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Chapter 4. Hot Rod Client API

Data Grid Hot Rod client API provides interfaces for creating caches remotely, manipulating data, monitoring the topology of clustered caches, and more.

4.1. RemoteCache API

The collection methods keySet, entrySet and values are backed by the remote cache. That is that every method is called back into the RemoteCache. This is useful as it allows for the various keys, entries or values to be retrieved lazily, and not requiring them all be stored in the client memory at once if the user does not want.

These collections adhere to the Map specification being that add and addAll are not supported but all other methods are supported.

One thing to note is the Iterator.remove and Set.remove or Collection.remove methods require more than 1 round trip to the server to operate. You can check out the RemoteCache Javadoc to see more details about these and the other methods.

Iterator Usage

The iterator method of these collections uses retrieveEntries internally, which is described below. If you notice retrieveEntries takes an argument for the batch size. There is no way to provide this to the iterator. As such the batch size can be configured via system property infinispan.client.hotrod.batch_size or through the ConfigurationBuilder when configuring the RemoteCacheManager.

Also the retrieveEntries iterator returned is Closeable as such the iterators from keySet, entrySet and values return an AutoCloseable variant. Therefore you should always close these `Iterator`s when you are done with them. 

try (CloseableIterator<Map.Entry<K, V>> iterator = remoteCache.entrySet().iterator()) {

      }

What if I want a deep copy and not a backing collection?

Previous version of RemoteCache allowed for the retrieval of a deep copy of the keySet. This is still possible with the new backing map, you just have to copy the contents yourself. Also you can do this with entrySet and values, which we didn’t support before. 

Set<K> keysCopy = remoteCache.keySet().stream().collect(Collectors.toSet());

4.1.1. Unsupported Methods

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.

Reference

4.2. Remote Iterator API

Data Grid provides a remote iterator API to retrieve entries where memory resources are constrained or if you plan to do server-side filtering or conversion. 

// Retrieve all entries in batches of 1000
int batchSize = 1000;
try (CloseableIterator<Entry<Object, Object>> iterator = remoteCache.retrieveEntries(null, batchSize)) {
     while(iterator.hasNext()) {
        // Do something
     }
}

// Filter by segment
Set<Integer> segments = ...
try (CloseableIterator<Entry<Object, Object>> iterator = remoteCache.retrieveEntries(null, segments, batchSize)) {
     while(iterator.hasNext()) {
        // Do something
     }
}

// Filter by custom filter
try (CloseableIterator<Entry<Object, Object>> iterator = remoteCache.retrieveEntries("myFilterConverterFactory", segments, batchSize)) {
     while(iterator.hasNext()) {
        // Do something
     }
}

4.2.1. Deploying Custom Filters to Data Grid Server

Deploy custom filters to Data Grid server instances.

Procedure

  1. Create a factory that extends KeyValueFilterConverterFactory. 

    import java.io.Serializable;

import org.infinispan.filter.AbstractKeyValueFilterConverter;
import org.infinispan.filter.KeyValueFilterConverter;
import org.infinispan.filter.KeyValueFilterConverterFactory;
import org.infinispan.filter.NamedFactory;
import org.infinispan.metadata.Metadata;

//@NamedFactory annotation defines the factory name
@NamedFactory(name = "myFilterConverterFactory")
public class MyKeyValueFilterConverterFactory implements KeyValueFilterConverterFactory {

   @Override
   public KeyValueFilterConverter<String, SampleEntity1, SampleEntity2> getFilterConverter() {
      return new MyKeyValueFilterConverter();
   }
   // Filter implementation. Should be serializable or externalizable for DIST caches
   static class MyKeyValueFilterConverter extends AbstractKeyValueFilterConverter<String, SampleEntity1, SampleEntity2> implements Serializable {
      @Override
      public SampleEntity2 filterAndConvert(String key, SampleEntity1 entity, Metadata metadata) {
         // returning null will case the entry to be filtered out
         // return SampleEntity2 will convert from the cache type SampleEntity1
      }

      @Override
      public MediaType format() {
         // returns the MediaType that data should be presented to this converter.
         // When omitted, the server will use "application/x-java-object".
         // Returning null will cause the filter/converter to be done in the storage format.
      }
   }
}

  2. Create a JAR that contains a META-INF/services/org.infinispan.filter.KeyValueFilterConverterFactory file. This file should include the fully qualified class name of the filter factory class implementation.

    If the filter uses custom key/value classes, you must include them in your JAR file so that the filter can correctly unmarshall key and/or value instances.

  3. Add the JAR file to the server/lib directory of your Data Grid server installation directory.

Reference

4.3. MetadataValue API

Use the MetadataValue interface for versioned operations.

The following example shows a remove operation that occurs only if the version of the value for the entry is unchanged: 

RemoteCacheManager remoteCacheManager = new RemoteCacheManager();
      RemoteCache<String, String> remoteCache = remoteCacheManager.getCache();

      remoteCache.put("car", "ferrari");
      VersionedValue valueBinary = remoteCache.getWithMetadata("car");

      assert remoteCache.remove("car", valueBinary.getVersion());
      assert !remoteCache.containsKey("car");

Reference

4.4. Streaming API

Data Grid provides a Streaming API that implements methods that return instances of InputStream and OutputStream so you can stream large objects between Hot Rod clients and Data Grid servers.

Consider the following example of a large object: 

StreamingRemoteCache<String> streamingCache = remoteCache.streaming();
OutputStream os = streamingCache.put("a_large_object");
os.write(...);
os.close();

You could read the object through streaming as follows: 

StreamingRemoteCache<String> streamingCache = remoteCache.streaming();
InputStream is = streamingCache.get("a_large_object");
for(int b = is.read(); b >= 0; b = is.read()) {
   // iterate
}
is.close();
Note

The Streaming API does not marshall values, which means you cannot access the same entries using both the Streaming and Non-Streaming API at the same time. You can, however, implement a custom marshaller to handle this case.

The InputStream returned by the RemoteStreamingCache.get(K key) method implements the VersionedMetadata interface, so you can retrieve version and expiration information as follows: 

StreamingRemoteCache<String> streamingCache = remoteCache.streaming();
InputStream is = streamingCache.get("a_large_object");
long version = ((VersionedMetadata) is).getVersion();
for(int b = is.read(); b >= 0; b = is.read()) {
   // iterate
}
is.close();
Note

Conditional write methods (putIfAbsent(), replace()) perform the actual condition check after the value is completely sent to the server. In other words, when the close() method is invoked on the OutputStream.

Reference

4.5. Counter API

The CounterManager interface is the entry point to define, retrieve and remove counters.

Hot Rod clients can retrieve the CounterManager interface as in the following example: 

// create or obtain your RemoteCacheManager
RemoteCacheManager manager = ...;

// retrieve the CounterManager
CounterManager counterManager = RemoteCounterManagerFactory.asCounterManager(manager);

Reference

4.6. Creating Event Listeners

Java Hot Rod clients can register listeners to receive cache-entry level events. Cache entry created, modified and removed events are supported.

Creating a client listener is very similar to embedded listeners, except that different annotations and event classes are used. Here’s an example of a client listener that prints out each event received: 

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

@ClientListener(converterFactoryName = "static-converter")
public class EventPrintListener {

   @ClientCacheEntryCreated
   public void handleCreatedEvent(ClientCacheEntryCreatedEvent e) {
      System.out.println(e);
   }

   @ClientCacheEntryModified
   public void handleModifiedEvent(ClientCacheEntryModifiedEvent e) {
      System.out.println(e);
   }

   @ClientCacheEntryRemoved
   public void handleRemovedEvent(ClientCacheEntryRemovedEvent e) {
      System.out.println(e);
   }

}

ClientCacheEntryCreatedEvent and ClientCacheEntryModifiedEvent instances provide information on the affected key, and the version of the entry. This version can be used to invoke conditional operations on the server, such as replaceWithVersion or removeWithVersion.

ClientCacheEntryRemovedEvent events are only sent when the remove operation succeeds. In other words, if a remove operation is invoked but no entry is found or no entry should be removed, no event is generated. Users interested in removed events, even when no entry was removed, can develop event customization logic to generate such events. More information can be found in the customizing client events section.

All ClientCacheEntryCreatedEvent, ClientCacheEntryModifiedEvent and ClientCacheEntryRemovedEvent event instances also provide a boolean isCommandRetried() method that will return true if the write command that caused this had to be retried again due to a topology change. This could be a sign that this event has been duplicated or another event was dropped and replaced (eg: ClientCacheEntryModifiedEvent replaced ClientCacheEntryCreatedEvent).

Once the client listener implementation has been created, it needs to be registered with the server. To do so, execute: 

RemoteCache<?, ?> cache = ...
cache.addClientListener(new EventPrintListener());

4.6.1. Removing Event Listeners

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

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

4.6.2. Filtering Events

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.

  1. Create a JAR file that contains the filter implementation.

    If the cache uses custom key/value classes, these must be included in the JAR so that the callbacks can be executed with the correctly unmarshalled key and/or value instances. If the client listener has useRawData enabled, this is not necessary since the callback key/value instances will be provided in binary format.

  2. Create a META-INF/services/org.infinispan.notifications.cachelistener.filter.CacheEventFilterFactory file within the JAR file and within it, write the fully qualified class name of the filter class implementation.
  3. Add the JAR file to the server/lib directory of your Data Grid server installation directory.

  4. Link the client listener with this cache event filter factory by adding the factory name to the @ClientListener annotation: 

    @ClientListener(filterFactoryName = "static-filter")
public class EventPrintListener { ... }

  5. Register the listener with the server: 

    RemoteCache<?, ?> cache = ...
cache.addClientListener(new EventPrintListener());

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);
Warning

Filter instances have to marshallable when they are deployed in a cluster so that the filtering can happen right where the event is generated, even if the even is generated in a different node to where the listener is registered. To make them marshallable, either make them extend Serializable, Externalizable, or provide a custom Externalizer for them.

4.6.3. Skipping Notifications

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");

4.6.4. Customizing Events

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.

Warning

The target type of the converter must be either Serializable or Externalizable. In this particular case of converters, providing an Externalizer will not work by default since the default Hot Rod client marshaller does not support them.

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.

  1. Create a JAR file with the converter implementation within it.

    If the cache uses custom key/value classes, these must be included in the JAR so that the callbacks can be executed with the correctly unmarshalled key and/or value instances. If the client listener has useRawData enabled, this is not necessary since the callback key/value instances will be provided in binary format.

  2. Create a META-INF/services/org.infinispan.notifications.cachelistener.filter.CacheEventConverterFactory file within the JAR file and within it, write the fully qualified class name of the converter class implementation.
  3. Add the JAR file to the server/lib directory of your Data Grid server installation directory.

  4. Link the client listener with this converter factory by adding the factory name to the @ClientListener annotation: 

    @ClientListener(converterFactoryName = "static-converter")
public class CustomEventPrintListener { ... }

  5. Register the listener with the server: 

    RemoteCache<?, ?> cache = ...
cache.addClientListener(new CustomEventPrintListener());

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});
Warning

Converter instances have to marshallable when they are deployed in a cluster, so that the conversion can happen right where the event is generated, even if the event is generated in a different node to where the listener is registered. To make them marshallable, either make them extend Serializable, Externalizable, or provide a custom Externalizer for them.

4.6.5. Filter and Custom Events

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.

  1. Create a JAR file with the converter implementation within it.

    If the cache uses custom key/value classes, these must be included in the JAR so that the callbacks can be executed with the correctly unmarshalled key and/or value instances. If the client listener has useRawData enabled, this is not necessary since the callback key/value instances will be provided in binary format.

  2. Create a META-INF/services/org.infinispan.notifications.cachelistener.filter.CacheEventFilterConverterFactory file within the JAR file and within it, write the fully qualified class name of the converter class implementation.
  3. Add the JAR file to the server/lib directory of your Data Grid server installation directory.

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);

4.6.6. Event Marshalling

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:

  1. Create a JAR file with the converter implementation within it.
  2. Create a META-INF/services/org.infinispan.commons.marshall.Marshaller file within the JAR file and within it, write the fully qualified class name of the marshaller class implementation.
  3. Add the JAR file to the server/lib directory of your Data Grid server installation directory.

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

4.6.6.1. Deploying Protostream Marshallers

If a cache stores Protobuf content, as it happens when using ProtoStream marshaller in the Hot Rod client, it’s not necessary to deploy a custom marshaller since the format is already support by the server: there are transcoders from Protobuf format to most common formats like JSON and POJO.

When using filters/converters with those caches, and it’s desirable to use filter/converters with Java Objects rather binary Protobuf data, it’s necessary to configure the extra ProtoStream marshallers so that the server can unmarshall the data before filtering/converting. To do so, you must configure the required SerializationContextInitializer(s) as part of the Data Grid server configuration.

See Cache Encoding and Marshalling for more information.

4.6.7. Listener State Handling

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.

4.6.8. Listener Failure Handling

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.

4.7. Hot Rod Java Client Transactions

You can configure and use Hot Rod clients in JTA Transactions.

To participate in a transaction, the Hot Rod client requires the TransactionManager with which it interacts and whether it participates in the transaction through the Synchronization or XAResource interface.

Important

Transactions are optimistic in that clients acquire write locks on entries during the prepare phase. To avoid data inconsistency, be sure to read about Detecting Conflicts with Transactions.

4.7.1. Configuring the Server

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:

  • Isolation level must be REPEATABLE_READ.
  • PESSIMISTIC locking mode is recommended but OPTIMISTIC can be used.
  • Transaction mode should be NON_XA or NON_DURABLE_XA. Hot Rod transactions should not use FULL_XA because it degrades performance.

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.

4.7.2. Configuring Hot Rod Clients

Transactional RemoteCache are configured per-cache basis. The exception is the transaction’s timeout which is global, because a single transaction can interact with multiple RemoteCaches.

The following example shows how to configure a transactional RemoteCache for cache my-cache: 

org.infinispan.client.hotrod.configuration.ConfigurationBuilder cb = new org.infinispan.client.hotrod.configuration.ConfigurationBuilder();
//other client configuration parameters
cb.transactionTimeout(1, TimeUnit.MINUTES);
cb.remoteCache("my-cache")
   .transactionManagerLookup(GenericTransactionManagerLookup.getInstance())
   .transactionMode(TransactionMode.NON_XA);

See ConfigurationBuilder and RemoteCacheConfigurationBuilder 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.cache.my-cache.transaction.transaction_manager_lookup = org.infinispan.client.hotrod.transaction.lookup.GenericTransactionManagerLookup
infinispan.client.hotrod.cache.my-cache.transaction.transaction_mode = NON_XA
infinispan.client.hotrod.transaction.timeout = 60000

4.7.2.1. TransactionManagerLookup Interface

TransactionManagerLookup provides an entry point to fetch a TransactionManager.

Available implementations of TransactionManagerLookup:

GenericTransactionManagerLookup
A lookup class that locates TransactionManagers running in Java EE application servers. Defaults to the RemoteTransactionManager if it cannot find a TransactionManager. This is the default for Hot Rod Java clients.
Tip

In most cases, GenericTransactionManagerLookup is suitable. However, you can implement the TransactionManagerLookup interface if you need to integrate a custom TransactionManager.

RemoteTransactionManagerLookup
A basic, and volatile, TransactionManager if no other implementation is available. Note that this implementation has significant limitations when handling concurrent transactions and recovery.

4.7.3. Transaction Modes

TransactionMode controls how a RemoteCache interacts with the TransactionManager.

Important

Configure transaction modes on both the Data Grid server and your client application. If clients attempt to perform transactional operations on non-transactional caches, runtime exceptions can occur.

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:

NONE
The RemoteCache does not interact with the TransactionManager. This is the default mode and is non-transactional.
NON_XA
The RemoteCache interacts with the TransactionManager via Synchronization.
NON_DURABLE_XA
The RemoteCache interacts with the TransactionManager via XAResource. Recovery capabilities are disabled.
FULL_XA
The RemoteCache interacts with the TransactionManager via XAResource. Recovery capabilities are enabled. Invoke the XaResource.recover() method to retrieve transactions to recover.

4.7.4. Detecting Conflicts with Transactions

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.

Note

Transactions use versions to detect changes instead of checking value equality.

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

  • If true, the TransactionManager fetches the most recent value from the server before performing write operations. However, the forceReturnValue parameter applies only to write operations that access the key for the first time.
  • If false, the TransactionManager does not fetch the most recent value from the server before performing write operations.
Note

This parameter does not affect conditional write operations such as replace or putIfAbsent because they require the most recent value.

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

Transaction 1 (TX1)

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

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

Transaction 2 (TX2)

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".

  • If forceReturnValue = true, the cache.put() operation fetches the value for "k" from the server in both TX1 and TX2. The transaction that acquires the lock for "k" first then commits. The other transaction rolls back during the commit phase because the transaction can detect that "k" has a value other than "v".
  • If forceReturnValue = false, the cache.put() operation does not fetch the value for "k" from the server and returns null. Both TX1 and TX2 can successfully commit, which results in a conflict. This occurs because neither transaction can detect that the initial value of "k" changed.

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

Transaction 1 (TX1)

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

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

Transaction 2 (TX2)

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.

4.7.5. Using the Configured Transaction Manager and Transaction Mode

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.remoteCache("my-cache")
    .transactionManagerLookup(RemoteTransactionManagerLookup.getInstance())
    .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();
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