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Replicated Map

A Replicated Map is a distributed key-value data structure where the data is replicated to all members in the cluster. It provides full replication of entries to all members for high speed access.

The following are the features of Replicated Map:

When you have a Replicated Map in the cluster, your clients can communicate with any cluster member.
All cluster members are able to perform write operations.
It supports all methods of the interface java.util.Map.
It supports automatic initial fill up when a new member is started.
It provides statistics for entry access, write and update so that you can monitor it using Hazelcast Management Center.
New members joining to the cluster pull all the data from the existing members.
You can listen to entry events using listeners. See the Using EntryListener on Replicated Map section.

Replicating Instead of Partitioning

A Replicated Map does not partition data (it does not spread data to different cluster members); instead, it replicates the data to all members.

Replication leads to higher memory consumption. However, a Replicated Map has faster read and write access since the data is available on all members.

Writes could take place on local/remote members in order to provide write-order, eventually being replicated to all other members.

Replicated Map is suitable for objects, catalog data, or idempotent calculable data (such as HTML pages). It fully implements the java.util.Map interface, but it lacks the methods from java.util.concurrent.ConcurrentMap since there are no atomic guarantees to writes or reads.

If Replicated Map is used from a unisocket client and this unisocket client is connected to a lite member, the entry listeners cannot be registered/de-registered.

You cannot use Replicated Map from a lite member. A com.hazelcast.replicatedmap.ReplicatedMapCantBeCreatedOnLiteMemberException is thrown if com.hazelcast.core.HazelcastInstance.getReplicatedMap(name) is invoked on a lite member.

Example Replicated Map Code

Here is an example of Replicated Map code. The HazelcastInstance’s getReplicatedMap method gets the Replicated Map, and the Replicated Map’s put method creates map entries.

        HazelcastInstance hz = Hazelcast.newHazelcastInstance();
        Map<String, String> map = hz.getReplicatedMap("map");

        map.put("1", "Tokyo");
        map.put("2", "Paris");
        map.put("3", "New York");

        System.out.println("Finished loading map");
        hz.shutdown();

HazelcastInstance.getReplicatedMap() returns com.hazelcast.core.ReplicatedMap which, as stated above, extends the java.util.Map interface.

The com.hazelcast.core.ReplicatedMap interface has some additional methods for registering entry listeners or retrieving values in an expected order.

Considerations for Replicated Map

If you have a large cluster or very high occurrences of updates, the Replicated Map may not scale linearly as expected since it has to replicate update operations to all members in the cluster.

Since the replication of updates is performed in an asynchronous manner, we recommend you enable back pressure in case your system has high occurrences of updates. See the Back Pressure section to learn how to enable it.

Replicated Map has an anti-entropy system that converges values to a common one if some of the members are missing replication updates.

Replicated Map does not guarantee eventual consistency because there are some edge cases that fail to provide consistency.

Replicated Map uses the internal partition system of Hazelcast in order to serialize updates happening on the same key at the same time. This happens by sending updates of the same key to the same Hazelcast member in the cluster.

Due to the asynchronous nature of replication, a Hazelcast member could die before successfully replicating a "write" operation to other members after sending the "write completed" response to its caller during the write process. In this scenario, Hazelcast’s internal partition system promotes one of the replicas of the partition as the primary one. The new primary partition does not have the latest "write" since the dead member could not successfully replicate the update. (This leaves the system in a state that the caller is the only one that has the update and the rest of the cluster have not.) In this case even the anti-entropy system simply could not converge the value since the source of true information is lost for the update. This leads to a break in the eventual consistency because different values can be read from the system for the same key.

Other than the aforementioned scenario, the Replicated Map behaves like an eventually consistent system with read-your-writes and monotonic-reads consistency.

Configuration Design for Replicated Map

There are several technical design decisions you should consider when you configure a Replicated Map.

Initial Provisioning

If a new member joins the cluster, there are two ways you can handle the initial provisioning that is executed to replicate all existing values to the new member. Each involves how you configure the async fill up.

First, you can configure async fill up to true, which does not block reads while the fill up operation is underway. That way, you have immediate access on the new member, but it will take time until all the values are eventually accessible. Not yet replicated values are returned as non-existing (null).

Second, you can configure for a synchronous initial fill up (by configuring the async fill up to false), which blocks every read or write access to the map until the fill up operation is finished. Use this with caution since it might block your application from operating.

Configuring Replicated Map

Replicated Map can be configured programmatically or declaratively.

Declarative Configuration:

You can declare your Replicated Map configuration in the Hazelcast configuration file hazelcast.xml. See the following example:

XML
YAML

<hazelcast>
    ...
    <replicatedmap name="default">
        <in-memory-format>BINARY</in-memory-format>
        <async-fillup>true</async-fillup>
        <statistics-enabled>true</statistics-enabled>
        <entry-listeners>
            <entry-listener include-value="true">
                com.hazelcast.examples.EntryListener
            </entry-listener>
       </entry-listeners>
       <split-brain-protection-ref>splitbrainprotection-name</split-brain-protection-ref>
    </replicatedmap>
    ...
</hazelcast>

hazelcast:
  replicatedmap:
    default:
      in-memory-format: BINARY
      async-fillup: true
      statistics-enabled: true
      entry-listeners:
        - class-name: com.hazelcast.examples.EntryListener
      split-brain-protection-ref: splitbrainprotection-name

Replicated Map has the following configuration elements:

in-memory-format: Internal storage format. See the In-Memory Format section. Its default value is OBJECT.
async-fillup: Specifies whether the Replicated Map is available for reads before the initial replication is completed. Its default value is true. If set to false, i.e., synchronous initial fill up, no exception is thrown when the Replicated Map is not yet ready, but null values can be seen until the initial replication is completed.
statistics-enabled: Specifies whether the statistics gathering is enabled for your Replicated Map. If set to false, you cannot collect statistics in your implementation (using getLocalReplicatedMapStats()) and also Hazelcast Management Center will not show them. Its default value is true.
entry-listener: Full canonical classname of the EntryListener implementation.
- entry-listener#include-value: Specifies whether the event includes the value or not. Sometimes the key is enough to react on an event. In those situations, setting this value to false saves a deserialization cycle. Its default value is true.
- entry-listener#local: Not used for Replicated Map since listeners are always local.
split-brain-protection-ref: Name of quorum configuration that you want this Replicated Map to use. See the Split-Brain Protection for Replicated Map section.

Programmatic Configuration:

You can configure a Replicated Map programmatically, as you can do for all other data structures in Hazelcast. You must create the configuration upfront, when you instantiate the HazelcastInstance. A basic example of how to configure the Replicated Map using the programmatic approach is shown in the following snippet.

        Config config = new Config();

        ReplicatedMapConfig replicatedMapConfig =
                config.getReplicatedMapConfig( "default" );

        replicatedMapConfig.setInMemoryFormat( InMemoryFormat.BINARY )
                .setSplitBrainProtectionName( "splitbrainprotectionname" );

All properties that can be configured using the declarative configuration are also available using programmatic configuration by transforming the tag names into getter or setter names.

In-Memory Format on Replicated Map

Currently, you can use the following in-memory-format options with the Replicated Map:

OBJECT (default): The data is stored in deserialized form. This configuration is the default choice since the data replication is mostly used for high speed access. Please be aware that changing the values without a Map.put() is not reflected on the other members but is visible on the changing members for later value accesses.
BINARY: The data is stored in serialized binary format and has to be deserialized on every request. This option offers higher encapsulation since changes to values are always discarded as long as the newly changed object is not explicitly Map.put() into the map again.

Using EntryListener on Replicated Map

A com.hazelcast.core.EntryListener used on a Replicated Map serves the same purpose as it would on other data structures in Hazelcast. You can use it to react on add, update and remove operations. Replicated Maps do not yet support eviction.

Difference in EntryListener on Replicated Map

The fundamental difference in Replicated Map behavior, compared to the other data structures, is that an EntryListener only reflects changes on local data. Since replication is asynchronous, all listener events are fired only when an operation is finished on a local member. Events can fire at different times on different members.

Example of Replicated Map EntryListener

Here is a code example for using EntryListener on a Replicated Map.

The HazelcastInstance s getReplicatedMap method gets a Replicated Map (customers), and the ReplicatedMap s addEntryListener method adds an entry listener to the Replicated Map. Then, the ReplicatedMap s put method adds a Replicated Map entry and updates it. The method remove removes the entry.

        HazelcastInstance hz = Hazelcast.newHazelcastInstance();
        ReplicatedMap<String, String> map = hz.getReplicatedMap("somemap");
        map.addEntryListener(new MyEntryListener());
        System.out.println("EntryListener registered");
    }

    private static class MyEntryListener implements EntryListener<String, String> {

        @Override
        public void entryAdded(EntryEvent<String, String> event) {
            System.out.println("entryAdded: " + event);
        }

        @Override
        public void entryRemoved(EntryEvent<String, String> event) {
            System.out.println("entryRemoved: " + event);
        }

        @Override
        public void entryUpdated(EntryEvent<String, String> event) {
            System.out.println("entryUpdated: " + event);
        }

        @Override
        public void entryEvicted(EntryEvent<String, String> event) {
            System.out.println("entryEvicted: " + event);
        }
        @Override
        public void entryExpired(EntryEvent<String, String> event) {
            System.out.println( "Entry expired: " + event );
        }
        @Override
        public void mapEvicted(MapEvent event) {
            System.out.println("mapEvicted:" + event);

        }

        @Override
        public void mapCleared(MapEvent event) {
            System.out.println("mapCleared: " + event);
        }

Split-Brain Protection for Replicated Map

Replicated Map can be configured to check for a minimum number of available members before applying its operations (see the Split-Brain Protection section). This is a check to avoid performing successful queue operations on all parts of a cluster during a network partition.

The following is a list of methods, grouped by the protection types, that support split-brain protection checks:

WRITE, READ_WRITE:
- clear
- put
- putAll
- remove
READ, READ_WRITE:
- containsKey
- containsValue
- entrySet
- get
- isEmpty
- keySet
- size
- values

Configuring Split-Brain Protection

Split-brain protection for Replicated Map can be configured programmatically using the method setSplitBrainProtectionName(), or declaratively using the element split-brain-protection-ref. Following is an example declarative configuration:

XML
YAML

<hazelcast>
    ...
    <replicatedmap name="default">
        <split-brain-protection-ref>splitbrainprotection-name</split-brain-protection-ref>
    </replicatedmap>
    ...
</hazelcast>

hazelcast:
  replicatedmap:
    default:
      split-brain-protection-ref: splitbrainprotection-name

The value of split-brain-protection-ref should be the split-brain protection configuration name which you configured under the split-brain-protection element as explained in the Split-Brain Protection section.

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