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;

BlockingQueue<MyTask> q = Hazelcast.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
}

If you have 10 million tasks in your "tasks" queue and you are running that code over 10 JVMs (or servers), then each server carries 1 million task objects (plus backups). 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 beSerializable. Maximum capacity per JVM and the TTL (Time to Live) for a queue can be configured as shown in the example below.

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

        <!--
            Maximum number of seconds for each item to stay in the queue. Items that are
            not consumed in <time-to-live-seconds> will get automatically evicted from the queue.
            Any integer between 0 and Integer.MAX_VALUE. 0 means infinite. Default is 0.
        -->
        <time-to-live-seconds>0</time-to-live-seconds>
    </queue>
</hazelcast>

As of version 1.9.3, distributed queues are backed by distributed maps. Thus, queues can have custom backup counts and persistent storage. Hazelcast will generate cluster-wide unique id for each element in the queue. Sample configuration:

<hazelcast>
    ...
    <queue name="tasks">
        <!--
            Maximum size of the queue. When a JVM's local queue size reaches the maximum,
            all put/offer operations will get blocked until the queue size
            of the JVM goes down below the maximum.
            Any integer between 0 and Integer.MAX_VALUE. 0 means Integer.MAX_VALUE. Default is 0.
        -->
        <max-size-per-jvm>10000</max-size-per-jvm>
        
        <!--
            Name of the map configuration that will be used for the backing distributed
            map for this queue.
        -->
        <backing-map-ref>queue-map</backing-map-ref>
    </queue>

    <map name="queue-map">

        <backup-count>1</backup-count>

        <map-store enabled="true">

            <class-name>com.your,company.storage.DBMapStore</class-name>

            <write-delay-seconds>0</write-delay-seconds>

        </map-store>

        ...

    </map>
</hazelcast>

If the backing map has no map-store defined then your distributed queue will be in-memory only. If the backing map has a map-store defined then Hazelcast will call your MapStore implementation to persist queue elements. Even if you reboot your cluster Hazelcast will rebuild the queue with its content. When implementing a MapStore for the backing map, note that type of the key is always Long and values are the elements you place into the queue. So make sure MapStore.loadAllKeys returns Set<Long> for instance.

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