In-Memory Data Grid - Hazelcast | Documentation

Core architecture:

Serialization

Map

Queue

Multimap

Topic

Transactions

Client

In this section, we list the changes what users should take into account before upgrading to Hazelcast 3.0 from earlier versions of Hazelcast.

In this short tutorial, we will create simple Java application using Hazelcast distributed map and queue. Then we will run our application twice to have two nodes (JVMs) clustered and finalize this tutorial with connecting to our cluster from another Java application by using Hazelcast Native Java Client API.

Connecting Hazelcast Cluster with Java Client API

What is Next?

Hazelcast will partition your map entries; and almost evenly distribute onto all Hazelcast members. Distributed maps have 1 backup by default so that if a member goes down, we don't lose data. Backup operations are synchronous so when a map.put(key, value) returns, it is guaranteed that the entry is replicated to one other node. For the reads, it is also guaranteed that map.get(key) returns the latest value of the entry. Consistency is strictly enforced.

import com.hazelcast.core.Hazelcast;
import java.util.Map;
import java.util.Collection;
import com.hazelcast.config.Config;

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
Map<String, Customer> mapCustomers = hz.getMap("customers");
mapCustomers.put("1", new Customer("Joe", "Smith"));
mapCustomers.put("2", new Customer("Ali", "Selam"));
mapCustomers.put("3", new Customer("Avi", "Noyan"));

Collection<Customer> colCustomers = mapCustomers.values();
for (Customer customer : colCustomers) {
    // process customer
}

Hazelcast.getMap() actually returns com.hazelcast.core.IMap which extends java.util.concurrent.ConcurrentMap interface. So methods like ConcurrentMap.putIfAbsent(key,value) and ConcurrentMap.replace(key,value) can be used on distributed map as shown in the example below.

import com.hazelcast.core.Hazelcast;
import java.util.concurrent.ConcurrentMap;

Customer getCustomer (String id) {
    ConcurrentMap<String, Customer> map = Hazelcast.getMap("customers");
    Customer customer = map.get(id);
    if (customer == null) {
        customer = new Customer (id);
        customer = map.putIfAbsent(id, customer);
    }
    return customer;
}               

public boolean updateCustomer (Customer customer) {
    ConcurrentMap<String, Customer> map = Hazelcast.getMap("customers");
    return (map.replace(customer.getId(), customer) != null);            
}
                
public boolean removeCustomer (Customer customer) {
    ConcurrentMap<String, Customer> map = Hazelcast.getMap("customers");
    return map.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 throwjava.util.ConcurrentModificationException.

Also see:

<hazelcast>
    ...
    <map name="default">
        <!--
            Number of sync-backups. If 1 is set as the backup-count for example,
            then all entries of the map will be copied to another JVM for
            fail-safety. Valid numbers are 0 (no backup), 1, 2, 3.
        -->
        <backup-count>1</backup-count>

        <!--
            Number of async-backups. If 1 is set as the backup-count for example,
            then all entries of the map will be copied to another JVM for
            fail-safety. Valid numbers are 0 (no backup), 1, 2, 3.
        -->
        <async-backup-count>1</async-backup-count>

        <!--
            Can we read the local backup entries? Default value is false for
            strong consistency. Being able to read backup data will give you
            greater performance.
        -->
        <read-backup-data>false</read-backup-data>

        ...
    </map>
</hazelcast>

<hazelcast>
    ...
    <map name="default">
        <!--
            Number of backups. If 1 is set as the backup-count for example,
            then all entries of the map will be copied to another JVM for
            fail-safety. Valid numbers are 0 (no backup), 1, 2, 3.
        -->
        <backup-count>1</backup-count>

        <!--
            Maximum number of seconds for each entry to stay in the map. Entries that are
            older than <time-to-live-seconds> and not updated for <time-to-live-seconds>
            will get automatically evicted from the map.
            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 for each entry to stay idle in the map. Entries that are
            idle(not touched) for more than <max-idle-seconds> will get
            automatically evicted from the map.
            Entry is touched if get, put or containsKey is called.
            Any integer between 0 and Integer.MAX_VALUE.
            0 means infinite. Default is 0.
        -->
        <max-idle-seconds>0</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>

        <!--
            Maximum size of the map. When max size is reached,
            map 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 policy="PER_NODE">5000</max-size>

        <!--
            When max. size is reached, specified percentage of
            the map will be evicted. Any integer between 0 and 100.
            If 25 is set for example, 25% of the entries will
            get evicted.
        -->
        <eviction-percentage>25</eviction-percentage>
    </map>
</hazelcast>

<hazelcast>
    ...
    <map name="default">
        ...
        <map-store enabled="true">
            <!--
               Name of the class implementing MapLoader and/or MapStore.
               The class should implement at least of these interfaces and
               contain no-argument constructor. Note that the inner classes are not supported.
            -->
            <class-name>com.hazelcast.examples.DummyStore</class-name>
            <!--
               Number of seconds to delay to call the MapStore.store(key, value).
               If the value is zero then it is write-through so MapStore.store(key, value)
               will be called as soon as the entry is updated.
               Otherwise it is write-behind so updates will be stored after write-delay-seconds
               value by calling Hazelcast.storeAll(map). Default value is 0.
            -->
            <write-delay-seconds>0</write-delay-seconds>
        </map-store>
    </map>
</hazelcast>

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;
}
}

import com.hazelcast.core.IMap;
import com.hazelcast.query.SqlPredicate;

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
IMap map = hz.getMap("employee");

Set<Employee> employees = (Set<Employee>) map.values(new SqlPredicate("active AND age < 30"));

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;


Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
IMap map = hz.getMap("employee");

EntryObject e = new PredicateBuilder().getEntryObject();
Predicate predicate = e.is("active").and(e.get("age").lessThan(30));

Set<Employee> employees = (Set<Employee>) map.values(predicate);

IMap imap = Hazelcast.getMap("employees");
imap.addIndex("age", true);        // ordered, since we have ranged queries for this field
imap.addIndex("active", false);    // not ordered, because boolean field cannot have range

    /**
     * Adds an continuous entry listener for this map. Listener will get notified
     * for map add/remove/update/evict events filtered by given predicate.
     *
     * @param listener  entry listener
     * @param predicate predicate for filtering entries
     */
    void addEntryListener(EntryListener<K, V> listener, Predicate<K, V> predicate, K key, boolean includeValue);
                

/**
     * Applies the user defined EntryProcessor to the entry mapped by the key.
     * Returns the the object which is result of the process() method of EntryProcessor.
     * <p/>
     *
     * @return result of entry process.
     */
    Object executeOnKey(K key, EntryProcessor entryProcessor);


    /**
     * Applies the user defined EntryProcessor to the all entries in the map.
     * Returns the results mapped by each key in the map.
     * <p/>
     *
     */
    Map<K,Object> executeOnAllKeys(EntryProcessor entryProcessor);

public interface EntryProcessor<K, V> extends Serializable {

    Object process(Map.Entry<K, V> entry);

    EntryBackupProcessor<K, V> getBackupProcessor();
}

public interface EntryBackupProcessor<K, V> extends Serializable {

    void processBackup(Map.Entry<K, V> entry);
}

public class EntryProcessorTest {

    @Test
    public void testMapEntryProcessor() throws InterruptedException {
        Config cfg = new Config();
        cfg.getMapConfig("default").setInMemoryFormat(MapConfig.InMemoryFormat.OBJECT);
        HazelcastInstance instance1 = Hazelcast.newHazelcastInstance(cfg);
        HazelcastInstance instance2 = Hazelcast.newHazelcastInstance(cfg);
        IMap<Integer, Integer> map = instance1.getMap("testMapEntryProcessor");
        map.put(1, 1);
        EntryProcessor entryProcessor = new IncrementorEntryProcessor();
        map.executeOnKey(1, entryProcessor);
        assertEquals(map.get(1), (Object) 2);
        instance1.getLifecycleService().shutdown();
        instance2.getLifecycleService().shutdown();
    }


    @Test
    public void testMapEntryProcessorAllKeys() throws InterruptedException {
        StaticNodeFactory nodeFactory = new StaticNodeFactory(2);
        Config cfg = new Config();
        cfg.getMapConfig("default").setInMemoryFormat(MapConfig.InMemoryFormat.OBJECT);
        HazelcastInstance instance1 = nodeFactory.newHazelcastInstance(cfg);
        HazelcastInstance instance2 = nodeFactory.newHazelcastInstance(cfg);
        IMap<Integer, Integer> map = instance1.getMap("testMapEntryProcessorAllKeys");
        int size = 100;
        for (int i = 0; i < size; i++) {
            map.put(i, i);
        }
        EntryProcessor entryProcessor = new IncrementorEntryProcessor();
        Map<Integer, Object> res = map.executeOnAllKeys(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));
        }
        instance1.getLifecycleService().shutdown();
        instance2.getLifecycleService().shutdown();
    }

    static class IncrementorEntryProcessor 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 IncrementorEntryProcessor.this;
        }

        public void processBackup(Map.Entry entry) {
            entry.setValue((Integer) entry.getValue() + 1);
        }
    }


}

    /**
     * Adds an interceptor for this map. Added interceptor will intercept operations
     * and execute user defined methods and will cancel operations if user defined method throw exception.
     * <p/>
     *
     * @param interceptor map interceptor
     * @return id of registered interceptor
     */
    String addInterceptor(MapInterceptor interceptor);

    /**
     * Removes the given interceptor for this map. So it will not intercept operations anymore.
     * <p/>
     *
     * @param id registration id of map interceptor
     */
    void removeInterceptor(String id);

public interface MapInterceptor extends Serializable {

    /**
     * Intercept get operation before returning value.
     * Return another object to change the return value of get(..)
     * Returning null will cause the get(..) operation return original value, namely return null if you do not want to change anything.
     * <p/>
     *
     * @param value the original value to be returned as the result of get(..) operation
     * @return the new value that will be returned by get(..) operation
     */
    Object interceptGet(Object value);

    /**
     * Called after get(..) operation is completed.
     * <p/>
     *
     * @param value the value returned as the result of get(..) operation
     */
    void afterGet(Object value);

    /**
     * Intercept put operation before modifying map data.
     * Return the object to be put into the map.
     * Returning null will cause the put(..) operation to operate as expected, namely no interception.
     * Throwing an exception will cancel the put operation.
     * <p/>
     *
     * @param oldValue the value currently in map
     * @param newValue the new value to be put
     * @return new value after intercept operation
     */
    Object interceptPut(Object oldValue, Object newValue);

    /**
     * Called after put(..) operation is completed.
     * <p/>
     *
     * @param value the value returned as the result of put(..) operation
     */
    void afterPut(Object value);

    /**
     * Intercept remove operation before removing the data.
     * Return the object to be returned as the result of remove operation.
     * Throwing an exception will cancel the remove operation.
     * <p/>
     *
     * @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.
     * <p/>
     *
     * @param value the value returned as the result of remove(..) operation
     */
    void afterRemove(Object value);

}

public class InterceptorTest {
    final String mapName = "map";


    @Test
    public void testMapInterceptor() throws InterruptedException {
        Config cfg = new Config();
        HazelcastInstance instance1 = Hazelcast.newHazelcastInstance(cfg);
        HazelcastInstance instance2 = Hazelcast.newHazelcastInstance(cfg);
        final IMap<Object, Object> map = instance1.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
        }
    }
}

<hazelcast>
    ...
    <map name="my-read-mostly-map">
        ...
        <near-cache>
            <!--
                Maximum size of the near cache. When max size is reached,
                cache is evicted based on the policy defined.
                Any integer between 0 and Integer.MAX_VALUE. 0 means
                Integer.MAX_VALUE. Default is 0.
            -->
            <max-size>5000</max-size>
            <!--
                Maximum number of seconds for each entry to stay in the near cache. Entries that are
                older than <time-to-live-seconds> will get automatically evicted from the near cache.
                Any integer between 0 and Integer.MAX_VALUE. 0 means infinite. Default is 0.
            -->
            <time-to-live-seconds>0</time-to-live-seconds>

            <!--
                Maximum number of seconds each entry can stay in the near cache as untouched (not-read).
                Entries that are not read (touched) more than <max-idle-seconds> value will get removed
                from the near cache.
                Any integer between 0 and Integer.MAX_VALUE. 0 means
                Integer.MAX_VALUE. Default is 0.
            -->
            <max-idle-seconds>60</max-idle-seconds>

            <!--
                Valid values are:
                NONE (no extra eviction, <time-to-live-seconds> may still apply),
                LRU  (Least Recently Used),
                LFU  (Least Frequently Used).
                NONE is the default.
                Regardless of the eviction policy used, <time-to-live-seconds> will still apply.
            -->
            <eviction-policy>LRU</eviction-policy>

            <!--
                Should the cached entries get evicted if the entries are changed (updated or removed).
                true of false. Default is true.
            -->
            <invalidate-on-change>true</invalidate-on-change>

        </near-cache>
    </map>
</hazelcast>

import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.EntryView;

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
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 learn about wildcard configuration feature, see Wildcard Configuration page.

Hazelcast distributed queue is an implementation ofjava.util.concurrent.BlockingQueue.

import com.hazelcast.core.Hazelcast;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.TimeUnit;
import com.hazelcast.config.Config;

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
BlockingQueue<MyTask> q = hz.getQueue("tasks");
q.put(new MyTask());
MyTask task = q.take();

boolean offered = q.offer(new MyTask(), 10, TimeUnit.SECONDS);
task = q.poll (5, TimeUnit.SECONDS);
if (task != null) {
    //process task
}

FIFO ordering will apply to all queue operations cluster-wide. User objects (such as MyTask in the example above), that are (en/de)queued have to be Serializable. By configuring max-size for queue one can obtain a bounded queue.

Sample configuration:

<hazelcast>
    ...
    <queue name="tasks">
        <!--
            Maximum size of the queue. When queue size reaches the maximum,
            all put operations will get blocked until the queue size
            goes down below the maximum.
            Any integer between 0 and Integer.MAX_VALUE. 0 means Integer.MAX_VALUE. Default is 0.
        -->
        <max-size>10000</max-size>

        <!--
        Number of backups. If 1 is set as the backup-count for example,
        then all entries of the map will be copied to another JVM for
        fail-safety. Valid numbers are 0 (no backup), 1, 2 ... 6.
        Default is 1.
        -->
        <backup-count>1</backup-count>

        <!--
            Number of async backups. 0 means no backup.
        -->
        <async-backup-count>0</async-backup-count>

        <!--
            QueueStore implementation to persist items.
            'binary' property indicates that storing items will be in binary format
            'memory-limit' property enables 'overflow to store' after reaching limit
            'bulk-load' property enables bulk-loading from store
        -->
        <queue-store>
            <class-name>com.hazelcast.QueueStore</class-name>
            <properties>
                <property name="binary">false</property>
                <property name="memory-limit">1000</property>
                <property name="bulk-load">250</property>
            </properties>
        </queue-store>
    </queue>
</hazelcast>

        <queue-store>
            <class-name>com.hazelcast.QueueStoreImpl</class-name>
            <properties>
                <property name="binary">false</property>
                <property name="memory-limit">10000</property>
                <property name="bulk-load">500</property>
            </properties>
        </queue-store>
    </queue>


</hazelcast>

To learn about wildcard configuration feature, see Wildcard Configuration page.

MultiMap is a specialized map where you can associate a key with multiple values. Just like any other distributed data structure implementation in Hazelcast, MultiMap is distributed/partitioned and thread-safe.

import com.hazelcast.core.MultiMap;
import com.hazelcast.core.Hazelcast;
import java.util.Collection;
import com.hazelcast.config.Config;

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);

// a multimap to hold <customerId, Order> pairs
MultiMap<String, Order> mmCustomerOrders = hz.getMultiMap("customerOrders");
mmCustomerOrders.put("1", new Order ("iPhone", 340));
mmCustomerOrders.put("1", new Order ("MacBook", 1200));
mmCustomerOrders.put("1", new Order ("iPod", 79));

// get orders of the customer with customerId 1.
Collection<Order> colOrders = mmCustomerOrders.get ("1");
for (Order order : colOrders) {
    // process order
}

// remove specific key/value pair
boolean removed = mmCustomerOrders.remove("1", new Order ("iPhone", 340));

Hazelcast provides distribution mechanism for publishing messages that are delivered to multiple subscribers which is also known as publish/subscribe (pub/sub) messaging model. Publish and subscriptions are cluster-wide. When a member subscribes for a topic, it is actually registering for messages published by any member in the cluster, including the new members joined after you added the listener. Messages are ordered, meaning, listeners(subscribers) will process the messages in the order they are actually 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 order. Therefore there is only single thread invoking onMessage. There is also globalOrderEnabled option in topic configuration, which is disabled by default. When enabled, it guarantees all nodes listening the same topic will get messages in same order. The user shouldn't keep the thread busy and preferably dispatch it via an Executor.

import com.hazelcast.core.Topic;
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.MessageListener;
import com.hazelcast.config.Config;

public class Sample implements MessageListener<MyEvent> {

    public static void main(String[] args) {
        Sample sample = new Sample();
        Config cfg = new Config();
        HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
        ITopic topic = hz.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 static final Executor messageExecutor = Executors.newSingleThreadExecutor();
}

To learn about wildcard configuration feature, see Wildcard Configuration page.

Distributed Set is distributed and concurrent implementation ofjava.util.Set. Set doesn't allow duplicate elements, so elements in the set should have proper hashCode and equals methods.

import com.hazelcast.core.Hazelcast;
import java.util.Set;
import java.util.Iterator;
import com.hazelcast.config.Config;

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);

java.util.Set set = hz.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 it = set.iterator();
while (it.hasNext()) { 
    Price price = (Price) it.next(); 
    //analyze
}

Distributed List is very similar to distributed set, but it allows duplicate elements.

import com.hazelcast.core.Hazelcast;
import java.util.List;
import java.util.Iterator;
import com.hazelcast.config.Config;

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);

java.util.List 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 it = list.iterator();
while (it.hasNext()) { 
    Price price = (Price) it.next(); 
    //analyze
}

import com.hazelcast.core.Hazelcast;
import com.hazelcast.config.Config;
import java.util.concurrent.locks.Lock;

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
Lock lock = hz.getLock(myLockedObject);
lock.lock();
try {
    // do something here
} finally {
    lock.unlock();
} 
 

java.util.concurrent.locks.Lock.tryLock() with timeout is also supported. All operations on the Lock that Hazelcast.getLock(Object obj) returns are cluster-wide and Lock behaves just like java.util.concurrent.lock.ReentrantLock.

if (lock.tryLock (5000, TimeUnit.MILLISECONDS)) {
    try {  
       // do some stuff here..  
    } 
    finally {  
      lock.unlock();  
    }   
} 

Locks are fail-safe. If a member holds a lock and some of the members go down, cluster will keep your locks safe and available. Moreover, when a member leaves the cluster, all the locks acquired by this dead member will be removed so that these locks can be available for live members immediately.

Hazelcast allows you to register for entry events to get notified when entries added, updated or removed. Listeners are cluster-wide. When a member adds a listener, it is actually registering for events originated in any member in the cluster. When a new member joins, events originated at the new member will also be delivered. All events are ordered, meaning, listeners will receive and process the events in the order they are actually occurred.

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; 
import com.hazelcast.config.Config;

public class Sample implements ItemListener, EntryListener {

    public static void main(String[] args) { 
        Sample sample = new Sample();
        Config cfg = new Config();
        HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
        IQueue queue = hz.getQueue ("default");
        IMap   map   = hz.getMap   ("default");
        ISet   set   = hz.getSet   ("default");
        //listen for all added/updated/removed entries
        queue.addItemListener(sample, true);
        set.addItemListener  (sample, true); 
        map.addEntryListener (sample, true);        
        //listen for an entry with specific key 
        map.addEntryListener (sample, "keyobj");        
    } 

    public void entryAdded(EntryEvent event) {
        System.out.println("Entry added key=" + event.getKey() + ", value=" + event.getValue());
    }

    public void entryRemoved(EntryEvent event) {
        System.out.println("Entry removed key=" + event.getKey() + ", value=" + event.getValue());
    }

    public void entryUpdated(EntryEvent event) {
        System.out.println("Entry update key=" + event.getKey() + ", value=" + event.getValue());
    } 

    public void entryEvicted(EntryEvent event) {
        System.out.println("Entry evicted key=" + event.getKey() + ", value=" + event.getValue());
    } 
    
    public void itemAdded(Object item) {
        System.out.println("Item added = " + item);
    }

    public void itemRemoved(Object item) {
        System.out.println("Item removed = " + item);
    }     
}
       

For a faster serialization of objects, Hazelcast recommends to implement com.hazelcast.nio.serialization.IdentifiedDataSerializable which is slightly better version of com.hazelcast.nio.serialization.DataSerializable.

Here is an example of a class implementing com.hazelcast.nio.serialization.DataSerializable interface.

public class Address implements com.hazelcast.nio.serialization.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();
    }
}

Lets take a look at another example which is encapsulating a DataSerializable field.

public class Employee implements com.hazelcast.nio.serialization.DataSerializable {
    private String firstName;
    private String lastName;
    private int age;
    private double salary;
    private Address address; //address itself is DataSerializable

    public Employee() {}

    //getters setters..

    public void writeData(ObjectDataOutput out) throws IOException {
        out.writeUTF(firstName);
        out.writeUTF(lastName);
        out.writeInt(age);
        out.writeDouble (salary);
        address.writeData (out);
    }

    public void readData (ObjectDataInput in) throws IOException {
        firstName = in.readUTF();
        lastName  = in.readUTF();
        age       = in.readInt();
        salary       = in.readDouble();
        address   = new Address();
        // since Address is DataSerializable let it read its own internal state
        address.readData (in);
    }
}

As you can see, since address field itself isDataSerializable, it is calling address.writeData(out) when writing and address.readData(in) when reading. Also note that the order of writing and reading fields should be the same. While Hazelcast serializes a DataSerializable it writes the className first and when de-serializes it, className is used to instantiate the object using reflection. IdentifiedDataSerializable To avoid the reflection and long class names IdentifiedDataSerializable can be used instead ofDataSerializable. Note that IdentifiedDataSerializable extends DataSerializable and introduces two new methods.

IdentifiedDataSerializable uses getId() instead of className and uses getFactoryId() to load the class given the Id. To complete the implementation a com.hazelcast.nio.serialization.DataSerializableFactory should also be implemented and registered into SerializationConfig which can be accessed from Config.getSerializationConfig(). The Factories responsibility is to return an instance of the right IdentifiedDataSerializable object, given the id. So far this is the most efficient way of Serialization that Hazelcast supports of the shelf.

As an alternative to the existing serialization methods, Hazelcast offers a Portable serialization that have 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 is able to navigate in the byte[] and de-serialize only the required field without actually de-serializing the whole object which improves the Query performance.

With multiversion support, you can have two nodes where each of them having different versions of the same Object and Hazelcast will store both meta information and use the correct one to serialize and de-serialize Portable objects depending on the node. This is very helpfull when you are doing a rolling upgrade without shutting down the cluster.

Also note that Portable serialization is totally language independent and is used as the binary protocol between Hazelcast server and clients.

A sample Portable implementation of a Foo class will look like the following.

public class Foo implements Portable{
    final static int ID = 5;

    private String foo;

    public String getFoo() {
        return foo;
    }

    public void setFoo(String foo) {
        this.foo = foo;
    }

    @Override
    public int getFactoryId() {
        return 1;
    }

    @Override
    public int getClassId() {
        return ID;
    }

    @Override
    public void writePortable(PortableWriter writer) throws IOException {
        writer.writeUTF("foo", foo);
    }

    @Override
    public void readPortable(PortableReader reader) throws IOException {
        foo = reader.readUTF("foo");
    }
}
        

Similar to IdentifiedDataSerializable, a Portable Class must provide classId andfactoryId. The Factory object will be used to create the Portable object given the classId.

A sample Factory could be implemented as following:

public class MyPortableFactory implements PortableFactory {

    @Override
    public Portable create(int classId) {
        if (Foo.ID == classId)
            return new Foo();
        else return null;
     }
}
            

The last step is to register the Factory to the SerializationConfig.

Programmatic Configuration

Config config = new Config();
config.getSerializationConfig().addPortableFactory(1, new MyPortableFactory());
                

XML Configuration

<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 same as the factoryId that Foo class returns.

TODO: ClassDefinitionBuilder...

Hazelcast lets you to plug a custom serializer to be used for serialization of objects.

public class Foo {
    private String foo;
    public String getFoo() {
        return foo;
    }
    public void setFoo(String foo) {
        this.foo = foo;
    }
}
            

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 {
        final InputStream inputStream = (InputStream) in;
        XMLDecoder decoder = new XMLDecoder(inputStream);
        return (Foo) decoder.readObject();
    }

    @Override
    public void destroy() {
    }
}

            

Programmatic Configuration

SerializerConfig sc = new SerializerConfig().
        setImplementation(new FooXmlSerializer()).
        setTypeClass(Foo.class);
Config config = new Config();
config.getSerializationConfig().addSerializerConfig(sc);
            

XML Configuration

<hazelcast>
    <serialization>
        <serializers>
            <serializer type-class="com.www.Foo">com.www.FooXmlSerializer</serializer>
        </serializers>
    </serialization>
</hazelcast>
    

Hazelcast allows you to register for membership events to get notified when members added or removed. You can also get the set of cluster members.

import com.hazelcast.core.*;
import com.hazelcast.config.Config;

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
Cluster cluster = hz.getCluster();
cluster.addMembershipListener(new MembershipListener(){
    public void memberAdded(MembershipEvent membersipEvent) {
        System.out.println("MemberAdded " + membersipEvent);
    }

    public void memberRemoved(MembershipEvent membersipEvent) {
        System.out.println("MemberRemoved " + membersipEvent);
    }
});

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());
}

Hazelcast IdGenerator creates cluster-wide unique IDs. Generated IDs are long type primitive values between 0 and Long.MAX_VALUE . Id generation occurs almost at the speed of AtomicLong.incrementAndGet() . 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.

import com.hazelcast.core.IdGenerator;
import com.hazelcast.core.Hazelcast;

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
IdGenerator idGenerator = hz.getIdGenerator("customer-ids");
idGenerator.init(123L); //Optional
long id = idGenerator.newId();

Hazelcast can be used in transactional context. Basically create a TransactionContext which can be used to begin, commit, rollback transaction. Obtain transaction aware instances of queues, maps, sets, lists, multimaps via TransactionContext, work with them and commit/rollback in one shot. Hazelcast supports LOCAL(One Phase) and TWO_PHASE transactions. Default behaviour is TWO_PHASE.

import java.util.Queue;
import java.util.Map;
import java.util.Set;
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.Transaction; 

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);

TransactionOptions options = new TransactionOptions().setTransactionType(TransactionType.LOCAL);
TransactionContext context = hz.newTransactionContext()
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();
}

Isolation is always REPEATABLE_READ . If you are in a transaction, you can read the data in your transaction and the data that is already committed and if not in a transaction, you can only read the committed data. Implementation is different for queue and map/set. For queue operations (offer,poll), offered and/or polled objects are copied to the owner member in order to safely commit/rollback. For map/set, 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 transaction is set to commit, Hazelcast will release the locks and apply the differences. When rolling back, Hazelcast will simply releases the locks and discard the differences.

Not yet implemented as of version 3.0

Distributed executor service is a distributed implementation of java.util.concurrent.ExecutorService. It allows you to execute your code in cluster. In this chapter, all the code samples are based on the Echo class above. Please note that Echo class is Serializable . You can ask Hazelcast to execute your code (Runnable, Callable):

import com.hazelcast.core.Member;
import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.IExecutorService;
import java.util.concurrent.Callable;
import java.util.concurrent.Future;   
import java.util.Set;
import com.hazelcast.config.Config;


public void echoOnTheMember(String input, Member member) throws Exception {
   Callable<String> task = new Echo(input);
   HazelcastInstance hz = Hazelcast.newHazelcastInstance();
   IExecutorService executorService = hz.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 hz = Hazelcast.newHazelcastInstance();
   IExecutorService executorService = hz.getExecutorService("default");
   Future<String> future = executorService.submitToKeyOwner(task, key);
   String echoResult = future.get();
}

public void echoOnSomewhere(String input) throws Exception { 
   HazelcastInstance hz = Hazelcast.newHazelcastInstance();
   IExecutorService executorService = hz.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 hz = Hazelcast.newHazelcastInstance();
   IExecutorService executorService = hz.getExecutorService("default");
   Map<Member, Future<String>> futures = executorService.submitToMembers(new Echo(input), members);
   for (Future<String> future : futures.values()) {
        String echoResult = future.get();
        // ...
   }
}

Note that you can obtain the set of cluster members via Hazelcast.getCluster().getMembers() call.

What if the code you execute in cluster takes longer than acceptable. If you cannot stop/cancel that task it will keep eating your resources. Standard Java executor framework solves this problem with by introducing cancel() api and 'encouraging' us to code and design for cancellations, which is highly ignored part of software development.

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 callable class above calculates the fibonacci number for a given number. In the calculate method, we are checking to see if the current thread is interrupted so that code can be responsive to cancellations once the execution started. Following fib() method submits the Fibonacci calculation task for number 'n' and waits maximum 3 seconds for result. If the execution doesn't complete in 3 seconds, future.get() will throw TimeoutException and upon catching it we interruptibly cancel the execution for saving some CPU cycles.

long fib(int n) throws Exception {
    Config cfg = new Config();
    HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
    IExecutorService es = hz.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 will take less than 3 seconds but fib(50) will take way longer. (This is not the example for writing better fibonacci calculation code but for showing how to cancel a running execution that takes too long.) 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. So if you are willing to be able to cancel already running task then your task has to be designed to handle interruption. If calculate (int n) method didn't have if (Thread.currentThread().isInterrupted()) line, then you wouldn't be able to cancel the execution after it started.

ExecutionCallback allows you to asynchronously get notified when the execution is done.

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);
    }
}

import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.ExecutionCallback;
import com.hazelcast.core.IExecutorService;
import java.util.concurrent.Future;
import com.hazelcast.config.Config;

Config cfg = new Config();
HazelcastInstance hz = Hazelcast.newHazelcastInstance(cfg);
IExecutorService es = hz.getExecutorService();
Callable<Long> task = new Fibonacci(10);

es.submit(task, new ExecutionCallback<Long> () {

    public void onResponse(Long response) {
        System.out.println("Fibonacci calculation result = " + response);
    }

    public void onFailure(Throwable t) {
        t.printStackTrace();
    }

});

By specifying group-name and group-password, you can separate your clusters in a simple way; dev group, production group, test group, app-a group etc...

<hazelcast>
    <group>
        <name>dev</name>
        <password>dev-pass</password>
    </group>
    ...
</hazelcast>

You can also set the groupName with Config API. JVM can host multiple Hazelcast instances (nodes). Each node can only participate in one group and it only joins to its own group, does not mess with others. Following code creates 3 separate Hazelcast nodes, h1 belongs to app1 cluster, while h2 and h3 are belong to app2 cluster.

Config configApp1 = new Config();
configApp1.getGroupConfig().setName("app1");

Config configApp2 = new Config();
configApp2.getGroupConfig().setName("app2");

HazelcastInstance h1 = Hazelcast.newHazelcastInstance(configApp1);
HazelcastInstance h2 = Hazelcast.newHazelcastInstance(configApp2);
HazelcastInstance h3 = Hazelcast.newHazelcastInstance(configApp2);

Hazelcast distributes key objects into partitions (blocks) using a consistent hashing algorithm and those partitions are assigned to nodes. That means an entry is stored in a node which is owner of partition to that entry's key is assigned. Number of total partitions is default 271 and can be changed with configuration property hazelcast.map.partition.count. Along with those partitions, there are also copies of them as backups. Backup partitions can have multiple copies due to backup count defined in configuration, such as first backup partition, second backup partition etc. As a rule, a node can not hold more than one copy of a partition (ownership or backup). By default Hazelcast distributes partitions and their backup copies randomly and equally among cluster nodes assuming all nodes in the cluster are identical.

What if some nodes share same JVM or physical machine or chassis and you want backups of these nodes to be assigned to nodes in another machine or chassis? What if processing or memory capacities of some nodes are different and you do not want equal number of partitions to be assigned to all nodes?

You can group nodes in the same JVM (or physical machine) or nodes located in the same chassis. Or you can group nodes to create identical capacity. We call these groups partition groups. This way partitions are assigned to those partition groups instead of single nodes. And backups of these partitions are located in another partition group.

When you enable partition grouping, Hazelcast presents three choices to configure partition groups at the moments.

Hazelcast provides various event listener extensions to receive specific event types. These are:

These listeners can be added to and removed from related object using Hazelcast API. Such as

MembershipListener listener = new MyMembershipListener();
hazelcastInstance.getCluster().addMembershipListener(listener);
hazelcastInstance.getCluster().removeMembershipListener(listener);

EntryListener listener = new MyEntryListener();
IMap map = hazelcastInstance.getMap("default");
String id =map.addEntryListener(listener, true);
map.removeEntryListener(id);

ItemListener listener = new MyItemListener();
IQueue queue = hazelcastInstance.getQueue("default");
queue.addItemListener(listener, true);
queue.removeItemListener(listener);

Downside of attaching listeners using API is possibility of missing events between creation of object and registering listener. To overcome this race condition Hazelcast introduces registration of listeners in configuration. Listeners can be registered using either Hazelcast XML configuration, Config API or Spring configuration.

Hazelcast supports wildcard configuration of Maps, Queues and Topics. Using an asterisk (*) character in the name, different instances of Maps, Queues and Topics can be configured by a single configuration.

Note that, with a limitation of a single usage, asterisk (*) can be placed anywhere inside the configuration name.

For instance a map named 'com.hazelcast.test.mymap' can be configured using one of these configurations;

<map name="com.hazelcast.test.*">
...
</map>

<map name="com.hazel*">
...
</map>

<map name="*.test.mymap">
...
</map>

<map name="com.*test.mymap">
...
</map>

Or a queue 'com.hazelcast.test.myqueue'

<queue name="*hazelcast.test.myqueue">
...
</queue>

<queue name="com.hazelcast.*.myqueue">
...
</queue>

There are some advanced configuration properties to tune some aspects of Hazelcast. These can be set as property name and value pairs through configuration xml, configuration API or JVM system property.

Hazelcast has a flexible logging configuration and doesn't depend on any logging framework except JDK logging. It has in-built adaptors for a number of logging frameworks and also supports custom loggers by providing logging interfaces.

To use built-in adaptors you should set hazelcast.logging.type property to one of predefined types below.

You can set hazelcast.logging.type through configuration xml, configuration API or JVM system property.

To use custom logging feature you should implement com.hazelcast.logging.LoggerFactory and com.hazelcast.logging.ILogger interfaces and set system property hazelcast.logging.class to your custom LoggerFactory class name.

java -Dhazelcast.logging.class=foo.bar.MyLoggingFactory

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

LogListener listener = new LogListener() {
    public void log(LogEvent logEvent) {
        // do something
    }
}
LoggingService loggingService = Hazelcast.getLoggingService();
loggingService.addLogListener(Level.INFO, listener):
        

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

To be able to use Hazelcast Enterprise Edition, you need to set license key in configuration.

You can declare Hazelcast beans for Spring context using beans namespace (default spring beans namespace) as well to declare hazelcast maps, queues and others. Hazelcast-Spring integration requires either hazelcast-spring jar or hazelcast-all jar in the classpath.

<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>

Hazelcast has Spring integration (requires version 2.5 or greater) since 1.9.1 using hazelcast namespace.

After that you can configure Hazelcast instance (node):

<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"
                cache-value="true"
                eviction-policy="NONE"
                merge-policy="com.hazelcast.map.merge.PassThroughMergePolicy"/>
    </hz:config>
</hz:hazelcast>

You can easily configure map-store and near-cache too. (For map-store you should set either class-name or 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>

It's possible to use placeholders instead of concrete values. For instance, use property file app-default.properties for group configuration:

<bean class="org.springframework.beans.factory.config.PropertyPlaceholderConfigurer">
    <property name="locations">
        <list>
            <value>classpath:/app-default.properties</value>
        </list>
    </property>
</bean>

<hz:hazelcast id="instance">
    <hz:config>
        <hz:group
            name="${cluster.group.name}"
            password="${cluster.group.password}"/>
        <!-- ... -->
    </hz:config>
</hz:hazelcast>

Similar for client

<hz:client id="client">
        <hz:group name="${cluster.group.name}" password="${cluster.group.password}"/>
        <hz:network>
            <hz:member>10.10.1.2:5701</hz:member>
            <hz:member>10.10.1.3:5701</hz:member>
        </hz:network>
    </hz:client>

Hazelcast also supportslazy-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>

You can declare beans for the following Hazelcast objects:

Example:

<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: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: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"/>

Injecting Typed Collections/Maps

Spring tries to create a newMap/ Collection instance and fill the new instance by iterating and converting values of the originalMap/ Collection (IMap, IQueue etc.) to required types when generic type parameters of the originalMap/ Collection and the target property/attribute do not match.

Since HazelcastMaps/Collections are designed to hold very large data which a single machine can not carry, iterating through whole values can cause out of memory errors.

To avoid this issue either target property/attribute can be declared as un-typedMap/ Collection

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.

public class SomeBean {

    IMap<K, V> map;

    IQueue<E> queue;

    public SomeBean(IMap map) { // instead of IMap<K, V> map
        this.map = map;
    }

    ...

    public void setQueue(IQueue queue) { // instead of IQueue<E> queue
        this.queue = queue;
    }
    ...
}

For more info see Spring issue-3407 .

It's often desired to access Spring managed beans, to apply bean properties or to apply factory callbacks such as ApplicationContextAware, BeanNameAware or to apply bean post-processing such as InitializingBean, @PostConstruct like annotations while using Hazelcast distributed ExecutorService or more generally any Hazelcast managed object. Achieving those features are as simple as adding @SpringAware annotation to your distributed object types. Once you have configured HazelcastInstance as explained in Spring configuration, just mark any distributed type with @SpringAware annotation.

<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-3.0.xsd">

    <context:annotation-config />

    <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" />
                    <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>

ExecutorService example:

@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(final ApplicationContext applicationContext)
        throws BeansException {
        context = applicationContext;
    }

    @Autowired
    public void setSomeBean(final SomeBean someBean) {
        this.someBean = someBean;
    }
}

HazelcastInstance hazelcast = (HazelcastInstance) context.getBean("hazelcast");
SomeBean bean = (SomeBean) context.getBean("someBean");

Future<Long> f = hazelcast.getExecutorService().submit(new SomeTask());
Assert.assertEquals(bean.value, f.get().longValue());

// choose a member
Member member = hazelcast.getCluster().getMembers().iterator().next();

Future<Long> f2 = (Future<Long>) hazelcast.getExecutorService()
    .submitToMember(new SomeTask(), member);
Assert.assertEquals(bean.value, f2.get().longValue());

Distributed Map value example:

@SpringAware
@Component("someValue")
@Scope("prototype")
public class SomeValue implements Serializable, ApplicationContextAware {

    transient ApplicationContext context;

    transient SomeBean someBean;

    transient boolean init = false;

    public void setApplicationContext(final ApplicationContext applicationContext)
        throws BeansException {
        context = applicationContext;
    }

    @Autowired
    public void setSomeBean(final SomeBean someBean) {
        this.someBean = someBean;
    }

    @PostConstruct
    public void init() {
        someBean.doSomethingUseful();
        init = true;
    }
    ...
}

On Node-1;

HazelcastInstance hazelcast = (HazelcastInstance) context.getBean("hazelcast");
SomeValue value = (SomeValue) context.getBean("someValue")
IMap<String, SomeValue> map = hazelcast.getMap("values");
map.put("key", value);

On Node-2;

HazelcastInstance hazelcast = (HazelcastInstance) context.getBean("hazelcast");
IMap<String, SomeValue> map = hazelcast.getMap("values");
SomeValue value = map.get("key");
Assert.assertTrue(value.init);

Note that, Spring managed properties/fields are marked as transient.

As of version 3.1, Spring Framework provides support for adding caching into an existing Spring application. To use Hazelcast as Spring cache provider, you should just define a com.hazelcast.spring.cache.HazelcastCacheManager bean and register it as Spring cache manager.

<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>

For more info see Spring Cache Abstraction .

If you are using Hibernate with Hazelcast as 2nd level cache provider, you can easily create RegionFactory instances within Spring configuration (by Spring version 3.1). That way it is possible to use same HazelcastInstance as Hibernate L2 cache instance.

<hz:hibernate-region-factory id="regionFactory" instance-ref="instance" />
...
<bean id="sessionFactory" class="org.springframework.orm.hibernate3.LocalSessionFactoryBean" scope="singleton">
    <property name="dataSource" ref="dataSource"/>
    <property name="cacheRegionFactory" ref="regionFactory" />
    ...
</bean>

Hazelcast supports JPA persistence integrated with Spring Data-JPA module. Your POJOs are mapped and persisted to your relational database. To use JPA persistence first you should create a Repository interface extending CrudRepository class with object type that you want to persist..

package com.hazelcast.jpa.repository;

import com.hazelcast.jpa.Product;
import org.springframework.data.repository.CrudRepository;

public interface ProductRepository extends CrudRepository<Product, Long> {

}

Then you should add your data source and repository definition to you Spring configuration,

<jpa:repositories
       base-package="com.hazelcast.jpa.repository" />

    <bean class="com.hazelcast.jpa.SpringJPAMapStore" id="jpamapstore">
        <property name="crudRepository" ref="productRepository" />
    </bean>

    <bean class="org.apache.commons.dbcp.BasicDataSource" destroy-method="close" id="dataSource">
        <property name="driverClassName" value="com.mysql.jdbc.Driver"/>
             <property name="url" value="jdbc:mysql://localhost:3306/YOUR_DB"/>
             <property name="username" value="YOUR_USERNAME"/>
             <property name="password" value="YOUR_PASSWORD"/>
    </bean>

    <bean id="entityManagerFactory"
      class="org.springframework.orm.jpa.LocalContainerEntityManagerFactoryBean">
      <property name="dataSource" ref="dataSource" />
      <property name="jpaVendorAdapter">
        <bean class="org.springframework.orm.jpa.vendor.HibernateJpaVendorAdapter">
          <property name="generateDdl" value="true" />
          <property name="database" value="MYSQL" />
        </bean>
      </property>
        <property name="persistenceUnitName" value="jpa.sample" />
    </bean>

    <bean class="org.springframework.orm.jpa.JpaTransactionManager"
      id="transactionManager">
      <property name="entityManagerFactory"
          ref="entityManagerFactory" />
      <property name="jpaDialect">
        <bean class="org.springframework.orm.jpa.vendor.HibernateJpaDialect" />
      </property>
    </bean>
    

In the example configuration above, Hibernate and MYSQL is configured, you change them according your ORM and database selection. Also you should define your persistence unit with persistence.xml under META-INF directory.

<?xml version="1.0" encoding="UTF-8"?>
<persistence version="2.0" xmlns="http://java.sun.com/xml/ns/persistence" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://java.sun.com/xml/ns/persistence http://java.sun.com/xml/ns/persistence/persistence_2_0.xsd">
	<persistence-unit name="jpa.sample" />
</persistence>

By default, the key is expected to be the same with id of the JPA object. You can change this behaviour and customize MapStore implementation extending SpringJPAMapStore class. For more info see Spring Data JPA Reference .

With version 2.1, Hazelcast will support MongoDB persistence integrated with Spring Data-MongoDB module. Spring MongoDB module maps your objects to equivalent MongoDB objects. To persist your objects into MongoDB you should define MongoDB mapstore in your Spring configuration as follows:

<mongo:mongo id="mongo" host="localhost" port="27017"/>

<bean id="mongoTemplate"
      class="org.springframework.data.mongodb.core.MongoTemplate">
    <constructor-arg ref="mongo"/>
    <constructor-arg name="databaseName" value="test"/>
</bean>

<bean class="com.hazelcast.spring.mongodb.MongoMapStore" id="mongomapstore">
    <property name="mongoTemplate" ref="mongoTemplate" />
</bean>

Then you can set this as mapstore for maps that you want to persist into MongoDB.

<hz:map name="user">
    <hz:map-store enabled="true" implementation="mongomapstore"
                  write-delay-seconds="0">
    </hz:map-store>
</hz:map>

By default, the key is set as id of the MongoDB object. You can override MongoMapStore class for you custom needs. For more info see Spring Data MongoDB Reference .

Native Client enables you to do 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 or connects to all of them and delegate operations smartly. When the relied cluster member dies, client will transparently switch to another live member.

There can be hundreds, even thousands of clients connected to the cluster. But by default there is 40 threads on server side that will handle all the requests. You may want to increase hazelcast.executor.client.thread.count property for better performance.

Imagine a trading application where all the trading data stored and managed in a 10 node Hazelcast cluster. Swing/Web applications at traders' desktops can use Native Java Client to access and modify the data in the Hazelcast cluster.

Currently Hazelcast has Native Java and C# Client available. C# Client implementation has not yet finished as of version 3.

import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.client.HazelcastClient;

import java.util.Map;
import java.util.Collection;


ClientConfig clientConfig = new ClientConfig();
clientConfig.getGroupConfig().setName("dev").setPassword("dev-pass");
clientConfig.addAddress("10.90.0.1", "10.90.0.2:5702");

HazelcastInstance client = HazelcastClient.newHazelcastClient(clientConfig);
//All cluster operations that you can do with ordinary HazelcastInstance
Map<String, Customer> mapCustomers = client.getMap("customers");
mapCustomers.put("1", new Customer("Joe", "Smith"));
mapCustomers.put("2", new Customer("Ali", "Selam"));
mapCustomers.put("3", new Customer("Avi", "Noyan"));

Collection<Customer> colCustomers = mapCustomers.values();
for (Customer customer : colCustomers) {
     // process customer
}

A Memcache client written in any language can talk directly to Hazelcast cluster. No additional configuration is required. Here is an example: Let's say your cluster's members are:

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]
 }

And 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 doesn't matter which member the client connects to because Hazelcast cluster looks as one giant machine (Single System Image). PHP client code sample:

<?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 is connecting to 10.20.17.1 and using port5701. Java client code sample 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 following:

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 is to separate memcache maps from hazelcast maps.

An entry written with a memcache client can be read by another memcache client written in another language.

Let's say your cluster's members are:

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]
 }

And you have a distributed map named 'stocks'. You can put a new key1/value1 entry into this map by issuing HTTP POST call to http://10.20.17.1:5701/hazelcast/rest/maps/stocks/key1 URL. Your http post call's content body should contain the value (value1). You can retrieve this entry via HTTP GET call to http://10.20.17.1:5701/hazelcast/rest/maps/stocks/key1. You can also retrieve this entry from another member such ashttp://10.20.17.3:5701/hazelcast/rest/maps/stocks/key1.

RESTful access is provided through any member of your cluster. So you can even put an HTTP load-balancer in-front of your cluster members for load-balancing and fault-tolerance.

Now go ahead and install a REST plugin for your browser and explore further.

Hazelcast also stores the mime-type of your POST request if it contains any. So if, for example, you post binary of an image file and set the mime-type of the HTTP POST request to image/jpeg then this mime-type will be part of the response of your HTTP GET request for that entry.

Let's say you also have a task queue named 'tasks'. You can offer a new item into the queue via HTTP POST and take and item from the queue via HTTP DELETE.

HTTP POST http://10.20.17.1:5701/hazelcast/rest/queues/tasks <CONTENT> means

Hazelcast.getQueue("tasks").offer(<CONTENT>);

and HTTP DELETE http://10.20.17.1:5701/hazelcast/rest/queues/tasks/3 means

Hazelcast.getQueue("tasks").poll(3, SECONDS);

Note that you will have to handle the failures on REST polls as there is no transactional guarantee.

Hazelcast Management Center enables you to monitor and manage your servers running hazelcast. With Management Center, in addition to monitoring overall state of your clusters, you can also analyze and browse your data structures in details. You can also update map configurations and take thread dump from nodes. With its scripting module, you can run scritps (JavaScript, Groovy etc.) on your servers. Version 2.0 is a web based tool so you can deploy it into your internal server and serve your users.

Map instances are listed on the left panel. When you click on a map, a new tab for monitoring this map instance is opened on the right. In this tab, you can monitor metrics also re-configure the map.

Queues is the second data structure that you can monitor in management center. You can activate the Queue Tab by clicking the instance name listed on the left panel under queues part. The queue page consists of the charts monitoring data about the queue. You can change the data to be monitored by clicking on the chart. Available options are Size, Polls, Offers.

You can monitor your topics' metrics by clicking the topic name listed on the left panel under topics part. There are two charts which reflects live data, and a datatable lists the live data distributed among members.

The current members in the cluster are listed on the bottom side of the left panel. You can monitor each member on tab page displayed by clicking on member items.

System logs part helps you track internal operations and detect problems. To see system logs first you should set a Log level other than "None". In left menu there are inputs by which you can filter the displayed logs dynamically. Also you can Export your logs and send the file to Hazelcast support team, so they can analyze and help you solving your problem.

In scripting part, you can execute your own code on your cluster. In the left part you can select members, on which the code will be executed. Also you can select over scripting languages: Javascript, Groovy, JRuby, BeanShell. This part is only enabled for users with read/write permissions for current cluster.

Time Travel mode is activated by clicking clock icon on top right toolbar. In time travel mode, the time is paused and the full state of the cluster is displayed according the time selected on time slider. You can change time either by Prev/Next buttons or sliding the slider. Also you can change the day by clicking calendar icon. Management center stores the states in you local disk, while your web server is alive. So if you slide to a time when you do not have data, the reports will be seen as empty.

The console tool enables you execute commands on the cluster. You can read or write on instances but first you should set namespace. For example if you have a map with name "mapCustomers". To get a customer with key "Jack" you should first set the namespace with command "ns mapCustomers". Then you can take the object by "m.get Jack" Here is the command list:

-- General commands
echo true|false                      //turns on/off echo of commands (default false)
silent true|false                    //turns on/off silent of command output (default false)
#<number> <command>                  //repeats <number> time <command>, replace $i in <command> with current iteration (0..<number-1>)
&<number> <command>                  //forks <number> threads to execute <command>, replace $t in <command> with current thread number (0..<number-1>
     When using #x or &x, is is advised to use silent true as well.
     When using &x with m.putmany and m.removemany, each thread will get a different share of keys unless a start key index is specified
jvm                                  //displays info about the runtime
who                                  //displays info about the cluster
whoami                               //displays info about this cluster member
ns <string>                          //switch the namespace for using the distributed queue/map/set/list <string> (defaults to "default"
@<file>                              //executes the given <file> script. Use '//' for comments in the script

-- Queue commands
q.offer <string>                     //adds a string object to the queue
q.poll                               //takes an object from the queue
q.offermany <number> [<size>]        //adds indicated number of string objects to the queue ('obj<i>' or byte[<size>])
q.pollmany <number>                  //takes indicated number of objects from the queue
q.iterator [remove]                  //iterates the queue, remove if specified
q.size                               //size of the queue
q.clear                              //clears the queue

-- Set commands
s.add <string>                       //adds a string object to the set
s.remove <string>                    //removes the string object from the set
s.addmany <number>                   //adds indicated number of string objects to the set ('obj<i>')
s.removemany <number>                //takes indicated number of objects from the set
s.iterator [remove]                  //iterates the set, removes if specified
s.size                               //size of the set
s.clear                              //clears the set

-- Lock commands
lock <key>                           //same as Hazelcast.getLock(key).lock()
tryLock <key>                        //same as Hazelcast.getLock(key).tryLock()
tryLock <key> <time>                 //same as tryLock <key> with timeout in seconds
unlock <key>                         //same as Hazelcast.getLock(key).unlock()

-- Map commands
m.put <key> <value>                  //puts an entry to the map
m.remove <key>                       //removes the entry of given key from the map
m.get <key>                          //returns the value of given key from the map
m.putmany <number> [<size>] [<index>]//puts indicated number of entries to the map ('key<i>':byte[<size>], <index>+(0..<number>)
m.removemany <number> [<index>]      //removes indicated number of entries from the map ('key<i>', <index>+(0..<number>)
     When using &x with m.putmany and m.removemany, each thread will get a different share of keys unless a start key <index> is specified
m.keys                               //iterates the keys of the map
m.values                             //iterates the values of the map
m.entries                            //iterates the entries of the map
m.iterator [remove]                  //iterates the keys of the map, remove if specified
m.size                               //size of the map
m.clear                              //clears the map
m.destroy                            //destroys the map
m.lock <key>                         //locks the key
m.tryLock <key>                      //tries to lock the key and returns immediately
m.tryLock <key> <time>               //tries to lock the key within given seconds
m.unlock <key>                       //unlocks the key

-- List commands:
l.add <string>
l.add <index> <string>
l.contains <string>
l.remove <string>
l.remove <index>
l.set <index> <string>
l.iterator [remove]
l.size
l.clear
-- IAtomicLong commands:
a.get
a.set <long>
a.inc
a.dec
-- Executor Service commands:
execute	<echo-input>				//executes an echo task on random member
execute0nKey	<echo-input> <key>		//executes an echo task on the member that owns the given key
execute0nMember <echo-input> <key>	//executes an echo task on the member with given index
execute0nMembers <echo-input> 		//executes an echo task on all of the members

Hazelcast is the distributed implementation of several structures that exist in Java. Most of the time it behaves as you expect. However there are some design choices in Hazelcast that violate some contracts. This page will list those violations.

Hazelcast allows you to create more than one member on the same JVM. Each member is called HazelcastInstance and each will have its own configuration, socket and threads, so you can treat them as totally separate members. This enables us to write and run cluster unit tests on single JVM. As you can use this feature for creating separate members different applications running on the same JVM (imagine running multiple webapps on the same JVM), you can also use this feature for testing Hazelcast cluster.

Let's say you want to test if two members have the same size of a map.

@Test
public void testTwoMemberMapSizes() {
    // start the first member
    HazelcastInstance h1 = Hazelcast.newHazelcastInstance(null);
    // get the map and put 1000 entries
    Map map1 = h1.getMap("testmap");
    for (int i = 0; i < 1000; i++) {
        map1.put(i, "value" + i);
    }
    // check the map size
    assertEquals(1000, map1.size());
    // start the second member
    HazelcastInstance h2 = Hazelcast.newHazelcastInstance(null);
    // get the same map from the second member
    Map map2 = h2.getMap("testmap");
    // check the size of map2
    assertEquals(1000, map2.size());
    // check the size of map1 again
    assertEquals(1000, map1.size());
}

In the test above, everything happened in the same thread. When developing multi-threaded test, coordination of the thread executions has to be carefully handled. Usage of CountDownLatch for thread coordination is highly recommended. You can certainly use other things. Here is an example where we need to listen for messages and make sure that we got these messages:

@Test
public void testTopic() {
    // start two member cluster
    HazelcastInstance h1 = Hazelcast.newHazelcastInstance(null);
    HazelcastInstance h2 = Hazelcast.newHazelcastInstance(null);
    String topicName = "TestMessages";
    // get a topic from the first member and add a messageListener
    ITopic<String> topic1 = h1.getTopic(topicName);
    final CountDownLatch latch1 = new CountDownLatch(1);
    topic1.addMessageListener(new MessageListener() {
        public void onMessage(Object msg) {
            assertEquals("Test1", msg);
            latch1.countDown();
        }
    });
    // get a topic from the second member and add a messageListener
    ITopic<String> topic2 = h2.getTopic(topicName);
    final CountDownLatch latch2 = new CountDownLatch(2);
    topic2.addMessageListener(new MessageListener() {
        public void onMessage(Object msg) {
            assertEquals("Test1", msg);
            latch2.countDown();
        }
    });
    // publish the first message, both should receive this
    topic1.publish("Test1");
    // shutdown the first member
    h1.shutdown();
    // publish the second message, second member's topic should receive this
    topic2.publish("Test1");
    try {
        // assert that the first member's topic got the message
        assertTrue(latch1.await(5, TimeUnit.SECONDS));
        // assert that the second members' topic got two messages
        assertTrue(latch2.await(5, TimeUnit.SECONDS));
    } catch (InterruptedException ignored) {
    }
}

Also remember to call Hazelcast.shutdownAll() after each test case to make sure that there is no other running member left from the previous tests.

@After
public void cleanup() throws Exception {
    Hazelcast.shutdownAll();
}

Need more info? Check out existing tests.

Random order of planned features.