This Reference Manual covers all editions of Hazelcast IMDG. Throughout this manual:

You can see the features for all Hazelcast IMDG editions in the following architecture diagram.

For more information on Hazelcast IMDG’s Architecture, please see the white paper An Architect’s View of Hazelcast.

You can extend Hazelcast IMDG’s functionality by using its plugins. These plugins have their own lifecycles. Please see Plugins page to learn about Hazelcast plugins you can use. Hazelcast plugins are marked with label throughout this manual. See also the Hazelcast Plugins chapter for more information.

Hazelcast IMDG and Hazelcast Reference Manual are free and provided under the Apache License, Version 2.0. Hazelcast IMDG Enterprise and Hazelcast IMDG Enterprise HD 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 the commercial support for Hazelcast IMDG and Hazelcast IMDG Enterprise, please see hazelcast.com.

Please refer to the Release Notes document for the new features, enhancements and fixes performed for each Hazelcast IMDG release.

You can contribute to the Hazelcast IMDG code, report a bug, or request an enhancement. Please see the following resources.

Hazelcast partners with leading hardware and software technologies, system integrators, resellers and OEMs including Amazon Web Services, Vert.x, Azul Systems, C2B2. Please see the Partners page for the full list of and information on our partners.

Hazelcast uses phone home data to learn about usage of Hazelcast IMDG.

Hazelcast IMDG member instances call our phone home server initially when they are started and then every 24 hours. This applies to all the instances joined to the cluster.

What is sent in?

The following information is sent in a phone home:

Phone Home Code

The phone home code itself is open source. Please see here.

Disabling Phone Homes

Set the hazelcast.phone.home.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.

You can also disable the phone home using the environment variable HZ_PHONE_HOME_ENABLED. Simply add the following line to your .bash_profile:

Phone Home URLs

For versions 1.x and 2.x: http://www.hazelcast.com/version.jsp.

For versions 3.x up to 3.6: http://versioncheck.hazelcast.com/version.jsp.

For versions after 3.6: http://phonehome.hazelcast.com/ping.

The following sections explain the installation of Hazelcast IMDG and Hazelcast IMDG Enterprise. It also includes notes and changes to consider when upgrading Hazelcast.

Following table summarizes the version compatibility between Hazelcast IMDG and various vendors' Java Virtual Machines (JVMs).

Java project Jigsaw brought a new Module System into Java 9 and newer. Hazelcast supports running in the modular environment. If you want to run your application with Hazelcast libraries on the modulepath, use following module names:

Don’t use hazelcast-all-<version>.jar or hazelcast-enterprise-all-<version>.jar on the modulepath as it could lead to problems in module dependencies for your application. You can still use them on the classpath.

The Java Module System comes with stricter visibility rules. It affects Hazelcast which uses internal Java API to reach the best performance results.

Hazelcast needs java.se module and access to the following Java packages for a proper work:

You can provide the access to the above mentioned packages by using --add-exports and --add-opens (for the reflective access) Java arguments.

Example: Running a member on the classpath

Example: Running a member on the modulepath

This example expects hazelcast-<version>.jar placed in the lib directory.

Having installed Hazelcast, you can get started.

In this short tutorial, you perform the following activities.

Let’s begin.

Hazelcast also offers a tool, Management Center, that enables you to monitor your cluster. You can download it from Hazelcast website’s download page. You can use it to monitor your maps, queues and other distributed data structures and members. Please see the Hazelcast Management Center Reference Manual 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.

When you download and extract the Hazelcast ZIP or TAR.GZ package, you will see the following scripts under the /bin folder that provide basic functionalities for member and cluster management.

The following are the names and descriptions of each script:

A simple option for deploying Hazelcast is Hazelcast Cloud. It delivers enterprise-grade Hazelcast software in the cloud. You can deploy, scale and update your Hazelcast easily using Hazelcast Cloud; it maintains the clusters for you. You can use Hazelcast Cloud as a low-latency high-performance caching or data layer for your microservices, and it is also a nice solution for state management of serverless functions (AWS Lambda).

Hazelcast Cloud uses Docker and Kubernetes, and is powered by Hazelcast IMDG Enterprise HD. It is initially available on Amazon Web Services (AWS), to be followed by Microsoft Azure and Google Cloud Platform (GCP). Since it is based on Hazelcast IMDG Enterprise HD, it features advanced functionalities such as TLS, multi-region, persistence, and high availability.

Note that Hazelcast Cloud is currently in beta. See here for more information and applying for a beta.

You can deploy your Hazelcast project onto an 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, which you can download at hazelcast.org. Please refer to this sample project for more information.

You can deploy your Hazelcast cluster onto a Microsoft Azure environment. For this, your cluster should make use of Hazelcast Discovery Plugin for Microsoft Azure. You can find information about this plugin on its GitHub repository at Hazelcast Azure.

For information on how to automatically deploy your cluster onto Azure, please see the Deployment section of Hazelcast Azure plugin repository.

Starting with Hazelcast 3.7, you can deploy your Hazelcast cluster onto Pivotal Cloud Foundry. It is available as a Pivotal Cloud Foundry Tile which you can download at here. You can find the installation and usage instructions and the release notes documents at https://docs.pivotal.io/partners/hazelcast/index.html.

You can deploy your Hazelcast projects using the Docker containers. Hazelcast has the following images on Docker:

After you pull an image from the Docker registry, you can run your image to start the Management Center or a Hazelcast instance with Hazelcast’s default configuration. All repositories provide the latest stable releases but you can pull a specific release, too. You can also specify environment variables when running the image.

If you want to start a customized Hazelcast instance, you can extend the Hazelcast image by providing your own configuration file.

This feature is provided as a Hazelcast plugin. Please see its own GitHub repo at Hazelcast Docker for details on configurations and usages.

Hazelcast shards are called Partitions. By default, Hazelcast has 271 partitions. Given a key, we serialize, hash and mod 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 members for redundancy.

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 the preferred way. In Embedded deployment, 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.

See the below illustration.

Hazelcast provides native clients (Java, .NET and C++), Memcache and REST clients, Scala, Python and Node.js client implementations.

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.

Note that Hazelcast member libraries are available only in Java. Therefore, embedding a member to a business service, it is only possible with Java. Applications written in other languages (.NET, C++, Node.js, etc.) can use Hazelcast client libraries to access the cluster. See the Hazelcast Clients chapter for information on the clients and other language implementations.

If you want low-latency data access, as in the Embedded deployment, and you also want the scalability advantages of the Client/Server deployment, you can consider defining Near Caches for your clients. This enables the frequently used data to be kept in the client’s local memory. Please refer to the Configuring Client Near Cache section.

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 that help 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.

In the case of TTL, if the access is less frequent than the TTL, the result is always 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, it means there are consistency issues. This can be avoided by having the members communicate with each other so that entry invalidations can be propagated.

We can conclude that an ideal cache would combine TTL and write-through features. There are several cache servers and in-memory database solutions in this field. However, these are stand-alone single instances with a distribution mechanism that is provided by other technologies to an extent. So, we are back to square one; we 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 that it does not have a master member. Each cluster member is configured to be the same in terms of functionality. The oldest member (the first member created in the cluster) automatically performs the data assignment to cluster members. If the oldest member dies, the second oldest member takes over.

Another main feature of Hazelcast is that the data is held entirely in-memory. This is fast. In the case of a failure, such as a member crash, no data is lost since Hazelcast distributes copies of the data across all the cluster members.

As shown in the feature list in the Distributed Data Structures chapter, Hazelcast supports a number of distributed data structures and distributed computing utilities. These provide 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 that can contain hundreds or thousands of data entries each, depending on the memory capacity of your system. Each Hazelcast partition can have multiple replicas, which are distributed among the cluster members. One of the replicas becomes the primary and other replicas are called backups. Cluster member which owns primary replica of a partition is called partition owner. When you read or write a particular data entry, you transparently talk to the owner of the partition that contains the data entry.

By default, Hazelcast offers 271 partitions. When you start a cluster with a single member, it owns all of 271 partitions (i.e., it keeps primary replicas for 271 partitions). The following illustration shows the partitions in a Hazelcast cluster with single member.

When you start a second member on that cluster (creating a Hazelcast cluster with two members), the partition replicas are distributed as shown in the illustration here.

In the illustration, the partition replicas with black text are primaries and the partition replicas with blue text are backups. The first member has primary replicas of 135 partitions (black) and each of these partitions are backed up in the second member (i.e., the second member owns the backup replicas) (blue). At the same time, the first member also has the backup replicas of the second member’s primary partition replicas.

As you add more members, Hazelcast moves some of the primary and backup partition replicas to the new members one by one, making all members equal and redundant. Thanks to the consistent hashing algorithm, only the minimum amount of partitions are moved to scale out Hazelcast. The following is an illustration of the partition replica distributions in a Hazelcast cluster with four members.

Hazelcast distributes partitions' primary and backup replicas equally among the members of the cluster. Backup replicas of the partitions are maintained for redundancy.

Starting with Hazelcast 3.6, lite members are introduced. Lite members are a new type of members that do not own any partition. Lite members are intended for use in computationally-heavy task executions and listener registrations. Although they do not own any partitions, they can access partitions that are owned by other members in the cluster.

Hazelcast can be used:

This is the configuration option where you use an XML or a YAML configuration file. When you download and unzip hazelcast-<version> .zip, you see the following files present in the /bin folder, which are standard configuration files:

A part of hazelcast.xml is shown as an example below.

The identical part of the configuration extracted from hazelcast.yaml is shown as below.

Besides declarative configuration, you can configure your cluster programmatically. For this you can create a Config object, set/change its properties and attributes and use this Config object to create a new Hazelcast member. Following is an example code which configures some network and Hazelcast Map properties.

To create a Hazelcast member with the above example configuration, pass the configuration object as shown below:

You can also create a named Hazelcast member. In this case, you should set instanceName of Config object as shown below:

To retrieve an existing Hazelcast member by its name, use the following:

To retrieve all existing Hazelcast members, use the following:

If you want to specify your own configuration file to create Config, Hazelcast supports several ways including filesystem, classpath, InputStream and URL.

Building Config from the XML declarative configuration:

Building Config from the YAML declarative configuration:

You can use system properties to configure some aspects of Hazelcast. You set these properties as name and value pairs through declarative configuration, programmatic configuration or JVM system property. Following are examples for each option.

Declarative Configuration:

Programmatic Configuration:

Using JVM’s System class or -D argument:

System.setProperty( "hazelcast.property.foo", "value" );

or

java -Dhazelcast.property.foo=value

You will see Hazelcast system properties mentioned throughout this Reference Manual as required in some of the chapters and sections. All Hazelcast system properties are listed in the System Properties appendix with their descriptions, default values and property types as a reference for you.

If you use Hazelcast with Spring you can declare beans using the namespace hazelcast. When you add the namespace declaration to the element beans in the Spring context file, you can start to use the namespace shortcut hz to be used as a bean declaration. Following is an example Hazelcast configuration when integrated with Spring:

Please see the Integration with Spring section for more information on Hazelcast-Spring integration.

As described above, Hazelcast can be configured in a declarative or programmatic way; configuration must be completed before starting a Hazelcast member and this configuration cannot be altered at runtime, thus we refer to this as static configuration.

It is possible to dynamically add configuration for certain data structures at runtime; these can be added by invoking one of the Config.add*Config methods on the Config object obtained from a running member’s HazelcastInstance.getConfig() method. For example:

Dynamic configuration elements must be fully configured before the invocation of add*Config method: at that point, the configuration object is delivered to every member of the cluster and added to each member’s dynamic configuration, so mutating the configuration object after the add*Config invocation does not have an effect.

As dynamically added data structure configuration is propagated across all cluster members, failures may occur due to conditions such as timeout and network partition. The configuration propagation mechanism internally retries adding the configuration whenever a membership change is detected. However if an exception is thrown from add*Config method, the configuration may have been partially propagated to some cluster members and adding the configuration should be retried by the user.

Adding new dynamic configuration is supported for all add*Config methods except:

Keep in mind that this feature also works for Hazelcast Java clients. See the following example:

When you start a Hazelcast member without passing a Config object, as explained in the Configuring Programmatically section, Hazelcast checks the member’s configuration as follows:

Before configuring Hazelcast, please try to work with the default configuration to see if it works for you. This default configuration should be fine for most of the users. If not, you can consider to modify the configuration to be more suitable for your environment.

You can give a custom strategy to match an item name to a configuration pattern. By default Hazelcast uses a simplified wildcard matching. See Using Wildcards section for this. A custom configuration pattern matcher can be given by using either member or client config objects, as shown below:

And the following is an example pattern matcher:

Hazelcast supports wildcard configuration for all distributed data structures that can be configured using Config, that is, for all except IAtomicLong, IAtomicReference. Using an asterisk (*) character in the name, different instances of maps, queues, topics, semaphores, etc. can be configured by a single configuration.

A single asterisk (*) can be placed anywhere inside the configuration name.

For instance, a map named com.hazelcast.test.mymap can be configured using one of the following configurations:

A queue named com.hazelcast.test.myqueue can be configured using one of the following configurations:

In your Hazelcast and/or Hazelcast Client declarative configuration, you can use variables to set the values of the elements. This is valid when you set a system property programmatically or you use the command line interface. You can use a variable in the declarative configuration to access the values of the system properties you set.

For example, see the following command that sets two system properties.

Let’s get the values of these system properties in the declarative configuration of Hazelcast, as shown below.

In the XML configuration:

In the YAML configuration:

This also applies to the declarative configuration of Hazelcast Java Client, as shown below.

If you do not want to rely on the system properties, you can use the XmlConfigBuilder or YamlConfigBuilder and explicitly set a Properties instance, as shown below.

Variable replacers are used to replace custom strings during loading the configuration, e.g., they can be used to mask sensitive information such as usernames and passwords. Of course their usage is not limited to security related information.

Variable replacers implement the interface com.hazelcast.config.replacer.spi.ConfigReplacer and they are configured only declaratively: in the Hazelcast’s declarative configuration files, i.e., hazelcast.xml, hazelcast.yaml and hazelcast-client .xml, hazelcast-client.yaml. You can refer to ConfigReplacers Javadoc for basic information on how a replacer works.

Variable replacers are configured within the element <config-replacers> under <hazelcast>, as shown below.

In the XML configuration:

In the YAML configuration:

As you can see, <config-replacers> is the parent element for your replacers, which are declared using the <replacer> sub-elements. You can define multiple replacers under the <config-replacers>. Here are the descriptions of elements and attributes used for the replacer configuration:

The following replacer classes are provided by Hazelcast as example implementations of the ConfigReplacer interface. Note that you can also implement your own replacers.

Each example replacer is explained in the below sections.

A Hazelcast cluster is a network of cluster members that run Hazelcast. Cluster members 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.

Please note that, after a cluster is formed, communication between cluster members is always via TCP/IP, regardless of the discovery mechanism used.

Hazelcast uses the following discovery mechanisms.

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.

The following is an example declarative configuration.

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 tries to resolve one interface to bind from the member elements.

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, as they just multicast to all the other members for listening. Whether multicast is possible or allowed depends on your environment.

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.

The following is an example declarative configuration.

Pay attention to the multicast-timeout-seconds element. multicast-timeout-seconds specifies the time in seconds that a member should wait for a valid multicast response from another member running in the network before declaring itself the leader member (the first member joined to the cluster) and creating its own cluster. This only applies to the startup of members 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 member waits 60 seconds before moving on. Be careful when providing a high value. Also, be careful not to set the value too low, or the members might give up too early and create their own cluster.

Hazelcast members and native Java clients can find each other with multicast discovery plugin. This plugin is implemented using Hazelcast Discovery SPI. You should configure the plugin both at Hazelcast members and Java clients in order to use multicast discovery.

To configure your cluster to have the multicast discovery plugin, follow these steps:

The following is an example declarative configuration.

The following are the multicast discovery plugin configuration properties with their descriptions.

You can create cluster groups. To do this, use the group configuration element.

You can separate your clusters in a simple way by specifying group names. Example groupings can be by development, production, test, app, etc. The following is an example declarative configuration.

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 and does not interact with other groups. The following code example creates three separate Hazelcast instances--h1 belongs to the production cluster, while h2 and h3 belong to the development cluster.

Hazelcast can dynamically load your custom classes or domain classes from a remote class repository, which typically includes lite members. For this purpose Hazelcast offers a distributed dynamic class loader.

Using this dynamic class loader, you can control the local caching of the classes loaded from other members, control the classes to be served to other members and create blacklists or whitelists of classes and packages. When you enable this feature, you don’t need to deploy your classes to all cluster members.

The following is the brief working mechanism of the User Code Deployment feature:

You can also deploy your codes from the client side for the following situations:

When this feature is enabled, the clients deploy these classes to the members. By this way, when a client adds a new class, the members do not require restarts to include the new classes in classpath.

You can also use the client permission policy to specify which clients are permitted to use User Code Deployment. Please see the Permissions.

Hazelcast distributes key objects into partitions using the consistent hashing algorithm. Multiple replicas are created for each partition and those partition replicas are distributed among Hazelcast members. An entry is stored in the members that own replicas of the partition to which the entry’s key is assigned. The total partition count is 271 by default; you can change it with the configuration property hazelcast.partition.count. Please see the System Properties appendix.

Hazelcast member that owns the primary replica of a partition is called as partition owner. Other replicas are called backups. Based on the configuration, a key object can be kept in multiple replicas of a partition. A member can hold at most one replica of a partition (ownership or backup).

By default, Hazelcast distributes partition replicas randomly and equally among the cluster members, assuming all members in the cluster are identical. But what if some members share the same JVM or physical machine or chassis and you want backups of these members to be assigned to members in another machine or chassis? What if processing or memory capacities of some members are different and you do not want an equal number of partitions to be assigned to all members?

To deal with such scenarios, you can group members in the same JVM (or physical machine) or members located in the same chassis. Or you can group members to create identical capacity. We call these groups partition groups. Partitions are assigned to those partition groups instead of individual members. Backup replicas of a partition which is owned by a partition group are located in other partition groups.

Hazelcast has a flexible logging configuration and does not depend on any logging framework except JDK logging. It has built-in adapters for a number of logging frameworks and it also supports custom loggers by providing logging interfaces.

To use built-in adapters, set the hazelcast.logging.type property to one of the predefined types below.

You can set hazelcast.logging.type through declarative configuration, programmatic configuration, or JVM system property.

Declarative Configuration:

Programmatic Configuration

System Property

If the provided logging mechanisms are not satisfactory, you can implement your own using the custom logging feature. To use it, implement the com.hazelcast.logging.LoggerFactory and com.hazelcast.logging.ILogger interfaces and set the system property hazelcast.logging.class as your custom LoggerFactory class name.

You can also listen to logging events generated by Hazelcast runtime by registering LogListeners to LoggingService.

Through the LoggingService, you can get the currently used ILogger implementation and log your own messages too.

Below are example configurations for Log4j2 and Log4j. Note that Hazelcast does not recommend any specific logging library, these examples are provided only to demonstrate how to configure the logging. You can use your custom logging as explained above.

All network related configurations are performed via the network element in the Hazelcast XML configuration file or the class NetworkConfig when using programmatic configuration. Following subsections describe the available configurations that you can perform under the network element.

A failure detector is responsible to determine if a member in the cluster is unreachable or crashed. The most important problem in failure detection is to distinguish whether a member is still alive but slow or has crashed. But according to the famous FLP result, it is impossible to distinguish a crashed member from a slow one in an asynchronous system. A workaround to this limitation is to use unreliable failure detectors. An unreliable failure detector allows a member to suspect that others have failed, usually based on liveness criteria but it can make mistakes to a certain degree.

Hazelcast has two built-in failure detectors; Deadline Failure Detector and Phi Accrual Failure Detector.

Since 3.9.1, Hazelcast provides yet another failure detector, Ping Failure Detector, that, if enabled, works in parallel with the above ones, but identifies failures on OSI Layer 3 (Network Layer). This detector is by default disabled.

Note that, Hazelcast also offers failure detectors for its Java client. Please refer to the Client Failure Detectors section for more information.