optimize the behavior to the needs of JCache. 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.
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 for you to perform 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 creating 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 that you 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
: Fully qualified class name of the cache key type. Its default value is java.lang.Object
.value-type#class-name
: Fully qualified class name of the cache value type. Its default value is 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 enable 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. Its default value is 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. Its default value is false.cache-loader-factory#class-name
: 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
: 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
: 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. Its default value is false.cache-entry-listener#synchronous
: If set to true, the javax.cache.event.CacheEntryListener
implementation will be called in a synchronous manner. Its default value is false.cache-entry-listener/entry-listener-factory#class-name
: 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
: 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, such as 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 that you use declarative configuration
and that you 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, we recommend that you instantiate
javax.cache.configuration.MutableConfiguration
and that you use the setters to configure Hazelcast as shown in the example in the
Example JCache Application 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. Hazelcast recommends 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
Scoping to Join Clusters.
To request 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 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 Scoping to Join Clusters.
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 Example JCache Application 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 Configuring JCache Provider 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 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
creates 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. 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. This implementation is quite straight forward.
CacheLoader
.load
method to compute or retrieve the value corresponding to key
.loadAll
method to compute or retrieve the values corresponding to keys
.An important note is that
any kind of exception has to be wrapped into javax.cache.integration.CacheLoaderException
.
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;
}
}
You use a javax.cache.integration.CacheWriter
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, there is currently 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.
The following example performs the following tasks.
CacheWriter
.write
method to write the specified entry to the underlying store.writeAll
method to write the specified entires to the underlying store.delete
method to delete the key entry from the store.deleteAll
method to delete the data and keys from the underlying store for the given collection of keys, if present.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 Implementing 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.
The following example performs the following tasks.
EntryProcessor
.process
method to process an entry.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. This application performs the following tasks:
onCreated
method to call after an entry is created.onUpdated
method to call after an entry is updated.onRemoved
method to call after an entry is removed.onExpired
method to call after an entry expires.printEvents
to print event information on the console.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. See Custom ExpiryPolicy.As mentioned before, you can scope a CacheManager
in the case of client to connect to multiple clusters. In the case of an embedded node, you can scope a CacheManager
to join different clusters at the same time. This process is called scoping. To apply scoping, 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 );
You can set this on the command line.
-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.
You can bind CacheManager
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) you must get the instance name from HazelcastInstance
instance via the 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 safer and the preferable way to get the name of the 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 and different namespaces on the CacheManager
level, and therefore they 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 Scoping to Join Clusters and Namespacing, 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 example:
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.
ICache Async Methods and ICache Convenience 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, eviction policy and quorum reference.
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" />
<partition-lost-listeners>
<partition-lost-listener>CachePartitionLostListenerImpl</partition-lost-listener>
</partition-lost-listeners>
<quorum-ref>quorum-name</quorum-ref>
</cache>
backup-count
: 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
: 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
: 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
: 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
: 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
: Eviction policy which compares values 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.partition-lost-listeners
: Defines listeners for dispatching partition lost events for the cache. For more information, please see the ICache Partition Lost Listener section.quorum-ref
: Name of quorum configuration that you want this cache to use.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.
RELATED INFORMATION
Please refer to the Cache Configuration section for a full description of ICache configuration.
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.
The asynchronous versions of the methods append the phrase Async
to the method name. The example code below uses 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
Defining a Custom ExpiryPolicy.
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 you can pass custom expiry policies 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. You can use them as shown in the following code example.
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 ICache Async Methods for the list of asynchronous method overloads.
Caches are generally not expected to grow to an infinite size. Implementing an expiry policy is one way you can 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, Hazelcast put a lot of effort into designing the eviction system to be as predictable as possible. All built-in implementations provide an amortized O(1) runtime. The default operation runtime is rendered as O(1), but it can be faster than the normal runtime cost if the algorithm finds an expired entry while sampling.
Most importantly, typical production systems have two common types of caches:
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
: User defined maximum cache size (cluster-wide).PartitionCount
: Number of partitions in the cluster (defaults to 271).BalancedPartitionSize
: 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
: 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
: 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 Configuring JCache Near Cache.
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 example configurations for JCache near cache.
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, you must explicitly configure eviction 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 ICache Async Methods and Defining a Custom ExpiryPolicy, 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 Implementing 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 you 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 Implementing 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.
You can listen to CachePartitionLostEvent
instances by registering an implementation
of CachePartitionLostListener
, which is also a sub-interface of java.util.EventListener
from ICache
.
Let's consider the following example code:
public static void main(String[] args) {
CachingProvider cachingProvider = Caching.getCachingProvider();
CacheManager cacheManager = cachingProvider.getCacheManager();
Cache<Object, Object> cache = cacheManager.getCache( ... );
ICache<Object, Object> unwrappedCache = cache.unwrap( ICache.class );
unwrappedCache.addPartitionLostListener(new CachePartitionLostListener() {
@Override
public void partitionLost(CachePartitionLostEvent event) {
System.out.println(event);
}
});
}
Within this example code, a CachePartitionLostListener
implementation is registered to a cache and assume that this cache is configured with 1 backup. For this particular cache and any of the partitions in the
system, if the partition owner member and its first backup member crash simultaneously, the
given CachePartitionLostListener
receives a
corresponding CachePartitionLostEvent
. If only a single member crashes in the cluster,
a CachePartitionLostEvent
is not fired for this cache since backups for the partitions
owned by the crashed member are kept on other members.
Please refer to the Partition Lost Listener section for more information about partition lost detection and partition lost events.
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 both 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 tells 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 Hibernate for Hazelcast:
hazelcast-hibernate3-<
hazelcastversion>.jar
or hazelcast-
hibernate4-<
hazelcastversion>.jar
into your classpath depending on your Hibernate version.hibernate.cfg.xml
).<property name="hibernate.cache.use_second_level_cache">true</property>
You can configure Hibernate RegionFactory with HazelcastCacheRegionFactory
or HazelcastLocalCacheRegionFactory
.
HazelcastCacheRegionFactory
uses standard Hazelcast Distributed Maps to cache the data, so all cache operations go through the wire.
<property name="hibernate.cache.region.factory_class">
com.hazelcast.hibernate.HazelcastCacheRegionFactory
</property>
All operations like get
, put
, and remove
will be performed using the Distributed Map logic. The only downside of using HazelcastCacheRegionFactory
may be 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.
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>
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.
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>
To avoid NullPointerException
when you have entities that have composite keys (using @IdClass
):
<property name="hibernate.session_factory_name">yourFactoryName</property>
NOTE: QueryCache is always LOCAL to the node and never distributed across Hazelcast Cluster.
To configure Hazelcast for Hibernate, put the configuration file named hazelcast.xml
into the root of your classpath. If Hazelcast cannot find hazelcast.xml
, then it will use the default configuration from hazelcast.jar
.
You can define a custom-named Hazelcast configuration XML file with one of these Hibernate configuration properties.
<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.
Hibernate Second Level Cache can use Hazelcast in two modes: Peer-to-Peer (P2P) and Client/Server (next section).
With P2P mode, each Hibernate deployment launches its own Hazelcast Instance. You can also configure Hibernate to use an existing instance, instead of creating a new HazelcastInstance
for each SessionFactory
. To do this, set the hibernate.cache.hazelcast.instance_name
Hibernate property to the HazelcastInstance
's name. For more information, please see Named Instance Scope.
Disabling shutdown during SessionFactory.close()
You can disable shutting down HazelcastInstance
during SessionFactory.close()
. To do this, set the Hibernate property hibernate.cache.hazelcast.shutdown_on_session_factory_close
to false. (In this case, you should not set the Hazelcast property hazelcast.shutdownhook.enabled
to false.) The default value is true
.
You can set up Hazelcast to connect to the cluster as Native Client. Native client is not a member; it connects to one of the cluster members and delegates all cluster wide operations to it. Client instance started in the Native Client mode uses Smart Routing: when the relied cluster member dies, the client transparently switches to another live member. All client operations are Retry-able, meaning that the client resends the request as many as 10 times in case of a failure. After the 10th retry, it throws an exception. You cannot change the routing mode and retry-able operation configurations of the Native Client instance used by Hibernate 2nd Level Cache. Please see the Smart Routing section and Retry-able Operation Failure section for more details.
<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.
NOTE: To use Native Client, add hazelcast-all-<version>.jar
into your remote cluster's classpath.
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 does not yet support transactional strategy.
If you are using XML based class configurations, add a cache element into your configuration with the usage attribute set to one of the read-only, read-write, or nonstrict-read-write strategies.
<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>
If you are using Hibernate-Annotations, then you can add a class-cache or collection-cache element into your Hibernate configuration file with the usage attribute set to read only, read/write, or nonstrict read/write.
<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"/>
Or alternatively, you can put Hibernate Annotation's @Cache annotation on your entities and collections.
@Cache(usage = CacheConcurrencyStrategy.READ_WRITE)
public class Cat implements Serializable {
...
}
Accessing underlying HazelcastInstance
If you need to access HazelcastInstance
used by Hibernate SessionFactory
, you can give a name to the HazelcastInstance
while configuring Hazelcast. Then it is possible to retrieve the instance using getHazelcastInstanceByName
static method of Hazelcast
.
Please refer to the Configuration Overview section to learn how to create a named Hazelcast instance.
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 do not have to be present in the server classpath.
To set up Hazelcast Session Clustering:
Put the hazelcast
and hazelcast-wm
jars in your WEB-INF/lib
folder. Optionally, if you wish to connect to a cluster as a client, add hazelcast-client
as well.
Put the following XML into the 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 the 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. The org.springframework.security.core.session.SessionRegistry
instance uses these events.
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.
Updating an attribute when deferred-write=false
:
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” characters and without a “-“ 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 that when using sticky sessions, you 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 to be distributed, you can mark those attributes as transient.
Transient attributes are kept in and when the server is shutdown, you lose 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.
This type of deployment is simple: just configure your Tomcat and launch. There is no need for an external Hazelcast cluster.
The following steps configure a sample P2P for 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.
```xml
<Server>
...
<Listener className="com.hazelcast.session.P2PLifecycleListener"/>
...
</Server>
```
Put a <Manager>
element into the file $CATALINA_HOME$/conf/context.xml
as shown below.
```xml
<Context>
...
<Manager className="com.hazelcast.session.HazelcastSessionManager"/>
...
</Context>
```
Start Tomcat instances with a configured load balancer and deploy the web application.
Optional Attributes for Listener Element
Optionally, you can add a 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
The following steps configure a sample Client/Server for 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.
```xml
<Server>
...
<Listener className="com.hazelcast.session.ClientServerLifecycleListener"/>
...
</Server>
```
Update the <Manager>
element in the $CATALINA_HOME$/conf/context.xml
as shown below.
```xml
<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
Optionally, you can add 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>
element. 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.
Requests always go to the same instance where the session was firstly created. By using a sticky session, you mostly eliminate session replication problems, except for the failover cases. In case of failovers, Hazelcast helps you to 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.
This type of deployment is simple: just configure your Jetty and launch. There is no need for an external Hazelcast cluster.
The following steps configure a sample P2P for 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
.Configure the Session ID Manager. You need to configure a com.hazelcast.session.HazelcastSessionIdManager
instance in jetty.xml
. Add the following lines to your jetty.xml
.
```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>
```
Configure the Session Manager. You can configure HazelcastSessionManager
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.
```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="idManager">
<Ref id="hazelcastIdMgr"/>
</Set>
</New>
</Arg>
</New>
</Set>
```
Start Jetty instances with a configured load balancer and deploy the web application.
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
.:
```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.
The following steps configure a sample Client/Server for 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
.Configure the Session ID Manager. You need to configure a com.hazelcast.session.HazelcastSessionIdManager
instance in jetty.xml
. Add the following lines to your jetty.xml
.
```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>
```
Configure the Session Manager. You can configure HazelcastSessionManager
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
.
```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
.
```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. Example code for a Hazelcast Instance declaration is listed below.
<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>
Configuring Classpath
Hazelcast-Spring integration requires the following JAR files in the classpath:
hazelcast-spring-
<version>.jar
hazelcast-
<version>.jar
or
hazelcast-all-
<version>.jar
Declaring Beans
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>
You can mark Hazelcast Distributed Objects 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 Configuring Spring 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 SpringAware 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 SpringAware 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. To perform Annotation-Based Configuration:
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 a second 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 Hibernate Configuring RegionFactory 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 High-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.
NOTE: This feature is supported for Hazelcast Enterprise 3.6 or higher.
This chapter explains the Hazelcast's Hot Restart Store feature which provides fast cluster restarts by storing the states of the cluster members into the disk. This feature is currently provided for Hazelcast map data structure and Hazelcast JCache implementation.
Hot Restart Store enables you to get your cluster up and running swiftly after a cluster restart that can be caused by member crashes, rolling upgrades or power/network interruptions.
???
???
???
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.
Hundreds or even thousands of clients can be 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, there will be 40 threads).
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 Java Native Client Library is introduced in the release package: hazelcast-client-new-<version>.jar
. This library contains clients which use the new Hazelcast Binary Client Protocol. This library does not exist 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 |
Continuous Query (Listener with Predicate) | Yes | Yes | 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 Hazelcast native 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.
You can configure connectionAttemptLimit
for the number of times you want the client to retry connecting. Please see Setting Connection Attempt Limit.
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, operations can fail due to various reasons. Read-only operations are retried by default. If you want to enable retry for the other operations, set the redoOperation
to true
. Please see Enabling Redo Operation.
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 Java client. When you use clients in other languages, you should check for the exceptions.
As a general rule, you configure these data structures on the server side and access them through a proxy on the client side.
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.
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.
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.
getLocalTopicStats
is not supported because locality is ambiguous for the client.
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.
Hazelcast provides the services discussed below for some common functionalities on the client side.
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.
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
}
});
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 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.
You can configure Hazelcast Java Client declaratively (XML) or programmatically (API).
For declarative configuration, the Hazelcast client looks at 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. The 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 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. An example 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 in 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.
Here is an example of configuring network for Java Client declaratively.
<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>
Here is an example of configuring network for Java Client programmatically.
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 the addresses for all the nodes.
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.
Smart routing defines whether the client mode is smart or dummy. The following are 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 Handling 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.
Connection timeout 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. This is also the case when an existing client-member connection goes down. The following are 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.
Connection timeout period is the duration in milliseconds between the connection attempts defined by ClientNetworkConfig.connectionAttemptLimit
. The following are 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 );
You can refer to the aws element section for the descriptions of above AWS configuration elements except inside-aws
and iam-role
, which are explained below.
If the inside-aws
element is not set, the private addresses of cluster members will always be converted to public addresses. Also, the client will use public addresses to connect to the members. 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 members.
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 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");
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 the client side must be the same as the name of IMap on the 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 LifecycleListener, MembershipListener and DistributedObjectListener.
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 cluster members and perform almost all operations that a member can perform. Clients differ from members 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 member.
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.
You can try the following C++ client code examples. You need to have a Hazelcast client member running for the code examples to 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, you can use as Key or Value of IMap
or as an Item in IQueue
.
You can use the native .NET client to connect to Hazelcast client members. You need 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 in the .NET Client as they are 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 cluster member (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.
In the following GET, POST, and DELETE examples, 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
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
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
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
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
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
$ 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 port 5701
. 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.
Hazelcast needs 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 cluster members. The data you need may not be present on the local member, and in that case, Hazelcast retrieves that data from another member. 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.
Byte, Boolean, Character, Short, Integer, Long, Float, Double, byte[], char[], short[], int[]
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.
Date, BigInteger, BigDecimal, Class, Externalizable, Serializable
Hazelcast optimizes all of the above object types. You do not need to worry about their (de)serializations.
For complex objects, use the following interfaces for serialization and deserialization.
java.io.Serializable
See Implementing Java Serializable and Externalizable.
java.io.Externalizable
See Implementing Java Externalizable.
com.hazelcast.nio.serialization.DataSerializable
See Implementing DataSerializable.
com.hazelcast.nio.serialization.IdentifiedDataSerializable
See Implementing IdentifiedDataSerializable
com.hazelcast.nio.serialization.Portable
, and
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, you need to have all the classes needed by the application on your classpath because Hazelcast does not download them automatically.
The table below 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 for Java Serializable.
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. For example, if the class relies on a hash map, the 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);
}
}
You explicitly perform writing and reading of fields. Perform reading in the same order as writing.
As mentioned in Implementing Java Serializable & Externalizable, Java serialization is an easy mechanism. However, it does 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.
Since the address
field itself is DataSerializable
, it calls address.writeData(out)
when writing and address.readData(in)
when reading. Also note that you should have writing and reading of the fields occur
in the same order. When Hazelcast serializes a DataSerializable
, it writes the className
first. When Hazelcast de-serializes it, className
is used to instantiate the object using reflection.
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 the address
field itself is DataSerializable
, it is calling address.writeData(out)
when writing and address.readData(in)
when reading. Also note that you should have writing and reading of the fields occur in the same order. 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 Implementing DataSerializable. 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, you should also implement com.hazelcast.nio.serialization.DataSerializableFactory
and register it 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. This is currently the most efficient way of Serialization that Hazelcast supports off the shelf.
Let's take a look at the following example code 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 you should implement 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 cluster members where each has 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 member. 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.
Here is example code for Portable implementation of a Foo class.
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
and factoryId
. The Factory object creates the Portable object given the classId
.
An example Factory
could be implemented as follows:
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 may have a newer version of the same class.
Portable serialization supports versioning. It is a global versioning, meaning that all portable classes that are serialized through a member get the globally configured portable version.
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 can also use the interface VersionedPortable which enables to upgrade the version per class, instead of global versioning. If you need to update only one class, you can use this interface. In this case, your class should implement VersionedPortable
instead of Portable
, and you can give the desired version using the method VersionedPortable.getClassVersion()
.
You should consider the following when you perform versioning.
int
to float
).Assume that a new member joins to the cluster with a class that has been modified and class' version has been upgraded due to this modification.
put
operations will include that new field. If this new member tries to get an object that was put from the older members, it will get null
for the newly added field.null
for the objects that are put by the new member.IncompatibleClassChangeError
is generated unless the change was made on a built-in type or the byte size of the new type is less than or equal to the old one. The following are example allowed type conversions:long
-> int
, byte
, char
, short
int
-> byte
, char
, short
If you have not modify a class at all, it will work as usual.
Be careful with 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
(Hazelcast Semaphore, Queue, etc.) in a cluster member 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 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 in a custom serializer for serializing your 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 you 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 you 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 Implementing StreamSerializer.
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 to see 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 set the HazelcastInstance field while using setHazelcastInstance
method and you not execute operations on the HazelcastInstance. The reason is that 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 explains how to use Hazelcast Management Center.
You can get various statistics from your distributed data structures via the Statistics API. Since the data structures are distributed in the cluster, the Statistics API provides statistics for the local portion (1/Number of Members in the Cluster) of data on each member (or node).
To get local map statistics, use the getLocalMapStats()
method from the IMap
interface. This method returns a
LocalMapStats
object that holds local map statistics.
Below is example code where the getLocalMapStats()
method and the getOwnedEntryCount()
method get the number of entries owned by this member.
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 the number of gets.
*/
long getTotalGetLatency();
/**
* Returns the total latency of remove operations. To get the average latency,
* divide by the number of gets.
*/
long getTotalRemoveLatency();
/**
* Returns the maximum latency of put operations. To get the average latency,
* divide by the number of puts.
*/
long getMaxPutLatency();
/**
* Returns the maximum latency of get operations. To get the average latency,
* divide by the number of gets.
*/
long getMaxGetLatency();
/**
* Returns the maximum latency of remove operations. To get the average latency,
* divide by the 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();
To get Near Cache statistics, use the getNearCacheStats()
method from the LocalMapStats
object.
This method returns a NearCacheStats
object that holds Near Cache statistics.
Below is example code where the getNearCacheStats()
method and the getRatio
method from NearCacheStats
get a Near Cache hit/miss ratio.
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();
To get MultiMap statistics, use the getLocalMultiMapStats()
method from the MultiMap
interface.
This method returns a LocalMultiMapStats
object that holds local MultiMap statistics.
Below is example code where the getLocalMultiMapStats()
method and the getLastUpdateTime
method from LocalMultiMapStats
get the last update time.
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 the number of puts.
*/
long getTotalPutLatency();
/**
* Returns the total latency of get operations. To get the average latency,
* divide by the number of gets.
*/
long getTotalGetLatency();
/**
* Returns the total latency of remove operations. To get the average latency,
* divide by the number of gets.
*/
long getTotalRemoveLatency();
/**
* Returns the maximum latency of put operations. To get the average latency,
* divide by the number of puts.
*/
long getMaxPutLatency();
/**
* Returns the maximum latency of get operations. To get the average latency,
* divide by the number of gets.
*/
long getMaxGetLatency();
/**
* Returns the maximum latency of remove operations. To get the average latency,
* divide by the 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();
To get local queue statistics, use the getLocalQueueStats()
method from the IQueue
interface.
This method returns a LocalQueueStats
object that holds local queue statistics.
Below is example code where the getLocalQueueStats()
method and the getAvgAge
method from LocalQueueStats
get the average age of items.
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 the number of null returning poll operations.
* Poll operation might return null if the queue is empty.
*/
long getEmptyPollOperationCount();
/**
* Returns the number of other operations.
*/
long getOtherOperationsCount();
/**
* Returns the number of event operations.
*/
long getEventOperationCount();
To get local topic statistics, use the getLocalTopicStats()
method from the ITopic
interface.
This method returns a LocalTopicStats
object that holds local topic statistics.
Below is example code where the getLocalTopicStats()
method and the getPublishOperationCount
method from LocalTopicStats
get the number of publish operations.
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();
To get local executor statistics, use the getLocalExecutorStats()
method from the IExecutorService
interface.
This method returns a LocalExecutorStats
object that holds local executor statistics.
Below is example code where the getLocalExecutorStats()
method and the getCompletedTaskCount
method from LocalExecutorStats
get the number of completed operations of the executor service.
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.
To achieve this, first add the following system properties to enable JMX agent:
-Dcom.sun.management.jmxremote
-Dcom.sun.management.jmxremote.port=\_portNo\_
(to specify JMX port, the default is 1099
) (optional)-Dcom.sun.management.jmxremote.authenticate=false
(to disable JMX auth) (optional)Then enable the Hazelcast property hazelcast.jmx
(please refer to the System Properties section) using one of the following ways:
<properties>
<property name="hazelcast.jmx">true</property>
</properties>
config.setProperty(GroupProperty.ENABLE_JMX, "true");
<hz:properties>
<hz: property name="hazelcast.jmx">true</hz:property>
</hz:properties>
-Dhazelcast.jmx=true
Hazelcast set the naming convention for MBeans as follows:
final ObjectName mapMBeanName = new ObjectName("com.hazelcast:instance=_hzInstance_1_dev,type=IMap,name=trial");
The MBeans name consists of the Hazelcast instance name, the type of the data structure, and that data structure's name. In the above sample, _hzInstance_1_dev
is the instance name, we connect to an IMap with the name trial
.
One of the ways you can connect to JMX agent is using jconsole, jvisualvm (with MBean plugin) or another JMX compliant monitoring tool.
The other way to connect is to use a custom JMX client.
First, you need to specify the URL where the Hazelcast JMX service is running. Please see the following sample code snippet. The port
in this sample should be the one that you define while setting the JMX remote port number (if different than the default port 1099
).
// Parameters for connecting to the JMX Service
int port = 1099;
String hostname = InetAddress.getLocalHost().getHostName();
JMXServiceURL url = new JMXServiceURL("service:jmx:rmi://" + hostname + ":" + port + "/jndi/rmi://" + hostname + ":" + port + "/jmxrmi");
Then use the URL you acquired to connect to the JMX service and get the JMXConnector
object. Using this object, get the MBeanServerConnection
object. The MBeanServerConnection
object will enable you to use the MBean methods. Please see the example code below.
// Connect to the JMX Service
JMXConnector jmxc = JMXConnectorFactory.connect(url, null);
MBeanServerConnection mbsc = jmxc.getMBeanServerConnection();
Once you get the MBeanServerConnection
object, you can call the getter methods of MBeans as follows:
System.out.println("\nTotal entries on map " + mbsc.getAttribute(mapMBeanName, "name") + " : "
+ mbsc.getAttribute(mapMBeanName, "localOwnedEntryCount"));
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. It also gives information on the Hazelcast Lite Member.
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.
The following example code does the above: registers for member events, notified when members are added or removed, and gets 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.
Lite members are the Hazelcast cluster members that do not store data. These members are used mainly to execute tasks and register listeners, and they do not have partitions.
You can form your cluster to include the regular Hazelcast members to store data and Hazelcast lite members to run heavy computations. The presence of the lite members do not affect the operations performed on the other members in the cluster. You can directly submit your tasks to the lite members, register listeners on them and invoke operations for the Hazelcast data structures on them (e.g. map.put()
and map.get()
).
You can enable a cluster member to be a lite member using declarative or programmatic configuration.
<hazelcast>
<lite-member enabled="true">
</hazelcast>
Config config = new Config();
config.setLiteMember(true);
NOTE: Note that you cannot change a member's role at runtime.
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.
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 cluster member, Hazelcast provides a graceful shutdown feature. You perform this shutdown by calling the method HazelcastInstance.shutdown()
. Once this method is called, it checks the following conditions to ensure the member is safe to shutdown.
Even if the above conditions are not met, HazelcastInstance.shutdown()
will force them to be completed. When this method eventually returns, the member 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, as in safe to shutdown without any data loss? For example, you may have some use cases like rolling upgrades, development/testing, or other logic that requires a cluster/member to be safe.
To provide this safety, 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 member is in a safe state. This check controls if the first backups of partitions of the given member are synced with the primary ones. Once it returns true
, the given member 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 starting with Hazelcast 3.3.
PartitionService partitionService = hazelcastInstance.getPartitionService();
if (partitionService.isClusterSafe()) {
hazelcastInstance.shutdown(); // or terminate
}
OR
PartitionService partitionService = hazelcastInstance.getPartitionService();
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 achieving better consistency by rejecting updates that do not pass a minimum threshold. This reduces the chance of concurrent updates to an entry from two partitioned clusters. Note that the consistency defined here is the best effort, it is 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, Transactional Map and Cache, 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 the cluster to continue operating. The following examples are configurations for this scenario.
<hazelcast>
....
<quorum name="quorumRuleWithThreeNodes" enabled="true">
<quorum-size>3</quorum-size>
</quorum>
<map name="default">
<quorum-ref>quorumRuleWithThreeNodes</quorum-ref>
</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);
NOTE: Please refer to the Quorum Configuration section for the full description of quorum configuration.
You can register quorum listeners to be notified about quorum results. Quorum listeners are local to the node where they are registered, so they receive only events that occurred on that local node.
Quorum listeners can be configured via declarative or programmatic configuration. The following examples are such 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-ref>quorumRuleWithThreeNodes</quorum-ref>
</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 you the ability to query quorum results over the Quorum
instances. Quorum instances let you query the quorum result of a particular quorum.
Here is a 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 the quorum instance for a particular quorum over the quorum service, as in the following example.
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 cluster members 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 members. With its scripting and console module, you can run scripts (JavaScript, Groovy, etc.) and commands on your members.
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 cluster members communicate with that web application. This means that your members should know the URL of the mancenter
application before they start.
Here are the steps.
mancenter
-version.war
file. You can directly start 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
java -jar mancenter-*version*.war 8080 mancenter
```
Or, instead of starting at the command line, you can deploy it to your web server (Tomcat, Jetty, etc.). Let us say it is running at http://localhost:8080/mancenter
.
http://localhost:8080/mancenter
is up.Configure your Hazelcast members by adding the URL of your web application to your hazelcast.xml
. Hazelcast members will send their states to this URL.
```xml
<management-center enabled="true">
http://localhost:8080/mancenter
</management-center>
```
Start your Hazelcast cluster.
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, 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 (cluster member), as shown below.
The first column lists the nodes with their IPs and ports. The next columns list the system load averages on each node for the last 1, 5 and 15 minutes. These average values are calculated as the sum of the count of runnable entities running on and queued to the available CPUs averaged over the last 1, 5 and 15 minutes. This calculation is operating system specific, typically a damped time-dependent average. If system load average is not available, these columns show negative values.
The last column (Chart) graphically shows the recent load on the CPUs. When you move the mouse cursor on a chart, you can see the CPU load at the time where the cursor is placed. Charts under this column shows the CPU loads approximately for the last 2 minutes. If recent CPU load is not available, you will see a negative value.
This part of the page provides information related to memory usages for each node (cluster member), 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 both non-Hazelcast entities and all Hazelcast entities except the map (i.e. the memory used by all entities subtracted by the memory used by map). 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 (cluster member) 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 member, and the get, put, removal, eviction, and hit and miss counts 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.
NOTE: You need to enable the statistics for caches to monitor them in the Management Center. Use the <statistics-enabled>
element or setStatisticsEnabled()
method in declarative or programmatic configuration, respectively, to enable the statistics. Please refer to the [Cache Configuration section] for more information.
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.
Use the Map Browser tool to retrieve properties of the entries stored in the selected map. To open the Map Browser tool, 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 the key's type are specified and the Browse button is clicked, the key's properties along with its value are listed.
Use the Map Config tool to set the selected map's attributes, such as the backup count, TTL, and eviction policy. To open the Map Config tool, 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.
You can change any attribute and click the Update button to save your changes.
Besides the Map Browser and Map Config tools, the map monitoring page has monitoring options that are 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 members, 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 entry 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 member in the map, as shown below.
From left to right, this table lists:
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.
Replicated Map instances are shown under the Replicated Maps menu item on the left. When you click on a Replicated Map, a new tab for monitoring that instance opens on the right, as shown below.
In this tab, you can monitor metrics and also re-configure the selected Replicated Map. All of the statistics are real-time monitoring statistics.
When you click on a desired monitoring, the chart is loaded with the selected option. Also you can open the chart in new window.
The Replicated Map Throughput Data Table provides information about operations (get, put, remove) performed on each member in the selected Replicated Map.
From left to right, this table lists:
You can select the period from the combo box placed at the top right corner of the window, in which the table data is 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 member, as shown below.
From left to right, this table lists the IP address and port, items and backup items on the queue of each member, 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 member, 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, the members 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 member, 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 Managing Maps.
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. Click the button to list the monitoring options, 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 members, the counts of pending, started and completed executors per second, and the execution time and average start latency of executors on each member. 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 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 and/or e-mail notifications by creating filters. In these filters, you can specify criteria for cluster members 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 or sent as an e-mail.
Once you click the Alerts button located on the toolbar, the page shown below appears.
If you want to enable the Management Center to send e-mail notifications to the Management Center Admin users, you need to configure the SMTP server. To do this, click on the Create STMP Config shown above. The form shown below appears.
In this form, specify the e-mail address from which the notifications will be sent and also its password. Then, provide the SMTP server host address and port. Finally, check the TLS Connection checkbox if the connection is secured by TLS (Transport Layer Security).
After you provide the required information, click on the Save Config button. After a processing period (for a couple of seconds), the form will be closed if the configuration is created successfully. In this case, an e-mail will be sent to the e-mail address you provided in the form stating that the SMTP configuration is successful and e-mail alert system is created.
If not, you will see an error message at the bottom of this form as shown below.
As you can see, the reasons can be wrong SMTP configuration or connectivity problems. In this case, please check the form fields and check for any causes for the connections issues with your server.
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. Then, select whether notification e-mails will be sent to the Management Center Admins using the Send Email Alert checkbox. Then, provide a time interval (in seconds) for which the e-mails with the same notification content will be sent using the Email Interval (secs) field. Finally, 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, click the Next button. On the next page, give a name for the filter. Then, select whether notification e-mails will be sent to the Management Center Admins using the Send Email Alert checkbox. Then, provide a time interval (in seconds) for which the e-mails with the same notification content will be sent using the Email Interval (secs) field. Finally, 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.
Use the Time Travel toolbar item 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 (Cluster Members)
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 LoginModule s during the authentication process. User supplied attributes from LoginModule
s are accessed by CallbackHandler s. 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, you 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
. You must create the credentials at the client side and send them to the connected member 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>
You can also define your 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()
when the method configure(SecurityConfig securityConfig, Properties properties)
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
<id-generator-permission name="name" principal="principal">
<endpoints>
...
</endpoints>
<actions>
...
</actions>
</id-generator-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) {
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 perform all the I/O. Instead, multiple threads perform the I/O. On each cluster member, the I/O threading is split up in 3 types of I/O threads:
You can configure the number of I/O threads using the hazelcast.io.thread.count
system property. Its default value is 3 per member. If 3 is used, in total there are 7 I/O threads: 1 accept I/O thread, 3 read I/O threads, and 3 write I/O threads. Each I/O thread has its own Selector instance and waits on the Selector.select
if there is nothing to do.
NOTE: You can also specify counts for input and output threads separately. There are hazelcast.io.input.thread.count
and hazelcast.io.output.thread.count
properties for this purpose. Please refer to the System Properties section for information on these properties and how to set them.
Hazelcast periodically scans utilization of each I/O thread and can decide to migrate a connection to a new thread if the existing thread is servicing a disproportionate number of I/O events. You can customize the scanning interval by configuring the hazelcast.io.balancer.interval.seconds
system property; its default interval is 20 seconds. You can disable the balancing process by setting this property to a negative value.
In case of the read I/O thread, when sufficient bytes for a packet have been received, the Packet
object is created. This Packet
object 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 practice. 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 suffers from this. That is why it is better to offload processing to a dedicated thread (pool) so that systems are better isolated.
If the events are produced at a higher rate than they are consumed, the queue grows 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 placed in 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). Please also see the Configuring Executor Threads section.
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 in the following sections.
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 consumes 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 the followings:
A single operation thread executes operations for multiple partitions; if there are 271 partitions and 10 partition threads, then roughly every operation thread executes operations for 27 partitions.
Each partition belongs to only 1 operation thread. All operations for a partition are always handled by exactly the same operation thread.
Concurrency control is not needed to deal with partition-aware operations because once a partition-aware operation is put in 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 partition. For example, if there
is a map employees
and a map orders
, the method employees.get("peter")
running on partition 25 may be blocked
by the method orders.get(1234)
also running on partition 25. If independent data structures 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 on a number of partitions. For example, thread 1 could be responsible for partitions 0, 10, 20, etc. and thread-2 could be responsible for partitions 1, 11, 21, etc. If an operation for partition 1 takes a lot of time, it blocks the execution of an operation for partition 11 because both of them are mapped to 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
as 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 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-aware 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 operations that are not partition-aware, 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.
A non-partition-aware operation thread does not execute an operation for a specific partition. Only partition-aware operation threads execute partition-aware operations.
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. You may not see this contention in production since performance is dominated by I/O and the system does not run many non-partition-aware operations.
In some cases, the system needs to run operations with a higher priority, e.g. an important system operation. To support priority operations, Hazelcast has the following features:
For partition-aware operations: Each partition thread has its own work queue and it also has a priority work queue. The partition thread always checks the priority queue before it processes work from its normal work queue.
For non-partition-aware operations: Next to the genericWorkQueue
, there is also a genericPriorityWorkQueue
. When a priority operation
needs to be run, it is put in the genericPriorityWorkQueue
. Like the partition-aware operation threads, a generic
operation thread first checks the genericPriorityWorkQueue
for work.
Since a worker thread blocks on the normal work queue (either partition specific or generic), a priority operation
may not be picked up because it is not put in the queue where it is blocking. Hazelcast always sends a 'kick the worker' operation that
only triggers the worker to wake up and check the priority queue.
When an Operation is invoked, a Future
is returned. Please see 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. Upon execution, a response is returned and placed on the genericWorkQueue
where it is executed by a
"generic operation thread". This thread signals the future
and notifies the blocked thread that a response is available.
Hazelcast has a plan of exposing this future
to the outside world, and we will provide the ability to register a completion listener so you can perform 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 acquires a lock and waits 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 run 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.
Change CLOUD_PROVIDER to 'static'
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