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Further, it provides the essential notions of multidimensional geometry applied to analysis. Written in an accessible style and with proofs given as clearly as possible, it is a valuable resource for graduate students in Mathematical Analysis and researchers dealing with Hoelder-continuous functions and their applications. Please sign in to write a review. If you have changed your email address then contact us and we will update your details. We have recently updated our Privacy Policy. The site uses cookies to offer you a better experience. By continuing to browse the site you accept our Cookie Policy, you can change your settings at any time.

We can order this Usually dispatched within 3 weeks. Quantity Add to basket. This item has been added to your basket View basket Checkout. Added to basket. A Student's Guide to Fourier Transforms. Schaum's Outline of Partial Differential Equations. Paul DuChateau. Paul Lockhart. The Cartoon Guide to Calculus.

Larry Gonick. Calculus for the Ambitious. Guide to Analysis. Mary Hart. The Calculus Lifesaver. Adrian Banner. Measurements and their Uncertainties. Ifan Hughes. Schaum's Outline of Differential Equations. Richard Bronson. Introductory Mathematics: Algebra and Analysis. Geoffrey C. Stochastic Differential Equations. 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.

If you have a Hazelcast release older than 3. The following are the configuration examples with the password element:. 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. Dynamic class loader first checks the local classes, i.

If it is there, Hazelcast does not try to load it from the remote class repository. Then, it checks the cache of classes loaded from the remote class repository for this, caching should have been enabled in your local, see the Configuring User Code Deployment section. If your class is found here, again, Hazelcast does not try to load it from the remote class repository. Finally, dynamic class loader checks the remote class repository. If a member in this repository returns the class, it means your class is found and to be used. You can also put this class into your local class cache as mentioned in the previous step.

User Code Deployment feature is not enabled by default. You can configure this feature declaratively or programmatically. Following are example configuration snippets:. Its default value is "false" and it is a mandatory attribute. Available values are as follows:. This is the default value and suitable when you load long-living objects, such as domain objects stored in a map.

OFF : Do not cache the loaded classes locally. It is suitable for loading runnables, callables, entry processors, etc. This is the default value. Classes loaded from other members are used locally, but they are not served to other members. For example, if you set it as "com. If you set it as "com. Class", then the "Class" and all classes having the "Class" as prefix in the "com.

There are some built-in prefixes which are blacklisted by default. These are as follows:. It allows to quickly configure remote loading only for classes from selected packages. It can be used together with blacklisting. For example, you can whitelist the prefix "com. Setting this to null allows to load classes from all members. See an example in the below section. As described above, the configuration element provider-filter is used to constrain a member to load classes only from a subset of all cluster members.

The value of the provider-filter must be set as a member attribute in the desired members from which the classes are to be loaded. See the following example usages provided as programmatic configurations. The below example configuration allows the Hazelcast member to load classes only from the members with the class-provider attribute set. It does not ask any other member to provide a locally unavailable class:.

And the below example configuration sets the attribute class-provider for a member. So, the above member loads classes from the members who have the attribute class-provider :. You have objects that run on the cluster via the clients such as Runnable , Callable and Entry Processors. You have new or amended user domain objects in-memory format of the IMap set to Object which need to be deployed into the cluster.

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. See the Permissions section. Client User Code Deployment feature is not enabled by default.

C.other: Other default operation rules

See the Member User Code Deployment section for more information on enabling it on the member side and its configuration properties. For example, assuming that you provide com. When you want to use a Hazelcast feature in a non-Java client, you need to make sure that the Hazelcast member recognizes it. Then you can run the start. The following is an example code which can be the Java equivalent of entry processor in Node. Then, you can start your Hazelcast member by using the start scripts start. The start scripts automatically adds your class and JAR files to the classpath.

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. The total partition count is by default; you can change it with the configuration property hazelcast. 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. When you enable partition grouping, Hazelcast presents the following choices for you to configure partition groups. You can group members automatically using the IP addresses of members, so members sharing the same network interface are grouped together.


FUKUSHIMA : Basic properties of Brownian motion and a capacity on the Wiener space

All members on the same host IP address or domain name form a single partition group. This helps to avoid data loss when a physical server crashes, because multiple replicas of the same partition are not stored on the same host. But if there are multiple network interfaces or domain names per physical machine, this assumption is invalid. This way, you can add different and multiple interfaces to a group.

You can also use wildcards in the interface addresses. For example, the users can create rack-aware or data warehouse partition groups using custom partition grouping. The following are declarative and programmatic configuration examples that show how to enable and use CUSTOM grouping:. You can give every member its own group. Each member is a group of its own and primary and backup partitions are distributed randomly not on the same physical member. This gives the least amount of protection and is the default configuration for a Hazelcast cluster. This grouping type provides good redundancy when Hazelcast members are on separate hosts.

However, if multiple instances run on the same host, this type is not a good option. As discovery services, these plugins put zone information to the Hazelcast member attributes map during the discovery process. That means backups are created in the other zones and each zone is accepted as one partition group.

A note on fractional integral operators on Herz spaces with variable exponent

The following is the list of supported attributes which is set by the Discovery Service plugins during a Hazelcast member start-up:. You can provide your own partition group implementation using the SPI configuration. To create your partition group implementation, you need to first extend the DiscoveryStrategy class of the discovery service plugin, override the method public PartitionGroupStrategy getPartitionGroupStrategy and return the PartitionGroupStrategy configuration in that overridden method.

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 the built-in adapters, set the hazelcast. You can set hazelcast. If the provided logging mechanisms are not satisfactory, you can implement your own using the custom logging feature.

To use it, implement the com. LoggerFactory and com. ILogger interfaces and set the system property hazelcast. You can also listen to logging events generated by Hazelcast runtime by registering LogListener s 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. To enable the debug logs for all Hazelcast operations uncomment the below line in the above configuration file:. If you do not need detailed logs, the default settings is enough.

Using the Hazelcast specific lines in the above configuration file, you can select to see specific logs cluster, partition, hibernate, etc. Its configuration is similar to that of Log4j2. Below is the JVM argument way of specifying the logging type and configuration file:. 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.

By default, a member selects its socket address as its public address. If both members set their public addresses to their defined addresses on NAT, then that way they can communicate with each other. In this case, their public addresses are not an address of a local network interface but a virtual address defined by NAT. It is optional to set and useful when you have a private cloud. Note that, the value for this element should be given in the format host IP address:port number.

You can specify the ports that Hazelcast uses to communicate between cluster members. Its default value is The following are example configurations. Meaning that, if you set the value of port as , as members are joining to the cluster, Hazelcast tries to find ports between and You can choose to change the port count in the cases like having large instances on a single machine or willing to have only a few ports to be assigned. The parameter port-count is used for this purpose, whose default value is In that case, you can disable the auto-increment feature of port by setting auto-increment to false.

The port-count attribute is not used when auto-increment feature is disabled. By default, Hazelcast lets the system pick up an ephemeral port during socket bind operation. To fulfill this requirement, you can configure Hazelcast to use only defined outbound ports. As shown in the programmatic configuration, you use the method addOutboundPort to add only one port. If you need to add a group of ports, then use the method addOutboundPortDefinition.

In the declarative configuration, the element ports can be used for both single and multiple port definitions. The join configuration element is used to discover Hazelcast members and enable them to form a cluster. These mechanisms are explained the Discovery Mechanisms section. This section describes all the sub-elements and attributes of join element. The multicast element includes parameters to fine tune the multicast join mechanism. Specify it when you want to create clusters within the same network. Values can be between See more information here.

For example, if you set it as 60 seconds, each member waits for 60 seconds until a leader member is selected. Its default value is 2 seconds. When a member wants to join to the cluster, its join request is rejected if it is not a trusted member. Values can be true or false. Cluster is only formed if the member with this IP address is found. Once members are connected to these well known ones, all member addresses are communicated with each other. You can also give comma separated IP addresses using the members element.

This is the maximum amount of time Hazelcast is going to try to connect to a well known member before giving up. Setting it to a too low value could mean that a member is not able to connect to a cluster. Setting it to a too high value means that member startup could slow down because of longer timeouts, for example when a well known member is not up. Increasing this value is recommended if you have many IPs listed and the members cannot properly build up the cluster.

Its default value is 5 seconds. The aws element includes parameters to allow the members to form a cluster on the Amazon EC2 environment. Its default value is us-east You need to specify this if the region is other than the default one. It is used to narrow the Hazelcast members to be within this group. It is optional. They are optional. Setting this value too low could mean that a member is not able to connect to a cluster.

Setting the value too high means that member startup could slow down because of longer timeouts for example, when a well known member is not up. The members need to be retrieved from that provider, e. The discovery-strategies element configures internal or external discovery strategies based on the Hazelcast Discovery SPI. For further information, see the Discovery SPI section and the vendor documentation of the used discovery strategy. It determines the private IP addresses of EC2 instances to be connected.

Give the AWSClient class the values for the parameters that you specified in the aws element, as shown below. You will see whether your EC2 instances are found. You can specify which network interfaces that Hazelcast should use. Servers mostly have more than one network interface, so you may want to list the valid IPs.

For instance, Interface If network interface configuration is enabled it is disabled by default and if Hazelcast cannot find a matching interface, then it prints a message on the console and does not start on that member. Hazelcast supports IPv6 addresses seamlessly This support is switched off by default, see the note at the end of this section. All you need is to define IPv6 addresses or interfaces in the network configuration. Interfaces configuration does not have this limitation, you can configure wildcard IPv6 interfaces in the same way as IPv4 interfaces.

JVM has two system properties for setting the preferred protocol stack IPv4 or IPv6 as well as the preferred address family types inet4 or inet6. On a dual stack machine, IPv6 stack is preferred by default, you can change this through the java. You can change this through java. See also additional details on IPv6 support in Java. By default, Hazelcast chooses the public and bind address.

Hölder condition

You can influence on the choice by defining a public-address in the configuration or by using other properties mentioned above. In some cases, though, these properties are not enough and the default address picking strategy chooses wrong addresses. This may be the case when deploying Hazelcast in some cloud environments, such as AWS, when using Docker or when the instance is deployed behind a NAT and the public-address property is not enough see the Public Address section. In these cases, it is possible to configure the bind and public address in a more advanced way.

You can provide an implementation of the com. MemberAddressProvider interface which provides the bind and public address. The implementation may then choose these addresses in any way - it may read from a system property or file or even invoke a web service to retrieve the public and private address. The details of the implementation depend heavily on the environment in which Hazelcast is deployed. As such, we now demonstrate how to configure Hazelcast to use a simplified custom member address provider SPI implementation. An example implementation is shown below:.

Note that if the bind address port is 0 then it uses a port as configured in the Hazelcast network configuration see the Port section. If the public address port is set to 0 then it broadcasts the same port that it is bound to. If you wish to bind to any local interface, you may return new InetSocketAddress InetAddress null, port from the getBindAddress address. The following configuration examples contain properties that are provided to the constructor of the provider class.

If you do not provide any properties, the class may have either a no-arg constructor or a constructor accepting a single java. Properties instance. On the other hand, if you do provide properties in the configuration, the class must have a constructor accepting a single java. 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.

This detector is by default disabled. Note that, Hazelcast also offers failure detectors for its Java client. See the Client Failure Detectors section for more information. To use Deadline Failure Detector configuration property hazelcast. If the network becomes slow or unreliable, the resulting mean and variance increase, there needs to be a longer period for which no heartbeat is received before the member is suspected.

The hazelcast. Since Phi Accrual Failure Detector is adaptive to network conditions, a much lower hazelcast. In addition to the above two properties, Phi Accrual Failure Detector has the following configuration properties:. After calculated phi exceeds this threshold, a member is considered as unreachable and marked as suspected. Default phi threshold is Too low standard deviation might result in too much sensitivity. To use Phi Accrual Failure Detector , configuration property hazelcast. It operates at Layer 3 of the OSI protocol and provides much quicker and more deterministic detection of hardware and other lower level events.

This detector may be configured to perform an extra check after a member is suspected by one of the other detectors, or it can work in parallel, which is the default. This way hardware and network level issues are detected more quickly. This failure detector is based on InetAddress. This is preferred. If there are not enough permissions, it can be configured to fallback on attempting a TCP Echo on port 7. In the latter case, both a successful connection or an explicit rejection is treated as "Host is Reachable". Or, it can be forced to use only RAW sockets. This is not preferred as each call creates a heavy weight socket and moreover the Echo service is typically disabled.

Supported OS: as of Java 1. This detector relies on ICMP, i. It tries to issue the ping attempts periodically, and their responses are used to determine the reachability of the remote member. Most operating systems allow this to the root users, however Unix based ones are more flexible and allow the use of custom privileges per process instead of requiring root access.

Therefore, this detector is supported only on Linux. As described in the above requirement, on Linux, you have the ability to define extra capabilities to a single process, which would allow the process to interact with the RAW sockets. To enable this capability run the following command:. When running with custom capabilities, the dynamic linker on Linux rejects loading the libs from untrusted paths. Run the following command:. To be able to use the Ping Failure Detector, please add the following properties in your Hazelcast declarative configuration file:.

Its default value is false. Its default value is milliseconds. Its default value is 3. Its default value is 0. In the above configuration, the Ping detector attempts 3 pings, one every second and waits up to 1 second for each to complete. If after 3 seconds, there was no successful ping, the member gets suspected. To enforce the Requirements , the property hazelcast. Below is a summary table of all possible configuration combinations of the ping failure detector. Legacy ping mode. This works hand-to-hand with the OSI Layer 7 failure detector see. Ping in this mode only kicks in after a period when there are no heartbeats received, in which case the remote Hazelcast member is pinged up to a configurable count of attempts.

If all those attempts fail, the member gets suspected. You can configure this attempt count using the hazelcast. Parallel ping detector, works in parallel with the configured failure detector. Checks periodically if members are live OSI Layer 3 and suspects them immediately, regardless of the other detectors. Up to and including Hazelcast 3.

This configuration scheme allows more flexibility when deploying Hazelcast as described in the following cases:. For security, it is possible to bind the member protocol server socket on a protected internal network interface, while the client connections can be established on another network interface accessible by the Hazelcast clients.

Different kinds of network connections can be established with different socket options. In the following example we introduce the advanced network configuration for a member to listen for member-to-member connections on the default port while listening for client connections on the port :. Running this example prints something similar to the following output, indicating that the member listens for the specified protocols on the respective configured ports:. You cannot define both elements in the declarative configuration, i. In the programmatic configuration, an enabled AdvancedNetworkConfig takes precedence over the NetworkConfig.

AdvancedNetworkConfig is disabled by default, therefore the unisocket member configuration under NetworkConfig is used in the default case. When using the advanced network configuration, the following configurations are defined member-wide:. In addition to the above, the advanced network configuration allows the configuration of multiple endpoints: each endpoint configuration applies for a specific protocol, e. An additional optional identifier can be configured to separate the configuration of multiple WAN protocol endpoints.

The default advanced network configuration defines a member endpoint configuration listening on port same as the single-socket Hazelcast member configuration. If no such endpoint is configured, then the clients will not be able to connect to the Hazelcast member. WAN : Multiple WAN endpoint configurations can be defined to determine the network settings of outgoing connections from the members of a source cluster to the target WAN cluster members or to establish server sockets on which a target WAN member can listen for the incoming connections from the source cluster.

The server socket endpoint configuration is common for all protocols.

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The elements comprising a server socket endpoint configuration are identical to their single-socket network configuration counterparts. The following declarative configuration example includes all the common server socket endpoint elements:. When using the declarative configuration, specific element names introduce the server socket endpoint configuration for each protocol:. When using the programmatic configuration, corresponding methods set the respective server socket endpoint configuration:.

Multiple WAN endpoint configurations can be defined to configure the outgoing connections and server sockets, depending on the role of the member in the WAN replication. The configuration examples are provided in the following sections for both active and passive side of the WAN replication. The members on the active cluster initiate connections to the target cluster members, so there is no need to create a server socket.

A plain EndpointConfig is created that supplies the configuration for the client side of connections that the active members will create:. The wan-endpoint-config element contains the same sub-elements as the member-server-socket-endpoint-config element described above except port , public-address and reuse-address. On the passive cluster, a server socket is configured on the members to listen for the incoming WAN connections, matching the network configuration SSL configuration, etc.

Can I multiplex protocols on a single advanced network endpoint? No, each endpoint configuration that defines a server socket must bind to a different socket address. You can only configure multiple server socket endpoints for WAN protocol. This chapter explains the procedure of upgrading the version of Hazelcast members in a running cluster without interrupting the operation of the cluster. Patch version : A version change after the second decimal point, e. Member codebase version : The major. For example, when running on hazelcast Cluster version : The major.

This ensures that cluster members are able to communicate using the same cluster protocol and determines the feature set exposed by the cluster. Hazelcast members operating on binaries of the same major and minor version numbers are compatible regardless of patch version. For example, in a cluster with members running on version 3. The compatibility guarantees described above are given in the context of rolling member upgrades and only apply to GA general availability releases. It is never advisable to run a cluster with members running on different patch or minor versions for prolonged periods of time.

The rolling upgrade process for this cluster, i. Wait until all partition migrations are completed; during migrations, membership changes member joins or removals are not allowed. Start the member and wait until it joins the cluster. You should see something like the following in your logs:. The version in brackets [3.

Once the member locates the existing cluster members, it sends its join request to the master. The master validates that the new member is allowed to join the cluster and lets the new member know that the cluster is currently operating at 3. The new member sets 3. At this point all members of the cluster have been upgraded to codebase version 3. In order to use 3. Using Management Center. Also note that you need to upgrade your Management Center version before upgrading the member version if you want to change cluster version using Management Center.

Management Center is compatible with the previous minor version of Hazelcast, starting with version 3. For example, Management Center 3. To change your cluster version to 3. For the IMDG versions before 3. IMDG 3. Therefore, a configuration change is needed specifically when performing a rolling member upgrade from IMDG 3. So, the steps listed in the above Rolling Upgrade Procedure section should be as follows:. The cluster can automatically upgrade its version. As soon as it detects that all its members have a version higher than the current cluster version, it upgrades the cluster version to match it.

This feature is disabled by default. To enable it, set the system property hazelcast. To avoid this, you can use the hazelcast. You should set it to the size of your cluster, and then Hazelcast will wait for the last member to join before it can proceed with the auto-upgrade. In the event of network partitions which split your cluster into two subclusters, split-brain handling works as explained in the Network Partitioning chapter , with the additional constraint that two subclusters only merge as long as they operate on the same cluster version.

This is a requirement to ensure that all members participating in each one of the subclusters are able to operate as members of the merged cluster at the same cluster version. With regards to rolling upgrades, the above constraint implies that if a network partition occurs while a change of cluster version is in progress, then with some unlucky timing, one subcluster may be upgraded to the new cluster version and another subcluster may have upgraded members but still operate at the old cluster version.

In order for the two subclusters to merge, it is necessary to change the cluster version of the subcluster that still operates on the old cluster version, so that both subclusters will be operating at the same, upgraded cluster version and able to merge as soon as the network partition is fixed. The following provide answers to the frequently asked questions related to rolling member upgrades. When a new member starts, it is not yet joined to a cluster; therefore its cluster version is still undetermined.

In order for the cluster version to be set, one of the following must happen:. So a standalone member running on codebase version 3. If it is found to be compatible, then the member joins and the master sends the cluster version, which is set on the joining member. Otherwise, the starting member fails to join and shuts down. What if a new Hazelcast minor version changes fundamental cluster protocol communication, like join messages? On startup, as answered in the above question How is the cluster version set? By default the newly started member uses the cluster protocol that corresponds to its codebase version until this member joins a cluster so for codebase 3.

Thus older client versions are compatible with next minor versions. Newer clients connected to a cluster operate at the lower version of capabilities until all members are upgraded and the cluster version upgrade occurs. It is not recommended due to potential network partitions. It is advised to always stop and start one member in each upgrade step. Can I upgrade my business app together with Hazelcast while doing a rolling member upgrade? Yes, but make sure to make the new version of your app compatible with the old one since there will be a timespan when both versions interoperate.

Checking if two versions of your app are compatible includes verifying binary and algorithmic compatibility and some other steps. It is worth mentioning that a business app upgrade is orthogonal to a rolling member upgrade. A rolling business app upgrade may be done without upgrading the members.

As mentioned in the Overview section , Hazelcast offers distributed implementations of Java interfaces. Below is the list of these implementations with links to the corresponding sections in this manual. Map is the distributed implementation of java. It lets you read from and write to a Hazelcast map with methods such as get and put. Queue is the distributed implementation of java. You can add an item in one member and remove it from another one. Set is the distributed and concurrent implementation of java. It does not allow duplicate elements and does not preserve their order.

List is similar to Hazelcast Set. The only difference is that it allows duplicate elements and preserves their order. Multimap is a specialized Hazelcast map. It is a distributed data structure where you can store multiple values for a single key. Replicated Map does not partition data. It does not spread data to different cluster members. Instead, it replicates the data to all members. Topic is the distributed mechanism for publishing messages that are delivered to multiple subscribers.

See the Topic section for more information. Hazelcast also has a structure called Reliable Topic which uses the same interface of Hazelcast Topic. The difference is that it is backed up by the Ringbuffer data structure. See the Reliable Topic section. Lock is the distributed implementation of java. When you use lock, the critical section that Hazelcast Lock guards is guaranteed to be executed by only one thread in the entire cluster.

ISemaphore is the distributed implementation of java. When performing concurrent activities, semaphores offer permits to control the thread counts. IAtomicLong is the distributed implementation of java. However, these operations involve remote calls and hence their performances differ from AtomicLong, due to being distributed.

IAtomicReference is the distributed implementation of java. When you need to deal with a reference in a distributed environment, you can use Hazelcast IAtomicReference.

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IdGenerator is used to generate cluster-wide unique identifiers. ID generation occurs almost at the speed of AtomicLong. This feature is deprecated, please use FlakeIdGenerator instead. ICountdownLatch is the distributed implementation of java. Hazelcast CountDownLatch is a gate keeper for concurrent activities. It enables the threads to wait for other threads to complete their operations. PN counter is a distributed data structure where each Hazelcast instance can increment and decrement the counter value and these updates are propagated to all replicas.

Event Journal is a distributed data structure that stores the history of mutation actions on map or cache. Data structures where each partition stores a part of the instance, namely partitioned data structures. Data structures where a single partition stores the whole instance, namely non-partitioned data structures. Besides these, Hazelcast also offers the Replicated Map structure as explained in the above Standard utility collections list.

Hazelcast offers a get method for most of its distributed objects. To load an object, first create a Hazelcast instance and then use the related get method on this instance. Following example code snippet creates an Hazelcast instance and a map on this instance. As to the configuration of distributed object, Hazelcast uses the default settings from the file hazelcast. Of course, you can provide an explicit configuration in this XML or programmatically according to your needs.

See the Understanding Configuration section. If you want to use an object you loaded in other places, you can safely reload it using its reference without creating a new Hazelcast instance customers in the above example. To destroy a Hazelcast distributed object, you can use the method destroy. This method clears and releases all resources of the object. Therefore, you must use it with care since a reload with the same object reference after the object is destroyed creates a new data structure without an error. See the following example code where one of the queues are destroyed and the other one is accessed.

Hazelcast is designed to create any distributed data structure whenever it is accessed, i. Therefore, keep in mind that a data structure is recreated when you perform an operation on it even after you have destroyed it. Hazelcast uses the name of a distributed object to determine which partition it will be put. Since these semaphores have different names, they will be placed into different partitions. If you want to put these two into the same partition, you use the symbol as shown below:.

Now, these two semaphores will be put into the same partition whose partition key is foo. Note that you can use the method getPartitionKey to learn the partition key of a distributed object. It may be useful when you want to create an object in the same partition of an existing object. See its usage as shown below:. If a member goes down, its backup replica which holds the same data dynamically redistributes the data, including the ownership and locks on them, to the remaining live members.

As a result, there will not be any data loss. There is no single cluster master that can be a single point of failure. Every member in the cluster has equal rights and responsibilities. No single member is superior. There is no dependency on an external 'server' or 'master'. Here is an example of how you can retrieve existing data structure instances map, queue, set, lock, topic, etc. Hazelcast Map IMap extends the interface java.

ConcurrentMap and hence java. It is the distributed implementation of Java map. Hazelcast partitions your map entries and their backups, and almost evenly distribute them onto all Hazelcast members. For example, if you have a member with objects to be stored in the cluster and then you start a second member, each member will both store objects and back up the objects in the other member.

Use the HazelcastInstance getMap method to get the map, then use the map put method to put an entry into the map. When you run this code, a cluster member is created with a map whose entries are distributed across the members' partitions. See the below illustration. For now, this is a single member cluster. This creates a cluster with two 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. IMap which extends the java. ConcurrentMap interface. Methods like ConcurrentMap. 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. Hazelcast distributes map entries onto multiple cluster members JVMs. Each member holds some portion of the data. Distributed maps have one backup by default. If a member goes down, your data is recovered using the backups in the cluster.

There are two types of backups as described below: sync and async. 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 is copied onto other member s. To create synchronous backups, select the number of backup copies using the backup-count property. When this count is 1, a map entry will have its backup on one other member in the cluster. If you set it to 2, then a map entry will have its backup on two other members. You can set it to 0 if you do not want your entries to be backed up, e.

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 members or deleted from backup members in case of remove and acknowledgements are received.

Therefore, backups are updated before a put operation is completed, provided that the cluster is stable. Sync backup operations have a blocking cost which may lead to latency issues. Asynchronous backups, on the other hand, do not block operations. To create asynchronous backups, select the number of async backups with the async-backup-count property. An example is shown below. See Consistency and Replication Model for more detail. 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. By default, map. 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 consistency. Enabling backup reads can improve performance but on the other hand it can cause stale reads while still preserving monotonic-reads property.

Please note that if you are performing a read from a backup, you should take into account that your hits to the keys in the backups are not reflected as hits to the original keys on the primary members. Therefore, even though there is a hit on a key in backups, your original key on the primary member may expire.

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. Hazelcast Map performs eviction based on partitions. Hazelcast uses the following equation to calculate the maximum size of a partition:. The eviction process starts according to this calculated partition maximum size when you try to put an entry. When entry count in that partition exceeds partition maximum size, eviction starts on that partition. This means you are at the eviction threshold since you set the max-size to When you try to put an entry:.

As a result of this eviction process, when you check the size of your map, it is After this eviction, subsequent put operations do not trigger the next eviction until the map size is again close to the max-size. It limits the lifetime of the entries relative to the time of the last write access performed on them. If it is not 0, the entries whose lifetime exceeds this period without any write access performed on them during this period are expired and evicted automatically.

An individual entry may have its own lifetime limit by using one of the methods accepting a TTL; see Evicting Specific Entries section. If there is no TTL value provided for the individual entry, it inherits the value set for this element. Valid values are integers between 0 and Integer. Its default value is 0, which means infinite no expiration and eviction. If it is not 0, entries are evicted regardless of the set eviction-policy described below. It limits the lifetime of the entries relative to the time of the last read or write access performed on them.

The entries whose idle period exceeds this limit are expired and evicted automatically. An entry is idle if no get , put , EntryProcessor. Its default value is 0, which means infinite. Valid values are:. NONE: Default policy. If set, no items are evicted and the property max-size described below is ignored. You still can combine it with time-to-live-seconds and max-idle-seconds. Apart from the above values, you can also develop and use your own eviction policy. See the Custom Eviction Policy section. When maximum size is reached, the map is evicted based on the policy defined. If you want max-size to work, set the eviction-policy property to a value other than NONE.

Its attributes are described below. This is the default policy. Storage size depends on the partition count in a cluster member. This attribute should not be used often. For instance, avoid using this attribute with a small cluster. If the cluster is small, it hosts more partitions, and therefore map entries, than that of a larger cluster. Thus, for a small cluster, eviction of the entries decreases performance the number of entries is large. If, for example, a JVM is configured to have MB and this value is 10, then the map entries will be evicted when used heap size exceeds MB.

If, for example, a JVM is configured to have MB and this value is 10, then the map entries will be evicted when free heap size is below MB. To put it briefly, Hazelcast maps have no restrictions on the size and may grow arbitrarily large, by default.

When it comes to reducing the size of a map, there are two concepts: expiration and eviction. Expiration puts a limit on the maximum lifetime of an entry stored inside the map. When the entry expires it cannot be retrieved from the map any longer and at some point in time it will be cleaned out from the map to free up the memory. Expiration, and hence the eviction based on the expiration, can be configured using the element time-to-live-seconds and max-idle-seconds as described above.

Eviction puts a limit on the maximum size of the map. If the size of the map grows larger than the maximum allowed size, an eviction policy decides which item to evict from the map to reduce its size. The maximum allowed size can be configured using the element max-size and the eviction policy can be configured using the element eviction-policy as described above. Eviction and expiration can be used together.

In this case, the expiration configurations time-to-live-seconds and max-idle-seconds continue to work as usual cleaning out the expired entries regardless of the map size. Note that locked map entries are not the subjects for eviction and expiration. In the above example, documents map starts to evict its entries from a member when the map size exceeds 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. If you want to evict some specific map entries, you can use the ttl and ttlUnit parameters of the method map. An example code line is given below.