Hot Rod Java Client Guide

Download and install the Data Grid Maven repository to a local file system, Apache HTTP server, or Maven repository manager if you do not want to use the public Red Hat Enterprise Maven repository.

Configure your Maven settings file, typically ~/.m2/settings.xml, to include the Red Hat GA repository. Alternatively, include the repository directly in your project pom.xml file.

The following configuration uses the public Red Hat Enterprise Maven repository. To use the Data Grid Maven repository that you downloaded from the Red Hat customer portal, change the value of url elements to the correct location.

<repositories>
  <repository>
    <id>redhat-ga</id>
    <name>Red Hat GA Repository</name>
    <url>https://maven.repository.redhat.com/ga/</url>
  </repository>
</repositories>
<pluginRepositories>
  <pluginRepository>
    <id>redhat-ga</id>
    <name>Red Hat GA Repository</name>
    <url>https://maven.repository.redhat.com/ga/</url>
  </pluginRepository>
</pluginRepositories>

Maven uses configuration files called Project Object Model (POM) files to define projects and manage builds. POM files are in XML format and describe the module and component dependencies, build order, and targets for the resulting project packaging and output.

The following example shows the Data Grid version and BOM:

<properties>
  <version.infinispan>10.1.8.Final-redhat-00001</version.infinispan>
</properties>

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>org.infinispan</groupId>
      <artifactId>infinispan-bom</artifactId>
      <version>${version.infinispan}</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

Clients that use topology-aware intelligence use request balancing for all requests. The default balancing strategy is round-robin, so topology-aware clients always send requests to servers in round-robin order.

For example, s1, s2, s3 are servers in a Data Grid cluster. Clients perform request balancing as follows:

CacheContainer cacheContainer = new RemoteCacheManager();
Cache<String, String> cache = cacheContainer.getCache();

//client sends put request to s1
cache.put("key1", "aValue");
//client sends put request to s2
cache.put("key2", "aValue");
//client sends get request to s3
String value = cache.get("key1");
//client dispatches to s1 again
cache.remove("key2");
//and so on...

Clients that use distribution-aware intelligence use request balancing only for failed requests. When requests fail, distribution-aware clients retry the request on the next available server.

infinispan.client.hotrod.request_balancing_strategy

Hot Rod clients can automatically failover when Data Grid cluster topologies change. For instance, Hot Rod clients that are topology-aware can detect when one or more Data Grid servers fail.

In addition to failover between clustered Data Grid servers, Hot Rod clients can failover between Data Grid clusters.

For example, you have a Data Grid cluster running in New York (NYC) and another cluster running in London (LON). Clients sending requests to NYC detect that no nodes are available so they switch to the cluster in LON. Clients then maintain connections to LON until you manually switch clusters or failover happens again.

Even though Hot Rod clients can failover, you should use transactional caches to ensure that operations do not partially complete and leave conflicting entries on different nodes.

Data Grid supports the following SASL authentications mechanisms with the Hot Rod connector:

To use the GSSAPI mechanism, you must create a LoginContext so your Hot Rod client can obtain a Ticket Granting Ticket (TGT).

Manually switch Hot Rod Java client connections between Data Grid clusters.

You can add implementations of the ProtoStream SerializationContextInitializer interface to Hot Rod client configurations so Data Grid marshalls custom Java objects.

infinispan.client.hotrod.context-initializers=org.infinispan.example.LibraryInitializerImpl,org.infinispan.example.AnotherExampleSciImpl

ConfigurationBuilder builder = new ConfigurationBuilder();
builder
      .addServer()
      .host("127.0.0.1")
      .port(11222)
      .addContextInitializers(new LibraryInitializerImpl(), new AnotherExampleSciImpl());
RemoteCacheManager rcm = new RemoteCacheManager(builder.build());

Configure Hot Rod clients to use custom marshallers.

The Data Grid RemoteCache API does not support all methods available in the Cache API and throws UnsupportedOperationException when unsupported methods are invoked.

Most of these methods do not make sense on the remote cache (e.g. listener management operations), or correspond to methods that are not supported by local cache as well (e.g. containsValue).

Certain atomic operations inherited from ConcurrentMap are also not supported with the RemoteCache API, for example:

boolean remove(Object key, Object value);
boolean replace(Object key, Object value);
boolean replace(Object key, Object oldValue, Object value);

However, RemoteCache offers alternative versioned methods for these atomic operations that send version identifiers over the network instead of whole value objects.

Deploy custom filters to Data Grid server instances.

When an client event listener is not needed any more, it can be removed:

EventPrintListener listener = ...
cache.removeClientListener(listener);

In order to avoid inundating clients with events, users can provide filtering functionality to limit the number of events fired by the server for a particular client listener. To enable filtering, a cache event filter factory needs to be created that produces filter instances:

import org.infinispan.notifications.cachelistener.filter.CacheEventFilterFactory;
import org.infinispan.filter.NamedFactory;

@NamedFactory(name = "static-filter")
public static class StaticCacheEventFilterFactory implements CacheEventFilterFactory {

   @Override
   public StaticCacheEventFilter getFilter(Object[] params) {
      return new StaticCacheEventFilter();
   }
}


// Serializable, Externalizable or marshallable with Infinispan Externalizers
// needed when running in a cluster
class StaticCacheEventFilter implements CacheEventFilter<Integer, String>, Serializable {
   @Override
   public boolean accept(Integer key, String oldValue, Metadata oldMetadata,
         String newValue, Metadata newMetadata, EventType eventType) {
      if (key.equals(1)) // static key
         return true;

      return false;
   }
}

The cache event filter factory instance defined above creates filter instances which statically filter out all entries except the one whose key is 1.

To be able to register a listener with this cache event filter factory, the factory has to be given a unique name, and the Hot Rod server needs to be plugged with the name and the cache event filter factory instance.

You can also register dynamic filter instances that filter based on parameters provided when the listener is registered are also possible. Filters use the parameters received by the filter factories to enable this option, for example:

import org.infinispan.notifications.cachelistener.filter.CacheEventFilterFactory;
import org.infinispan.notifications.cachelistener.filter.CacheEventFilter;

class DynamicCacheEventFilterFactory implements CacheEventFilterFactory {
   @Override
   public CacheEventFilter<Integer, String> getFilter(Object[] params) {
      return new DynamicCacheEventFilter(params);
   }
}

// Serializable, Externalizable or marshallable with Infinispan Externalizers
// needed when running in a cluster
class DynamicCacheEventFilter implements CacheEventFilter<Integer, String>, Serializable {
   final Object[] params;

   DynamicCacheEventFilter(Object[] params) {
      this.params = params;
   }

   @Override
   public boolean accept(Integer key, String oldValue, Metadata oldMetadata,
         String newValue, Metadata newMetadata, EventType eventType) {
      if (key.equals(params[0])) // dynamic key
         return true;

      return false;
   }
}

The dynamic parameters required to do the filtering are provided when the listener is registered:

RemoteCache<?, ?> cache = ...
cache.addClientListener(new EventPrintListener(), new Object[]{1}, null);

Include the SKIP_LISTENER_NOTIFICATION flag when calling remote API methods to perform operations without getting event notifications from the server. For example, to prevent listener notifications when creating or modifying values, set the flag as follows:

remoteCache.withFlags(Flag.SKIP_LISTENER_NOTIFICATION).put(1, "one");

The events generated by default contain just enough information to make the event relevant but they avoid cramming too much information in order to reduce the cost of sending them. Optionally, the information shipped in the events can be customised in order to contain more information, such as values, or to contain even less information. This customization is done with CacheEventConverter instances generated by a CacheEventConverterFactory:

import org.infinispan.notifications.cachelistener.filter.CacheEventConverterFactory;
import org.infinispan.notifications.cachelistener.filter.CacheEventConverter;
import org.infinispan.filter.NamedFactory;

@NamedFactory(name = "static-converter")
class StaticConverterFactory implements CacheEventConverterFactory {
   final CacheEventConverter<Integer, String, CustomEvent> staticConverter = new StaticCacheEventConverter();
   public CacheEventConverter<Integer, String, CustomEvent> getConverter(final Object[] params) {
      return staticConverter;
   }
}

// Serializable, Externalizable or marshallable with Infinispan Externalizers
// needed when running in a cluster
class StaticCacheEventConverter implements CacheEventConverter<Integer, String, CustomEvent>, Serializable {
   public CustomEvent convert(Integer key, String oldValue, Metadata oldMetadata, String newValue, Metadata newMetadata, EventType eventType) {
      return new CustomEvent(key, newValue);
   }
}

// Needs to be Serializable, Externalizable or marshallable with Infinispan Externalizers
// regardless of cluster or local caches
static class CustomEvent implements Serializable {
   final Integer key;
   final String value;
   CustomEvent(Integer key, String value) {
      this.key = key;
      this.value = value;
   }
}

In the example above, the converter generates a new custom event which includes the value as well as the key in the event. This will result in bigger event payloads compared with default events, but if combined with filtering, it can reduce its network bandwidth cost.

Handling custom events requires a slightly different client listener implementation to the one demonstrated previously. To be more precise, it needs to handle ClientCacheEntryCustomEvent instances:

import org.infinispan.client.hotrod.annotation.*;
import org.infinispan.client.hotrod.event.*;

@ClientListener
public class CustomEventPrintListener {

   @ClientCacheEntryCreated
   @ClientCacheEntryModified
   @ClientCacheEntryRemoved
   public void handleCustomEvent(ClientCacheEntryCustomEvent<CustomEvent> e) {
      System.out.println(e);
   }

}

The ClientCacheEntryCustomEvent received in the callback exposes the custom event via getEventData method, and the getType method provides information on whether the event generated was as a result of cache entry creation, modification or removal.

Similar to filtering, to be able to register a listener with this converter factory, the factory has to be given a unique name, and the Hot Rod server needs to be plugged with the name and the cache event converter factory instance.

Dynamic converter instances that convert based on parameters provided when the listener is registered are also possible. Converters use the parameters received by the converter factories to enable this option. For example:

import org.infinispan.notifications.cachelistener.filter.CacheEventConverterFactory;
import org.infinispan.notifications.cachelistener.filter.CacheEventConverter;

@NamedFactory(name = "dynamic-converter")
class DynamicCacheEventConverterFactory implements CacheEventConverterFactory {
   public CacheEventConverter<Integer, String, CustomEvent> getConverter(final Object[] params) {
      return new DynamicCacheEventConverter(params);
   }
}

// Serializable, Externalizable or marshallable with Infinispan Externalizers needed when running in a cluster
class DynamicCacheEventConverter implements CacheEventConverter<Integer, String, CustomEvent>, Serializable {
   final Object[] params;

   DynamicCacheEventConverter(Object[] params) {
      this.params = params;
   }

   public CustomEvent convert(Integer key, String oldValue, Metadata oldMetadata,
         String newValue, Metadata newMetadata, EventType eventType) {
      // If the key matches a key given via parameter, only send the key information
      if (params[0].equals(key))
         return new CustomEvent(key, null);

      return new CustomEvent(key, newValue);
   }
}

The dynamic parameters required to do the conversion are provided when the listener is registered:

RemoteCache<?, ?> cache = ...
cache.addClientListener(new EventPrintListener(), null, new Object[]{1});

If you want to do both event filtering and customization, it’s easier to implement org.infinispan.notifications.cachelistener.filter.CacheEventFilterConverter which allows both filter and customization to happen in a single step. For convenience, it’s recommended to extend org.infinispan.notifications.cachelistener.filter.AbstractCacheEventFilterConverter instead of implementing org.infinispan.notifications.cachelistener.filter.CacheEventFilterConverter directly. For example:

import org.infinispan.notifications.cachelistener.filter.CacheEventConverterFactory;
import org.infinispan.notifications.cachelistener.filter.CacheEventConverter;

@NamedFactory(name = "dynamic-filter-converter")
class DynamicCacheEventFilterConverterFactory implements CacheEventFilterConverterFactory {
   public CacheEventFilterConverter<Integer, String, CustomEvent> getFilterConverter(final Object[] params) {
      return new DynamicCacheEventFilterConverter(params);
   }
}

// Serializable, Externalizable or marshallable with Infinispan Externalizers needed when running in a cluster
//
class DynamicCacheEventFilterConverter extends AbstractCacheEventFilterConverter<Integer, String, CustomEvent>, Serializable {
   final Object[] params;

   DynamicCacheEventFilterConverter(Object[] params) {
      this.params = params;
   }

   public CustomEvent filterAndConvert(Integer key, String oldValue, Metadata oldMetadata,
         String newValue, Metadata newMetadata, EventType eventType) {
      // If the key matches a key given via parameter, only send the key information
      if (params[0].equals(key))
         return new CustomEvent(key, null);

      return new CustomEvent(key, newValue);
   }
}

Similar to filters and converters, to be able to register a listener with this combined filter/converter factory, the factory has to be given a unique name via the @NamedFactory annotation, and the Hot Rod server needs to be plugged with the name and the cache event converter factory instance.

From a client perspective, to be able to use the combined filter and converter class, the client listener must define the same filter factory and converter factory names, e.g.:

@ClientListener(filterFactoryName = "dynamic-filter-converter", converterFactoryName = "dynamic-filter-converter")
public class CustomEventPrintListener { ... }

The dynamic parameters required in the example above are provided when the listener is registered via either filter or converter parameters. If filter parameters are non-empty, those are used, otherwise, the converter parameters:

RemoteCache<?, ?> cache = ...
cache.addClientListener(new CustomEventPrintListener(), new Object[]{1}, null);

Hot Rod servers can store data in different formats, but in spite of that, Java Hot Rod client users can still develop CacheEventConverter or CacheEventFilter instances that work on typed objects. By default, filters and converter will use data as POJO (application/x-java-object) but it is possible to override the desired format by overriding the method format() from the filter/converter. If the format returns null, the filter/converter will receive data as it’s stored.

Hot Rod Java clients can be configured to use different org.infinispan.commons.marshall.Marshaller instances. If doing this and deploying CacheEventConverter or CacheEventFilter instances, to be able to present filters/converter with Java Objects rather than marshalled content, the server needs to be able to convert between objects and the binary format produced by the marshaller.

To deploy a Marshaller instance server-side, follow a similar method to the one used to deploy CacheEventConverter or CacheEventFilter instances:

Note that the Marshaller could be deployed in either a separate jar, or in the same jar as the CacheEventConverter and/or CacheEventFilter instances.

Client listener annotation has an optional includeCurrentState attribute that specifies whether state will be sent to the client when the listener is added or when there’s a failover of the listener.

By default, includeCurrentState is false, but if set to true and a client listener is added in a cache already containing data, the server iterates over the cache contents and sends an event for each entry to the client as a ClientCacheEntryCreated (or custom event if configured). This allows clients to build some local data structures based on the existing content. Once the content has been iterated over, events are received as normal, as cache updates are received. If the cache is clustered, the entire cluster wide contents are iterated over.

When a Hot Rod client registers a client listener, it does so in a single node in a cluster. If that node fails, the Java Hot Rod client detects that transparently and fails over all listeners registered in the node that failed to another node.

During this fail over the client might miss some events. To avoid missing these events, the client listener annotation contains an optional parameter called includeCurrentState which if set to true, when the failover happens, the cache contents can iterated over and ClientCacheEntryCreated events (or custom events if configured) are generated. By default, includeCurrentState is set to false.

Use callbacks to handle failover events:

@ClientCacheFailover
public void handleFailover(ClientCacheFailoverEvent e) {
  ...
}

This is very useful in use cases where the client has cached some data, and as a result of the fail over, taking in account that some events could be missed, it could decide to clear any locally cached data when the fail over event is received, with the knowledge that after the fail over event, it will receive events for the contents of the entire cache.

Caches in the server must also be transactional for clients to participate in JTA Transactions.

The following server configuration is required, otherwise transactions rollback only:

For example:

<replicated-cache name="hotrodReplTx">
  <locking isolation="REPEATABLE_READ"/>
  <transaction mode="NON_XA" locking="PESSIMISTIC"/>
</replicated-cache>

Hot Rod transactions have their own recovery mechanism.

When you create the RemoteCacheManager, you can set the default TransactionManager and TransactionMode that the RemoteCache uses.

The RemoteCacheManager lets you create only one configuration for transactional caches, as in the following example:

org.infinispan.client.hotrod.configuration.ConfigurationBuilder cb = new org.infinispan.client.hotrod.configuration.ConfigurationBuilder();
//other client configuration parameters
cb.transaction().transactionManagerLookup(GenericTransactionManagerLookup.getInstance());
cb.transaction().transactionMode(TransactionMode.NON_XA);
cb.transaction().timeout(1, TimeUnit.MINUTES)
RemoteCacheManager rmc = new RemoteCacheManager(cb.build());

The preceding configuration applies to all instances of a remote cache. If you need to apply different configurations to remote cache instances, you can override the RemoteCache configuration. See Overriding RemoteCacheManager Configuration.

See ConfigurationBuilder Javadoc for documentation on configuration parameters.

You can also configure the Java Hot Rod client with a properties file, as in the following example:

infinispan.client.hotrod.transaction.transaction_manager_lookup = org.infinispan.client.hotrod.transaction.lookup.GenericTransactionManagerLookup
infinispan.client.hotrod.transaction.transaction_mode = NON_XA
infinispan.client.hotrod.transaction.timeout = 60000

TransactionMode controls how a RemoteCache interacts with the TransactionManager.

Transaction modes are the same in both the Data Grid configuration and client settings. Use the following modes with your client, see the Data Grid configuration schema for the server:

Because RemoteCacheManager does not support different configurations for each cache instance. However, RemoteCacheManager includes the getCache(String) method that returns the RemoteCache instances and lets you override some configuration parameters, as follows:

Transactions use the initial values of keys to detect conflicts.

For example, "k" has a value of "v" when a transaction begins. During the prepare phase, the transaction fetches "k" from the server to read the value. If the value has changed, the transaction rolls back to avoid a conflict.

The forceReturnValue parameter controls write operations to the RemoteCache and helps avoid conflicts. It has the following values:

The following transactions provide an example where the forceReturnValue parameter can prevent conflicting write operations:

RemoteCache<String, String> cache = ...
TransactionManager tm = ...

tm.begin();
cache.put("k", "v1");
tm.commit();

RemoteCache<String, String> cache = ...
TransactionManager tm = ...

tm.begin();
cache.put("k", "v2");
tm.commit();

In this example, TX1 and TX2 are executed in parallel. The initial value of "k" is "v".

The following transactions include cache.get() operations to read the value for "k" before doing the cache.put() operations:

RemoteCache<String, String> cache = ...
TransactionManager tm = ...

tm.begin();
cache.get("k");
cache.put("k", "v1");
tm.commit();

RemoteCache<String, String> cache = ...
TransactionManager tm = ...

tm.begin();
cache.get("k");
cache.put("k", "v2");
tm.commit();

In the preceding examples, TX1 and TX2 both read the key so the forceReturnValue parameter does not take effect. One transaction commits, the other rolls back. However, the cache.get() operation requires an additional server request. If you do not need the return value for the cache.put() operation that server request is inefficient.

The following example shows how to use the TransactionManager and TransactionMode that you configure in the RemoteCacheManager:

//Configure the transaction manager and transaction mode.
org.infinispan.client.hotrod.configuration.ConfigurationBuilder cb = new org.infinispan.client.hotrod.configuration.ConfigurationBuilder();
cb.transaction().transactionManagerLookup(RemoteTransactionManagerLookup.getInstance());
cb.transaction().transactionMode(TransactionMode.NON_XA);

RemoteCacheManager rcm = new RemoteCacheManager(cb.build());

//The my-cache instance uses the RemoteCacheManager configuration.
RemoteCache<String, String> cache = rcm.getCache("my-cache");

//Return the transaction manager that the cache uses.
TransactionManager tm = cache.getTransactionManager();

//Perform a simple transaction.
tm.begin();
cache.put("k1", "v1");
System.out.println("K1 value is " + cache.get("k1"));
tm.commit();

The following example shows how to override TransactionManager with the getCache method:

//Configure the transaction manager and transaction mode.
org.infinispan.client.hotrod.configuration.ConfigurationBuilder cb = new org.infinispan.client.hotrod.configuration.ConfigurationBuilder();
cb.transaction().transactionManagerLookup(RemoteTransactionManagerLookup.getInstance());
cb.transaction().transactionMode(TransactionMode.NON_XA);

RemoteCacheManager rcm = new RemoteCacheManager(cb.build());

//Define a custom TransactionManager.
TransactionManager myCustomTM = ...

//Override the TransactionManager for the my-cache instance. Use the default configuration if null is returned.
RemoteCache<String, String> cache = rcm.getCache("my-cache", null, myCustomTM);

//Perform a simple transaction.
myCustomTM.begin();
cache.put("k1", "v1");
System.out.println("K1 value is " + cache.get("k1"));
myCustomTM.commit();

The following example shows how to override TransactionMode with the getCache method:

//Configure the transaction manager and transaction mode.
org.infinispan.client.hotrod.configuration.ConfigurationBuilder cb = new org.infinispan.client.hotrod.configuration.ConfigurationBuilder();
cb.transaction().transactionManagerLookup(RemoteTransactionManagerLookup.getInstance());
cb.transaction().transactionMode(TransactionMode.NON_XA);

RemoteCacheManager rcm = new RemoteCacheManager(cb.build());

//Override the transaction mode for the my-cache instance.
RemoteCache<String, String> cache = rcm.getCache("my-cache", TransactionMode.NON_DURABLE_XA, null);

//Return the transaction manager that the cache uses.
TransactionManager tm = cache.getTransactionManager();

//Perform a simple transaction.
tm.begin();
cache.put("k1", "v1");
System.out.println("K1 value is " + cache.get("k1"));
tm.commit();