1. The Cache API

1.1. The Cache interface

Infinispan’s Caches are manipulated through the Cache interface.

A Cache exposes simple methods for adding, retrieving and removing entries, including atomic mechanisms exposed by the JDK’s ConcurrentMap interface. Based on the cache mode used, invoking these methods will trigger a number of things to happen, potentially even including replicating an entry to a remote node or looking up an entry from a remote node, or potentially a cache store.

For simple usage, using the Cache API should be no different from using the JDK Map API, and hence migrating from simple in-memory caches based on a Map to Infinispan’s Cache should be trivial.

1.1.1. Performance Concerns of Certain Map Methods

Certain methods exposed in Map have certain performance consequences when used with Infinispan, such as size() , values() , keySet() and entrySet() . Specific methods on the keySet, values and entrySet are fine for use please see their Javadoc for further details.

Attempting to perform these operations globally would have large performance impact as well as become a scalability bottleneck. As such, these methods should only be used for informational or debugging purposes only.

It should be noted that using certain flags with the withFlags method can mitigate some of these concerns, please check each method’s documentation for more details.

1.1.2. Mortal and Immortal Data

Further to simply storing entries, Infinispan’s cache API allows you to attach mortality information to data. For example, simply using put(key, value) would create an immortal entry, i.e., an entry that lives in the cache forever, until it is removed (or evicted from memory to prevent running out of memory). If, however, you put data in the cache using put(key, value, lifespan, timeunit) , this creates a mortal entry, i.e., an entry that has a fixed lifespan and expires after that lifespan.

In addition to lifespan , Infinispan also supports maxIdle as an additional metric with which to determine expiration. Any combination of lifespans or maxIdles can be used.

1.1.3. putForExternalRead operation

Infinispan’s Cache class contains a different 'put' operation called putForExternalRead . This operation is particularly useful when Infinispan is used as a temporary cache for data that is persisted elsewhere. Under heavy read scenarios, contention in the cache should not delay the real transactions at hand, since caching should just be an optimization and not something that gets in the way.

To achieve this, putForExternalRead acts as a put call that only operates if the key is not present in the cache, and fails fast and silently if another thread is trying to store the same key at the same time. In this particular scenario, caching data is a way to optimise the system and it’s not desirable that a failure in caching affects the on-going transaction, hence why failure is handled differently. putForExternalRead is considered to be a fast operation because regardless of whether it’s successful or not, it doesn’t wait for any locks, and so returns to the caller promptly.

To understand how to use this operation, let’s look at basic example. Imagine a cache of Person instances, each keyed by a PersonId , whose data originates in a separate data store. The following code shows the most common pattern of using putForExternalRead within the context of this example:

// Id of the person to look up, provided by the application
PersonId id = ...;

// Get a reference to the cache where person instances will be stored
Cache<PersonId, Person> cache = ...;

// First, check whether the cache contains the person instance
// associated with with the given id
Person cachedPerson = cache.get(id);

if (cachedPerson == null) {
   // The person is not cached yet, so query the data store with the id
   Person person = dataStore.lookup(id);

   // Cache the person along with the id so that future requests can
   // retrieve it from memory rather than going to the data store
   cache.putForExternalRead(id, person);
} else {
   // The person was found in the cache, so return it to the application
   return cachedPerson;

Please note that putForExternalRead should never be used as a mechanism to update the cache with a new Person instance originating from application execution (i.e. from a transaction that modifies a Person’s address). When updating cached values, please use the standard put operation, otherwise the possibility of caching corrupt data is likely.

1.2. The AdvancedCache interface

In addition to the simple Cache interface, Infinispan offers an AdvancedCache interface, geared towards extension authors. The AdvancedCache offers the ability to inject custom interceptors, access certain internal components and to apply flags to alter the default behavior of certain cache methods. The following code snippet depicts how an AdvancedCache can be obtained:

AdvancedCache advancedCache = cache.getAdvancedCache();

1.2.1. Flags

Flags are applied to regular cache methods to alter the behavior of certain methods. For a list of all available flags, and their effects, see the Flag enumeration. Flags are applied using AdvancedCache.withFlags() . This builder method can be used to apply any number of flags to a cache invocation, for example:

advancedCache.withFlags(Flag.CACHE_MODE_LOCAL, Flag.SKIP_LOCKING)
   .put("hello", "world");

1.2.2. Custom Interceptors

The AdvancedCache interface also offers advanced developers a mechanism with which to attach custom interceptors. Custom interceptors allow developers to alter the behavior of the cache API methods, and the AdvancedCache interface allows developers to attach these interceptors programmatically, at run-time. See the AdvancedCache Javadocs for more details.

1.3. Listeners and Notifications

Infinispan offers a listener API, where clients can register for and get notified when events take place. This annotation-driven API applies to 2 different levels: cache level events and cache manager level events.

Events trigger a notification which is dispatched to listeners. Listeners are simple POJO s annotated with @Listener and registered using the methods defined in the Listenable interface.

Both Cache and CacheManager implement Listenable, which means you can attach listeners to either a cache or a cache manager, to receive either cache-level or cache manager-level notifications.

For example, the following class defines a listener to print out some information every time a new entry is added to the cache, in a non blocking fashion:

public class PrintWhenAdded {
  Queue<CacheEntryCreatedEvent> events = new ConcurrentLinkedQueue<>();

  public CompletionStage<Void> print(CacheEntryCreatedEvent event) {
    return null;


For more comprehensive examples, please see the Javadocs for @Listener.

1.3.1. Cache-level notifications

Cache-level events occur on a per-cache basis, and by default are only raised on nodes where the events occur. Note in a distributed cache these events are only raised on the owners of data being affected. Examples of cache-level events are entries being added, removed, modified, etc. These events trigger notifications to listeners registered to a specific cache.

Please see the Javadocs on the org.infinispan.notifications.cachelistener.annotation package for a comprehensive list of all cache-level notifications, and their respective method-level annotations.

Please refer to the Javadocs on the org.infinispan.notifications.cachelistener.annotation package for the list of cache-level notifications available in Infinispan.
Cluster Listeners

The cluster listeners should be used when it is desirable to listen to the cache events on a single node.

To do so all that is required is set to annotate your listener as being clustered.

@Listener (clustered = true)
public class MyClusterListener { .... }

There are some limitations to cluster listeners from a non clustered listener.

  1. A cluster listener can only listen to @CacheEntryModified, @CacheEntryCreated, @CacheEntryRemoved and @CacheEntryExpired events. Note this means any other type of event will not be listened to for this listener.

  2. Only the post event is sent to a cluster listener, the pre event is ignored.

Event filtering and conversion

All applicable events on the node where the listener is installed will be raised to the listener. It is possible to dynamically filter what events are raised by using a KeyFilter (only allows filtering on keys) or CacheEventFilter (used to filter for keys, old value, old metadata, new value, new metadata, whether command was retried, if the event is before the event (ie. isPre) and also the command type).

The example here shows a simple KeyFilter that will only allow events to be raised when an event modified the entry for the key Only Me.

public class SpecificKeyFilter implements KeyFilter<String> {
    private final String keyToAccept;

    public SpecificKeyFilter(String keyToAccept) {
      if (keyToAccept == null) {
        throw new NullPointerException();
      this.keyToAccept = keyToAccept;

    boolean accept(String key) {
      return keyToAccept.equals(key);

cache.addListener(listener, new SpecificKeyFilter("Only Me"));

This can be useful when you want to limit what events you receive in a more efficient manner.

There is also a CacheEventConverter that can be supplied that allows for converting a value to another before raising the event. This can be nice to modularize any code that does value conversions.

The mentioned filters and converters are especially beneficial when used in conjunction with a Cluster Listener. This is because the filtering and conversion is done on the node where the event originated and not on the node where event is listened to. This can provide benefits of not having to replicate events across the cluster (filter) or even have reduced payloads (converter).
Initial State Events

When a listener is installed it will only be notified of events after it is fully installed.

It may be desirable to get the current state of the cache contents upon first registration of listener by having an event generated of type @CacheEntryCreated for each element in the cache. Any additionally generated events during this initial phase will be queued until appropriate events have been raised.

This only works for clustered listeners at this time. ISPN-4608 covers adding this for non clustered listeners.
Duplicate Events

It is possible in a non transactional cache to receive duplicate events. This is possible when the primary owner of a key goes down while trying to perform a write operation such as a put.

Infinispan internally will rectify the put operation by sending it to the new primary owner for the given key automatically, however there are no guarantees in regards to if the write was first replicated to backups. Thus more than 1 of the following write events (CacheEntryCreatedEvent, CacheEntryModifiedEvent & CacheEntryRemovedEvent) may be sent on a single operation.

If more than one event is generated Infinispan will mark the event that it was generated by a retried command to help the user to know when this occurs without having to pay attention to view changes.

public class MyRetryListener {
  public void entryModified(CacheEntryModifiedEvent event) {
    if (event.isCommandRetried()) {
      // Do something

Also when using a CacheEventFilter or CacheEventConverter the EventType contains a method isRetry to tell if the event was generated due to retry.

1.3.2. Cache manager-level notifications

Cache manager-level events occur on a cache manager. These too are global and cluster-wide, but involve events that affect all caches created by a single cache manager. Examples of cache manager-level events are nodes joining or leaving a cluster, or caches starting or stopping.

Please see the Javadocs on the org.infinispan.notifications.cachemanagerlistener.annotation package for a comprehensive list of all cache manager-level notifications, and their respective method-level annotations.

1.3.3. Synchronicity of events

By default, all async notifications are dispatched in the notification thread pool. Sync notifications will delay the operation from continuing until the listener method completes or the CompletionStage completes (the former causing the thread to block). Alternatively, you could annotate your listener as asynchronous in which case the operation will continue immediately, while the notification is completed asynchronously on the notification thread pool. To do this, simply annotate your listener such:

Asynchronous Listener

@Listener (sync = false)
public class MyAsyncListener {
   void listen(CacheEntryCreatedEvent event) { }

Blocking Synchronous Listener

public class MySyncListener {
   void listen(CacheEntryCreatedEvent event) { }

Non-Blocking Listener

public class MyNonBlockingListener {
   CompletionStage<Void> listen(CacheEntryCreatedEvent event) { }
Asynchronous thread pool

To tune the thread pool used to dispatch such asynchronous notifications, use the <listener-executor /> XML element in your configuration file.

1.4. Asynchronous API

In addition to synchronous API methods like Cache.put() , Cache.remove() , etc., Infinispan also has an asynchronous, non-blocking API where you can achieve the same results in a non-blocking fashion.

These methods are named in a similar fashion to their blocking counterparts, with "Async" appended.  E.g., Cache.putAsync() , Cache.removeAsync() , etc.  These asynchronous counterparts return a Future containing the actual result of the operation.

For example, in a cache parameterized as Cache<String, String>, Cache.put(String key, String value) returns a String. Cache.putAsync(String key, String value) would return a Future<String>.

1.4.1. Why use such an API?

Non-blocking APIs are powerful in that they provide all of the guarantees of synchronous communications - with the ability to handle communication failures and exceptions - with the ease of not having to block until a call completes.  This allows you to better harness parallelism in your system.  For example:

Set<Future<?>> futures = new HashSet<Future<?>>();
futures.add(cache.putAsync(key1, value1)); // does not block
futures.add(cache.putAsync(key2, value2)); // does not block
futures.add(cache.putAsync(key3, value3)); // does not block

// the remote calls for the 3 puts will effectively be executed
// in parallel, particularly useful if running in distributed mode
// and the 3 keys would typically be pushed to 3 different nodes
// in the cluster

// check that the puts completed successfully
for (Future<?> f: futures) f.get();

1.4.2. Which processes actually happen asynchronously?

There are 4 things in Infinispan that can be considered to be on the critical path of a typical write operation. These are, in order of cost:

  • network calls

  • marshalling

  • writing to a cache store (optional)

  • locking

As of Infinispan 4.0, using the async methods will take the network calls and marshalling off the critical path.  For various technical reasons, writing to a cache store and acquiring locks, however, still happens in the caller’s thread.  In future, we plan to take these offline as well.  See this developer mail list thread about this topic.

1.4.3. Notifying futures

Strictly, these methods do not return JDK Futures, but rather a sub-interface known as a NotifyingFuture .  The main difference is that you can attach a listener to a NotifyingFuture such that you could be notified when the future completes.  Here is an example of making use of a notifying future:

FutureListener futureListener = new FutureListener() {

   public void futureDone(Future future) {
      try {
      } catch (Exception e) {
         // Future did not complete successfully
cache.putAsync("key", "value").attachListener(futureListener);

1.4.4. Further reading

The Javadocs on the Cache interface has some examples on using the asynchronous API, as does this article by Manik Surtani introducing the API.

1.5. Invocation Flags

An important aspect of getting the most of Infinispan is the use of per-invocation flags in order to provide specific behaviour to each particular cache call. By doing this, some important optimizations can be implemented potentially saving precious time and network resources. One of the most popular usages of flags can be found right in Cache API, underneath the putForExternalRead() method which is used to load an Infinispan cache with data read from an external resource. In order to make this call efficient, Infinispan basically calls a normal put operation passing the following flags: FAIL_SILENTLY , FORCE_ASYNCHRONOUS , ZERO_LOCK_ACQUISITION_TIMEOUT

What Infinispan is doing here is effectively saying that when putting data read from external read, it will use an almost-zero lock acquisition time and that if the locks cannot be acquired, it will fail silently without throwing any exception related to lock acquisition. It also specifies that regardless of the cache mode, if the cache is clustered, it will replicate asynchronously and so won’t wait for responses from other nodes. The combination of all these flags make this kind of operation very efficient, and the efficiency comes from the fact this type of putForExternalRead calls are used with the knowledge that client can always head back to a persistent store of some sorts to retrieve the data that should be stored in memory. So, any attempt to store the data is just a best effort and if not possible, the client should try again if there’s a cache miss.

1.5.1. Examples

If you want to use these or any other flags available, which by the way are described in detail the Flag enumeration , you simply need to get hold of the advanced cache and add the flags you need via the withFlags() method call. For example:

Cache cache = ...
   .put("local", "only");

It’s worth noting that these flags are only active for the duration of the cache operation. If the same flags need to be used in several invocations, even if they’re in the same transaction, withFlags() needs to be called repeatedly. Clearly, if the cache operation is to be replicated in another node, the flags are carried over to the remote nodes as well.

Suppressing return values from a put() or remove()

Another very important use case is when you want a write operation such as put() to not return the previous value. To do that, you need to use two flags to make sure that in a distributed environment, no remote lookup is done to potentially get previous value, and if the cache is configured with a cache loader, to avoid loading the previous value from the cache store. You can see these two flags in action in the following example:

Cache cache = ...
   .put("local", "only")

For more information, please check the Flag enumeration javadoc.

2. Functional Map API

Infinispan 8 introduces a new experimental API for interacting with your data which takes advantage of the functional programming additions and improved asynchronous programming capabilities available in Java 8.

Infinispan’s Functional Map API is a distilled map-like asynchronous API which uses functions to interact with data.

2.1. Asynchronous and Lazy

Being an asynchronous API, all methods that return a single result, return a CompletableFuture which wraps the result, so you can use the resources of your system more efficiently by having the possibility to receive callbacks when the CompletableFuture has completed, or you can chain or compose them with other CompletableFuture.

For those operations that return multiple results, the API returns instances of a ​Traversable interface which offers a lazy pull-style API for working with multiple results. ​Traversable ,​ being a lazy pull-style API, can still be asynchronous underneath since the user can decide to work on the traversable at a later stage, and the ​Traversable implementation itself can decide when to compute those results.

2.2. Function transparency

Since the content of the functions is transparent to Infinispan, the API has been split into 3 interfaces for read­-only ( R​eadOnlyMap )​, read­-write ( R​eadWriteMap )​ and write­-only ( W​riteOnlyMap )​ operations respectively, in order to provide hints to the Infinispan internals on the type of work needed to support functions.

2.3. Constructing Functional Maps

To construct any of the read-only, write-only or read-write map instances, an Infinispan AdvancedCache is required, which is retrieved from the Cache Manager, and using the AdvancedCache , static method factory methods are used to create R​eadOnlyMap , R​eadWriteMap or W​riteOnlyMap

import org.infinispan.commons.api.functional.FunctionalMap.*;
import org.infinispan.functional.impl.*;

AdvancedCache<String, String> cache = ...

FunctionalMapImpl<String, String> functionalMap = FunctionalMapImpl.create(cache);
ReadOnlyMap<String, String> readOnlyMap = ReadOnlyMapImpl.create(functionalMap);
WriteOnlyMap<String, String> writeOnlyMap = WriteOnlyMapImpl.create(functionalMap);
ReadWriteMap<String, String> readWriteMap = ReadWriteMapImpl.create(functionalMap);
At this stage, the Functional Map API is experimental and hence the way FunctionalMap, ReadOnlyMap, WriteOnlyMap and ReadWriteMap are constructed is temporary.

2.4. Read-Only Map API

Read-only operations have the advantage that no locks are acquired for the duration of the operation. Here’s an example on how to the equivalent operation for Map.get(K):

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;

ReadOnlyMap<String, String> readOnlyMap = ...
CompletableFuture<Optional<String>> readFuture = readOnlyMap.eval("key1", ReadEntryView::find);

Read-only map also exposes operations to retrieve multiple keys in one go:

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;
import org.infinispan.commons.api.functional.Traversable;

ReadOnlyMap<String, String> readOnlyMap = ...

Set<String> keys = new HashSet<>(Arrays.asList("key1", "key2"));
Traversable<String> values = readOnlyMap.evalMany(keys, ReadEntryView::get);

Finally, read-only map also exposes methods to read all existing keys as well as entries, which include both key and value information.

2.4.1. Read-Only Entry View

The function parameters for read-only maps provide the user with a read-only entry view to interact with the data in the cache, which include these operations:

  • key() method returns the key for which this function is being executed.

  • find() returns a Java 8 Optional wrapping the value if present, otherwise it returns an empty optional. Unless the value is guaranteed to be associated with the key, it’s recommended to use find() to verify whether there’s a value associated with the key.

  • get() returns the value associated with the key. If the key has no value associated with it, calling get() throws a NoSuchElementException. get() can be considered as a shortcut of ReadEntryView.find().get() which should be used only when the caller has guarantees that there’s definitely a value associated with the key.

  • findMetaParam(Class<T> type) allows metadata parameter information associated with the cache entry to be looked up, for example: entry lifespan, last accessed time…​etc. See Metadata Parameter Handling to find out more.

2.5. Write-Only Map API

Write-only operations include operations that insert or update data in the cache and also removals. Crucially, a write-only operation does not attempt to read any previous value associated with the key. This is an important optimization since that means neither the cluster nor any persistence stores will be looked up to retrieve previous values. In the main Infinispan Cache, this kind of optimization was achieved using a local-only per-invocation flag, but the use case is so common that in this new functional API, this optimization is provided as a first-class citizen.

Using write-only map API , an operation equivalent to javax.cache.Cache (JCache) 's void returning put can be achieved this way, followed by an attempt to read the stored value using the read-only map API:

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;

WriteOnlyMap<String, String> writeOnlyMap = ...
ReadOnlyMap<String, String> readOnlyMap = ...

CompletableFuture<Void> writeFuture = writeOnlyMap.eval("key1", "value1",
   (v, view) -> view.set(v));
CompletableFuture<String> readFuture = writeFuture.thenCompose(r ->
   readOnlyMap.eval("key1", ReadEntryView::get));

Multiple key/value pairs can be stored in one go using evalMany API:

WriteOnlyMap<String, String> writeOnlyMap = ...

Map<K, String> data = new HashMap<>();
data.put("key1", "value1");
data.put("key2", "value2");
CompletableFuture<Void> writerAllFuture = writeOnlyMap.evalMany(data, (v, view) -> view.set(v));
writerAllFuture.thenAccept(x -> "Write completed");

To remove all contents of the cache, there are two possibilities with different semantics. If using evalAll each cached entry is iterated over and the function is called with that entry’s information. Using this method also results in listeners being invoked.

WriteOnlyMap<String, String> writeOnlyMap = ...

CompletableFuture<Void> removeAllFuture = writeOnlyMap.evalAll(WriteEntryView::remove);
removeAllFuture.thenAccept(x -> "All entries removed");

The alternative way to remove all entries is to call truncate operation which clears the entire cache contents in one go without invoking any listeners and is best-effort:

WriteOnlyMap<String, String> writeOnlyMap = ...

CompletableFuture<Void> truncateFuture = writeOnlyMap.truncate();
truncateFuture.thenAccept(x -> "Cache contents cleared");

2.5.1. Write-Only Entry View

The function parameters for write-only maps provide the user with a write-only entry view to modify the data in the cache, which include these operations:

  • set(V, MetaParam.Writable…​) method allows for a new value to be associated with the cache entry for which this function is executed, and it optionally takes zero or more metadata parameters to be stored along with the value. See Metadata Parameter Handling for more information.

  • remove() method removes the cache entry, including both value and metadata parameters associated with this key.

2.6. Read-Write Map API

The final type of operations we have are read­write operations, and within this category CAS-like (Compare­And­Swap) operations can be found. This type of operations require previous value associated with the key to be read and for locks to be acquired before executing the function. The vast majority of operations within ConcurrentMap and JCache APIs fall within this category, and they can easily be implemented using the read-write map API . Moreover, with read-write map API , you can make CAS­like comparisons not only based on value equality but based on metadata parameter equality such as version information, and you can send back previous value or boolean instances to signal whether the CAS­like comparison succeeded.

Implementing a write operation that returns the previous value associated with the cache entry is easy to achieve with the read-write map API:

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;

ReadWriteMap<String, String> readWriteMap = ...

CompletableFuture<Optional<String>> readWriteFuture = readWriteMap.eval("key1", "value1",
   (v, view) -> {
      Optional<V> prev = rw.find();
      return prev;

ConcurrentMap.replace(K, V, V) is a replace function that compares the value present in the map and if it’s equals to the value passed in as first parameter, the second value is stored, returning a boolean indicating whether the replace was successfully completed. This operation can easily be implemented using the read-write map API:

ReadWriteMap<String, String> readWriteMap = ...

String oldValue = "old-value";
CompletableFuture<Boolean> replaceFuture = readWriteMap.eval("key1", "value1", (v, view) -> {
   return view.find().map(prev -> {
      if (prev.equals(oldValue)) {
         return true; // previous value present and equals to the expected one
      return false; // previous value associated with key does not match
   }).orElse(false); // no value associated with this key
replaceFuture.thenAccept(replaced -> System.out.printf("Value was replaced? %s%n", replaced));
The function in the example above captures oldValue which is an external value to the function which is valid use case.

Read-write map API contains evalMany and evalAll operations which behave similar to the write-only map offerings, except that they enable previous value and metadata parameters to be read.

2.6.1. Read-Write Entry View

The function parameters for read-write maps provide the user with the possibility to query the information associated with the key, including value and metadata parameters, and the user can also use this read-write entry view to modify the data in the cache.

The operations are exposed by read-write entry views are a union of the operations exposed by read-only entry views and write-only entry views.

2.7. Metadata Parameter Handling

Metadata parameters provide extra information about the cache entry, such as version information, lifespan, last accessed/used time…​etc. Some of these can be provided by the user, e.g. version, lifespan…​etc, but some others are computed internally and can only be queried, e.g. last accessed/used time.

The functional map API provides a flexible way to store metadata parameters along with an cache entry. To be able to store a metadata parameter, it must extend MetaParam.Writable interface, and implement the methods to allow the internal logic to extra the data. Storing is done via the set(V, MetaParam.Writable…​) method in the write-only entry view or read-write entry view function parameters.

Querying metadata parameters is available via the findMetaParam(Class) method available via read-write entry view or read-only entry views or function parameters.

Here is an example showing how to store metadata parameters and how to query them:

import java.time.Duration;
import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;
import org.infinispan.commons.api.functional.MetaParam.*;

WriteOnlyMap<String, String> writeOnlyMap = ...
ReadOnlyMap<String, String> readOnlyMap = ...

CompletableFuture<Void> writeFuture = writeOnlyMap.eval("key1", "value1",
   (v, view) -> view.set(v, new MetaLifespan(Duration.ofHours(1).toMillis())));
CompletableFuture<MetaLifespan> readFuture = writeFuture.thenCompose(r ->
   readOnlyMap.eval("key1", view -> view.findMetaParam(MetaLifespan.class).get()));

If the metadata parameter is generic, for example MetaEntryVersion<T> , retrieving the metadata parameter along with a specific type can be tricky if using .class static helper in a class because it does not return a Class<T> but only Class, and hence any generic information in the class is lost:

ReadOnlyMap<String, String> readOnlyMap = ...

CompletableFuture<String> readFuture = readOnlyMap.eval("key1", view -> {
   // If caller depends on the typed information, this is not an ideal way to retrieve it
   // If the caller does not depend on the specific type, this works just fine.
   Optional<MetaEntryVersion> version = view.findMetaParam(MetaEntryVersion.class);
   return view.get();

When generic information is important the user can define a static helper method that coerces the static class retrieval to the type requested, and then use that helper method in the call to findMetaParam:

class MetaEntryVersion<T> implements MetaParam.Writable<EntryVersion<T>> {
   public static <T> T type() { return (T) MetaEntryVersion.class; }

ReadOnlyMap<String, String> readOnlyMap = ...

CompletableFuture<String> readFuture = readOnlyMap.eval("key1", view -> {
   // The caller wants guarantees that the metadata parameter for version is numeric
   // e.g. to query the actual version information
   Optional<MetaEntryVersion<Long>> version = view.findMetaParam(MetaEntryVersion.type());
   return view.get();

Finally, users are free to create new instances of metadata parameters to suit their needs. They are stored and retrieved in the very same way as done for the metadata parameters already provided by the functional map API.

2.8. Invocation Parameter

Per-invocation parameters are applied to regular functional map API calls to alter the behaviour of certain aspects. Adding per invocation parameters is done using the withParams(Param<?>…​) method.

Param.FutureMode tweaks whether a method returning a CompletableFuture will span a thread to invoke the method, or instead will use the caller thread. By default, whenever a call is made to a method returning a CompletableFuture , a separate thread will be span to execute the method asynchronously. However, if the caller will immediately block waiting for the CompletableFuture to complete, spanning a different thread is wasteful, and hence Param.FutureMode.COMPLETED can be passed as per-invocation parameter to avoid creating that extra thread. Example:

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;
import org.infinispan.commons.api.functional.Param.*;

ReadOnlyMap<String, String> readOnlyMap = ...
ReadOnlyMap<String, String> readOnlyMapCompleted = readOnlyMap.withParams(FutureMode.COMPLETED);
Optional<String> readFuture = readOnlyMapCompleted.eval("key1", ReadEntryView::find).get();

Param.PersistenceMode controls whether a write operation will be propagated to a persistence store. The default behaviour is for all write-operations to be propagated to the persistence store if the cache is configured with a persistence store. By passing PersistenceMode.SKIP as parameter, the write operation skips the persistence store and its effects are only seen in the in-memory contents of the cache. PersistenceMode.SKIP can be used to implement an Cache.evict() method which removes data from memory but leaves the persistence store untouched:

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;
import org.infinispan.commons.api.functional.Param.*;

WriteOnlyMap<String, String> writeOnlyMap = ...
WriteOnlyMap<String, String> skiPersistMap = writeOnlyMap.withParams(PersistenceMode.SKIP);
CompletableFuture<Void> removeFuture = skiPersistMap.eval("key1", WriteEntryView::remove);

Note that there’s no need for another PersistenceMode option to skip reading from the persistence store, because a write operation can skip reading previous value from the store by calling a write-only operation via the WriteOnlyMap.

Finally, new Param implementations are normally provided by the functional map API since they tweak how the internal logic works. So, for the most part of users, they should limit themselves to using the Param instances exposed by the API. The exception to this rule would be advanced users who decide to add new interceptors to the internal stack. These users have the ability to query these parameters within the interceptors.

2.9. Functional Listeners

The functional map offers a listener API, where clients can register for and get notified when events take place. These notifications are post-event, so that means the events are received after the event has happened.

The listeners that can be registered are split into two categories: write listeners and read-write listeners.

2.9.1. Write Listeners

Write listeners enable user to register listeners for any cache entry write events that happen in either a read-write or write-only functional map.

Listeners for write events cannot distinguish between cache entry created and cache entry modify/update events because they don’t have access to the previous value. All they know is that a new non-null entry has been written.

However, write event listeners can distinguish between entry removals and cache entry create/modify-update events because they can query what the new entry’s value via ReadEntryView.find() method.

Adding a write listener is done via the WriteListeners interface which is accessible via both ReadWriteMap.listeners() and WriteOnlyMap.listeners() method.

A write listener implementation can be defined either passing a function to onWrite(Consumer<ReadEntryView<K, V>>) method, or passing a WriteListener implementation to add(WriteListener<K, V>) method. Either way, all these methods return an AutoCloseable instance that can be used to de-register the function listener:

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;
import org.infinispan.commons.api.functional.Listeners.WriteListeners.WriteListener;

WriteOnlyMap<String, String> woMap = ...

AutoCloseable writeFunctionCloseHandler = woMap.listeners().onWrite(written -> {
   // `written` is a ReadEntryView of the written entry
   System.out.printf("Written: %s%n", written.get());
AutoCloseable writeCloseHanlder = woMap.listeners().add(new WriteListener<String, String>() {
   public void onWrite(ReadEntryView<K, V> written) {
      System.out.printf("Written: %s%n", written.get());

// Either wrap handler in a try section to have it auto close...
try(writeFunctionCloseHandler) {
   // Write entries using read-write or write-only functional map API
// Or close manually

2.9.2. Read-Write Listeners

Read-write listeners enable users to register listeners for cache entry created, modified and removed events, and also register listeners for any cache entry write events.

Entry created, modified and removed events can only be fired when these originate on a read-write functional map, since this is the only one that guarantees that the previous value has been read, and hence the differentiation between create, modified and removed can be fully guaranteed.

Adding a read-write listener is done via the ReadWriteListeners interface which is accessible via ReadWriteMap.listeners() method.

If interested in only one of the event types, the simplest way to add a listener is to pass a function to either onCreate , onModify or onRemove methods. All these methods return an AutoCloseable instance that can be used to de-register the function listener:

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;

ReadWriteMap<String, String> rwMap = ...
AutoCloseable createClose = rwMap.listeners().onCreate(created -> {
   // `created` is a ReadEntryView of the created entry
   System.out.printf("Created: %s%n", created.get());
AutoCloseable modifyClose = rwMap.listeners().onModify((before, after) -> {
   // `before` is a ReadEntryView of the entry before update
   // `after` is a ReadEntryView of the entry after update
   System.out.printf("Before: %s%n", before.get());
   System.out.printf("After: %s%n", after.get());
AutoCloseable removeClose = rwMap.listeners().onRemove(removed -> {
   // `removed` is a ReadEntryView of the removed entry
   System.out.printf("Removed: %s%n", removed.get());
AutoCloseable writeClose = woMap.listeners().onWrite(written -> {
   // `written` is a ReadEntryView of the written entry
   System.out.printf("Written: %s%n", written.get());
// Either wrap handler in a try section to have it auto close...
try(createClose) {
   // Create entries using read-write functional map API
// Or close manually

If listening for two or more event types, it’s better to pass in an implementation of ReadWriteListener interface via the ReadWriteListeners.add() method. ReadWriteListener offers the same onCreate/onModify/onRemove callbacks with default method implementations that are empty:

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;
import org.infinispan.commons.api.functional.Listeners.ReadWriteListeners.ReadWriteListener;

ReadWriteMap<String, String> rwMap = ...
AutoCloseable readWriteClose = rwMap.listeners.add(new ReadWriteListener<String, String>() {
   public void onCreate(ReadEntryView<String, String> created) {
      System.out.printf("Created: %s%n", created.get());

   public void onModify(ReadEntryView<String, String> before, ReadEntryView<String, String> after) {
      System.out.printf("Before: %s%n", before.get());
      System.out.printf("After: %s%n", after.get());

   public void onRemove(ReadEntryView<String, String> removed) {
      System.out.printf("Removed: %s%n", removed.get());
AutoCloseable writeClose = rwMap.listeners.add(new WriteListener<String, String>() {
   public void onWrite(ReadEntryView<K, V> written) {
      System.out.printf("Written: %s%n", written.get());

// Either wrap handler in a try section to have it auto close...
try(readWriteClose) {
   // Create/update/remove entries using read-write functional map API
// Or close manually

2.10. Marshalling of Functions

Running functional map in a cluster of nodes involves marshalling and replication of the operation parameters under certain circumstances.

To be more precise, when write operations are executed in a cluster, regardless of read-write or write-only operations, all the parameters to the method and the functions are replicated to other nodes.

There are multiple ways in which a function can be marshalled. The simplest way, which is also the most costly option in terms of payload size, is to mark the function as Serializable:

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;

WriteOnlyMap<String, String> writeOnlyMap = ...

// Force a function to be Serializable
Consumer<WriteEntryView<String>> function =
   (Consumer<WriteEntryView<String>> & Serializable) wv -> wv.set("one");

CompletableFuture<Void> writeFuture = writeOnlyMap.eval("key1", function);

Infinispan provides overloads for all functional methods that make lambdas passed directly to the API serializable by default; the compiler automatically selects this overload if that’s possible. Therefore you can call

WriteOnlyMap<String, String> writeOnlyMap = ...
CompletableFuture<Void> writeFuture = writeOnlyMap.eval("key1", wv -> wv.set("one"));

without doing the cast described above.

A more economical way to marshall a function is to provide an Infinispan Externalizer for it:

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;
import org.infinispan.commons.marshall.Externalizer;
import org.infinispan.commons.marshall.SerializeFunctionWith;

WriteOnlyMap<String, String> writeOnlyMap = ...

// Force a function to be Serializable
Consumer<WriteEntryView<String>> function = new SetStringConstant<>();
CompletableFuture<Void> writeFuture = writeOnlyMap.eval("key1", function);

@SerializeFunctionWith(value = SetStringConstant.Externalizer0.class)
class SetStringConstant implements Consumer<WriteEntryView<String>> {
   public void accept(WriteEntryView<String> view) {

   public static final class Externalizer0 implements Externalizer<Object> {
      public void writeObject(ObjectOutput oo, Object o) {
         // No-op
      public Object readObject(ObjectInput input) {
         return new SetStringConstant<>();

To help users take advantage of the tiny payloads generated by Externalizer-based functions, the functional API comes with a helper class called org.infinispan.commons.marshall.MarshallableFunctions which provides marshallable functions for some of the most commonly user functions.

In fact, all the functions required to implement ConcurrentMap and JCache using the functional map API have been defined in MarshallableFunctions. For example, here is an implementation of JCache’s boolean putIfAbsent(K, V) using functional map API which can be run in a cluster:

import org.infinispan.commons.api.functional.EntryView.*;
import org.infinispan.commons.api.functional.FunctionalMap.*;
import org.infinispan.commons.marshall.MarshallableFunctions;

ReadWriteMap<String, String> readWriteMap = ...

CompletableFuture<Boolean> future = readWriteMap.eval("key1,
future.thenAccept(stored -> System.out.printf("Value was put? %s%n", stored));

2.11. Use Cases for Functional API

This new API is meant to complement existing Key/Value Infinispan API offerings, so you’ll still be able to use ConcurrentMap or JCache standard APIs if that’s what suits your use case best.

The target audience for this new API is either:

  • Distributed or persistent caching/in­memory­data­grid users that want to benefit from CompletableFuture and/or Traversable for async/lazy data grid or caching data manipulation. The clear advantage here is that threads do not need to be idle waiting for remote operations to complete, but instead these can be notified when remote operations complete and then chain them with other subsequent operations.

  • Users who want to go beyond the standard operations exposed by ConcurrentMap and JCache, for example, if you want to do a replace operation using metadata parameter equality instead of value equality, or if you want to retrieve metadata information from values and so on.

3. Encoding

Encoding is the data conversion operation done by Infinispan caches before storing data, and when reading back from storage.

3.1. Overview

Encoding allows dealing with a certain data format during API calls (map, listeners, stream, etc) while the format effectively stored is different.

The data conversions are handled by instances of org.infinispan.commons.dataconversion.Encoder :

public interface Encoder {

    * Convert data in the read/write format to the storage format.
    * @param content data to be converted, never null.
    * @return Object in the storage format.
   Object toStorage(Object content);

    * Convert from storage format to the read/write format.
    * @param content data as stored in the cache, never null.
    * @return data in the read/write format
   Object fromStorage(Object content);

     * Returns the {@link MediaType} produced by this encoder or null if the storage format is not known.
   MediaType getStorageFormat();

3.2. Default encoders

Infinispan automatically picks the Encoder depending on the cache configuration. The table below shows which internal Encoder is used for several configurations:

Mode Configuration Encoder Description




Passthrough encoder, no conversion done




Use the Infinispan internal marshaller to convert to byte[]. May delegate to the configured marshaller in the cache manager.




Use the Infinispan internal marshaller to convert to byte[], except for primitives and String.




Store byte[]s directly as received by remote clients

3.3. Overriding programmatically

Is is possible to override programmatically the encoding used for both keys and values, by calling the .withEncoding() method variants from AdvancedCache.

Example, consider the following cache configured as OFF_HEAP:

// Read and write POJO, storage will be byte[] since for
// OFF_HEAP the GlobalMarshallerEncoder is used internally:
cache.put(1, new Pojo())
Pojo value = cache.get(1)

// Get the content in its stored format by overriding
// the internal encoder with a no-op encoder (IdentityEncoder)
Cache<?,?> rawContent = cache.getAdvancedCache().withValueEncoding(IdentityEncoder.class)
byte[] marshalled = rawContent.get(1)

The override can be useful if any operation in the cache does not require decoding, such as counting number of entries, or calculating the size of byte[] of an OFF_HEAP cache.

3.4. Defining custom Encoders

A custom encoder can be registered in the EncoderRegistry.

Ensure that the registration is done in every node of the cluster, before starting the caches.

Consider a custom encoder used to compress/decompress with gzip:

public class GzipEncoder implements Encoder {

   public Object toStorage(Object content) {
      assert content instanceof String;
      return compress(content.toString());

   public Object fromStorage(Object content) {
      assert content instanceof byte[];
      return decompress((byte[]) content);

   private byte[] compress(String str) {
      try (ByteArrayOutputStream baos = new ByteArrayOutputStream();
           GZIPOutputStream gis = new GZIPOutputStream(baos)) {
         return baos.toByteArray();
      } catch (IOException e) {
         throw new RuntimeException("Unabled to compress", e);

   private String decompress(byte[] compressed) {
      try (GZIPInputStream gis = new GZIPInputStream(new ByteArrayInputStream(compressed));
           BufferedReader bf = new BufferedReader(new InputStreamReader(gis, "UTF-8"))) {
         StringBuilder result = new StringBuilder();
         String line;
         while ((line = bf.readLine()) != null) {
         return result.toString();
      } catch (IOException e) {
         throw new RuntimeException("Unable to decompress", e);

   public MediaType getStorageFormat() {
      return MediaType.parse("application/gzip");

   public boolean isStorageFormatFilterable() {
      return false;

   public short id() {
      return 10000;

It can be registered by:

GlobalComponentRegistry registry = cacheManager.getGlobalComponentRegistry();
EncoderRegistry encoderRegistry = registry.getComponent(EncoderRegistry.class);
encoderRegistry.registerEncoder(new GzipEncoder());

And then be used to write and read data from a cache:

AdvancedCache<String, String> cache = ...

// Decorate cache with the newly registered encoder, without encoding keys (IdentityEncoder)
// but compressing values
AdvancedCache<String, String> compressingCache = (AdvancedCache<String, String>) cache.withEncoding(IdentityEncoder.class, GzipEncoder.class);

// All values will be stored compressed...
compressingCache.put("297931749", "0412c789a37f5086f743255cfa693dd5");

// ... but API calls deals with String
String value = compressingCache.get("297931749");

// Bypassing the value encoder to obtain the value as it is stored
Object value = compressingCache.withEncoding(IdentityEncoder.class).get("297931749");

// value is a byte[] which is the compressed value

3.5. MediaType

A Cache can optionally be configured with a org.infinispan.commons.dataconversion.MediaType for keys and values. By describing the data format of the cache, Infinispan is able to convert data on the fly during cache operations.

The MediaType configuration is more suitable when storing binary data. When using server mode, it’s common to have a MediaType configured and clients such as REST or Hot Rod reading and writing in different formats.

The data conversion between MediaType formats are handled by instances of org.infinispan.commons.dataconversion.Transcoder

public interface Transcoder {

    * Transcodes content between two different {@link MediaType}.
    * @param content         Content to transcode.
    * @param contentType     The {@link MediaType} of the content.
    * @param destinationType The target {@link MediaType} to convert.
    * @return the transcoded content.
   Object transcode(Object content, MediaType contentType, MediaType destinationType);

    * @return all the {@link MediaType} handled by this Transcoder.
   Set<MediaType> getSupportedMediaTypes();

3.5.1. Configuration


      <key media-type="application/x-java-object; type=java.lang.Integer"/>
      <value media-type="application/xml; charset=UTF-8"/>


ConfigurationBuilder cfg = new ConfigurationBuilder();


3.5.2. Overriding the MediaType Programmatically

It’s possible to decorate the Cache with a different MediaType, allowing cache operations to be executed sending and receiving different data formats.


DefaultCacheManager cacheManager = new DefaultCacheManager();

// The cache will store POJO for keys and values
ConfigurationBuilder cfg = new ConfigurationBuilder();

cacheManager.defineConfiguration("mycache", cfg.build());

Cache<Integer, Person> cache = cacheManager.getCache("mycache");

cache.put(1, new Person("John","Doe"));

// Wraps cache using 'application/x-java-object' for keys but JSON for values
Cache<Integer, byte[]> jsonValuesCache = (Cache<Integer, byte[]>) cache.getAdvancedCache().withMediaType("application/x-java-object", "application/json");

byte[] json = jsonValuesCache.get(1);

Will return the value in JSON format:

Most Transcoders are installed when server mode is used; when using library mode, an extra dependency, org.infinispan:infinispan-server-core should be added to the project.

3.5.3. Transcoders and Encoders

Usually there will be none or only one data conversion involved in a cache operation:

  • No conversion by default on caches using in embedded or server mode;

  • Encoder based conversion for embedded caches without MediaType configured, but using OFF_HEAP or BINARY;

  • Transcoder based conversion for caches used in server mode with multiple REST and Hot Rod clients sending and receiving data in different formats. Those caches will have MediaType configured describing the storage.

But it’s possible to have both encoders and transcoders being used simultaneously for advanced use cases.

Consider an example, a cache that stores marshalled objects (with jboss marshaller) content but for security reasons a transparent encryption layer should be added in order to avoid storing "plain" data to an external store. Clients should be able to read and write data in multiple formats.

This can be achieved by configuring the cache with the the MediaType that describes the storage regardless of the encoding layer:

ConfigurationBuilder cfg = new ConfigurationBuilder();

The transparent encryption can be added by decorating the cache with a special Encoder that encrypts/decrypts with storing/retrieving, for example:

public class Scrambler implements Encoder {

   Object toStorage(Object content) {
      // Encrypt data

   Object fromStorage(Object content) {
      // Decrypt data

   MediaType getStorageFormat() {
      return "application/scrambled";


To make sure all data written to the cache will be stored encrypted, it’s necessary to decorate the cache with the Encoder above and perform all cache operations in this decorated cache:

Cache<?,?> secureStorageCache = cache.getAdvancedCache().withEncoding(Scrambler.class).put(k,v);

The capability of reading data in multiple formats can be added by decorating the cache with the desired MediaType:

// Obtain a stream of values in XML format from the secure cache

Internally, Infinispan will first apply the encoder fromStorage operation to obtain the entries, that will be in "application/x-jboss-marshalling" format and then apply a successive conversion to "application/xml" by using the adequate Transcoder.

4. The Embedded CacheManager

The CacheManager is Infinispan’s main entry point. You use a CacheManager to

  • configure and obtain caches

  • manage and monitor your nodes

  • execute code across a cluster

  • more…​

Depending on whether you are embedding Infinispan in your application or you are using it remotely, you will be dealing with either an EmbeddedCacheManager or a RemoteCacheManager. While they share some methods and properties, be aware that there are semantic differences between them. The following chapters focus mostly on the embedded implementation.

CacheManagers are heavyweight objects, and we foresee no more than one CacheManager being used per JVM (unless specific setups require more than one; but either way, this would be a minimal and finite number of instances).

The simplest way to create a CacheManager is:

EmbeddedCacheManager manager = new DefaultCacheManager();

which starts the most basic, local mode, non-clustered cache manager with no caches. CacheManagers have a lifecycle and the default constructors also call start(). Overloaded versions of the constructors are available, that do not start the CacheManager, although keep in mind that CacheManagers need to be started before they can be used to create Cache instances.

Once constructed, CacheManagers should be made available to any component that require to interact with it via some form of application-wide scope such as JNDI, a ServletContext or via some other mechanism such as an IoC container.

When you are done with a CacheManager, you must stop it so that it can release its resources: manager.stop();

This will ensure all caches within its scope are properly stopped, thread pools are shutdown. If the CacheManager was clustered it will also leave the cluster gracefully.

4.1. Obtaining caches

After you configure the CacheManager, you can obtain and control caches.

Invoke the getCache() method to obtain caches, as follows:

Cache<String, String> myCache = manager.getCache("myCache");

The preceding operation creates a cache named myCache, if it does not already exist, and returns it.

Using the getCache() method creates the cache only on the node where you invoke the method. In other words, it performs a local operation that must be invoked on each node across the cluster. Typically, applications deployed across multiple nodes obtain caches during initialization to ensure that caches are symmetric and exist on each node.

Invoke the createCache() method to create caches dynamically across the entire cluster, as follows:

Cache<String, String> myCache = manager.administration().createCache("myCache", "myTemplate");

The preceding operation also automatically creates caches on any nodes that subsequently join the cluster.

Caches that you create with the createCache() method are ephemeral by default. If the entire cluster shuts down, the cache is not automatically created again when it restarts.

Use the PERMANENT flag to ensure that caches can survive restarts, as follows:

Cache<String, String> myCache = manager.administration().withFlags(AdminFlag.PERMANENT).createCache("myCache", "myTemplate");

For the PERMANENT flag to take effect, you must enable global state and set a configuration storage provider.

For more information about configuration storage providers, see GlobalStateConfigurationBuilder#configurationStorage().

4.2. Clustering Information

The EmbeddedCacheManager has quite a few methods to provide information as to how the cluster is operating. The following methods only really make sense when being used in a clustered environment (that is when a Transport is configured).

4.3. Member Information

When you are using a cluster it is very important to be able to find information about membership in the cluster including who is the owner of the cluster.

The getMembers() method returns all of the nodes in the current cluster.

The getCoordinator() method will tell you which one of the members is the coordinator of the cluster. For most intents you shouldn’t need to care who the coordinator is. You can use isCoordinator() method directly to see if the local node is the coordinator as well.

4.4. Other methods

This method provides you access to the underlying Transport that is used to send messages to other nodes. In most cases a user wouldn’t ever need to go to this level, but if you want to get Transport specific information (in this case JGroups) you can use this mechanism.

The stats provided here are coalesced from all of the active caches in this manager. These stats can be useful to see if there is something wrong going on with your cluster overall.

5. Locking and Concurrency

Infinispan makes use of multi-versioned concurrency control (MVCC) - a concurrency scheme popular with relational databases and other data stores. MVCC offers many advantages over coarse-grained Java synchronization and even JDK Locks for access to shared data, including:

  • allowing concurrent readers and writers

  • readers and writers do not block one another

  • write skews can be detected and handled

  • internal locks can be striped

5.1. Locking implementation details

Infinispan’s MVCC implementation makes use of minimal locks and synchronizations, leaning heavily towards lock-free techniques such as compare-and-swap and lock-free data structures wherever possible, which helps optimize for multi-CPU and multi-core environments.

In particular, Infinispan’s MVCC implementation is heavily optimized for readers. Reader threads do not acquire explicit locks for entries, and instead directly read the entry in question.

Writers, on the other hand, need to acquire a write lock. This ensures only one concurrent writer per entry, causing concurrent writers to queue up to change an entry.

To allow concurrent reads, writers make a copy of the entry they intend to modify, by wrapping the entry in an MVCCEntry. This copy isolates concurrent readers from seeing partially modified state. Once a write has completed, MVCCEntry.commit() will flush changes to the data container and subsequent readers will see the changes written.

5.1.1. How does it work in clustered caches?

In clustered caches, each key has a node responsible to lock the key. This node is called primary owner.

Non Transactional caches
  1. The write operation is sent to the primary owner of the key.

  2. The primary owner tries to lock the key.

    1. If it succeeds, it forwards the operation to the other owners;

    2. Otherwise, an exception is thrown.

If the operation is conditional and it fails on the primary owner, it is not forwarded to the other owners.
If the operation is executed locally in the primary owner, the first step is skipped.

5.1.2. Transactional caches

The transactional cache supports optimistic and pessimistic locking mode. Refer to Transaction Locking for more information.

5.1.3. Isolation levels

Isolation level affects what transactions can read when running concurrently with other transaction. Refer to Isolation Levels for more information.

5.1.4. The LockManager

The LockManager is a component that is responsible for locking an entry for writing. The LockManager makes use of a LockContainer to locate/hold/create locks. LockContainers come in two broad flavours, with support for lock striping and with support for one lock per entry.

5.1.5. Lock striping

Lock striping entails the use of a fixed-size, shared collection of locks for the entire cache, with locks being allocated to entries based on the entry’s key’s hash code. Similar to the way the JDK’s ConcurrentHashMap allocates locks, this allows for a highly scalable, fixed-overhead locking mechanism in exchange for potentially unrelated entries being blocked by the same lock.

The alternative is to disable lock striping - which would mean a new lock is created per entry. This approach may give you greater concurrent throughput, but it will be at the cost of additional memory usage, garbage collection churn, etc.

Default lock striping settings
lock striping is disabled by default, due to potential deadlocks that can happen if locks for different keys end up in the same lock stripe.

The size of the shared lock collection used by lock striping can be tuned using the concurrencyLevel attribute of the `<locking /> configuration element.

Configuration example:

<locking striping="false|true"/>


new ConfigurationBuilder().locking().useLockStriping(false|true);

5.1.6. Concurrency levels

In addition to determining the size of the striped lock container, this concurrency level is also used to tune any JDK ConcurrentHashMap based collections where related, such as internal to DataContainers. Please refer to the JDK ConcurrentHashMap Javadocs for a detailed discussion of concurrency levels, as this parameter is used in exactly the same way in Infinispan.

Configuration example:

<locking concurrency-level="32"/>


new ConfigurationBuilder().locking().concurrencyLevel(32);

5.1.7. Lock timeout

The lock timeout specifies the amount of time, in milliseconds, to wait for a contented lock.

Configuration example:

<locking acquire-timeout="10000"/>


new ConfigurationBuilder().locking().lockAcquisitionTimeout(10000);
new ConfigurationBuilder().locking().lockAcquisitionTimeout(10, TimeUnit.SECONDS);

5.1.8. Consistency

The fact that a single owner is locked (as opposed to all owners being locked) does not break the following consistency guarantee: if key K is hashed to nodes {A, B} and transaction TX1 acquires a lock for K, let’s say on A. If another transaction, TX2, is started on B (or any other node) and TX2 tries to lock K then it will fail with a timeout as the lock is already held by TX1. The reason for this is the that the lock for a key K is always, deterministically, acquired on the same node of the cluster, regardless of where the transaction originates.

5.2. Data Versioning

Infinispan supports two forms of data versioning: simple and external. The simple versioning is used in transactional caches for write skew check.

The external versioning is used to encapsulate an external source of data versioning within Infinispan, such as when using Infinispan with Hibernate which in turn gets its data version information directly from a database.

In this scheme, a mechanism to pass in the version becomes necessary, and overloaded versions of put() and putForExternalRead() will be provided in AdvancedCache to take in an external data version. This is then stored on the InvocationContext and applied to the entry at commit time.

Write skew checks cannot and will not be performed in the case of external data versioning.

6. Clustered Lock

A clustered lock is a lock which is distributed and shared among all nodes in the Infinispan cluster and currently provides a way to execute code that will be synchronized between the nodes in a given cluster.

6.1. Installation

In order to start using the clustered locks, you needs to add the dependency in your Maven pom.xml file:

  <!-- Replace ${version.infinispan} with the
  version of Infinispan that you're using. -->

6.2. ClusteredLock Configuration

Currently there is a single type of ClusteredLock supported : non reentrant, NODE ownership lock.

6.2.1. Ownership

  • NODE When a ClusteredLock is defined, this lock can be used from all the nodes in the Infinispan cluster. When the ownership is NODE type, this means that the owner of the lock is the Infinispan node that acquired the lock at a given time. This means that each time we get a ClusteredLock instance with the ClusteredCacheManager, this instance will be the same instance for each Infinispan node. This lock can be used to synchronize code between Infinispan nodes. The advantage of this lock is that any thread in the node can release the lock at a given time.

  • INSTANCE - not yet supported

When a ClusteredLock is defined, this lock can be used from all the nodes in the Infinispan cluster. When the ownership is INSTANCE type, this means that the owner of the lock is the actual instance we acquired when ClusteredLockManager.get("lockName") is called.

This means that each time we get a ClusteredLock instance with the ClusteredCacheManager, this instance will be a new instance. This lock can be used to synchronize code between Infinispan nodes and inside each Infinispan node. The advantage of this lock is that only the instance that called 'lock' can release the lock.

6.2.2. Reentrancy

When a ClusteredLock is configured reentrant, the owner of the lock can reacquire the lock as many consecutive times as it wants while holding the lock.

Currently, only non reentrant locks are supported. This means that when two consecutive lock calls are sent for the same owner, the first call will acquire the lock if it’s available, and the second call will block.

6.3. ClusteredLockManager Interface

The ClusteredLockManager interface, marked as experimental, is the entry point to define, retrieve and remove a lock. It automatically listen to the creation of EmbeddedCacheManager and proceeds with the registration of an instance of it per EmbeddedCacheManager. It starts the internal caches needed to store the lock state.

Retrieving the ClusteredLockManager is as simple as invoking the EmbeddedClusteredLockManagerFactory.from(EmbeddedCacheManager) as shown in the example below:

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

// retrieve the ClusteredLockManager
ClusteredLockManager clusteredLockManager = EmbeddedClusteredLockManagerFactory.from(manager);
public interface ClusteredLockManager {

   boolean defineLock(String name);

   boolean defineLock(String name, ClusteredLockConfiguration configuration);

   ClusteredLock get(String name);

   ClusteredLockConfiguration getConfiguration(String name);

   boolean isDefined(String name);

   CompletableFuture<Boolean> remove(String name);

   CompletableFuture<Boolean> forceRelease(String name);
  • defineLock : Defines a lock with the specified name and the default ClusteredLockConfiguration. It does not overwrite existing configurations.

  • defineLock(String name, ClusteredLockConfiguration configuration) : Defines a lock with the specified name and ClusteredLockConfiguration. It does not overwrite existing configurations.

  • ClusteredLock get(String name) : Get’s a ClusteredLock by it’s name. A call of defineLock must be done at least once in the cluster. See ownership level section to understand the implications of get method call.

Currently, the only ownership level supported is NODE.

  • ClusteredLockConfiguration getConfiguration(String name) :

Returns the configuration of a ClusteredLock, if such exists.

  • boolean isDefined(String name) : Checks if a lock is already defined.

  • CompletableFuture<Boolean> remove(String name) : Removes a ClusteredLock if such exists.

  • CompletableFuture<Boolean> forceRelease(String name) : Releases - or unlocks - a ClusteredLock, if such exists, no matter who is holding it at a given time. Calling this method may cause concurrency issues and has to be used in exceptional situations.

6.4. ClusteredLock Interface

ClusteredLock interface, marked as experimental, is the interface that implements the clustered locks.

public interface ClusteredLock {

   CompletableFuture<Void> lock();

   CompletableFuture<Boolean> tryLock();

   CompletableFuture<Boolean> tryLock(long time, TimeUnit unit);

   CompletableFuture<Void> unlock();

   CompletableFuture<Boolean> isLocked();

   CompletableFuture<Boolean> isLockedByMe();
  • lock : Acquires the lock. If the lock is not available then call blocks until the lock is acquired. Currently, there is no maximum time specified for a lock request to fail, so this could cause thread starvation.

  • tryLock Acquires the lock only if it is free at the time of invocation, and returns true in that case. This method does not block (or wait) for any lock acquisition.

  • tryLock(long time, TimeUnit unit) If the lock is available this method returns immediately with true. If the lock is not available then the call waits until :

    • The lock is acquired

    • The specified waiting time elapses

If the time is less than or equal to zero, the method will not wait at all.

  • unlock

Releases the lock. Only the holder of the lock may release the lock.

  • isLocked Returns true when the lock is locked and false when the lock is released.

  • isLockedByMe Returns true when the lock is owned by the caller and false when the lock is owned by someone else or it’s released.

6.4.1. Usage Examples

EmbeddedCache cm = ...;
ClusteredLockManager cclm = EmbeddedClusteredLockManagerFactory.from(cm);

  .thenCompose(result -> {
     if (result) {
      try {
          // manipulate protected state
          } finally {
             return lock.unlock();
     } else {
        // Do something else

6.4.2. ClusteredLockManager Configuration

You can configure ClusteredLockManager to use different strategies for locks, either declaratively or programmatically, with the following attributes:


Defines the total number of nodes in each cluster that store the states of clustered locks. The default value is -1, which replicates the value to all nodes.


Controls how clustered locks behave when clusters split into partitions or multiple nodes leave a cluster. You can set the following values:

  • AVAILABLE: Nodes in any partition can concurrently operate on locks.

  • CONSISTENT: Only nodes that belong to the majority partition can operate on locks. This is the default value.

The following is an example declarative configuration for ClusteredLockManager:

<?xml version="1.0" encoding="UTF-8"?>
    <cache-container default-cache="default">
        <local-cache name="default">
            <locking concurrency-level="100" acquire-timeout="1000"/>

        <clustered-locks xmlns="urn:infinispan:config:clustered-locks:10.1"
                         num-owners = "3"
            <clustered-lock name="lock1" />
            <clustered-lock name="lock2" />

7. Clustered Counters

Clustered counters are counters which are distributed and shared among all nodes in the Infinispan cluster. Counters can have different consistency levels: strong and weak.

Although a strong/weak consistent counter has separate interfaces, both support updating its value, return the current value and they provide events when its value is updated. Details are provided below in this document to help you choose which one fits best your uses-case.

7.1. Installation and Configuration

In order to start using the counters, you needs to add the dependency in your Maven pom.xml file:

  <!-- Replace ${version.infinispan} with the
  version of Infinispan that you're using. -->

The counters can be configured Infinispan configuration file or on-demand via the CounterManager interface detailed later in this document. A counters configured in Infinispan configuration file is created at boot time when the EmbeddedCacheManager is starting. Theses counters are started eagerly and they are available in all the cluster’s nodes.

<?xml version="1.0" encoding="UTF-8"?>
    <cache-container ...>
        <!-- if needed to persist counter, global state needs to be configured -->
        <!-- your caches configuration goes here -->
         <counters xmlns="urn:infinispan:config:counters:10.1" num-owners="3" reliability="CONSISTENT">
             <strong-counter name="c1" initial-value="1" storage="PERSISTENT"/>
             <strong-counter name="c2" initial-value="2" storage="VOLATILE">
                 <lower-bound value="0"/>
             <strong-counter name="c3" initial-value="3" storage="PERSISTENT">
                 <upper-bound value="5"/>
             <strong-counter name="c4" initial-value="4" storage="VOLATILE">
                 <lower-bound value="0"/>
                 <upper-bound value="10"/>
             <weak-counter name="c5" initial-value="5" storage="PERSISTENT" concurrency-level="1"/>

or programmatically, in the GlobalConfigurationBuilder:

GlobalConfigurationBuilder globalConfigurationBuilder = ...;
CounterManagerConfigurationBuilder builder = globalConfigurationBuilder.addModule(CounterManagerConfigurationBuilder.class);

On other hand, the counters can be configured on-demand, at any time after the EmbeddedCacheManager is initialized.

CounterManager manager = ...;
manager.defineCounter("c1", CounterConfiguration.builder(CounterType.UNBOUNDED_STRONG).initialValue(1).storage(Storage.PERSISTENT)build());
manager.defineCounter("c2", CounterConfiguration.builder(CounterType.BOUNDED_STRONG).initialValue(2).lowerBound(0).storage(Storage.VOLATILE).build());
manager.defineCounter("c3", CounterConfiguration.builder(CounterType.BOUNDED_STRONG).initialValue(3).upperBound(5).storage(Storage.PERSISTENT).build());
manager.defineCounter("c4", CounterConfiguration.builder(CounterType.BOUNDED_STRONG).initialValue(4).lowerBound(0).upperBound(10).storage(Storage.VOLATILE).build());
manager.defineCounter("c2", CounterConfiguration.builder(CounterType.WEAK).initialValue(5).concurrencyLevel(1).storage(Storage.PERSISTENT).build());
CounterConfiguration is immutable and can be reused.

The method defineCounter() will return true if the counter is successful configured or false otherwise. However, if the configuration is invalid, the method will throw a CounterConfigurationException. To find out if a counter is already defined, use the method isDefined().

CounterManager manager = ...
if (!manager.isDefined("someCounter")) {
    manager.define("someCounter", ...);

Per cluster attributes:

  • num-owners: Sets the number of counter’s copies to keep cluster-wide. A smaller number will make update operations faster but will support a lower number of server crashes. It must be positive and its default value is 2.

  • reliability: Sets the counter’s update behavior in a network partition. Default value is AVAILABLE and valid values are:

    • AVAILABLE: all partitions are able to read and update the counter’s value.

    • CONSISTENT: only the primary partition (majority of nodes) will be able to read and update the counter’s value. The remaining partitions can only read its value.

Per counter attributes:

  • initial-value [common]: Sets the counter’s initial value. Default is 0 (zero).

  • storage [common]: Sets the counter’s behavior when the cluster is shutdown and restarted. Default value is VOLATILE and valid values are:

    • VOLATILE: the counter’s value is only available in memory. The value will be lost when a cluster is shutdown.

    • PERSISTENT: the counter’s value is stored in a private and local persistent store. The value is kept when the cluster is shutdown and restored after a restart.

On-demand and VOLATILE counters will lose its value and configuration after a cluster shutdown. They must be defined again after the restart.
  • lower-bound [strong]: Sets the strong consistent counter’s lower bound. Default value is Long.MIN_VALUE.

  • upper-bound [strong]: Sets the strong consistent counter’s upper bound. Default value is Long.MAX_VALUE.

If neither the lower-bound or upper-bound are configured, the strong counter is set as unbounded.
The initial-value must be between lower-bound and upper-bound inclusive.
  • concurrency-level [weak]: Sets the number of concurrent updates. Its value must be positive and the default value is 16.

7.1.1. List counter names

To list all the counters defined, the method CounterManager.getCounterNames() returns a collection of all counter names created cluster-wide.

7.2. The CounterManager interface.

The CounterManager interface is the entry point to define, retrieve and remove a counter. It automatically listen to the creation of EmbeddedCacheManager and proceeds with the registration of an instance of it per EmbeddedCacheManager. It starts the caches needed to store the counter state and configures the default counters.

Retrieving the CounterManager is as simple as invoke the EmbeddedCounterManagerFactory.asCounterManager(EmbeddedCacheManager) as shown in the example below:

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

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

For Hot Rod client, the CounterManager is registered in the RemoteCacheManager and it can be retrieved like:

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

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

7.2.1. Remove a counter via CounterManager

use with caution.

There is a difference between remove a counter via the Strong/WeakCounter interfaces and the CounterManager. The CounterManager.remove(String) removes the counter value from the cluster and removes all the listeners registered in the counter in the local counter instance. In addition, the counter instance is no longer reusable and it may return an invalid results.

On the other side, the Strong/WeakCounter removal only removes the counter value. The instance can still be reused and the listeners still works.

The counter is re-created if it is accessed after a removal.

7.3. The Counter

A counter can be strong (StrongCounter) or weakly consistent (WeakCounter) and both is identified by a name. They have a specific interface but they share some logic, namely, both of them are asynchronous ( a CompletableFuture is returned by each operation), provide an update event and can be reset to its initial value.

If you don’t want to use the async API, it is possible to return a synchronous counter via sync() method. The API is the same but without the CompletableFuture return value.

The following methods are common to both interfaces:

String getName();
CompletableFuture<Long> getValue();
CompletableFuture<Void> reset();
<T extends CounterListener> Handle<T> addListener(T listener);
CounterConfiguration getConfiguration();
CompletableFuture<Void> remove();
SyncStrongCounter sync(); //SyncWeakCounter for WeakCounter
  • getName() returns the counter name (identifier).

  • getValue() returns the current counter’s value.

  • reset() allows to reset the counter’s value to its initial value.

  • addListener() register a listener to receive update events. More details about it in the Notification and Events section.

  • getConfiguration() returns the configuration used by the counter.

  • remove() removes the counter value from the cluster. The instance can still be used and the listeners are kept.

  • sync() creates a synchronous counter.

The counter is re-created if it is accessed after a removal.

7.3.1. The StrongCounter interface: when the consistency or bounds matters.

The strong counter provides uses a single key stored in Infinispan cache to provide the consistency needed. All the updates are performed under the key lock to updates its values. On other hand, the reads don’t acquire any locks and reads the current value. Also, with this scheme, it allows to bound the counter value and provide atomic operations like compare-and-set/swap.

A StrongCounter can be retrieved from the CounterManager by using the getStrongCounter() method. As an example:

CounterManager counterManager = ...
StrongCounter aCounter = counterManager.getStrongCounter("my-counter);
Since every operation will hit a single key, the StrongCounter has a higher contention rate.

The StrongCounter interface adds the following method:

default CompletableFuture<Long> incrementAndGet() {
   return addAndGet(1L);

default CompletableFuture<Long> decrementAndGet() {
   return addAndGet(-1L);

CompletableFuture<Long> addAndGet(long delta);

CompletableFuture<Boolean> compareAndSet(long expect, long update);

CompletableFuture<Long> compareAndSwap(long expect, long update);
  • incrementAndGet() increments the counter by one and returns the new value.

  • decrementAndGet() decrements the counter by one and returns the new value.

  • addAndGet() adds a delta to the counter’s value and returns the new value.

  • compareAndSet() and compareAndSwap() atomically set the counter’s value if the current value is the expected.

A operation is considered completed when the CompletableFuture is completed.
The difference between compare-and-set and compare-and-swap is that the former returns true if the operation succeeds while the later returns the previous value. The compare-and-swap is successful if the return value is the same as the expected.
Bounded StrongCounter

When bounded, all the update method above will throw a CounterOutOfBoundsException when they reached the lower or upper bound. The exception has the following methods to check which side bound has been reached:

public boolean isUpperBoundReached();
public boolean isLowerBoundReached();
Uses cases

The strong counter fits better in the following uses cases:

  • When counter’s value is needed after each update (example, cluster-wise ids generator or sequences)

  • When a bounded counter is needed (example, rate limiter)

Usage Examples
StrongCounter counter = counterManager.getStrongCounter("unbounded_counter");

// incrementing the counter
System.out.println("new value is " + counter.incrementAndGet().get());

// decrement the counter's value by 100 using the functional API
counter.addAndGet(-100).thenApply(v -> {
   System.out.println("new value is " + v);
   return null;

// alternative, you can do some work while the counter is updated
CompletableFuture<Long> f = counter.addAndGet(10);
// ... do some work ...
System.out.println("new value is " + f.get());

// and then, check the current value
System.out.println("current value is " + counter.getValue().get());

// finally, reset to initial value
System.out.println("current value is " + counter.getValue().get());

// or set to a new value if zero
System.out.println("compare and set succeeded? " + counter.compareAndSet(0, 1));

And below, there is another example using a bounded counter:

StrongCounter counter = counterManager.getStrongCounter("bounded_counter");

// incrementing the counter
try {
    System.out.println("new value is " + counter.addAndGet(100).get());
} catch (ExecutionException e) {
    Throwable cause = e.getCause();
    if (cause instanceof CounterOutOfBoundsException) {
       if (((CounterOutOfBoundsException) cause).isUpperBoundReached()) {
          System.out.println("ops, upper bound reached.");
       } else if (((CounterOutOfBoundsException) cause).isLowerBoundReached()) {
          System.out.println("ops, lower bound reached.");

// now using the functional API
counter.addAndGet(-100).handle((v, throwable) -> {
   if (throwable != null) {
      Throwable cause = throwable.getCause();
      if (cause instanceof CounterOutOfBoundsException) {
         if (((CounterOutOfBoundsException) cause).isUpperBoundReached()) {
            System.out.println("ops, upper bound reached.");
         } else if (((CounterOutOfBoundsException) cause).isLowerBoundReached()) {
            System.out.println("ops, lower bound reached.");
      return null;
   System.out.println("new value is " + v);
   return null;

Compare-and-set vs Compare-and-swap examples:

StrongCounter counter = counterManager.getStrongCounter("my-counter");
long oldValue, newValue;
do {
   oldValue = counter.getValue().get();
   newValue = someLogic(oldValue);
} while (!counter.compareAndSet(oldValue, newValue).get());

With compare-and-swap, it saves one invocation counter invocation (counter.getValue())

StrongCounter counter = counterManager.getStrongCounter("my-counter");
long oldValue = counter.getValue().get();
long currentValue, newValue;
do {
   currentValue = oldValue;
   newValue = someLogic(oldValue);
} while ((oldValue = counter.compareAndSwap(oldValue, newValue).get()) != currentValue);

7.3.2. The WeakCounter interface: when speed is needed

The WeakCounter stores the counter’s value in multiple keys in Infinispan cache. The number of keys created is configured by the concurrency-level attribute. Each key stores a partial state of the counter’s value and it can be updated concurrently. It main advantage over the StrongCounter is the lower contention in the cache. On other hand, the read of its value is more expensive and bounds are not allowed.

The reset operation should be handled with caution. It is not atomic and it produces intermediates values. These value may be seen by a read operation and by any listener registered.

A WeakCounter can be retrieved from the CounterManager by using the getWeakCounter() method. As an example:

CounterManager counterManager = ...
StrongCounter aCounter = counterManager.getWeakCounter("my-counter);
Weak Counter Interface

The WeakCounter adds the following methods:

default CompletableFuture<Void> increment() {
   return add(1L);

default CompletableFuture<Void> decrement() {
   return add(-1L);

CompletableFuture<Void> add(long delta);

They are similar to the `StrongCounter’s methods but they don’t return the new value.

Uses cases

The weak counter fits best in uses cases where the result of the update operation is not needed or the counter’s value is not required too often. Collecting statistics is a good example of such an use case.


Below, there is an example of the weak counter usage.

WeakCounter counter = counterManager.getWeakCounter("my_counter");

// increment the counter and check its result
System.out.println("current value is " + counter.getValue().get());

CompletableFuture<Void> f = counter.add(-100);
//do some work
f.get(); //wait until finished
System.out.println("current value is " + counter.getValue().get());

//using the functional API
counter.reset().whenComplete((aVoid, throwable) -> System.out.println("Reset done " + (throwable == null ? "successfully" : "unsuccessfully"))).get();
System.out.println("current value is " + counter.getValue().get());

7.4. Notifications and Events

Both strong and weak counter supports a listener to receive its updates events. The listener must implement CounterListener and it can be registered by the following method:

<T extends CounterListener> Handle<T> addListener(T listener);

The CounterListener has the following interface:

public interface CounterListener {
   void onUpdate(CounterEvent entry);

The Handle object returned has the main goal to remove the CounterListener when it is not longer needed. Also, it allows to have access to the CounterListener instance that is it handling. It has the following interface:

public interface Handle<T extends CounterListener> {
   T getCounterListener();
   void remove();

Finally, the CounterEvent has the previous and current value and state. It has the following interface:

public interface CounterEvent {
   long getOldValue();
   State getOldState();
   long getNewValue();
   State getNewState();
The state is always State.VALID for unbounded strong counter and weak counter. State.LOWER_BOUND_REACHED and State.UPPER_BOUND_REACHED are only valid for bounded strong counters.
The weak counter reset() operation will trigger multiple notification with intermediate values.

8. Protocol Interoperability

Clients exchange data with Infinispan through endpoints such as REST or Hot Rod.

Each endpoint uses a different protocol so that clients can read and write data in a suitable format. Because Infinispan can interoperate with multiple clients at the same time, it must convert data between client formats and the storage formats.

To configure Infinispan endpoint interoperability, you should define the MediaType that sets the format for data stored in the cache.

8.1. Considerations with Media Types and Endpoint Interoperability

Configuring Infinispan to store data with a specific media type affects client interoperability.

Although REST clients do support sending and receiving encoded binary data, they are better at handling text formats such as JSON, XML, or plain text.

Memcached text clients can handle String-based keys and byte[] values but cannot negotiate data types with the server. These clients do not offer much flexibility when handling data formats because of the protocol definition.

Java Hot Rod clients are suitable for handling Java objects that represent entities that reside in the cache. Java Hot Rod clients use marshalling operations to serialize and deserialize those objects into byte arrays.

Similarly, non-Java Hot Rod clients, such as the C++, C#, and Javascript clients, are suitable for handling objects in the respective languages. However, non-Java Hot Rod clients can interoperate with Java Hot Rod clients using platform independent data formats.

8.2. REST, Hot Rod, and Memcached Interoperability with Text-Based Storage

You can configure key and values with a text-based storage format.

For example, specify text/plain; charset=UTF-8, or any other character set, to set plain text as the media type. You can also specify a media type for other text-based formats such as JSON (application/json) or XML (application/xml) with an optional character set.

The following example configures the cache to store entries with the text/plain; charset=UTF-8 media type:

      <key media-type="text/plain; charset=UTF-8"/>
      <value media-type="text/plain; charset=UTF-8"/>

To handle the exchange of data in a text-based format, you must configure Hot Rod clients with the org.infinispan.commons.marshall.StringMarshaller marshaller.

REST clients must also send the correct headers when writing and reading from the cache, as follows:

  • Write: Content-Type: text/plain; charset=UTF-8

  • Read: Accept: text/plain; charset=UTF-8

Memcached clients do not require any configuration to handle text-based formats.

This configuration is compatible with…​

REST clients


Java Hot Rod clients


Memcached clients


Non-Java Hot Rod clients


Querying and Indexing


Custom Java objects


8.3. REST, Hot Rod, and Memcached Interoperability with Custom Java Objects

If you store entries in the cache as marshalled, custom Java objects, you should configure the cache with the MediaType of the marshalled storage.

Java Hot Rod clients use the JBoss marshalling storage format as the default to store entries in the cache as custom Java objects.

The following example configures the cache to store entries with the application/x-jboss-marshalling media type:

<distributed-cache name="my-cache">
      <key media-type="application/x-jboss-marshalling"/>
      <value media-type="application/x-jboss-marshalling"/>

If you use the Protostream marshaller, configure the MediaType as application/x-protostream. For UTF8Marshaller, configure the MediaType as text/plain.

If only Hot Rod clients interact with the cache, you do not need to configure the MediaType.

Because REST clients are most suitable for handling text formats, you should use primitives such as java.lang.String for keys. Otherwise, REST clients must handle keys as bytes[] using a supported binary encoding.

REST clients can read values for cache entries in XML or JSON format. However, the classes must be available in the server.

To read and write data from Memcached clients, you must use java.lang.String for keys. Values are stored and returned as marshalled objects.

Some Java Memcached clients allow data transformers that marshall and unmarshall objects. You can also configure the Memcached server module to encode responses in different formats, such as 'JSON' which is language neutral. This allows non-Java clients to interact with the data even if the storage format for the cache is Java-specific.

Storing Java objects in the cache requires you to deploy entity classes to {ProductName}. See Deploying Entity Classes.

This configuration is compatible with…​

REST clients


Java Hot Rod clients


Memcached clients


Non-Java Hot Rod clients


Querying and Indexing


Custom Java objects


8.4. Java and Non-Java Client Interoperability with Protobuf

Storing data in the cache as Protobuf encoded entries provides a platform independent configuration that enables Java and Non-Java clients to access and query the cache from any endpoint.

If indexing is configured for the cache, Infinispan automatically stores keys and values with the application/x-protostream media type.

If indexing is not configured for the cache, you can configure it to store entries with the application/x-protostream media type as follows:

<distributed-cache name="my-cache">
      <key media-type="application/x-protostream"/>
      <value media-type="application/x-protostream"/>

Infinispan converts between application/x-protostream and application/json, which allows REST clients to read and write JSON formatted data. However REST clients must send the correct headers, as follows:

Read Header
Read: Accept: application/json
Write Header
Write: Content-Type: application/json

The application/x-protostream media type uses Protobuf encoding, which requires you to register a Protocol Buffers schema definition that describes the entities and marshallers that the clients use.

This configuration is compatible with…​

REST clients


Java Hot Rod clients


Non-Java Hot Rod clients


Querying and Indexing


Custom Java objects


8.5. Custom Code Interoperability

You can deploy custom code with Infinispan. For example, you can deploy scripts, tasks, listeners, converters, and merge policies. Because your custom code can access data directly in the cache, it must interoperate with clients that access data in the cache through different endpoints.

For example, you might create a remote task to handle custom objects stored in the cache while other clients store data in binary format.

To handle interoperability with custom code you can either convert data on demand or store data as Plain Old Java Objects (POJOs).

8.5.1. Converting Data On Demand

If the cache is configured to store data in a binary format such as application/x-protostream or application/x-jboss-marshalling, you can configure your deployed code to perform cache operations using Java objects as the media type. See Overriding the MediaType Programmatically.

This approach allows remote clients to use a binary format for storing cache entries, which is optimal. However, you must make entity classes available to the server so that it can convert between binary format and Java objects.

Additionally, if the cache uses Protobuf (application/x-protostream) as the binary format, you must deploy protostream marshallers so that {ProductName} can unmarshall data from your custom code.

8.5.2. Storing Data as POJOs

Storing unmarshalled Java objects in the server is not recommended. Doing so requires Infinispan to serialize data when remote clients read from the cache and then deserialize data when remote clients write to the cache.

The following example configures the cache to store entries with the application/x-java-object media type:

<distributed-cache name="my-cache">
      <key media-type="application/x-java-object"/>
      <value media-type="application/x-java-object"/>

Hot Rod clients must use a supported marshaller when data is stored as POJOs in the cache, either the JBoss marshaller or the default Java serialization mechanism. You must also deploy the classes must be deployed in the server.

REST clients must use a storage format that Infinispan can convert to and from Java objects, currently JSON or XML.

Storing Java objects in the cache requires you to deploy entity classes to Infinispan. See Deploying Entity Classes.

Memcached clients must send and receive a serialized version of the stored POJO, which is a JBoss marshalled payload by default. However if you configure the client encoding in the appropriate Memcached connector, you change the storage format so that Memcached clients use a platform neutral format such as JSON.

This configuration is compatible with…​

REST clients


Java Hot Rod clients


Non-Java Hot Rod clients


Querying and Indexing

Yes. However, querying and indexing works with POJOs only if the entities are annotated.

Custom Java objects


8.6. Deploying Entity Classes

If you plan to store entries in the cache as custom Java objects or POJOs, you must deploy entity classes to Infinispan. Clients always exchange objects as bytes[]. The entity classes represent those custom objects so that Infinispan can serialize and deserialize them.

To make entity classes available to the server, do the following:

  • Create a JAR file that contains the entities and dependencies.

  • Stop Infinispan if it is running.

    Infinispan loads entity classes during boot. You cannot make entity classes available to Infinispan if the server is running.

  • Copy the JAR file to the $INFINISPAN_HOME/standalone/deployments/ directory.

  • Specify the JAR file as a module in the cache manager configuration, as in the following example:

<cache-container name="local" default-cache="default">
     <module name="deployment.my-entities.jar"/>

8.7. Trying the Interoperability Demo

Try the demo for protocol interoperability using the Infinispan Docker image at: https://github.com/infinispan-demos/endpoint-interop

9. Marshalling

Marshalling is the process of converting Java objects into a binary format that can be transferred over the wire or stored to disk. Unmarshalling is the reverse process whereby data read from a binary format is transformed back into Java objects. Infinispan uses marshalling/unmarshalling to:

  • Transform data so that it can be sent to other Infinispan nodes in a cluster.

  • Transform data so that it can be stored in underlying cache stores.

  • Store data in Infinispan in a binary format to provide lazy deserialization capabilities.

Infinispan handles marshalling for all internal types. Users only need to be concerned with the marshalling of the Java objects that they will store in the cache.

9.1. Marshaller Implementations

9.1.1. ProtoStream (Default)

The default marshaller for Infinispan is ProtoStream, which marshalls data as Protocol Buffers. This is a platform independent format that utilises schemas to provide a structured representation of your Java objects that can evolve over time but also maintain backwards compatibility".

Infinispan directly integrates with the ProtoStream library by allowing users to configure implementations of the ProtoStream SerializationContextInitializer interface. These implementations are then used to initialise the various SerializationContexts used by Infinispan for marshalling, allowing custom user objects to be marshalled for storage and cluster communication. More details on how to generate/implement your own SerializationContextInitailizers can be found here.

SerializationContextInitailizers can be configured as follows:

Programmatic procedure
GlobalConfigurationBuilder builder = new GlobalConfigurationBuilder();
builder.addContextInitializers(new LibraryInitializerImpl(), new SCIImpl())
Declarative procedure
    <context-initializer class="org.infinispan.example.LibraryInitializerImpl"/>
    <context-initializer class="org.infinispan.example.another.SCIImpl"/>

9.1.2. Java Serialization Marshaller

Java serialization can also be used to marshall your objects. The only requirements are that your Java objects implement the Serializable interface, the JavaSerializationMarshaller is specified and that your objects are added to the serialization white list as part of the global configuration. For example, to utilise Java serialization with all classes in package "org.infinispan.example.*" and the class "org.infinispan.concrete.SomeClass" in the white list configure the CacheManager as follows:

Programmatic procedure
GlobalConfigurationBuilder builder = new GlobalConfigurationBuilder();
      .addJavaSerialWhiteList("org.infinispan.example.*", "org.infinispan.concrete.SomeClass");
Declarative procedure
<serialization marshaller="org.infinispan.commons.marshall.JavaSerializationMarshaller">

9.1.3. JBoss Marshalling

JBoss Marshalling is a Serialization-based marshalling library and was the default marshaller in older Infinispan versions.

  • JBoss Marshalling is deprecated and planned for removal in a future version of Infinispan.

  • Infinispan ignores implementations of the AdvancedExternalizer interface when persisting data unless you use JBoss Marshalling. However, AdvancedExternalizer is deprecated alongside JBoss Marshalling.

You should not plan to use serialization-based marshalling with Infinispan because it is deprecated functionality; however, the steps to do so are as follows:

  1. Add the infinipsan-jboss-marshalling dependency to your classpath.

  2. Configure JBossUserMarshaller as follows:

Programmatic procedure
GlobalConfigurationBuilder builder = new GlobalConfigurationBuilder();
Declarative procedure
<serialization marshaller="org.infinispan.jboss.marshalling.core.JBossUserMarshaller"/>

9.1.4. Kryo Marshalling

Infinispan provides a marshalling implementation that uses Kryo libraries.

Register custom Kryo schemas for object marshalling as follows:

  1. Implement a service provider for the SerializerRegistryService.java interface.

  2. Place all serializer registrations in the register(Kryo) method; where serializers are registered with the supplied Kryo object using the Kryo API, for example:

    kryo.register(ExampleObject.class, new ExampleObjectSerializer())
  3. Specify the full path of implementing classes in your deployment JAR file within:


Infinispan provides a Kryo Bundle that includes all runtime class files for the Kryo marshalling implementation. Download the JAR and add it to the server/lib directory of your Infinispan servers.


Kryo on GitHub

9.1.5. Protostuff Marshalling

Infinispan provides a marshalling implementation that uses Protostuff libraries.

Do one of the following to register custom Protostuff schemas for object marshalling:

  • Call the register() method.

    RuntimeSchema.register(ExampleObject.class, new ExampleObjectSchema());
  • Implement a service provider for the SerializerRegistryService.java interface that places all schema registrations in the register() method.

    You should then specify the full path of implementing classes in your deployment JAR file within:


Infinispan provides a Protostuff Bundle JAR that includes all runtime class files for the Protostuff marshalling implementation. Download the JAR and add it to the server/lib directory of your Infinispan servers.

9.1.6. Custom Implementation

You can provide custom marshaller classes with the Marshaller interface. For example:

Programmatic procedure
GlobalConfigurationBuilder builder = new GlobalConfigurationBuilder();
Declarative procedure
<serialization marshaller="org.infinispan.example.marshall.CustomMarshaller">

Custom marshaller implementations can access a configured white list via the initialize() method, which is called during startup.

9.2. Adding Java Classes to Deserialization White Lists

Infinispan does not allow deserialization of arbritrary Java classes for security reasons, which applies to JSON, XML, and marshalled byte[] content.

You must add Java classes to a deserialization white list, either using system properties or specifying them in the Infinispan configuration.

System properties
// Specify a comma-separated list of fully qualified class names

// Specify a regular expression to match classes
   <serialization version="1.0" marshaller="org.infinispan.marshall.TestObjectStreamMarshaller">

Java classes that you add to the deserialization whitelist apply to the Infinispan CacheContainer and can be deserialized by all caches that the CacheContainer controls.

9.3. Storing Deserialized Objects in Infinispan Servers

If you configure Infinispan caches to use the application/x-java-object MediaType as the storage format (storing POJOs instead of binary content), you must put class files for all custom objects on the classpath for Infinispan servers.

  • Add JAR files that contain custom classes and/or service provider implementations for marshaller implementations in the server/lib directory of your Infinispan servers.

9.4. Store As Binary

Store as binary enables data to be stored in its serialized form. This can be useful to achieve lazy deserialization, which is the mechanism by which Infinispan by which serialization and deserialization of objects is deferred till the point in time in which they are used and needed. This typically means that any deserialization happens using the thread context class loader of the invocation that requires deserialization, and is an effective mechanism to provide classloader isolation. By default lazy deserialization is disabled but if you want to enable it, you can do it like this:

Programmatic procedure
ConfigurationBuilder builder = ...
Declarative procedure
  • Via XML at the Cache level, either under <*-cache /> element:

   <binary />

9.4.1. Equality Considerations

When using lazy deserialization/storing as binary, keys and values are wrapped as WrappedBytes. It is this wrapper class that transparently takes care of serialization and deserialization on demand, and internally may have a reference to the object itself being wrapped, or the serialized, byte array representation of this object.

This has a particular effect on the behavior of equality. The equals() method of this class will either compare binary representations (byte arrays) or delegate to the wrapped object instance’s equals() method, depending on whether both instances being compared are in serialized or deserialized form at the time of comparison. If one of the instances being compared is in one form and the other in another form, then one instance is either serialized or deserialized.

This will affect the way keys stored in the cache will work, when storeAsBinary is used, since comparisons happen on the key which will be wrapped by a MarshalledValue. Implementers of equals() methods on their keys need to be aware of the behavior of equality comparison, when a key is wrapped as a MarshalledValue, as detailed above.

9.4.2. Store-by-value via defensive copying

The configuration storeAsBinary offers the possibility to enable defensive copying, which allows for store-by-value like behaviour.

Infinispan marshalls objects the moment they’re stored, hence changes made to object references are not stored in the cache, not even for local caches. This provides store-by-value like behaviour. Enabling storeAsBinary can be achieved:

Programmatic procedure
ConfigurationBuilder builder = ...
Declarative procedure
  • Via XML at the Cache level, either under <*-cache /> or <default /> elements:

<store-as-binary keys="true" values="true"/>

9.5. Infinispan ProtoStream Serialization Library

Infinispan uses the Protostream serialization library to encode and decode Java objects into the Protocol Buffers (Protobuf) format, which is a platform-independent protocol for structural representation of data.

9.5.1. Concepts

.proto Files
Protocol Buffers is a broad subject, we will not detail it here in great detail, so please consult the Developer Guide for an in-depth explanation of the encoding format and best practices.

Protocol Buffers, Protobuf for short, provide a platform independent encoding format that utilises .proto schema files to provide a structured representation of entities that can be easily evolved over time whilst maintaining backwards compatibility.

Protobuf is all about structured data, so the first thing to do is to define the structure of your data. This is accomplished by declaring Protobuf message types in .proto files, as shown in the example below.

package book_sample;

message Book {
    optional string title = 1;
    optional string description = 2;
    optional int32 publicationYear = 3; // no native Date type available in Protobuf

    repeated Author authors = 4;

message Author {
    optional string name = 1;
    optional string surname = 2;

In this example .proto file, we define an entity (message type in Protobuf speak) named Book, which is contained in the package book_sample. The Book entity declares several fields of primitive types and a repeatable field (Protobuf’s way of representing an array) named authors, which is the Author message type also declared in library.proto.

There are a few important notes we need to make about Protobuf messages:

  • Nesting of messages is possible, but the resulting structure is strictly a tree, never a graph

  • There is no concept of type inheritance

  • Collections are not supported but arrays can be easily emulated using repeated fields


As described in the previous section, a fundamental concept of the Protobuf format is the definition of messages in the .proto schema to determine how an entity is represented. However, in order for our Java applications to utilise the Protobuf format to transmit/store data, it’s necessary for our Java objects to be encoded. This is handled by the ProtoStream library and its configured Marshaller implementations, which convert plain old Java objects into the Protobuf format.


A fundamental component of the ProtoStream library is the SerializationContext. This is a repository for Protobuf type definitions, loaded from .proto files, and their accompanying Marshaller implementations. All ProtoStream marshalling operations happen in the context of a provided SerializationContext.

9.5.2. Usage

ProtoStream is able to handle the following types, as well as their unboxed equivalent in the case of primitive types, without any additional configuration:

  • String

  • Integer

  • Long

  • Double

  • Float

  • Boolean

  • byte[]

  • Byte

  • Short

  • Character

  • java.util.Date

  • java.time.Instant

Support for additional Java objects is possible by configuring the SerializationContext using SerializationContextInitializer implementations.

Infinispan directly integrates with the ProtoStream library by allowing users to configure implementations of the ProtoStream SerializationContextInitializer interface. These implementations are then used to initialise the various SerializationContext instances used by Infinispan for marshalling, therefore allowing custom user objects to be marshalled for storage and cluster communication.

Generating SerializationContextInitializers

The simplest way to create a SerializastionContextInitializer is to use the org:infinispan.protostream:protostream-processor artifact to automatically generate the following:

  • .proto schema

  • All marshaller implementations

  • The SerializationContextInitializer implementation used to register the schemas and marshaller with a SerializationContext

This requires Java annotations to be added to the Java object(s) that require marshalling, in addition to a class annotated with @AutoProtoSchemaBuilder.

The protostream-processor then processes these objects at compile-time, using the meta data in the ProtoStream annotations to generate the required .proto schema and corresponding marshaller implementations.

For example, let’s reconsider the Book and Author messages which we manually defined [previously]. The manual writing of the .proto file, can be replaced with the following Java annotations.

import org.infinispan.protostream.annotations.ProtoFactory;
import org.infinispan.protostream.annotations.ProtoField;

public class Book {
   @ProtoField(number = 1)
   final String title;

   @ProtoField(number = 2)
   final String description;

   @ProtoField(number = 3, defaultValue = "0")
   final int publicationYear;

   @ProtoField(number = 4, collectionImplementation = ArrayList.class)
   final List<Author> authors;

   Book(String title, String description, int publicationYear, List<Author> authors) {
      this.title = title;
      this.description = description;
      this.publicationYear = publicationYear;
      this.authors = authors;
   // public Getter methods omitted for brevity
import org.infinispan.protostream.annotations.ProtoFactory;
import org.infinispan.protostream.annotations.ProtoField;

public class Author {
   @ProtoField(number = 1)
   final String name;

   @ProtoField(number = 2)
   final String surname;

   Author(String name, String surname) {
      this.name = name;
      this.surname = surname;
   // public Getter methods omitted for brevity

We then define an interface which is annotated with @AutoProtoSchemaBuilder and extends SerializationContextInitializer.

      includeClasses = {
      schemaFileName = "library.proto",
      schemaFilePath = "proto/",
      schemaPackageName = "book_sample")
interface LibraryInitializer extends SerializationContextInitializer {

During compile-time, the protostream-processor generates a concrete implementation of the above interface which can then be used to initialize a ProtoStream SerializationContext. By default, the name of the implementation is the annotated class name plus the "Impl" suffix. The generated schema file can be found at target/classes/proto/library.proto, which is dictated by the provided schemaFileName and schemaFilePath values.

The generated .proto file and LibraryInitializerImpl.java are shown below.

// File name: library.proto
// Generated from : org.infinispan.commons.marshall.LibraryInitializer

syntax = "proto2";

package book_sample;

message Book {

    optional string title = 1;

    optional string description = 2;

    optional int32 publicationYear = 3 [default = 0];

    repeated Author authors = 4;

message Author {

    optional string name = 1;

    optional string surname = 2;
 Generated by org.infinispan.protostream.annotations.impl.processor.AutoProtoSchemaBuilderAnnotationProcessor
 for class org.infinispan.commons.marshall.LibraryInitializer
 annotated with @org.infinispan.protostream.annotations.AutoProtoSchemaBuilder(dependsOn=, service=false, autoImportClasses=false, excludeClasses=, includeClasses=org.infinispan.commons.marshall.Book,org.infinispan.commons.marshall.Author, basePackages={}, value={}, schemaPackageName="book_sample", schemaFilePath="proto/", schemaFileName="library.proto", className="")

package org.infinispan.commons.marshall;

 * WARNING: Generated code!
@javax.annotation.Generated(value = "org.infinispan.protostream.annotations.impl.processor.AutoProtoSchemaBuilderAnnotationProcessor",
      comments = "Please do not edit this file!")
   className = "LibraryInitializerImpl",
   schemaFileName = "library.proto",
   schemaFilePath = "proto/",
   schemaPackageName = "book_sample",
   service = false,
   autoImportClasses = false,
   classes = {
public class LibraryInitializerImpl implements org.infinispan.commons.marshall.LibraryInitializer {

   public String getProtoFileName() { return "library.proto"; }

   public String getProtoFile() { return org.infinispan.protostream.FileDescriptorSource.getResourceAsString(getClass(), "/proto/library.proto"); }

   public void registerSchema(org.infinispan.protostream.SerializationContext serCtx) {
      serCtx.registerProtoFiles(org.infinispan.protostream.FileDescriptorSource.fromString(getProtoFileName(), getProtoFile()));

   public void registerMarshallers(org.infinispan.protostream.SerializationContext serCtx) {
      serCtx.registerMarshaller(new org.infinispan.commons.marshall.Book$___Marshaller_cdc76a682a43643e6e1d7e43ba6d1ef6f794949a45e1a8bc961046cda44c9a85());
      serCtx.registerMarshaller(new org.infinispan.commons.marshall.Author$___Marshaller_9b67e1c1ecea213b4207541b411fb9af2ae6f658610d2a4ca9126484d57786d1());
Manually Implementing SerializationContextInitializers

Although generating resources is the easiest and most performant way to utilise ProtoStream, this method might not always be viable. For example, if you are not able to modify the Java object classes to add the required annotations. For such use cases, it’s possible to manually define the .proto schema and create a manual marshaller implementation.

Continuing with the Book and Author examples, first we need to manually create a library.proto file with our message schemas.

package book_sample;

message Book {
    optional string title = 1;
    optional string description = 2;
    optional int32 publicationYear = 3; // no native Date type available in Protobuf

    repeated Author authors = 4;

message Author {
    optional string name = 1;
    optional string surname = 2;

Then, we need to implement a marshaller for both the Book and Author classes using the org.infinispan.protostream.MessageMarshaller interface.

import org.infinispan.protostream.MessageMarshaller;

public class BookMarshaller implements MessageMarshaller<Book> {

   public String getTypeName() {
      return "book_sample.Book";

   public Class<? extends Book> getJavaClass() {
      return Book.class;

   public void writeTo(ProtoStreamWriter writer, Book book) throws IOException {
      writer.writeString("title", book.getTitle());
      writer.writeString("description", book.getDescription());
      writer.writeInt("publicationYear", book.getPublicationYear());
      writer.writeCollection("authors", book.getAuthors(), Author.class);

   public Book readFrom(MessageMarshaller.ProtoStreamReader reader) throws IOException {
      String title = reader.readString("title");
      String description = reader.readString("description");
      int publicationYear = reader.readInt("publicationYear");
      List<Author> authors = reader.readCollection("authors", new ArrayList<>(), Author.class);
      return new Book(title, description, publicationYear, authors);
import org.infinispan.protostream.MessageMarshaller;

public class AuthorMarshaller implements MessageMarshaller<Author> {

   public String getTypeName() {
      return "book_sample.Author";

   public Class<? extends Author> getJavaClass() {
      return Author.class;

   public void writeTo(ProtoStreamWriter writer, Author author) throws IOException {
      writer.writeString("name", author.getName());
      writer.writeString("surname", author.getSurname());

   public Author readFrom(MessageMarshaller.ProtoStreamReader reader) throws IOException {
      String name = reader.readString("name");
      String surname = reader.readString("surname");
      return new Author(name, surname);

Finally, we need to create a SerializationContextInitializer implementation that registers the library.proto file and the two marshallers with a ProtoStream SerializationContext.

import org.infinispan.protostream.FileDescriptorSource;
import org.infinispan.protostream.SerializationContext;
import org.infinispan.protostream.SerializationContextInitializer;

public class ManualSerializationContextInitializer implements SerializationContextInitializer {
   public String getProtoFileName() {
      return "library.proto";

   public String getProtoFile() throws UncheckedIOException {
      // Assumes that the file is located in a Jar's resources, we must provide the path to the library.proto file
      return FileDescriptorSource.getResourceAsString(getClass(), "/" + getProtoFileName());

   public void registerSchema(SerializationContext serCtx) {
      serCtx.registerProtoFiles(FileDescriptorSource.fromString(getProtoFileName(), getProtoFile()));

   public void registerMarshallers(SerializationContext serCtx) {
      serCtx.registerMarshaller(new AuthorMarshaller());
      serCtx.registerMarshaller(new BookMarshaller());

10. CDI Support

Infinispan includes integration with Contexts and Dependency Injection (better known as CDI) via Infinispan’s infinispan-cdi-embedded or infinispan-cdi-remote module. CDI is part of Java EE specification and aims for managing beans' lifecycle inside the container. The integration allows to inject Cache interface and bridge Cache and CacheManager events. JCache annotations (JSR-107) are supported by infinispan-jcache and infinispan-jcache-remote artifacts. For more information have a look at Chapter 11 of the JCACHE specification.

10.1. Maven Dependencies

To include CDI support for Infinispan in your project, use one of the following dependencies:

pom.xml for Embedded mode

<dependency> <groupId>org.infinispan</groupId> <artifactId>infinispan-cdi-embedded</artifactId> <!-- Replace ${version.infinispan} with the version of Infinispan that you’re using. -→ <version>${version.infinispan}</version> </dependency>

pom.xml for Remote mode

<dependency> <groupId>org.infinispan</groupId> <artifactId>infinispan-cdi-remote</artifactId> <!-- Replace ${version.infinispan} with the version of Infinispan that you’re using. -→ <version>${version.infinispan}</version> </dependency>

Which version of Infinispan should I use?
We recommend using the latest final version Infinispan.

10.2. Embedded cache integration

10.2.1. Inject an embedded cache

By default you can inject the default Infinispan cache. Let’s look at the following example:

Default cache injection
import javax.inject.Inject;

public class GreetingService {

    private Cache<String, String> cache;

    public String greet(String user) {
        String cachedValue = cache.get(user);
        if (cachedValue == null) {
            cachedValue = "Hello " + user;
            cache.put(user, cachedValue);
        return cachedValue;

If you want to use a specific cache rather than the default one, you just have to provide your own cache configuration and cache qualifier. See example below:

Qualifier example
import javax.inject.Qualifier;

@Target({ElementType.FIELD, ElementType.PARAMETER, ElementType.METHOD})
public @interface GreetingCache {
Injecting Cache with qualifier
import org.infinispan.configuration.cache.Configuration;
import org.infinispan.configuration.cache.ConfigurationBuilder;
import org.infinispan.cdi.ConfigureCache;
import javax.enterprise.inject.Produces;

public class Config {

    @ConfigureCache("greeting-cache") // This is the cache name.
    @GreetingCache // This is the cache qualifier.
    public Configuration greetingCacheConfiguration() {
        return new ConfigurationBuilder()

    // The same example without providing a custom configuration.
    // In this case the default cache configuration will be used.
    public Configuration greetingCacheConfiguration;

To use this cache in the GreetingService add the @GeetingCache qualifier on your cache injection point.

10.2.2. Override the default embedded cache manager and configuration

You can override the default cache configuration used by the default EmbeddedCacheManager. For that, you just have to create a Configuration producer with default qualifiers as illustrated in the following snippet:

Overriding Configuration
public class Config {

    // By default CDI adds the @Default qualifier if no other qualifier is provided.
    public Configuration defaultEmbeddedCacheConfiguration() {
        return new ConfigurationBuilder()

It’s also possible to override the default EmbeddedCacheManager. The newly created manager must have default qualifiers and Application scope.

Overriding EmbeddedCacheManager
import javax.enterprise.context.ApplicationScoped;

public class Config {

    public EmbeddedCacheManager defaultEmbeddedCacheManager() {
      return new DefaultCacheManager(new ConfigurationBuilder()

10.2.3. Configure the transport for clustered use

To use Infinispan in a clustered mode you have to configure the transport with the GlobalConfiguration. To achieve that override the default cache manager as explained in the previous section. Look at the following snippet:

Overriding default EmbeddedCacheManager
package org.infinispan.configuration.global.GlobalConfigurationBuilder;

public EmbeddedCacheManager defaultClusteredCacheManager() {
    return new DefaultCacheManager(
        new GlobalConfigurationBuilder().transport().defaultTransport().build(),
        new ConfigurationBuilder().memory().size(7).build()

10.3. Remote cache integration

10.3.1. Inject a remote cache

With the CDI integration it’s also possible to use a RemoteCache as illustrated in the following snippet:

Injecting RemoteCache
public class GreetingService {

    private RemoteCache<String, String> cache;

    public String greet(String user) {
        String cachedValue = cache.get(user);
        if (cachedValue == null) {
            cachedValue = "Hello " + user;
            cache.put(user, cachedValue);
        return cachedValue;

If you want to use another cache, for example the greeting-cache, add the @Remote qualifier on the cache injection point which contains the cache name.

Injecting RemoteCache with qualifier
public class GreetingService {

    private RemoteCache<String, String> cache;


Adding the @Remote cache qualifier on each injection point might be error prone. That’s why the remote cache integration provides another way to achieve the same goal. For that you have to create your own qualifier annotated with @Remote:

RemoteCache qualifier
@Target({ElementType.FIELD, ElementType.PARAMETER, ElementType.METHOD})
public @interface RemoteGreetingCache {

To use this cache in the GreetingService add the qualifier @RemoteGreetingCache qualifier on your cache injection.

10.3.2. Override the default remote cache manager

Like the embedded cache integration, the remote cache integration comes with a default remote cache manager producer. This default RemoteCacheManager can be overridden as illustrated in the following snippet:

Overriding default RemoteCacheManager
public class Config {

    public RemoteCacheManager defaultRemoteCacheManager() {
        return new RemoteCacheManager(localhost, 1544);

10.4. Use a custom remote/embedded cache manager for one or more cache

It’s possible to use a custom cache manager for one or more cache. You just need to annotate the cache manager producer with the cache qualifiers. Look at the following example:

public class Config {

   public EmbeddedCacheManager specificEmbeddedCacheManager() {
      return new DefaultCacheManager(new ConfigurationBuilder()

   public RemoteCacheManager specificRemoteCacheManager() {
       return new RemoteCacheManager("localhost", 1544);

With the above code the GreetingCache or the RemoteGreetingCache will be associated with the produced cache manager.

Producer method scope
To work properly the producers must have the scope @ApplicationScoped . Otherwise each injection of cache will be associated to a new instance of cache manager.

10.5. Use JCache caching annotations

There is now a separate module for JSR 107 (JCACHE) integration, including API.

When CDI integration and JCache artifacts are present on the classpath, it is possible to use JCache annotations with CDI managed beans. These annotations provide a simple way to handle common use cases. The following caching annotations are defined in this specification:

  • @CacheResult - caches the result of a method call

  • @CachePut - caches a method parameter

  • @CacheRemoveEntry - removes an entry from a cache

  • @CacheRemoveAll - removes all entries from a cache

Annotations target type
These annotations must only be used on methods.

To use these annotations, proper interceptors need to be declared in beans.xml file:

Interceptors for managed environments such as Application Servers
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://xmlns.jcp.org/xml/ns/javaee"
   xsi:schemaLocation="http://xmlns.jcp.org/xml/ns/javaee http://xmlns.jcp.org/xml/ns/javaee/beans_1_1.xsd"
   version="1.2" bean-discovery-mode="annotated">

Interceptors for unmanaged environments such as standalone applications
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://xmlns.jcp.org/xml/ns/javaee"
   xsi:schemaLocation="http://xmlns.jcp.org/xml/ns/javaee http://xmlns.jcp.org/xml/ns/javaee/beans_1_1.xsd"
   version="1.2" bean-discovery-mode="annotated">


The following snippet of code illustrates the use of @CacheResult annotation. As you can see it simplifies the caching of the Greetingservice#greet method results.

Using JCache annotations
import javax.cache.interceptor.CacheResult;

public class GreetingService {

    public String greet(String user) {
        return "Hello" + user;

The first version of the GreetingService and the above version have exactly the same behavior. The only difference is the cache used. By default it’s the fully qualified name of the annotated method with its parameter types (e.g. org.infinispan.example.GreetingService.greet(java.lang.String)).

Using other cache than default is rather simple. All you need to do is to specify its name with the cacheName attribute of the cache annotation. For example:

Specifying cache name for JCache
@CacheResult(cacheName = "greeting-cache")

10.6. Use Cache events and CDI

It is possible to receive Cache and Cache Manager level events using CDI Events. You can achieve it using @Observes annotation as shown in the following snippet:

Event listeners based on CDI
import javax.enterprise.event.Observes;
import org.infinispan.notifications.cachemanagerlistener.event.CacheStartedEvent;
import org.infinispan.notifications.cachelistener.event.*;

public class GreetingService {

    // Cache level events
    private void entryRemovedFromCache(@Observes CacheEntryCreatedEvent event) {

    // Cache Manager level events
    private void cacheStarted(@Observes CacheStartedEvent event) {

11. JCache (JSR-107) provider

Infinispan provides an implementation of JCache 1.0 API ( JSR-107 ). JCache specifies a standard Java API for caching temporary Java objects in memory. Caching java objects can help get around bottlenecks arising from using data that is expensive to retrieve (i.e. DB or web service), or data that is hard to calculate. Caching these type of objects in memory can help speed up application performance by retrieving the data directly from memory instead of doing an expensive roundtrip or recalculation. This document specifies how to use JCache with Infinispan’s implementation of the specification, and explains key aspects of the API.

11.1. Dependencies

In order to start using Infinispan JCache implementation, a single dependency needs to be added to the Maven pom.xml file:

  <!-- Replace ${version.infinispan} with the
  version of Infinispan that you're using. -->

11.2. Create a local cache

Creating a local cache, using default configuration options as defined by the JCache API specification, is as simple as doing the following:

import javax.cache.*;
import javax.cache.configuration.*;

// Retrieve the system wide cache manager
CacheManager cacheManager = Caching.getCachingProvider().getCacheManager();
// Define a named cache with default JCache configuration
Cache<String, String> cache = cacheManager.createCache("namedCache",
      new MutableConfiguration<String, String>());
By default, the JCache API specifies that data should be stored as storeByValue, so that object state mutations outside of operations to the cache, won’t have an impact in the objects stored in the cache. Infinispan has so far implemented this using serialization/marshalling to make copies to store in the cache, and that way adhere to the spec. Hence, if using default JCache configuration with Infinispan, data stored must be marshallable.

Alternatively, JCache can be configured to store data by reference (just like Infinispan or JDK Collections work). To do that, simply call:

Cache<String, String> cache = cacheManager.createCache("namedCache",
      new MutableConfiguration<String, String>().setStoreByValue(false));

11.3. Create a remote cache

Creating a remote cache (client-server mode), using default configuration options as defined by the JCache API specification, is as simple as doing the following:

import javax.cache.*;
import javax.cache.configuration.*;

// Retrieve the system wide cache manager via org.infinispan.jcache.remote.JCachingProvider
CacheManager cacheManager = Caching.getCachingProvider("org.infinispan.jcache.remote.JCachingProvider").getCacheManager();
// Define a named cache with default JCache configuration
Cache<String, String> cache = cacheManager.createCache("remoteNamedCache",
      new MutableConfiguration<String, String>());
In order to use the org.infinispan.jcache.remote.JCachingProvider, infinispan-jcache-remote-<version>.jar and all its transitive dependencies need to be on put your classpath.

11.4. Store and retrieve data

Even though JCache API does not extend neither java.util.Map not java.util.concurrent.ConcurrentMap, it providers a key/value API to store and retrieve data:

import javax.cache.*;
import javax.cache.configuration.*;

CacheManager cacheManager = Caching.getCacheManager();
Cache<String, String> cache = cacheManager.createCache("namedCache",
      new MutableConfiguration<String, String>());
cache.put("hello", "world"); // Notice that javax.cache.Cache.put(K) returns void!
String value = cache.get("hello"); // Returns "world"

Contrary to standard java.util.Map, javax.cache.Cache comes with two basic put methods called put and getAndPut. The former returns void whereas the latter returns the previous value associated with the key. So, the equivalent of java.util.Map.put(K) in JCache is javax.cache.Cache.getAndPut(K).

Even though JCache API only covers standalone caching, it can be plugged with a persistence store, and has been designed with clustering or distribution in mind. The reason why javax.cache.Cache offers two put methods is because standard java.util.Map put call forces implementors to calculate the previous value. When a persistent store is in use, or the cache is distributed, returning the previous value could be an expensive operation, and often users call standard java.util.Map.put(K) without using the return value. Hence, JCache users need to think about whether the return value is relevant to them, in which case they need to call javax.cache.Cache.getAndPut(K) , otherwise they can call java.util.Map.put(K, V) which avoids returning the potentially expensive operation of returning the previous value.

11.5. Comparing java.util.concurrent.ConcurrentMap and javax.cache.Cache APIs

Here’s a brief comparison of the data manipulation APIs provided by java.util.concurrent.ConcurrentMap and javax.cache.Cache APIs.

Operation java.util.concurrent.ConcurrentMap<K, V> javax.cache.Cache<K, V>

store and no return


void put(K key)

store and return previous value

V put(K key)

V getAndPut(K key)

store if not present

V putIfAbsent(K key, V value)

boolean putIfAbsent(K key, V value)


V get(Object key)

V get(K key)

delete if present

V remove(Object key)

boolean remove(K key)

delete and return previous value

V remove(Object key)

V getAndRemove(K key)

delete conditional

boolean remove(Object key, Object value)

boolean remove(K key, V oldValue)

replace if present

V replace(K key, V value)

boolean replace(K key, V value)

replace and return previous value

V replace(K key, V value)

V getAndReplace(K key, V value)

replace conditional

boolean replace(K key, V oldValue, V newValue)

boolean replace(K key, V oldValue, V newValue)

Comparing the two APIs, it’s obvious to see that, where possible, JCache avoids returning the previous value to avoid operations doing expensive network or IO operations. This is an overriding principle in the design of JCache API. In fact, there’s a set of operations that are present in java.util.concurrent.ConcurrentMap , but are not present in the javax.cache.Cache because they could be expensive to compute in a distributed cache. The only exception is iterating over the contents of the cache:

Operation java.util.concurrent.ConcurrentMap<K, V> javax.cache.Cache<K, V>

calculate size of cache

int size()


return all keys in the cache

Set<K> keySet()


return all values in the cache

Collection<V> values()


return all entries in the cache

Set<Map.Entry<K, V>> entrySet()


iterate over the cache

use iterator() method on keySet, values or entrySet

Iterator<Cache.Entry<K, V>> iterator()

11.6. Clustering JCache instances

Infinispan JCache implementation goes beyond the specification in order to provide the possibility to cluster caches using the standard API. Given a Infinispan configuration file configured to replicate caches like this:

   <cache-container default-cache="namedCache">
      <transport cluster="jcache-cluster" />
      <replicated-cache name="namedCache" />

You can create a cluster of caches using this code:

import javax.cache.*;
import java.net.URI;

// For multiple cache managers to be constructed with the standard JCache API
// and live in the same JVM, either their names, or their classloaders, must
// be different.
// This example shows how to force their classloaders to be different.
// An alternative method would have been to duplicate the XML file and give
// it a different name, but this results in unnecessary file duplication.
ClassLoader tccl = Thread.currentThread().getContextClassLoader();
CacheManager cacheManager1 = Caching.getCachingProvider().getCacheManager(
      URI.create("infinispan-jcache-cluster.xml"), new TestClassLoader(tccl));
CacheManager cacheManager2 = Caching.getCachingProvider().getCacheManager(
      URI.create("infinispan-jcache-cluster.xml"), new TestClassLoader(tccl));

Cache<String, String> cache1 = cacheManager1.getCache("namedCache");
Cache<String, String> cache2 = cacheManager2.getCache("namedCache");

cache1.put("hello", "world");
String value = cache2.get("hello"); // Returns "world" if clustering is working

// --

public static class TestClassLoader extends ClassLoader {
  public TestClassLoader(ClassLoader parent) {

12. Multimap Cache

MutimapCache is a type of Infinispan Cache that maps keys to values in which each key can contain multiple values.

12.1. Installation and configuration

  <!-- Replace ${version.infinispan} with the
  version of Infinispan that you're using. -->

12.2. MultimapCache API

MultimapCache API exposes several methods to interact with the Multimap Cache. All these methods are non-blocking in most of the cases. See [limitations]

public interface MultimapCache<K, V> {

   CompletableFuture<Void> put(K key, V value);

   CompletableFuture<Collection<V>> get(K key);

   CompletableFuture<Boolean> remove(K key);

   CompletableFuture<Boolean> remove(K key, V value);

   CompletableFuture<Void> remove(Predicate<? super V> p);

   CompletableFuture<Boolean> containsKey(K key);

   CompletableFuture<Boolean> containsValue(V value);

   CompletableFuture<Boolean> containsEntry(K key, V value);

   CompletableFuture<Long> size();

   boolean supportsDuplicates();


12.2.1. CompletableFuture<Void> put(K key, V value)

Puts a key-value pair in the multimap cache.

MultimapCache<String, String> multimapCache = ...;

multimapCache.put("girlNames", "marie")
             .thenCompose(r1 -> multimapCache.put("girlNames", "oihana"))
             .thenCompose(r3 -> multimapCache.get("girlNames"))
             .thenAccept(names -> {
                              System.out.println("Marie is a girl name");

                              System.out.println("Oihana is a girl name");

The output of this code is as follows:

Marie is a girl name
Oihana is a girl name

12.2.2. CompletableFuture<Collection<V>> get(K key)

Asynchronous that returns a view collection of the values associated with key in this multimap cache, if any. Any changes to the retrieved collection won’t change the values in this multimap cache. When this method returns an empty collection, it means the key was not found.

12.2.3. CompletableFuture<Boolean> remove(K key)

Asynchronous that removes the entry associated with the key from the multimap cache, if such exists.

12.2.4. CompletableFuture<Boolean> remove(K key, V value)

Asynchronous that removes a key-value pair from the multimap cache, if such exists.

12.2.5. CompletableFuture<Void> remove(Predicate<? super V> p)

Asynchronous method. Removes every value that match the given predicate.

12.2.6. CompletableFuture<Boolean> containsKey(K key)

Asynchronous that returns true if this multimap contains the key.

12.2.7. CompletableFuture<Boolean> containsValue(V value)

Asynchronous that returns true if this multimap contains the value in at least one key.

12.2.8. CompletableFuture<Boolean> containsEntry(K key, V value)

Asynchronous that returns true if this multimap contains at least one key-value pair with the value.

12.2.9. CompletableFuture<Long> size()

Asynchronous that returns the number of key-value pairs in the multimap cache. It doesn’t return the distinct number of keys.

12.2.10. boolean supportsDuplicates()

Asynchronous that returns true if the multimap cache supports duplicates. This means that the content of the multimap can be 'a' → ['1', '1', '2']. For now this method will always return false, as duplicates are not yet supported. The existence of a given value is determined by 'equals' and `hashcode' method’s contract.

12.3. Creating a Multimap Cache

Currently the MultimapCache is configured as a regular cache. This can be done either by code or XML configuration. See how to configure a regular Cache in the section link to [configure a cache].

12.3.1. Embedded mode

// create or obtain your EmbeddedCacheManager
EmbeddedCacheManager cm = ... ;

// create or obtain a MultimapCacheManager passing the EmbeddedCacheManager
MultimapCacheManager multimapCacheManager = EmbeddedMultimapCacheManagerFactory.from(cm);

// define the configuration for the multimap cache
multimapCacheManager.defineConfiguration(multimapCacheName, c.build());

// get the multimap cache
multimapCache = multimapCacheManager.get(multimapCacheName);

12.4. Limitations

In almost every case the Multimap Cache will behave as a regular Cache, but some limitations exist in the current version.

12.4.1. Support for duplicates

Duplicates are not supported yet. This means that the multimap won’t contain any duplicate key-value pair. Whenever put method is called, if the key-value pair already exist, this key-value par won’t be added. Methods used to check if a key-value pair is already present in the Multimap are the equals and hashcode.

12.4.2. Eviction

For now, the eviction works per key, and not per key-value pair. This means that whenever a key is evicted, all the values associated with the key will be evicted too. Eviction per key-value could be supported in the future.

12.4.3. Transactions

Implicit transactions are supported through the auto-commit and all the methods are non blocking. Explicit transactions work without blocking in most of the cases. Methods that will block are size, containsEntry and remove(Predicate<? super V> p)

13. Infinispan Transactions

Infinispan can be configured to use and to participate in JTA compliant transactions.

Alternatively, if transaction support is disabled, it is equivalent to using autocommit in JDBC calls, where modifications are potentially replicated after every change (if replication is enabled).

On every cache operation Infinispan does the following:

  1. Retrieves the current Transaction associated with the thread

  2. If not already done, registers XAResource with the transaction manager to be notified when a transaction commits or is rolled back.

In order to do this, the cache has to be provided with a reference to the environment’s TransactionManager. This is usually done by configuring the cache with the class name of an implementation of the TransactionManagerLookup interface. When the cache starts, it will create an instance of this class and invoke its getTransactionManager() method, which returns a reference to the TransactionManager.

Infinispan ships with several transaction manager lookup classes:

Transaction manager lookup implementations
  • EmbeddedTransactionManagerLookup: This provides with a basic transaction manager which should only be used for embedded mode when no other implementation is available. This implementation has some severe limitations to do with concurrent transactions and recovery.

  • JBossStandaloneJTAManagerLookup: If you’re running Infinispan in a standalone environment, or in JBoss AS 7 and earlier, and WildFly 8, 9, and 10, this should be your default choice for transaction manager. It’s a fully fledged transaction manager based on JBoss Transactions which overcomes all the deficiencies of the EmbeddedTransactionManager.

  • WildflyTransactionManagerLookup: If you’re running Infinispan in WildFly 11 or later, this should be your default choice for transaction manager.

  • GenericTransactionManagerLookup: This is a lookup class that locate transaction managers in the most popular Java EE application servers. If no transaction manager can be found, it defaults on the EmbeddedTransactionManager.

WARN: DummyTransactionManagerLookup has been deprecated in 9.0 and it will be removed in the future. Use EmbeddedTransactionManagerLookup instead.

Once initialized, the TransactionManager can also be obtained from the Cache itself:

//the cache must have a transactionManagerLookupClass defined
Cache cache = cacheManager.getCache();

//equivalent with calling TransactionManagerLookup.getTransactionManager();
TransactionManager tm = cache.getAdvancedCache().getTransactionManager();

13.1. Configuring transactions

Transactions are configured at cache level. Below is the configuration that affects a transaction behaviour and a small description of each configuration attribute.


or programmatically:

ConfigurationBuilder builder = new ConfigurationBuilder();
    .transactionManagerLookup(new GenericTransactionManagerLookup())
  • isolation - configures the isolation level. Check section Isolation Levels for more details. Default is REPEATABLE_READ.

  • write-skew - enables write skew checks (deprecated). Infinispan automatically sets this attribute in Library Mode. Default is false for READ_COMMITTED. Default is true for REPEATABLE_READ. See Write Skews for more details.

  • locking - configures whether the cache uses optimistic or pessimistic locking. Check section Transaction Locking for more details. Default is OPTIMISTIC.

  • auto-commit - if enable, the user does not need to start a transaction manually for a single operation. The transaction is automatically started and committed. Default is true.

  • complete-timeout - the duration in milliseconds to keep information about completed transactions. Default is 60000.

  • mode - configures whether the cache is transactional or not. Default is NONE. The available options are:

    • NONE - non transactional cache

    • FULL_XA - XA transactional cache with recovery enabled. Check section Transaction recovery for more details about recovery.

    • NON_DURABLE_XA - XA transactional cache with recovery disabled.

    • NON_XA - transactional cache with integration via Synchronization instead of XA. Check section Enlisting Synchronizations for details.

    • BATCH- transactional cache using batch to group operations. Check section Batching for details.

  • notifications - enables/disables triggering transactional events in cache listeners. Default is true.

  • protocol - configures the protocol uses. Default is DEFAULT. Values available are:

    • DEFAULT - uses the traditional Two-Phase-Commit protocol. It is described below.

    • TOTAL_ORDER - uses total order ensured by the Transport to commit transactions. Check section Total Order based commit protocol for details.

  • reaper-interval - the time interval in millisecond at which the thread that cleans up transaction completion information kicks in. Defaults is 30000.

  • recovery-cache - configures the cache name to store the recovery information. Check section Transaction recovery for more details about recovery. Default is recoveryInfoCacheName.

  • stop-timeout - the time in millisecond to wait for ongoing transaction when the cache is stopping. Default is 30000.

  • transaction-manager-lookup - configures the fully qualified class name of a class that looks up a reference to a javax.transaction.TransactionManager. Default is org.infinispan.transaction.lookup.GenericTransactionManagerLookup.

  • Versioning scheme - configure the version scheme to use when write skew is enabled with optimistic or total order transactions. Check section Write Skews for more details. Default is NONE.

For more details on how Two-Phase-Commit (2PC) is implemented in Infinispan and how locks are being acquired see the section below. More details about the configuration settings are available in Configuration reference.

13.2. Isolation levels

Infinispan offers two isolation levels - READ_COMMITTED and REPEATABLE_READ.

These isolation levels determine when readers see a concurrent write, and are internally implemented using different subclasses of MVCCEntry, which have different behaviour in how state is committed back to the data container.

Here’s a more detailed example that should help understand the difference between READ_COMMITTED and REPEATABLE_READ in the context of Infinispan. With READ_COMMITTED, if between two consecutive read calls on the same key, the key has been updated by another transaction, the second read may return the new updated value:

Thread1: tx1.begin()
Thread1: cache.get(k) // returns v
Thread2:                                       tx2.begin()
Thread2:                                       cache.get(k) // returns v
Thread2:                                       cache.put(k, v2)
Thread2:                                       tx2.commit()
Thread1: cache.get(k) // returns v2!
Thread1: tx1.commit()

With REPEATABLE_READ, the final get will still return v. So, if you’re going to retrieve the same key multiple times within a transaction, you should use REPEATABLE_READ.

However, as read-locks are not acquired even for REPEATABLE_READ, this phenomena can occur:

cache.get("A") // returns 1
cache.get("B") // returns 1

Thread1: tx1.begin()
Thread1: cache.put("A", 2)
Thread1: cache.put("B", 2)
Thread2:                                       tx2.begin()
Thread2:                                       cache.get("A") // returns 1
Thread1: tx1.commit()
Thread2:                                       cache.get("B") // returns 2
Thread2:                                       tx2.commit()

13.3. Transaction locking

13.3.1. Pessimistic transactional cache

From a lock acquisition perspective, pessimistic transactions obtain locks on keys at the time the key is written.

  1. A lock request is sent to the primary owner (can be an explicit lock request or an operation)

  2. The primary owner tries to acquire the lock:

    1. If it succeed, it sends back a positive reply;

    2. Otherwise, a negative reply is sent and the transaction is rollback.

As an example:

cache.put(k1,v1); //k1 is locked.
cache.remove(k2); //k2 is locked when this returns

When cache.put(k1,v1) returns, k1 is locked and no other transaction running anywhere in the cluster can write to it. Reading k1 is still possible. The lock on k1 is released when the transaction completes (commits or rollbacks).

For conditional operations, the validation is performed in the originator.

13.3.2. Optimistic transactional cache

With optimistic transactions locks are being acquired at transaction prepare time and are only being held up to the point the transaction commits (or rollbacks). This is different from the 5.0 default locking model where local locks are being acquire on writes and cluster locks are being acquired during prepare time.

  1. The prepare is sent to all the owners.

  2. The primary owners try to acquire the locks needed:

    1. If locking succeeds, it performs the write skew check.

    2. If the write skew check succeeds (or is disabled), send a positive reply.

    3. Otherwise, a negative reply is sent and the transaction is rolled back.

As an example:

transactionManager.commit(); //at prepare time, K1 and K2 is locked until committed/rolled back.
For conditional commands, the validation still happens on the originator.

13.3.3. What do I need - pessimistic or optimistic transactions?

From a use case perspective, optimistic transactions should be used when there is not a lot of contention between multiple transactions running at the same time. That is because the optimistic transactions rollback if data has changed between the time it was read and the time it was committed (with write skew check enabled).

On the other hand, pessimistic transactions might be a better fit when there is high contention on the keys and transaction rollbacks are less desirable. Pessimistic transactions are more costly by their nature: each write operation potentially involves a RPC for lock acquisition.

13.4. Write Skews

Write skews occur when two transactions independently and simultaneously read and write to the same key. The result of a write skew is that both transactions successfully commit updates to the same key but with different values.

In Library Mode, Infinispan automatically performs write skew checks to ensure data consistency for REPEATABLE_READ isolation levels in optimistic transactions. This allows Infinispan to detect and roll back one of the transactions.

The write-skew attribute is deprecated for Library Mode. In Remote Client/Server Mode, this attribute is not a valid declaration.

When operating in LOCAL mode, write skew checks rely on Java object references to compare differences, which provides a reliable technique for checking for write skews.

In clustered environments, you should configure data versioning to ensure reliable write skew checks. Infinispan provides an implementation of the EntryVersion interface called SIMPLE versioning, which is backed by a long that is incremented each time the entry is updated.

<versioning scheme="SIMPLE|NONE" />


new ConfigurationBuilder().versioning().scheme(SIMPLE);

13.4.1. Forcing write locks on keys in pessimitic transactions

To avoid write-skews with pessimistic transactions, lock keys at read-time with Flag.FORCE_WRITE_LOCK.

  • In non-transactional caches, Flag.FORCE_WRITE_LOCK does not work. The get() call reads the key value but does not acquire locks remotely.

  • You should use Flag.FORCE_WRITE_LOCK with transactions in which the entity is updated later in the same transaction.

Compare the following code snippets for an example of Flag.FORCE_WRITE_LOCK:

// begin the transaction
if (!cache.getAdvancedCache().lock(key)) {
   // abort the transaction because the key was not locked
} else {
   cache.put(key, value);
   // commit the transaction
// begin the transaction
try {
   // throws an exception if the key is not locked.
   cache.put(key, value);
} catch (CacheException e) {
   // mark the transaction rollback-only
// commit or rollback the transaction

13.5. Dealing with exceptions

If a CacheException (or a subclass of it) is thrown by a cache method within the scope of a JTA transaction, then the transaction is automatically marked for rollback.

13.6. Enlisting Synchronizations

By default Infinispan registers itself as a first class participant in distributed transactions through XAResource. There are situations where Infinispan is not required to be a participant in the transaction, but only to be notified by its lifecycle (prepare, complete): e.g. in the case Infinispan is used as a 2nd level cache in Hibernate.

Infinispan allows transaction enlistment through Synchronization. To enable it just use NON_XA transaction mode.

Synchronizations have the advantage that they allow TransactionManager to optimize 2PC with a 1PC where only one other resource is enlisted with that transaction (last resource commit optimization). E.g. Hibernate second level cache: if Infinispan registers itself with the TransactionManager as a XAResource than at commit time, the TransactionManager sees two XAResource (cache and database) and does not make this optimization. Having to coordinate between two resources it needs to write the tx log to disk. On the other hand, registering Infinispan as a Synchronisation makes the TransactionManager skip writing the log to the disk (performance improvement).

13.7. Batching

Batching allows atomicity and some characteristics of a transaction, but not full-blown JTA or XA capabilities. Batching is often a lot lighter and cheaper than a full-blown transaction.

Generally speaking, one should use batching API whenever the only participant in the transaction is an Infinispan cluster. On the other hand, JTA transactions (involving TransactionManager) should be used whenever the transactions involves multiple systems. E.g. considering the "Hello world!" of transactions: transferring money from one bank account to the other. If both accounts are stored within Infinispan, then batching can be used. If one account is in a database and the other is Infinispan, then distributed transactions are required.
You do not have to have a transaction manager defined to use batching.

13.7.1. API

Once you have configured your cache to use batching, you use it by calling startBatch() and endBatch() on Cache. E.g.,

Cache cache = cacheManager.getCache();
// not using a batch
cache.put("key", "value"); // will replicate immediately

// using a batch
cache.put("k1", "value");
cache.put("k2", "value");
cache.put("k2", "value");
cache.endBatch(true); // This will now replicate the modifications since the batch was started.

// a new batch
cache.put("k1", "value");
cache.put("k2", "value");
cache.put("k3", "value");
cache.endBatch(false); // This will "discard" changes made in the batch

13.7.2. Batching and JTA

Behind the scenes, the batching functionality starts a JTA transaction, and all the invocations in that scope are associated with it. For this it uses a very simple (e.g. no recovery) internal TransactionManager implementation. With batching, you get:

  1. Locks you acquire during an invocation are held until the batch completes

  2. Changes are all replicated around the cluster in a batch as part of the batch completion process. Reduces replication chatter for each update in the batch.

  3. If synchronous replication or invalidation are used, a failure in replication/invalidation will cause the batch to roll back.

  4. All the transaction related configurations apply for batching as well.

13.8. Transaction recovery

Recovery is a feature of XA transactions, which deal with the eventuality of a resource or possibly even the transaction manager failing, and recovering accordingly from such a situation.

13.8.1. When to use recovery

Consider a distributed transaction in which money is transferred from an account stored in an external database to an account stored in Infinispan. When TransactionManager.commit() is invoked, both resources prepare successfully (1st phase). During the commit (2nd) phase, the database successfully applies the changes whilst Infinispan fails before receiving the commit request from the transaction manager. At this point the system is in an inconsistent state: money is taken from the account in the external database but not visible yet in Infinispan (since locks are only released during 2nd phase of a two-phase commit protocol). Recovery deals with this situation to make sure data in both the database and Infinispan ends up in a consistent state.

13.8.2. How does it work

Recovery is coordinated by the transaction manager. The transaction manager works with Infinispan to determine the list of in-doubt transactions that require manual intervention and informs the system administrator (via email, log alerts, etc). This process is transaction manager specific, but generally requires some configuration on the transaction manager.  

Knowing the in-doubt transaction ids, the system administrator can now connect to the Infinispan cluster and replay the commit of transactions or force the rollback. Infinispan provides JMX tooling for this - this is explained extensively in the Transaction recovery and reconciliation section.

13.8.3. Configuring recovery   

Recovery is not enabled by default in Infinispan. If disabled, the TransactionManager won’t be able to work with Infinispan to determine the in-doubt transactions. The Transaction configuration section shows how to enable it.

NOTE: recovery-cache attribute is not mandatory and it is configured per-cache.

For recovery to work, mode must be set to FULL_XA, since full-blown XA transactions are needed.
Enable JMX support

In order to be able to use JMX for managing recovery JMX support must be explicitly enabled.

13.8.4. Recovery cache

In order to track in-doubt transactions and be able to reply them, Infinispan caches all transaction state for future use. This state is held only for in-doubt transaction, being removed for successfully completed transactions after when the commit/rollback phase completed.

This in-doubt transaction data is held within a local cache: this allows one to configure swapping this info to disk through cache loader in the case it gets too big. This cache can be specified through the recovery-cache configuration attribute. If not specified Infinispan will configure a local cache for you.

It is possible (though not mandated) to share same recovery cache between all the Infinispan caches that have recovery enabled. If the default recovery cache is overridden, then the specified recovery cache must use a TransactionManagerLookup that returns a different transaction manager than the one used by the cache itself.

13.8.5. Integration with the transaction manager

Even though this is transaction manager specific, generally a transaction manager would need a reference to a XAResource implementation in order to invoke XAResource.recover() on it. In order to obtain a reference to an Infinispan XAResource following API can be used:

XAResource xar = cache.getAdvancedCache().getXAResource();

It is a common practice to run the recovery in a different process from the one running the transaction.

13.8.6. Reconciliation

The transaction manager informs the system administrator on in-doubt transaction in a proprietary way. At this stage it is assumed that the system administrator knows transaction’s XID (a byte array).

A normal recovery flow is:

  • STEP 1: The system administrator connects to an Infinispan server through JMX, and lists the in doubt transactions. The image below demonstrates JConsole connecting to an Infinispan node that has an in doubt transaction.

Figure 1. Show in-doubt transactions

The status of each in-doubt transaction is displayed(in this example " PREPARED "). There might be multiple elements in the status field, e.g. "PREPARED" and "COMMITTED" in the case the transaction committed on certain nodes but not on all of them.  

  • STEP 2: The system administrator visually maps the XID received from the transaction manager to an Infinispan internal id, represented as a number. This step is needed because the XID, a byte array, cannot conveniently be passed to the JMX tool (e.g. JConsole) and then re-assembled on Infinispan’s side.

  • STEP 3: The system administrator forces the transaction’s commit/rollback through the corresponding jmx operation, based on the internal id. The image below is obtained by forcing the commit of the transaction based on its internal id.

Figure 2. Force commit
All JMX operations described above can be executed on any node, regardless of where the transaction originated.
Force commit/rollback based on XID

XID-based JMX operations for forcing in-doubt transactions' commit/rollback are available as well: these methods receive byte[] arrays describing the XID instead of the number associated with the transactions (as previously described at step 2). These can be useful e.g. if one wants to set up an automatic completion job for certain in-doubt transactions. This process is plugged into transaction manager’s recovery and has access to the transaction manager’s XID objects.

13.8.7. Want to know more?

The recovery design document describes in more detail the insides of transaction recovery implementation.

13.9. Total Order based commit protocol

The Total Order based protocol is a multi-master scheme (in this context, multi-master scheme means that all nodes can update all the data) as the (optimistic/pessimist) locking mode implemented in Infinispan. This commit protocol relies on the concept of totally ordered delivery of messages which, informally, implies that each node which delivers a set of messages, delivers them in the same order.

This protocol comes with this advantages.

  1. transactions can be committed in one phase, as they are delivered in the same order by the nodes that receive them.

  2. it mitigates distributed deadlocks.

The weaknesses of this approach are the fact that its implementation relies on a single thread per node which delivers the transaction and its modification, and the slightly cost of total ordering the messages in Transport.

Thus, this protocol delivers best performance in scenarios of high contention , in which it can benefit from the single-phase commit and the deliver thread is not the bottleneck.

Currently, the Total Order based protocol is available only in transactional caches for replicated and distributed modes.

13.9.1. Overview

The Total Order based commit protocol only affects how transactions are committed by Infinispan and the isolation level and write skew affects it behaviour.

When write skew is disabled, the transaction can be committed/rolled back in single phase. The data consistency is guaranteed by the Transport that ensures that all owners of a key will deliver the same transactions set by the same order.

On other hand, when write skew is enabled, the protocol adapts and uses one phase commit when it is safe. In XaResource enlistment, we can use one phase if the TransactionManager request a commit in one phase (last resource commit optimization) and the Infinispan cache is configured in replicated mode. This optimization is not safe in distributed mode because each node performs the write skew check validation in different keys subset. When in Synchronization enlistment, the TransactionManager does not provide any information if Infinispan is the only resource enlisted (last resource commit optimization), so it is not possible to commit in a single phase.

Commit in one phase

When the transaction ends, Infinispan sends the transaction (and its modification) in total order. This ensures all the transactions are deliver in the same order in all the involved Infinispan nodes. As a result, when a transaction is delivered, it performs a deterministic write skew check over the same state (if enabled), leading to the same outcome (transaction commit or rollback).

total order 1pc
Figure 3. 1-phase commit

The figure above demonstrates a high level example with 3 nodes. Node1 and Node3 are running one transaction each and lets assume that both transaction writes on the same key. To make it more interesting, lets assume that both nodes tries to commit at the same time, represented by the first colored circle in the figure. The blue circle represents the transaction tx1 and the green the transaction tx2 . Both nodes do a remote invocation in total order (to-send) with the transaction’s modifications. At this moment, all the nodes will agree in the same deliver order, for example, tx1 followed by tx2 . Then, each node delivers tx1 , perform the validation and commits the modifications. The same steps are performed for tx2 but, in this case, the validation will fail and the transaction is rollback in all the involved nodes.

Commit in two phases

In the first phase, it sends the modification in total order and the write skew check is performed. The result of the write skew check is sent back to the originator. As soon as it has the confirmation that all keys are successfully validated, it give a positive response to the TransactionManager. On other hand, if it receives a negative reply, it returns a negative response to the TransactionManager. Finally, the transaction is committed or aborted in the second phase depending of the TransactionManager request.

total order 2pc
Figure 4. 2-phase commit

The figure above shows the scenario described in the first figure but now committing the transactions using two phases. When tx1 is deliver, it performs the validation and it replies to the TransactionManager. Next, lets assume that tx2 is deliver before the TransactionManager request the second phase for tx1. In this case, tx2 will be enqueued and it will be validated only when tx1 is completed. Eventually, the TransactionManager for tx1 will request the second phase (the commit) and all the nodes are free to perform the validation of tx2 .

Transaction Recovery

Transaction recovery is currently not available for Total Order based commit protocol.

State Transfer

For simplicity reasons, the total order based commit protocol uses a blocking version of the current state transfer. The main differences are:

  1. enqueue the transaction deliver while the state transfer is in progress;

  2. the state transfer control messages (CacheTopologyControlCommand) are sent in total order.

This way, it provides a synchronization between the state transfer and the transactions deliver that is the same all the nodes. Although, the transactions caught in the middle of state transfer (i.e. sent before the state transfer start and deliver after it) needs to be re-sent to find a new total order involving the new joiners.

total order joing during st
Figure 5. Node joining during transaction

The figure above describes a node joining. In the scenario, the tx2 is sent in topologyId=1 but when it is received, it is in topologyId=2 . So, the transaction is re-sent involving the new nodes.

13.9.2. Configuration

To use total order in your cache, you need to add the TOA protocol in your jgroups.xml configuration file.

<tom.TOA />
Check the JGroups Manual for more details.
If you are interested in detail how JGroups guarantees total order, check the link::http://jgroups.org/manual/index.html#TOA[TOA manual].

Also, you need to set the protocol=TOTAL_ORDER in the <transaction> element, as shown in Transaction configuration.

13.9.3. When to use it?

Total order shows benefits when used in write intensive and high contented workloads. It avoids contention in the lock keys.

14. Indexing and Querying

14.1. Overview

Infinispan supports indexing and searching of Java Pojo(s) or objects encoded via Protocol Buffers stored in the grid using powerful search APIs which complement its main Map-like API.

Querying is possible both in library and client/server mode (for Java, C#, Node.js and other clients), and Infinispan can index data using Apache Lucene, offering an efficient full-text capable search engine in order to cover a wide range of data retrieval use cases.

Indexing configuration relies on a schema definition, and for that Infinispan can use annotated Java classes when in library mode, and protobuf schemas for remote clients written in other languages. By standardizing on protobuf, Infinispan allows full interoperability between Java and non-Java clients.

Apart from indexed queries, Infinispan can run queries over non-indexed data (indexless queries) and over partially indexed data (hybrid queries).

In terms of Search APIs, Infinispan has its own query language called Ickle, which is string-based and adds support for full-text querying. The Query DSL can be used for both embedded and remote java clients when full-text is not required; for Java embedded clients Infinispan offers the Hibernate Search Query API which supports running Lucene queries in the grid, apart from advanced search capabilities like Faceted and Spatial search.

Finally, Infinispan has support for Continuous Queries, which works in a reverse manner to the other APIs: instead of creating, executing a query and obtain results, it allows a client to register queries that will be evaluated continuously as data in the cluster changes, generating notifications whenever the changed data matches the queries.

14.2. Embedded Querying

Embedded querying is available when Infinispan is used as a library. No protobuf mapping is required, and both indexing and searching are done on top of Java objects. When in library mode, it is possible to run Lucene queries directly and use all the available Query APIs and it also allows flexible indexing configurations to keep latency to a minimal.

14.2.1. Quick example

We’re going to store Book instances in an Infinispan cache called "books". Book instances will be indexed, so we enable indexing for the cache, letting Infinispan configure the indexing automatically:

Infinispan configuration:

        <transport cluster="infinispan-cluster"/>
        <distributed-cache name="books">
            <indexing index="PRIMARY_OWNER" auto-config="true"/>

Obtaining the cache:

import org.infinispan.Cache;
import org.infinispan.manager.DefaultCacheManager;
import org.infinispan.manager.EmbeddedCacheManager;

EmbeddedCacheManager manager = new DefaultCacheManager("infinispan.xml");
Cache<String, Book> cache = manager.getCache("books");

Each Book will be defined as in the following example; we have to choose which properties are indexed, and for each property we can optionally choose advanced indexing options using the annotations defined in the Hibernate Search project.

import org.hibernate.search.annotations.*;
import java.util.Date;
import java.util.HashSet;
import java.util.Set;

//Values you want to index need to be annotated with @Indexed, then you pick which fields and how they are to be indexed:
public class Book {
   @Field String title;
   @Field String description;
   @Field @DateBridge(resolution=Resolution.YEAR) Date publicationYear;
   @IndexedEmbedded Set<Author> authors = new HashSet<Author>();
public class Author {
   @Field String name;
   @Field String surname;
   // hashCode() and equals() omitted

Now assuming we stored several Book instances in our Infinispan Cache , we can search them for any matching field as in the following example.

Using a Lucene Query:

// get the search manager from the cache:
SearchManager searchManager = org.infinispan.query.Search.getSearchManager(cache);

// create any standard Lucene query, via Lucene's QueryParser or any other means:
org.apache.lucene.search.Query fullTextQuery = //any Apache Lucene Query

// convert the Lucene query to a CacheQuery:
CacheQuery cacheQuery = searchManager.getQuery( fullTextQuery );

// get the results:
List<Object> found = cacheQuery.list();

A Lucene Query is often created by parsing a query in text format such as "title:infinispan AND authors.name:sanne", or by using the query builder provided by Hibernate Search.

// get the search manager from the cache:
SearchManager searchManager = org.infinispan.query.Search.getSearchManager( cache );

// you could make the queries via Lucene APIs, or use some helpers:
QueryBuilder queryBuilder = searchManager.buildQueryBuilderForClass(Book.class).get();

// the queryBuilder has a nice fluent API which guides you through all options.
// this has some knowledge about your object, for example which Analyzers
// need to be applied, but the output is a fairly standard Lucene Query.
org.apache.lucene.search.Query luceneQuery = queryBuilder.phrase()
                  .sentence("a book on highly scalable query engines")

// the query API itself accepts any Lucene Query, and on top of that
// you can restrict the result to selected class types:
CacheQuery query = searchManager.getQuery(luceneQuery, Book.class);

// and there are your results!
List objectList = query.list();

for (Object book : objectList) {

Apart from list() you have the option for streaming results, or use pagination.

For searches that do not require Lucene or full-text capabilities and are mostly about aggregation and exact matches, we can use the Infinispan Query DSL API:

import org.infinispan.query.dsl.QueryFactory;
import org.infinispan.query.dsl.Query;
import org.infinispan.query.Search;

// get the query factory:
QueryFactory queryFactory = Search.getQueryFactory(cache);

Query q = queryFactory.from(Book.class)

List<Book> list = q.list();

Finally, we can use an Ickle query directly, allowing for Lucene syntax in one or more predicates:

import org.infinispan.query.dsl.QueryFactory;
import org.infinispan.query.dsl.Query;

// get the query factory:
QueryFactory queryFactory = Search.getQueryFactory(cache);

Query q = queryFactory.create("from Book b where b.author.name = 'Stephen' and " +
                "b.description : (+'dark' -'tower')");

List<Book> list = q.list();

14.2.2. Indexing

Indexing in Infinispan happens on a per-cache basis and by default a cache is not indexed. Enabling indexing is not mandatory but queries using an index will have a vastly superior performance. On the other hand, enabling indexing can impact negatively the write throughput of a cluster, so make sure to check the query performance guide for some strategies to minimize this impact depending on the cache type and use case.

General format

To enable indexing via XML, you need to add the <indexing> element plus the index (index mode) to your cache configuration, and optionally pass additional properties.

   <cache-container default-cache="default">
      <replicated-cache name="default">
         <indexing index="ALL">
            <property name="property.name">some value</property>


import org.infinispan.configuration.cache.*;

ConfigurationBuilder cacheCfg = ...
      .addProperty("property name", "propery value")
Index names

Each property inside the index element is prefixed with the index name, for the index named org.infinispan.sample.Car the directory_provider is local-heap:

  <indexing index="ALL">
      <property name="org.infinispan.sample.Car.directory_provider">local-heap</property>
      .addProperty("org.infinispan.sample.Car.directory_provider", "local-heap")

Infinispan creates an index for each entity existent in a cache, and it allows to configure those indexes independently. For a class annotated with @Indexed, the index name is the fully qualified class name, unless overridden with the name argument in the annotation.

In the snippet below, the default storage for all entities is infinispan, but Boat instances will be stored on local-heap in an index named boatIndex. Airplane entities will also be stored in local-heap. Any other entity’s index will be configured with the property prefixed by default.

package org.infinispan.sample;

@Indexed(name = "boatIndex")
public class Boat {


public class Airplane {

    <indexing index="ALL">
        <property name="default.directory_provider">infinispan</property>
        <property name="boatIndex.directory_provider">local-heap</property>
        <property name="org.infinispan.sample.Airplane.directory_provider">
Specifying indexed Entities

Infinispan can automatically recognize and manage indexes for different entity types in a cache. Future versions of Infinispan will remove this capability so it’s recommended to declare upfront which types are going to be indexed (list them by their fully qualified class name). This can be done via xml:

   <cache-container default-cache="default">
      <replicated-cache name="default">
         <indexing index="ALL">

or programmatically:


In server mode, the class names listed under the 'indexed-entities' element must use the 'extended' class name format which is composed of a JBoss Modules module identifier, a slot name, and the fully qualified class name, these three components being separated by the ':' character, (eg. "com.acme.my-module-with-entity-classes:my-slot:com.acme.query.test.Car"). The entity classes must be located in the referenced module, which can be either a user supplied module deployed in the 'modules' folder of your server or a plain jar deployed in the 'deployments' folder. The module in question will become an automatic dependency of your Cache, so its eventual redeployment will cause the cache to be restarted.

Only for server, if you fail to follow the requirement of using 'extended' class names and use a plain class name its resolution will fail due to missing class because the wrong ClassLoader is being used (the Infinispan’s internal class path is being used).
Index mode

An Infinispan node typically receives data from two sources: local and remote. Local translates to clients manipulating data using the map API in the same JVM; remote data comes from other Infinispan nodes during replication or rebalancing.

The index mode configuration defines, from a node in the cluster point of view, which data gets indexed.

Possible values:

  • ALL: all data is indexed, local and remote.

  • LOCAL: only local data is indexed.

  • PRIMARY_OWNER: Only entries containing keys that the node is primary owner will be indexed, regardless of local or remote origin.

  • NONE: no data is indexed. Equivalent to not configure indexing at all.

Index Managers

Index managers are central components in Infinispan Querying responsible for the indexing configuration, distribution and internal lifecycle of several query components such as Lucene’s IndexReader and IndexWriter. Each Index Manager is associated with a Directory Provider, which defines the physical storage of the index.

Regarding index distribution, Infinispan can be configured with shared or non-shared indexes.

Shared indexes

A shared index is a single, distributed, cluster-wide index for a certain cache. The main advantage is that the index is visible from every node and can be queried as if the index were local, there is no need to broadcast queries to all members and aggregate the results. The downside is that Lucene does not allow more than a single process writing to the index at the same time, and the coordination of lock acquisitions needs to be done by a proper shared index capable index manager. In any case, having a single write lock cluster-wise can lead to some degree of contention under heavy writing.

Infinispan supports a shared index leveraging the Infinispan Directory Provider, which stores indexes in a separate set of caches, called InfinispanIndexManager.

Effect of the index mode

Shared indexes should not use the ALL index mode since it’d lead to redundant indexing: since there is a single index cluster wide, the entry would get indexed when inserted via Cache API, and another time when Infinispan replicates it to another node. The ALL mode is usually associates with non-shared indexes in order to create full index replicas on each node.


This index manager uses the Infinispan Directory Provider, and is suitable for creating shared indexes. Index mode should be set to LOCAL in this configuration.


<distributed-cache name="default" >
    <indexing index="PRIMARY_OWNER">
        <property name="default.indexmanager">org.infinispan.query.indexmanager.InfinispanIndexManager</property>
        <!-- Optional: tailor each cache used internally by the InfinispanIndexManager -->
        <property name="default.locking_cachename">LuceneIndexesLocking_custom</property>
        <property name="default.data_cachename">LuceneIndexesData_custom</property>
        <property name="default.metadata_cachename">LuceneIndexesMetadata_custom</property>

<!-- Optional -->
<replicated-cache name="LuceneIndexesLocking_custom">
    <indexing index="NONE" />
    <!-- extra configuration -->

<!-- Optional -->
<replicated-cache name="LuceneIndexesMetadata_custom">
    <indexing index="NONE" />
    <!-- extra configuration -->

<!-- Optional -->
<distributed-cache name="LuceneIndexesData_custom">
    <indexing index="NONE" />
    <!-- extra configuration -->

Indexes are stored in a set of clustered caches, called by default LuceneIndexesData, LuceneIndexesMetadata and LuceneIndexesLocking.

The LuceneIndexesLocking cache is used to store Lucene locks, and it is a very small cache: it will contain one entry per entity (index).

The LuceneIndexesMetadata cache is used to store info about the logical files that are part of the index, such as names, chunks and sizes and it is also small in size.

The LuceneIndexesData cache is where most of the index is located: it is much bigger then the other two but should be smaller than the data in the cache itself, thanks to Lucene’s efficient storing techniques.

It’s not necessary to redefine the configuration of those 3 cases, Infinispan will pick sensible defaults. Reasons re-define them would be performance tuning for a specific scenario, or for example to make them persistent by configuring a cache store.

In order to avoid index corruption when two or more nodes of the cluster try to write to the index at the same time, the InfinispanIndexManager internally elects a master in the cluster (which is the JGroups coordinator) and forwards all indexing works to this master.

Non-shared indexes

Non-shared indexes are independent indexes at each node. This setup is particularly advantageous for replicated caches where each node has all the cluster data and thus can hold all the indexes as well, offering optimal query performance with zero network latency when querying. Another advantage is, since the index is local to each node, there is less contention during writes due to the fact that each node is subjected to its own index lock, not a cluster wide one.

Since each node might hold a partial index, it may be necessary to link#query_clustered_query_api[broadcast] queries in order to get correct search results, which can add latency. If the cache is REPL, though, the broadcast is not necessary: each node can hold a full local copy of the index and queries runs at optimal speed taking advantage of a local index.

Infinispan has two index managers suitable for non-shared indexes: directory-based and near-real-time. Storage wise, non-shared indexes can be located in ram, filesystem, or Infinispan local caches.

Effect of the index mode

The directory-based and near-real-time index managers can be associated with different index modes, resulting in different index distributions.

REPL caches combined with the ALL index mode will result in a full copy of the cluster-wide index on each node. This mode allows queries to become effectively local without network latency. This is the recommended mode to index any REPL cache, and that’s the choice picked by the auto-config when the a REPL cache is detected. The ALL mode should not be used with DIST caches.

REPL or DIST caches combined with LOCAL index mode will cause each node to index only data inserted from the same JVM, causing an uneven distribution of the index. In order to obtain correct query results, it’s necessary to use broadcast queries.

REPL or DIST caches combined with PRIMARY_OWNER will also need broadcast queries. Differently from the LOCAL mode, each node’s index will contain indexed entries which key is primarily owned by the node according to the consistent hash, leading to a more evenly distributed indexes among the nodes.

directory-based index manager

This is the default Index Manager used when no index manager is configured. The directory-based index manager is used to manage indexes backed by a local lucene directory. It supports ram, filesystem and non-clustered infinispan storage.

Filesystem storage

This is the default storage, and used when index manager configuration is omitted. The index is stored in the filesystem using a MMapDirectory. It is the recommended storage for local indexes. Although indexes are persistent on disk, they get memory mapped by Lucene and thus offer decent query performance.


<replicated-cache name="myCache">
   <indexing index="ALL">
      <!-- Optional: define base folder for indexes -->
      <property name="default.indexBase">${java.io.tmpdir}/baseDir</property>

Infinispan will create a different folder under default.indexBase for each entity (index) present in the cache.

Ram storage

Index is stored in memory using a Lucene RAMDirectory. Not recommended for large indexes or highly concurrent situations. Indexes stored in Ram are not persistent, so after a cluster shutdown a re-index is needed. Configuration:

<replicated-cache name="myCache">
   <indexing index="ALL">
      <property name="default.directory_provider">local-heap</property>

Infinispan storage

Infinispan storage makes use of the Infinispan Lucene directory that saves the indexes to a set of caches; those caches can be configured like any other Infinispan cache, for example by adding a cache store to have indexes persisted elsewhere apart from memory. In order to use Infinispan storage with a non-shared index, it’s necessary to use LOCAL caches for the indexes:

<replicated-cache name="default">
    <indexing index="ALL">
        <property name="default.locking_cachename">LuceneIndexesLocking_custom</property>
        <property name="default.data_cachename">LuceneIndexesData_custom</property>
        <property name="default.metadata_cachename">LuceneIndexesMetadata_custom</property>

<local-cache name="LuceneIndexesLocking_custom">
    <indexing index="NONE" />

<local-cache name="LuceneIndexesMetadata_custom">
    <indexing index="NONE" />

<local-cache name="LuceneIndexesData_custom">
    <indexing index="NONE" />
near-real-time index manager

Similar to the directory-based index manager but takes advantage of the Near-Real-Time features of Lucene. It has better write performance than the directory-based because it flushes the index to the underlying store less often. The drawback is that unflushed index changes can be lost in case of a non-clean shutdown. Can be used in conjunction with local-heap, filesystem and local infinispan storage. Configuration for each different storage type is the same as the directory-based index manager.

Example with ram:

<replicated-cache name="default">
    <indexing index="ALL">
        <property name="default.indexmanager">near-real-time</property>
        <property name="default.directory_provider">local-heap</property>

Example with filesystem:

<replicated-cache name="default">
    <indexing index="ALL">
        <property name="default.indexmanager">near-real-time</property>
External indexes

Apart from having shared and non-shared indexes managed by Infinispan itself, it is possible to offload indexing to a third party search engine: currently Infinispan supports Elasticsearch as a external index storage.

Elasticsearch IndexManager (experimental)

This index manager forwards all indexes to an external Elasticsearch server. This is an experimental integration and some features may not be available, for example indexNullAs for @IndexedEmbedded annotations is not currently supported.


<indexing index="PRIMARY_OWNER">
    <property name="default.indexmanager">elasticsearch</property>
    <property name="default.elasticsearch.host">link:http://elasticHost:9200</property>
    <!-- other elasticsearch configurations -->

The index mode should be set to LOCAL, since Infinispan considers Elasticsearch as a single shared index. More information about Elasticsearch integration, including the full description of the configuration properties can be found at the Hibernate Search manual.

Automatic configuration

The attribute auto-config provides a simple way of configuring indexing based on the cache type. For replicated and local caches, the indexing is configured to be persisted on disk and not shared with any other processes. Also, it is configured so that minimum delay exists between the moment an object is indexed and the moment it is available for searches (near real time).

<local-cache name="default">
    <indexing index="PRIMARY_OWNER" auto-config="true"/>
it is possible to redefine any property added via auto-config, and also add new properties, allowing for advanced tuning.

The auto config adds the following properties for replicated and local caches:

Property name value description



Filesystem based index. More details at Hibernate Search documentation



indexing operation in exclusive mode, allowing Hibernate Search to optimize writes



make use of Lucene near real time feature, meaning indexed objects are promptly available to searches



Reuse index reader across several queries, thus avoiding reopening it

For distributed caches, the auto-config configure indexes in Infinispan itself, internally handled as a master-slave mechanism where indexing operations are sent to a single node which is responsible to write to the index.

The auto config properties for distributed caches are:

Property name value description



Indexes stored in Infinispan. More details at Hibernate Search documentation



indexing operation in exclusive mode, allowing Hibernate Search to optimize writes



Delegates index writing to a single node in the Infinispan cluster



Reuse index reader across several queries, avoiding reopening it


Occasionally you might need to rebuild the Lucene index by reconstructing it from the data stored in the Cache. You need to rebuild the index if you change the definition of what is indexed on your types, or if you change for example some Analyzer parameter, as Analyzers affect how the index is written. Also, you might need to rebuild the index if you had it destroyed by some system administration mistake. To rebuild the index just get a reference to the MassIndexer and start it; beware it might take some time as it needs to reprocess all data in the grid!

// Blocking execution
SearchManager searchManager = Search.getSearchManager(cache);

// Non blocking execution
CompletableFuture<Void> future = searchManager.getMassIndexer().startAsyc();
This is also available as a start JMX operation on the MassIndexer MBean registered under the name org.infinispan:type=Query,manager="{name-of-cache-manager}",cache="{name-of-cache}",component=MassIndexer.
Mapping Entities

Infinispan relies on the rich API of Hibernate Search in order to define fine grained configuration for indexing at entity level. This configuration includes which fields are annotated, which analyzers should be used, how to map nested objects and so on. Detailed documentation is available at the Hibernate Search manual.


Unlike Hibernate Search, using @DocumentId to mark a field as identifier does not apply to Infinispan values; in Infinispan the identifier for all @Indexed objects is the key used to store the value. You can still customize how the key is indexed using a combination of @Transformable , custom types and custom FieldBridge implementations.

@Transformable keys

The key for each value needs to be indexed as well, and the key instance must be transformed in a String. Infinispan includes some default transformation routines to encode common primitives, but to use a custom key you must provide an implementation of org.infinispan.query.Transformer .

Registering a key Transformer via annotations

You can annotate your key class with org.infinispan.query.Transformable and your custom transformer implementation will be picked up automatically:

@Transformable(transformer = CustomTransformer.class)
public class CustomKey {

public class CustomTransformer implements Transformer {
   public Object fromString(String s) {
      return new CustomKey(...);

   public String toString(Object customType) {
      CustomKey ck = (CustomKey) customType;
      return ...

Registering a key Transformer via the cache indexing configuration

You can use the key-transformers xml element in both embedded and server config:

<replicated-cache name="test">
    <indexing index="ALL" auto-config="true">
            <key-transformer key="com.mycompany.CustomKey" transformer="com.mycompany.CustomTransformer"/>

or alternatively, you can achieve the same effect by using the Java configuration API (embedded mode):

   ConfigurationBuilder builder = ...
         .addKeyTransformer(CustomKey.class, CustomTransformer.class);

Registering a Transformer programmatically at runtime

Using this technique, you don’t have to annotate your custom key type and you also do not add the transformer to the, cache indexing configuration, instead, you can add it to the SearchManagerImplementor dynamically at runtime by invoking org.infinispan.query.spi.SearchManagerImplementor.registerKeyTransformer(Class<?>, Class<? extends Transformer>):

org.infinispan.query.spi.SearchManagerImplementor manager = Search.getSearchManager(cache).unwrap(SearchManagerImplementor.class);
manager.registerKeyTransformer(keyClass, keyTransformerClass);
This approach is deprecated since 10.0 because it can lead to situations when a newly started node receives cache entries via initial state transfer and is not able to index them because the needed key transformers are not yet registered (and can only be registered after the Cache has been fully started). This undesirable situation is avoided if you register your key transformers using the other available approaches (configuration and annotation).
Programmatic mapping

Instead of using annotations to map an entity to the index, it’s also possible to configure it programmatically.

In the following example we map an object Author which is to be stored in the grid and made searchable on two properties but without annotating the class.

import org.apache.lucene.search.Query;
import org.hibernate.search.cfg.Environment;
import org.hibernate.search.cfg.SearchMapping;
import org.hibernate.search.query.dsl.QueryBuilder;
import org.infinispan.Cache;
import org.infinispan.configuration.cache.Configuration;
import org.infinispan.configuration.cache.ConfigurationBuilder;
import org.infinispan.configuration.cache.Index;
import org.infinispan.manager.DefaultCacheManager;
import org.infinispan.query.CacheQuery;
import org.infinispan.query.Search;
import org.infinispan.query.SearchManager;

import java.io.IOException;
import java.lang.annotation.ElementType;
import java.util.Properties;

SearchMapping mapping = new SearchMapping();
       .property("name", ElementType.METHOD).field()
       .property("surname", ElementType.METHOD).field();

Properties properties = new Properties();
properties.put(Environment.MODEL_MAPPING, mapping);
properties.put("hibernate.search.[other options]", "[...]");

Configuration infinispanConfiguration = new ConfigurationBuilder()

DefaultCacheManager cacheManager = new DefaultCacheManager(infinispanConfiguration);

Cache<Long, Author> cache = cacheManager.getCache();
SearchManager sm = Search.getSearchManager(cache);

Author author = new Author(1, "Manik", "Surtani");
cache.put(author.getId(), author);

QueryBuilder qb = sm.buildQueryBuilderForClass(Author.class).get();
Query q = qb.keyword().onField("name").matching("Manik").createQuery();
CacheQuery cq = sm.getQuery(q, Author.class);
assert cq.getResultSize() == 1;

14.2.3. Querying APIs

You can query Infinispan using:

  • Lucene or Hibernate Search Queries. Infinispan exposes the Hibernate Search DSL, which produces Lucene queries. You can run Lucene queries on single nodes or broadcast queries to multiple nodes in an Infinispan cluster.

  • Ickle queries, a custom string-based query language with full-text extensions.

Hibernate Search

Apart from supporting Hibernate Search annotations to configure indexing, it’s also possible to query the cache using other Hibernate Search APIs

Running Lucene queries

To run a Lucene query directly, simply create and wrap it in a CacheQuery:

import org.infinispan.query.Search;
import org.infinispan.query.SearchManager;
import org.apache.lucene.Query;

SearchManager searchManager = Search.getSearchManager(cache);
Query query = searchManager.buildQueryBuilderForClass(Book.class).get()
CacheQuery<Book> cacheQuery = searchManager.getQuery(query);
Using the Hibernate Search DSL

The Hibernate Search DSL can be used to create the Lucene Query, example:

import org.infinispan.query.Search;
import org.infinispan.query.SearchManager;
import org.apache.lucene.search.Query;

Cache<String, Book> cache = ...

SearchManager searchManager = Search.getSearchManager(cache);

Query luceneQuery = searchManager

List<Object> results = searchManager.getQuery(luceneQuery).list();

For a detailed description of the query capabilities of this DSL, see the relevant section of the Hibernate Search manual.

Infinispan support Faceted Searches by using the Hibernate Search FacetManager:

// Cache is indexed
Cache<Integer, Book> cache = ...

// Obtain the Search Manager
SearchManager searchManager = Search.getSearchManager(cache);

// Create the query builder
QueryBuilder queryBuilder = searchManager.buildQueryBuilderForClass(Book.class).get();

// Build any Lucene Query. Here it's using the DSL to do a Lucene term query on a book name
Query luceneQuery = queryBuilder.keyword().wildcard().onField("name").matching("bitcoin").createQuery();

// Wrap into a cache Query
CacheQuery<Book> query = searchManager.getQuery(luceneQuery);

// Define the Facet characteristics
FacetingRequest request = queryBuilder.facet()

// Associated the FacetRequest with the query
FacetManager facetManager = query.getFacetManager().enableFaceting(request);

// Obtain the facets
List<Facet> facetList = facetManager.getFacets("year_facet");

A Faceted search like above will return the number books that match 'bitcoin' released on a yearly basis, for example:

AbstractFacet{facetingName='year_facet', fieldName='year', value='2008', count=1}
AbstractFacet{facetingName='year_facet', fieldName='year', value='2009', count=1}
AbstractFacet{facetingName='year_facet', fieldName='year', value='2010', count=1}
AbstractFacet{facetingName='year_facet', fieldName='year', value='2011', count=1}
AbstractFacet{facetingName='year_facet', fieldName='year', value='2012', count=1}
AbstractFacet{facetingName='year_facet', fieldName='year', value='2016', count=1}
AbstractFacet{facetingName='year_facet', fieldName='year', value='2015', count=2}
AbstractFacet{facetingName='year_facet', fieldName='year', value='2013', count=3}

For more info about Faceted Search, see Hibernate Search Faceting

Spatial Queries

Infinispan also supports Spatial Queries, allowing to combining full-text with restrictions based on distances, geometries or geographic coordinates.

Example, we start by using the @Spatial annotation in our entity that will be searched, together with @Latitude and @Longitude:

public class Restaurant {

      private Double latitude;

      private Double longitude;

      @Field(store = Store.YES)
      String name;

      // Getters, Setters and other members omitted


to run spatial queries, the Hibernate Search DSL can be used:

// Cache is configured as indexed
Cache<String, Restaurant> cache = ...

// Obtain the SearchManager
Searchmanager searchManager = Search.getSearchManager(cache);

// Build the Lucene Spatial Query
Query query = Search.getSearchManager(cache).buildQueryBuilderForClass(Restaurant.class).get()
            .within( 2, Unit.KM )
              .ofLatitude( centerLatitude )
              .andLongitude( centerLongitude )

// Wrap in a cache Query
CacheQuery<Restaurant> cacheQuery = searchManager.getQuery(query);

List<Restaurant> nearBy = cacheQuery.list();

It’s possible to specify a query mode for indexed queries. IndexedQueryMode.BROADCAST allows to broadcast a query to each node of the cluster, retrieve the results and combine them before returning to the caller. It is suitable for use in conjunction with non-shared indexes, since each node’s local index will have only a subset of the data indexed.

IndexedQueryMode.FETCH will execute the query in the caller. If all the indexes for the cluster wide data are available locally, performance will be optimal, otherwise this query mode may involve fetching indexes data from remote nodes.

The IndexedQueryMode is supported for Ickle queries and Lucene Queries (but not for Query DSL).


CacheQuery<Person> broadcastQuery = Search.getSearchManager(cache).getQuery(new MatchAllDocsQuery(), IndexedQueryMode.BROADCAST);

List<Person> result = broadcastQuery.list();
Infinispan Query DSL
The Query DSL (QueryBuilder and related interfaces) are deprecated and will be removed in next major version. Please use Ickle queries instead.

Infinispan provides its own query DSL, independent of Lucene and Hibernate Search. Decoupling the query API from the underlying query and indexing mechanism makes it possible to introduce new alternative engines in the future, besides Lucene, and still being able to use the same uniform query API. The current implementation of indexing and searching is still based on Hibernate Search and Lucene so all indexing related aspects presented in this chapter still apply.

The new API simplifies the writing of queries by not exposing the user to the low level details of constructing Lucene query objects and also has the advantage of being available to remote Hot Rod clients. But before delving into further details, let’s examine first a simple example of writing a query for the Book entity from the previous example.

Query example using Infinispan’s query DSL
import org.infinispan.query.dsl.*;

// get the DSL query factory from the cache, to be used for constructing the Query object:
QueryFactory qf = org.infinispan.query.Search.getQueryFactory(cache);

// create a query for all the books that have a title which contains "engine":
org.infinispan.query.dsl.Query query = qf.from(Book.class)

// get the results:
List<Book> list = query.list();

The API is located in the org.infinispan.query.dsl package. A query is created with the help of the QueryFactory instance which is obtained from the per-cache SearchManager. Each QueryFactory instance is bound to the same Cache instance as the SearchManager, but it is otherwise a stateless and thread-safe object that can be used for creating multiple queries in parallel.

Query creation starts with the invocation of the from(Class entityType) method which returns a QueryBuilder object that is further responsible for creating queries targeted to the specified entity class from the given cache.

A query will always target a single entity type and is evaluated over the contents of a single cache. Running a query over multiple caches or creating queries that target several entity types (joins) is not supported.

The QueryBuilder accumulates search criteria and configuration specified through the invocation of its DSL methods and is ultimately used to build a Query object by the invocation of the QueryBuilder.build() method that completes the construction. Being a stateful object, it cannot be used for constructing multiple queries at the same time (except for nested queries) but can be reused afterwards.

This QueryBuilder is different from the one from Hibernate Search but has a somewhat similar purpose, hence the same name. We are considering renaming it in near future to prevent ambiguity.

Executing the query and fetching the results is as simple as invoking the list() method of the Query object. Once executed the Query object is not reusable. If you need to re-execute it in order to obtain fresh results then a new instance must be obtained by calling QueryBuilder.build().

Filtering operators

Constructing a query is a hierarchical process of composing multiple criteria and is best explained following this hierarchy.

The simplest possible form of a query criteria is a restriction on the values of an entity attribute according to a filtering operator that accepts zero or more arguments. The entity attribute is specified by invoking the having(String attributePath) method of the query builder which returns an intermediate context object (FilterConditionEndContext) that exposes all the available operators. Each of the methods defined by FilterConditionEndContext is an operator that accepts an argument, except for between which has two arguments and isNull which has no arguments. The arguments are statically evaluated at the time the query is constructed, so if you’re looking for a feature similar to SQL’s correlated sub-queries, that is not currently available.

// a single query criterion
QueryBuilder qb = ...
qb.having("title").eq("Hibernate Search in Action");
Table 1. FilterConditionEndContext exposes the following filtering operators:
Filter Arguments Description


Collection values

Checks that the left operand is equal to one of the elements from the Collection of values given as argument.


Object…​ values

Checks that the left operand is equal to one of the (fixed) list of values given as argument.


Object value

Checks that the left argument (which is expected to be an array or a Collection) contains the given element.


Collection values

Checks that the left argument (which is expected to be an array or a Collection) contains all the elements of the given collection, in any order.


Object…​ values

Checks that the left argument (which is expected to be an array or a Collection) contains all of the the given elements, in any order.


Collection values

Checks that the left argument (which is expected to be an array or a Collection) contains any of the elements of the given collection.


Object…​ values

Checks that the left argument (which is expected to be an array or a Collection) contains any of the the given elements.


Checks that the left argument is null.


String pattern

Checks that the left argument (which is expected to be a String) matches a wildcard pattern that follows the JPA rules.


Object value

Checks that the left argument is equal to the given value.


Object value

Alias for eq.


Object value

Checks that the left argument is greater than the given value.


Object value

Checks that the left argument is greater than or equal to the given value.


Object value

Checks that the left argument is less than the given value.


Object value

Checks that the left argument is less than or equal to the given value.


Object from, Object to

Checks that the left argument is between the given range limits.

It’s important to note that query construction requires a multi-step chaining of method invocation that must be done in the proper sequence, must be properly completed exactly once and must not be done twice, or it will result in an error. The following examples are invalid, and depending on each case they lead to criteria being ignored (in benign cases) or an exception being thrown (in more serious ones).

// Incomplete construction. This query does not have any filter on "title" attribute yet,
// although the author may have intended to add one.
QueryBuilder qb1 = ...
Query q1 = qb1.build(); // consequently, this query matches all Book instances regardless of title!

// Duplicated completion. This results in an exception at run-time.
// Maybe the author intended to connect two conditions with a boolean operator,
// but this does NOT actually happen here.
QueryBuilder qb2 = ...
qb2.having("title").like("%Data Grid%");
qb2.having("description").like("%clustering%");   // will throw java.lang.IllegalStateException: Sentence already started. Cannot use 'having(..)' again.
Query q2 = qb2.build();
Filtering based on attributes of embedded entities

The having method also accepts dot separated attribute paths for referring to embedded entity attributes, so the following is a valid query:

// match all books that have an author named "Manik"
Query query = queryFactory.from(Book.class)

Each part of the attribute path must refer to an existing indexed attribute in the corresponding entity or embedded entity class respectively. It’s possible to have multiple levels of embedding.

Boolean conditions

Combining multiple attribute conditions with logical conjunction (and) and disjunction (or) operators in order to create more complex conditions is demonstrated in the following example. The well known operator precedence rule for boolean operators applies here, so the order of DSL method invocations during construction is irrelevant. Here and operator still has higher priority than or even though or was invoked first.

// match all books that have "Data Grid" in their title
// or have an author named "Manik" and their description contains "clustering"
Query query = queryFactory.from(Book.class)
  .having("title").like("%Data Grid%")

Boolean negation is achieved with the not operator, which has highest precedence among logical operators and applies only to the next simple attribute condition.

// match all books that do not have "Data Grid" in their title and are authored by "Manik"
Query query = queryFactory.from(Book.class)
  .not().having("title").like("%Data Grid%")
Nested conditions

Changing the precedence of logical operators is achieved with nested filter conditions. Logical operators can be used to connect two simple attribute conditions as presented before, but can also connect a simple attribute condition with the subsequent complex condition created with the same query factory.

// match all books that have an author named "Manik" and their title contains
// "Data Grid" or their description contains "clustering"
Query query = queryFactory.from(Book.class)
  .and(queryFactory.having("title").like("%Data Grid%")

In some use cases returning the whole domain object is overkill if only a small subset of the attributes are actually used by the application, especially if the domain entity has embedded entities. The query language allows you to specify a subset of attributes (or attribute paths) to return - the projection. If projections are used then the Query.list() will not return the whole domain entity but will return a List of Object[], each slot in the array corresponding to a projected attribute.

// match all books that have "Data Grid" in their title or description
// and return only their title and publication year
Query query = queryFactory.from(Book.class)
  .select("title", "publicationYear")
  .having("title").like("%Data Grid%")
  .or().having("description").like("%Data Grid%"))

Ordering the results based on one or more attributes or attribute paths is done with the QueryBuilder.orderBy( ) method which accepts an attribute path and a sorting direction. If multiple sorting criteria are specified, then the order of invocation of orderBy method will dictate their precedence. But you have to think of the multiple sorting criteria as acting together on the tuple of specified attributes rather than in a sequence of individual sorting operations on each attribute.

// match all books that have "Data Grid" in their title or description
// and return them sorted by the publication year and title
Query query = queryFactory.from(Book.class)
  .orderBy("publicationYear", SortOrder.DESC)
  .orderBy("title", SortOrder.ASC)
  .having("title").like("%Data Grid%")
  .or().having("description").like("%Data Grid%"))

You can limit the number of returned results by setting the maxResults property of QueryBuilder. This can be used in conjunction with setting the startOffset in order to achieve pagination of the result set.

// match all books that have "clustering" in their title
// sorted by publication year and title
// and return 3'rd page of 10 results
Query query = queryFactory.from(Book.class)
  .orderBy("publicationYear", SortOrder.DESC)
  .orderBy("title", SortOrder.ASC)
Even if the results being fetched are limited to maxResults you can still find the total number of matching results by calling Query.getResultSize().
Grouping and Aggregation

Infinispan has the ability to group query results according to a set of grouping fields and construct aggregations of the results from each group by applying an aggregation function to the set of values that fall into each group. Grouping and aggregation can only be applied to projection queries. The supported aggregations are: avg, sum, count, max, min. The set of grouping fields is specified with the groupBy(field) method, which can be invoked multiple times. The order used for defining grouping fields is not relevant. All fields selected in the projection must either be grouping fields or else they must be aggregated using one of the grouping functions described below. A projection field can be aggregated and used for grouping at the same time. A query that selects only grouping fields but no aggregation fields is legal. ⁠

Example: Grouping Books by author and counting them.

Query query = queryFactory.from(Book.class)
    .select(Expression.property("author"), Expression.count("title"))
A projection query in which all selected fields have an aggregation function applied and no fields are used for grouping is allowed. In this case the aggregations will be computed globally as if there was a single global group.

The following aggregation functions may be applied to a field: avg, sum, count, max, min

  • avg() - Computes the average of a set of numbers. Accepted values are primitive numbers and instances of java.lang.Number. The result is represented as java.lang.Double. If there are no non-null values the result is null instead.

  • count() - Counts the number of non-null rows and returns a java.lang.Long. If there are no non-null values the result is 0 instead.

  • max() - Returns the greatest value found. Accepted values must be instances of java.lang.Comparable. If there are no non-null values the result is null instead.

  • min() - Returns the smallest value found. Accepted values must be instances of java.lang.Comparable. If there are no non-null values the result is null instead.

  • sum() - Computes the sum of a set of Numbers. If there are no non-null values the result is null instead. The following table indicates the return type based on the specified field.

Table 2. Table sum return type
Field Type Return Type

Integral (other than BigInteger)


Float or Double






Evaluation of queries with grouping and aggregation

Aggregation queries can include filtering conditions, like usual queries. Filtering can be performed in two stages: before and after the grouping operation. All filter conditions defined before invoking the groupBy method will be applied before the grouping operation is performed, directly to the cache entries (not to the final projection). These filter conditions may reference any fields of the queried entity type, and are meant to restrict the data set that is going to be the input for the grouping stage. All filter conditions defined after invoking the groupBy method will be applied to the projection that results from the projection and grouping operation. These filter conditions can either reference any of the groupBy fields or aggregated fields. Referencing aggregated fields that are not specified in the select clause is allowed; however, referencing non-aggregated and non-grouping fields is forbidden. Filtering in this phase will reduce the amount of groups based on their properties. Sorting may also be specified similar to usual queries. The ordering operation is performed after the grouping operation and can reference any of the groupBy fields or aggregated fields.

Using Named Query Parameters

Instead of building a new Query object for every execution it is possible to include named parameters in the query which can be substituted with actual values before execution. This allows a query to be defined once and be efficiently executed many times. Parameters can only be used on the right-hand side of an operator and are defined when the query is created by supplying an object produced by the org.infinispan.query.dsl.Expression.param(String paramName) method to the operator instead of the usual constant value. Once the parameters have been defined they can be set by invoking either Query.setParameter(parameterName, value) or Query.setParameters(parameterMap) as shown in the examples below. ⁠

import org.infinispan.query.Search;
import org.infinispan.query.dsl.*;

QueryFactory queryFactory = Search.getQueryFactory(cache);
// Defining a query to search for various authors and publication years
Query query = queryFactory.from(Book.class)

// Set actual parameter values
query.setParameter("authorName", "Doe");
query.setParameter("publicationYear", 2010);

// Execute the query
List<Book> found = query.list();

Alternatively, multiple parameters may be set at once by supplying a map of actual parameter values: ⁠

Setting multiple named parameters at once
import java.util.Map;
import java.util.HashMap;


Map<String, Object> parameterMap = new HashMap<>();
parameterMap.put("authorName", "Doe");
parameterMap.put("publicationYear", 2010);

A significant portion of the query parsing, validation and execution planning effort is performed during the first execution of a query with parameters. This effort is not repeated during subsequent executions leading to better performance compared to a similar query using constant values instead of query parameters.
More Query DSL samples

Probably the best way to explore using the Query DSL API is to have a look at our tests suite. QueryDslConditionsTest is a fine example.


Create relational and full-text queries in both Library and Remote Client-Server mode with the Ickle query language.

Ickle is string-based and has the following characteristics:

  • Query Java classes and supports Protocol Buffers.

  • Queries can target a single entity type.

  • Queries can filter on properties of embedded objects, including collections.

  • Supports projections, aggregations, sorting, named parameters.

  • Supports indexed and non-indexed execution.

  • Supports complex boolean expressions.

  • Supports full-text queries.

  • Does not support computations in expressions, such as user.age > sqrt(user.shoeSize+3).

  • Does not support joins.

  • Does not support subqueries.

  • Is supported across various {Infinispan} APIs. Whenever a Query is produced by the QueryBuilder is accepted, including continuous queries or in event filters for listeners.

To use the API, first obtain a QueryFactory to the cache and then call the .create() method, passing in the string to use in the query. For instance:

QueryFactory qf = Search.getQueryFactory(remoteCache);
Query q = qf.create("from sample_bank_account.Transaction where amount > 20");

When using Ickle all fields used with full-text operators must be both Indexed and Analysed.

Ickle Query Language Parser Syntax

The parser syntax for the Ickle query language has some notable rules:

  • Whitespace is not significant.

  • Wildcards are not supported in field names.

  • A field name or path must always be specified, as there is no default field.

  • && and || are accepted instead of AND or OR in both full-text and JPA predicates.

  • ! may be used instead of NOT.

  • A missing boolean operator is interpreted as OR.

  • String terms must be enclosed with either single or double quotes.

  • Fuzziness and boosting are not accepted in arbitrary order; fuzziness always comes first.

  • != is accepted instead of <>.

  • Boosting cannot be applied to >,>=,<, operators. Ranges may be used to achieve the same result.

Fuzzy Queries

To execute a fuzzy query add ~ along with an integer, representing the distance from the term used, after the term. For instance

Query fuzzyQuery = qf.create("from sample_bank_account.Transaction where description : 'cofee'~2");
Range Queries

To execute a range query define the given boundaries within a pair of braces, as seen in the following example:

Query rangeQuery = qf.create("from sample_bank_account.Transaction where amount : [20 to 50]");

Phrase Queries

A group of words may be searched by surrounding them in quotation marks, as seen in the following example:

Query q = qf.create("from sample_bank_account.Transaction where description : 'bus fare'");

Proximity Queries

To execute a proximity query, finding two terms within a specific distance, add a ~ along with the distance after the phrase. For instance, the following example will find the words canceling and fee provided they are not more than 3 words apart:

Query proximityQuery = qf.create("from sample_bank_account.Transaction where description : 'canceling fee'~3 ");
Wildcard Queries

Both single-character and multi-character wildcard searches may be performed:

  • A single-character wildcard search may be used with the ? character.

  • A multi-character wildcard search may be used with the * character.

To search for text or test the following single-character wildcard search would be used:

Query wildcardQuery = qf.create("from sample_bank_account.Transaction where description : 'te?t'");

To search for test, tests, or tester the following multi-character wildcard search would be useD:

Query wildcardQuery = qf.create("from sample_bank_account.Transaction where description : 'test*'");
Regular Expression Queries

Regular expression queries may be performed by specifing a pattern between /. Ickle uses Lucene’s regular expression syntax, so to search for the words moat or boat the following could be used:

Query regExpQuery = qf.create("from sample_library.Book  where title : /[mb]oat/");
Boosting Queries

Terms may be boosted by adding a ^ after the term to increase their relevance in a given query, the higher the boost factor the more relevant the term will be. For instance to search for titles containing beer and wine with a higher relevance on beer, by a factor of 3, the following could be used:

Query boostedQuery = qf.create("from sample_library.Book where title : beer^3 OR wine");
Continuous Query

Continuous Queries allow an application to register a listener which will receive the entries that currently match a query filter, and will be continuously notified of any changes to the queried data set that result from further cache operations. This includes incoming matches, for values that have joined the set, updated matches, for matching values that were modified and continue to match, and outgoing matches, for values that have left the set. By using a Continuous Query the application receives a steady stream of events instead of having to repeatedly execute the same query to discover changes, resulting in a more efficient use of resources. For instance, all of the following use cases could utilize Continuous Queries:

  • Return all persons with an age between 18 and 25 (assuming the Person entity has an age property and is updated by the user application).

  • Return all transactions higher than $2000.

  • Return all times where the lap speed of F1 racers were less than 1:45.00s (assuming the cache contains Lap entries and that laps are entered live during the race).

Continuous Query Execution

A continuous query uses a listener that is notified when:

  • An entry starts matching the specified query, represented by a Join event.

  • A matching entry is updated and continues to match the query, represented by an Update event.

  • An entry stops matching the query, represented by a Leave event.

When a client registers a continuous query listener it immediately begins to receive the results currently matching the query, received as Join events as described above. In addition, it will receive subsequent notifications when other entries begin matching the query, as Join events, or stop matching the query, as Leave events, as a consequence of any cache operations that would normally generate creation, modification, removal, or expiration events. Updated cache entries will generate Update events if the entry matches the query filter before and after the operation. To summarize, the logic used to determine if the listener receives a Join, Update or Leave event is:

  1. If the query on both the old and new values evaluate false, then the event is suppressed.

  2. If the query on the old value evaluates false and on the new value evaluates true, then a Join event is sent.

  3. If the query on both the old and new values evaluate true, then an Update event is sent.

  4. If the query on the old value evaluates true and on the new value evaluates false, then a Leave event is sent.

  5. If the query on the old value evaluates true and the entry is removed or expired, then a Leave event is sent.

Continuous Queries can use the full power of the Query DSL except: grouping, aggregation, and sorting operations.
Running Continuous Queries

To create a continuous query you’ll start by creating a Query object first. This is described in the Query DSL section. Then you’ll need to obtain the ContinuousQuery (org.infinispan.query.api.continuous.ContinuousQuery) object of your cache and register the query and a continuous query listener (org.infinispan.query.api.continuous.ContinuousQueryListener) with it. A ContinuousQuery object associated to a cache can be obtained by calling the static method org.infinispan.client.hotrod.Search.getContinuousQuery(RemoteCache<K, V> cache) if running in remote mode or org.infinispan.query.Search.getContinuousQuery(Cache<K, V> cache) when running in embedded mode. Once the listener has been created it may be registered by using the addContinuousQueryListener method of ContinuousQuery:

continuousQuery.addContinuousQueryListener(query, listener);

The following example demonstrates a simple continuous query use case in embedded mode: ⁠

Registering a Continuous Query
import org.infinispan.query.api.continuous.ContinuousQuery;
import org.infinispan.query.api.continuous.ContinuousQueryListener;
import org.infinispan.query.Search;
import org.infinispan.query.dsl.QueryFactory;
import org.infinispan.query.dsl.Query;

import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;


// We have a cache of Persons
Cache<Integer, Person> cache = ...

// We begin by creating a ContinuousQuery instance on the cache
ContinuousQuery<Integer, Person> continuousQuery = Search.getContinuousQuery(cache);

// Define our query. In this case we will be looking for any Person instances under 21 years of age.
QueryFactory queryFactory = Search.getQueryFactory(cache);
Query query = queryFactory.from(Person.class)

final Map<Integer, Person> matches = new ConcurrentHashMap<Integer, Person>();

// Define the ContinuousQueryListener
ContinuousQueryListener<Integer, Person> listener = new ContinuousQueryListener<Integer, Person>() {
    public void resultJoining(Integer key, Person value) {
        matches.put(key, value);

    public void resultUpdated(Integer key, Person value) {
        // we do not process this event

    public void resultLeaving(Integer key) {

// Add the listener and the query
continuousQuery.addContinuousQueryListener(query, listener);


// Remove the listener to stop receiving notifications

As Person instances having an age less than 21 are added to the cache they will be received by the listener and will be placed into the matches map, and when these entries are removed from the cache or their age is modified to be greater or equal than 21 they will be removed from matches.

Removing Continuous Queries

To stop the query from further execution just remove the listener:

Notes on performance of Continuous Queries

Continuous queries are designed to provide a constant stream of updates to the application, potentially resulting in a very large number of events being generated for particularly broad queries. A new temporary memory allocation is made for each event. This behavior may result in memory pressure, potentially leading to OutOfMemoryErrors (especially in remote mode) if queries are not carefully designed. To prevent such issues it is strongly recommended to ensure that each query captures the minimal information needed both in terms of number of matched entries and size of each match (projections can be used to capture the interesting properties), and that each ContinuousQueryListener is designed to quickly process all received events without blocking and to avoid performing actions that will lead to the generation of new matching events from the cache it listens to.

14.3. Remote Querying

Apart from supporting indexing and searching of Java entities to embedded clients, Infinispan introduced support for remote, language neutral, querying.

This leap required two major changes:

  • Since non-JVM clients cannot benefit from directly using Apache Lucene's Java API, Infinispan defines its own new query language, based on an internal DSL that is easily implementable in all languages for which we currently have an implementation of the Hot Rod client.

  • In order to enable indexing, the entities put in the cache by clients can no longer be opaque binary blobs understood solely by the client. Their structure has to be known to both server and client, so a common way of encoding structured data had to be adopted. Furthermore, allowing multi-language clients to access the data requires a language and platform-neutral encoding. Google’s Protocol Buffers was elected as an encoding format for both over-the-wire and storage due to its efficiency, robustness, good multi-language support and support for schema evolution.

14.3.1. Storing Protobuf encoded entities

Remote clients that want to be able to index and query their stored entities must do so using the ProtoStream marshaller. This is key for the search capability to work. But it’s also possible to store Protobuf entities just for gaining the benefit of platform independence and not enable indexing if you do not need it.

14.3.2. Indexing of Protobuf encoded entries

After configuring the client as described in the previous section you can start configuring indexing for your caches on the server side. Activating indexing and the various indexing specific configurations is identical to embedded mode and is detailed in the Querying Infinispan chapter.

There is however an extra configuration step involved. While in embedded mode the indexing metadata is obtained via Java reflection by analyzing the presence of various Hibernate Search annotations on the entry’s class, this is obviously not possible if the entry is protobuf encoded. The server needs to obtain the relevant metadata from the same descriptor (.proto file) as the client. The descriptors are stored in a dedicated cache on the server named '___protobuf_metadata'. Both keys and values in this cache are plain strings. Registering a new schema is therefore as simple as performing a put operation on this cache using the schema’s name as key and the schema file itself as the value. Alternatively you can use the CLI (via the cache-container=*:register-proto-schemas() operation), the Management Console or the ProtobufMetadataManager MBean via JMX. Be aware that, when security is enabled, access to the schema cache via the remote protocols requires that the user belongs to the '___schema_manager' role.

Even if indexing is enabled for a cache no fields of Protobuf encoded entries will be indexed unless you use the @Indexed and @Field protobuf schema documentation annotations in order to specify what fields need to get indexed.

14.3.3. A remote query example

In this example, we will show you how to configure the client to utilise the example LibraryInitializerImpl, put some data in the cache and then try to search for it. Note, the following example assumes that Indexing has been enabled by registering the required .proto files with the ___protobuf_metadata cache.

ConfigurationBuilder clientBuilder = new ConfigurationBuilder();
    .addContextInitializers(new LibraryInitializerImpl());

RemoteCacheManager remoteCacheManager = new RemoteCacheManager(clientBuilder.build());

Book book1 = new Book();
book1.setTitle("Infinispan in Action");
remoteCache.put(1, book1);

Book book2 = new Book();
book2.setTile("Hibernate Search in Action");
remoteCache.put(2, book2);

QueryFactory qf = Search.getQueryFactory(remoteCache);
Query query = qf.from(Book.class)
            .having("title").like("%Hibernate Search%")

List<Book> list = query.list(); // Voila! We have our book back from the cache!

The key part of creating a query is obtaining the QueryFactory for the remote cache using the org.infinispan.client.hotrod.Search.getQueryFactory() method. Once you have this creating the query is similar to embedded mode which is covered in this section.

14.3.4. Analysis

Analysis is a process that converts input data into one or more terms that you can index and query.

Default Analyzers

Infinispan provides a set of default analyzers as follows:

Definition Description


Splits text fields into tokens, treating whitespace and punctuation as delimiters.


Tokenizes input streams by delimiting at non-letters and then converting all letters to lowercase characters. Whitespace and non-letters are discarded.


Splits text streams on whitespace and returns sequences of non-whitespace characters as tokens.


Treats entire text fields as single tokens.


Stems English words using the Snowball Porter filter.


Generates n-gram tokens that are 3 grams in size by default.


Splits text fields into larger size tokens than the standard analyzer, treating whitespace as a delimiter and converts all letters to lowercase characters.

These analyzer definitions are based on Apache Lucene and are provided "as-is". For more information about tokenizers, filters, and CharFilters, see the appropriate Lucene documentation.

Using Analyzer Definitions

To use analyzer definitions, reference them by name in the .proto schema file.

  1. Include the Analyze.YES attribute to indicate that the property is analyzed.

  2. Specify the analyzer definition with the @Analyzer annotation.

The following example shows referenced analyzer definitions:

/* @Indexed */
message TestEntity {

    /* @Field(store = Store.YES, analyze = Analyze.YES, analyzer = @Analyzer(definition = "keyword")) */
    optional string id = 1;

    /* @Field(store = Store.YES, analyze = Analyze.YES, analyzer = @Analyzer(definition = "simple")) */
    optional string name = 2;
Creating Custom Analyzer Definitions

If you require custom analyzer definitions, do the following:

  1. Create an implementation of the ProgrammaticSearchMappingProvider interface packaged in a JAR file.

  2. Provide a file named org.infinispan.query.spi.ProgrammaticSearchMappingProvider in the META-INF/services/ directory of your JAR. This file should contain the fully qualified class name of your implementation.

  3. Copy the JAR to the standalone/deployments directory of your Infinispan installation.

    Your deployment must be available to the Infinispan server during startup. You cannot add the deployment if the server is already running.

    The following is an example implementation of the ProgrammaticSearchMappingProvider interface:

    import org.apache.lucene.analysis.core.LowerCaseFilterFactory;
    import org.apache.lucene.analysis.core.StopFilterFactory;
    import org.apache.lucene.analysis.standard.StandardFilterFactory;
    import org.apache.lucene.analysis.standard.StandardTokenizerFactory;
    import org.hibernate.search.cfg.SearchMapping;
    import org.infinispan.Cache;
    import org.infinispan.query.spi.ProgrammaticSearchMappingProvider;
    public final class MyAnalyzerProvider implements ProgrammaticSearchMappingProvider {
       public void defineMappings(Cache cache, SearchMapping searchMapping) {
                .analyzerDef("standard-with-stop", StandardTokenizerFactory.class)
  4. Specify the JAR in the cache container configuration, for example:

    <cache-container name="mycache" default-cache="default">
         <module name="deployment.analyzers.jar"/>

14.4. Statistics

Query Statistics can be obtained from the SearchManager, as demonstrated in the following code snippet.

SearchManager searchManager = Search.getSearchManager(cache);
org.hibernate.search.stat.Statistics statistics = searchManager.getStatistics();
This data is also available via JMX through the Hibernate Search StatisticsInfoMBean registered under the name org.infinispan:type=Query,manager="{name-of-cache-manager}",cache="{name-of-cache}",component=Statistics. Please note this MBean is always registered by Infinispan but the statistics are collected only if statistics collection is enabled at cache level.
Hibernate Search has its own configuration properties hibernate.search.jmx_enabled and hibernate.search.generate_statistics for JMX statistics as explained here. Using them with Infinispan Query is forbidden as it will only lead to duplicated MBeans and unpredictable results.

14.5. Performance Tuning

14.5.1. Batch writing in SYNC mode

By default, the Index Managers work in sync mode, meaning when data is written to Infinispan, it will perform the indexing operations synchronously. This synchronicity guarantees indexes are always consistent with the data (and thus visible in searches), but can slowdown write operations since it will also perform a commit to the index. Committing is an extremely expensive operation in Lucene, and for that reason, multiple writes from different nodes can be automatically batched into a single commit to reduce the impact.

So, when doing data loads to Infinispan with index enabled, try to use multiple threads to take advantage of this batching.

If using multiple threads does not result in the required performance, an alternative is to load data with indexing temporarily disabled and run a re-indexing operation afterwards. This can be done writing data with the SKIP_INDEXING flag:


14.5.2. Writing using async mode

If it’s acceptable a small delay between data writes and when that data is visible in queries, an index manager can be configured to work in async mode. The async mode offers much better writing performance, since in this mode commits happen at a configurable interval.


<distributed-cache name="default">
    <indexing index="PRIMARY_OWNER">
        <property name="default.indexmanager">org.infinispan.query.indexmanager.InfinispanIndexManager</property>
        <!-- Index data in async mode -->
        <property name="default.worker.execution">async</property>
        <!-- Optional: configure the commit interval, default is 1000ms -->
        <property name="default.index_flush_interval">500</property>

14.5.3. Index reader async strategy

Lucene internally works with snapshots of the index: once an IndexReader is opened, it will only see the index changes up to the point it was opened; further index changes will not be visible until the IndexReader is refreshed. The Index Managers used in Infinispan by default will check the freshness of the index readers before every query and refresh them if necessary.

It is possible to tune this strategy to relax this freshness checking to a pre-configured interval by using the reader.strategy configuration set as async:

<distributed-cache name="default">
    <indexing index="PRIMARY_OWNER">
        <property name="default.indexmanager">org.infinispan.query.affinity.InfinispanIndexManager</property>
        <property name="default.reader.strategy">async</property>
        <!-- refresh reader every 1s, default is 5s -->
        <property name="default.reader.async_refresh_period_ms">1000</property>

14.5.4. Lucene Options

It is possible to apply tuning options in Lucene directly. For more details, see the Hibernate Search manual.

15. Executing code in the Grid

The main benefit of a Cache is the ability to very quickly lookup a value by its key, even across machines. In fact this use alone is probably the reason many users use Infinispan. However Infinispan can provide many more benefits that aren’t immediately apparent. Since Infinispan is usually used in a cluster of machines we also have features available that can help utilize the entire cluster for performing the user’s desired workload.

This section covers only executing code in the grid using an embedded cache, if you are using a remote cache you should review details about executing code in the remote grid.

15.1. Cluster Executor

Since you have a group of machines, it makes sense to leverage their combined computing power for executing code on all of them them. The cache manager comes with a nice utility that allows you to execute arbitrary code in the cluster. Note this feature requires no Cache to be used. This Cluster Executor can be retrieved by calling executor() on the EmbeddedCacheManager. This executor is retrievable in both clustered and non clustered configurations.

The ClusterExecutor is specifically designed for executing code where the code is not reliant upon the data in a cache and is used instead as a way to help users to execute code easily in the cluster.

This manager was built specifically using Java 8 and such has functional APIs in mind, thus all methods take a functional inteface as an argument. Also since these arguments will be sent to other nodes they need to be serializable. We even used a nice trick to ensure our lambdas are immediately Serializable. That is by having the arguments implement both Serializable and the real argument type (ie. Runnable or Function). The JRE will pick the most specific class when determining which method to invoke, so in that case your lambdas will always be serializable. It is also possible to use an Externalizer to possibly reduce message size further.

The manager by default will submit a given command to all nodes in the cluster including the node where it was submitted from. You can control on which nodes the task is executed on by using the filterTargets methods as is explained in the section.

15.1.1. Filtering execution nodes

It is possible to limit on which nodes the command will be ran. For example you may want to only run a computation on machines in the same rack. Or you may want to perform an operation once in the local site and again on a different site. A cluster executor can limit what nodes it sends requests to at the scope of same or different machine, rack or site level.

EmbeddedCacheManager manager = ...;

To use this topology base filtering you must enable topology aware consistent hashing through Server Hinting.

You can also filter using a predicate based on the Address of the node. This can also be optionally combined with topology based filtering in the previous code snippet.

We also allow the target node to be chosen by any means using a Predicate that will filter out which nodes can be considered for execution. Note this can also be combined with Topology filtering at the same time to allow even more fine control of where you code is executed within the cluster.

EmbeddedCacheManager manager = ...;
// Just filter
manager.executor().filterTargets(a -> a.equals(..)).submit(...)
// Filter only those in the desired topology
manager.executor().filterTargets(ClusterExecutionPolicy.SAME_SITE, a -> a.equals(..)).submit(...)

15.1.2. Timeout

Cluster Executor allows for a timeout to be set per invocation. This defaults to the distributed sync timeout as configured on the Transport Configuration. This timeout works in both a clustered and non clustered cache manager. The executor may or may not interrupt the threads executing a task when the timeout expires. However when the timeout occurs any Consumer or Future will be completed passing back a TimeoutException. This value can be overridden by ivoking the timeout method and supplying the desired duration.

15.1.3. Single Node Submission

Cluster Executor can also run in single node submission mode instead of submitting the command to all nodes it will instead pick one of the nodes that would have normally received the command and instead submit it it to only one. Each submission will possibly use a different node to execute the task on. This can be very useful to use the ClusterExecutor as a java.util.concurrent.Executor which you may have noticed that ClusterExecutor implements.

EmbeddedCacheManager manager = ...;

When running in single node submission it may be desirable to also allow the Cluster Executor handle cases where an exception occurred during the processing of a given command by retrying the command again. When this occurs the Cluster Executor will choose a single node again to resubmit the command to up to the desired number of failover attempts. Note the chosen node could be any node that passes the topology or predicate check. Failover is enabled by invoking the overridden singleNodeSubmission method. The given command will be resubmitted again to a single node until either the command completes without exception or the total submission amount is equal to the provided failover count.

15.1.4. Example: PI Approximation

This example shows how you can use the ClusterExecutor to estimate the value of PI.

Pi approximation can greatly benefit from parallel distributed execution via Cluster Executor. Recall that area of the square is Sa = 4r2 and area of the circle is Ca=pi*r2. Substituting r2 from the second equation into the first one it turns out that pi = 4 * Ca/Sa. Now, image that we can shoot very large number of darts into a square; if we take ratio of darts that land inside a circle over a total number of darts shot we will approximate Ca/Sa value. Since we know that pi = 4 * Ca/Sa we can easily derive approximate value of pi. The more darts we shoot the better approximation we get. In the example below we shoot 1 billion darts but instead of "shooting" them serially we parallelize work of dart shooting across the entire Infinispan cluster. Note this will work in a cluster of 1 was well, but will be slower.

public class PiAppx {

   public static void main (String [] arg){
      EmbeddedCacheManager cacheManager = ..
      boolean isCluster = ..

      int numPoints = 1_000_000_000;
      int numServers = isCluster ? cacheManager.getMembers().size() : 1;
      int numberPerWorker = numPoints / numServers;

      ClusterExecutor clusterExecutor = cacheManager.executor();
      long start = System.currentTimeMillis();
      // We receive results concurrently - need to handle that
      AtomicLong countCircle = new AtomicLong();
      CompletableFuture<Void> fut = clusterExecutor.submitConsumer(m -> {
         int insideCircleCount = 0;
         for (int i = 0; i < numberPerWorker; i++) {
            double x = Math.random();
            double y = Math.random();
            if (insideCircle(x, y))
         return insideCircleCount;
      }, (address, count, throwable) -> {
         if (throwable != null) {
            System.out.println("Address: " + address + " encountered an error: " + throwable);
         } else {
      fut.whenComplete((v, t) -> {
         // This is invoked after all nodes have responded with a value or exception
         if (t != null) {
            System.out.println("Exception encountered while waiting:" + t);
         } else {
            double appxPi = 4.0 * countCircle.get() / numPoints;

            System.out.println("Distributed PI appx is " + appxPi +
                  " using " + numServers + " node(s), completed in " + (System.currentTimeMillis() - start) + " ms");

      // May have to sleep here to keep alive if no user threads left

   private static boolean insideCircle(double x, double y) {
      return (Math.pow(x - 0.5, 2) + Math.pow(y - 0.5, 2))
            <= Math.pow(0.5, 2);

16. Streams

You may want to process a subset or all data in the cache to produce a result. This may bring thoughts of Map Reduce. Infinispan allows the user to do something very similar but utilizes the standard JRE APIs to do so. Java 8 introduced the concept of a Stream which allows functional-style operations on collections rather than having to procedurally iterate over the data yourself. Stream operations can be implemented in a fashion very similar to MapReduce. Streams, just like MapReduce allow you to perform processing upon the entirety of your cache, possibly a very large data set, but in an efficient way.

Streams are the preferred method when dealing with data that exists in the cache because streams automatically adjust to cluster topology changes.

Also since we can control how the entries are iterated upon we can more efficiently perform the operations in a cache that is distributed if you want it to perform all of the operations across the cluster concurrently.

A stream is retrieved from the entrySet, keySet or values collections returned from the Cache by invoking the stream or parallelStream methods.

16.1. Common stream operations

This section highlights various options that are present irrespective of what type of underlying cache you are using.

16.2. Key filtering

It is possible to filter the stream so that it only operates upon a given subset of keys. This can be done by invoking the filterKeys method on the CacheStream. This should always be used over a Predicate filter and will be faster if the predicate was holding all keys.

If you are familiar with the AdvancedCache interface you may be wondering why you even use getAll over this keyFilter. There are some small benefits (mostly smaller payloads) to using getAll if you need the entries as is and need them all in memory in the local node. However if you need to do processing on these elements a stream is recommended since you will get both distributed and threaded parallelism for free.

16.3. Segment based filtering

This is an advanced feature and should only be used with deep knowledge of Infinispan segment and hashing techniques. These segments based filtering can be useful if you need to segment data into separate invocations. This can be useful when integrating with other tools such as Apache Spark.

This option is only supported for replicated and distributed caches. This allows the user to operate upon a subset of data at a time as determined by the KeyPartitioner. The segments can be filtered by invoking filterKeySegments method on the CacheStream. This is applied after the key filter but before any intermediate operations are performed.

16.4. Local/Invalidation

A stream used with a local or invalidation cache can be used just the same way you would use a stream on a regular collection. Infinispan handles all of the translations if necessary behind the scenes and works with all of the more interesting options (ie. storeAsBinary and a cache loader). Only data local to the node where the stream operation is performed will be used, for example invalidation only uses local entries.

16.5. Example

The code below takes a cache and returns a map with all the cache entries whose values contain the string "JBoss"

Map<Object, String> jbossValues = cache.entrySet().stream()
              .filter(e -> e.getValue().contains("JBoss"))
              .collect(Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue));

16.6. Distribution/Replication/Scattered

This is where streams come into their stride. When a stream operation is performed it will send the various intermediate and terminal operations to each node that has pertinent data. This allows processing the intermediate values on the nodes owning the data, and only sending the final results back to the originating nodes, improving performance.

16.6.1. Rehash Aware

Internally the data is segmented and each node only performs the operations upon the data it owns as a primary owner. This allows for data to be processed evenly, assuming segments are granular enough to provide for equal amounts of data on each node.

When you are utilizing a distributed cache, the data can be reshuffled between nodes when a new node joins or leaves. Distributed Streams handle this reshuffling of data automatically so you don’t have to worry about monitoring when nodes leave or join the cluster. Reshuffled entries may be processed a second time, and we keep track of the processed entries at the key level or at the segment level (depending on the terminal operation) to limit the amount of duplicate processing.

It is possible but highly discouraged to disable rehash awareness on the stream. This should only be considered if your request can handle only seeing a subset of data if a rehash occurs. This can be done by invoking CacheStream.disableRehashAware() The performance gain for most operations when a rehash doesn’t occur is completely negligible. The only exceptions are for iterator and forEach, which will use less memory, since they do not have to keep track of processed keys.

Please rethink disabling rehash awareness unless you really know what you are doing.

16.6.2. Serialization

Since the operations are sent across to other nodes they must be serializable by Infinispan marshalling. This allows the operations to be sent to the other nodes.

The simplest way is to use a CacheStream instance and use a lambda just as you would normally. Infinispan overrides all of the various Stream intermediate and terminal methods to take Serializable versions of the arguments (ie. SerializableFunction, SerializablePredicate…​) You can find these methods at CacheStream. This relies on the spec to pick the most specific method as defined here.

In our previous example we used a Collector to collect all the results into a Map. Unfortunately the Collectors class doesn’t produce Serializable instances. Thus if you need to use these, there are two ways to do so:

One option would be to use the CacheCollectors class which allows for a Supplier<Collector> to be provided. This instance could then use the Collectors to supply a Collector which is not serialized.

Map<Object, String> jbossValues = cache.entrySet().stream()
              .filter(e -> e.getValue().contains("Jboss"))
              .collect(CacheCollectors.serializableCollector(() -> Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue)));

Alternatively, you can avoid the use of CacheCollectors and instead use the overloaded collect methods that take Supplier<Collector>. These overloaded collect methods are only available via CacheStream interface.

Map<Object, String> jbossValues = cache.entrySet().stream()
              .filter(e -> e.getValue().contains("Jboss"))
              .collect(() -> Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue));

If however you are not able to use the Cache and CacheStream interfaces you cannot utilize Serializable arguments and you must instead cast the lambdas to be Serializable manually by casting the lambda to multiple interfaces. It is not a pretty sight but it gets the job done.

Map<Object, String> jbossValues = map.entrySet().stream()
              .filter((Serializable & Predicate<Map.Entry<Object, String>>) e -> e.getValue().contains("Jboss"))
              .collect(CacheCollectors.serializableCollector(() -> Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue)));

The recommended and most performant way is to use an AdvancedExternalizer as this provides the smallest payload. Unfortunately this means you cannot use lamdbas as advanced externalizers require defining the class before hand.

You can use an advanced externalizer as shown below:

   Map<Object, String> jbossValues = cache.entrySet().stream()
              .filter(new ContainsFilter("Jboss"))
              .collect(() -> Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue));

   class ContainsFilter implements Predicate<Map.Entry<Object, String>> {
      private final String target;

      ContainsFilter(String target) {
         this.target = target;

      public boolean test(Map.Entry<Object, String> e) {
         return e.getValue().contains(target);

   class JbossFilterExternalizer implements AdvancedExternalizer<ContainsFilter> {

      public Set<Class<? extends ContainsFilter>> getTypeClasses() {
         return Util.asSet(ContainsFilter.class);

      public Integer getId() {
         return CUSTOM_ID;

      public void writeObject(ObjectOutput output, ContainsFilter object) throws IOException {

      public ContainsFilter readObject(ObjectInput input) throws IOException, ClassNotFoundException {
         return new ContainsFilter(input.readUTF());

You could also use an advanced externalizer for the collector supplier to reduce the payload size even further.

   Map<Object, String> jbossValues = cache.entrySet().stream()
              .filter(new ContainsFilter("Jboss"))

 class ToMapCollectorSupplier<K, U> implements Supplier<Collector<Map.Entry<K, U>, ?, Map<K, U>>> {
      static final ToMapCollectorSupplier INSTANCE = new ToMapCollectorSupplier();

      private ToMapCollectorSupplier() { }

      public Collector<Map.Entry<K, U>, ?, Map<K, U>> get() {
         return Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue);

   class ToMapCollectorSupplierExternalizer implements AdvancedExternalizer<ToMapCollectorSupplier> {

      public Set<Class<? extends ToMapCollectorSupplier>> getTypeClasses() {
         return Util.asSet(ToMapCollectorSupplier.class);

      public Integer getId() {
         return CUSTOM_ID;

      public void writeObject(ObjectOutput output, ToMapCollectorSupplier object) throws IOException {

      public ToMapCollectorSupplier readObject(ObjectInput input) throws IOException, ClassNotFoundException {
         return ToMapCollectorSupplier.INSTANCE;

16.7. Parallel Computation

Distributed streams by default try to parallelize as much as possible. It is possible for the end user to control this and actually they always have to control one of the options. There are 2 ways these streams are parallelized.

Local to each node When a stream is created from the cache collection the end user can choose between invoking stream or parallelStream method. Depending on if the parallel stream was picked will enable multiple threading for each node locally. Note that some operations like a rehash aware iterator and forEach operations will always use a sequential stream locally. This could be enhanced at some point to allow for parallel streams locally.

Users should be careful when using local parallelism as it requires having a large number of entries or operations that are computationally expensive to be faster. Also it should be noted that if a user uses a parallel stream with forEach that the action should not block as this would be executed on the common pool, which is normally reserved for computation operations.

Remote requests When there are multiple nodes it may be desirable to control whether the remote requests are all processed at the same time concurrently or one at a time. By default all terminal operations except the iterator perform concurrent requests. The iterator, method to reduce overall memory pressure on the local node, only performs sequential requests which actually performs slightly better.

If a user wishes to change this default however they can do so by invoking the sequentialDistribution or parallelDistribution methods on the CacheStream.

16.8. Task timeout

It is possible to set a timeout value for the operation requests. This timeout is used only for remote requests timing out and it is on a per request basis. The former means the local execution will not timeout and the latter means if you have a failover scenario as described above the subsequent requests each have a new timeout. If no timeout is specified it uses the replication timeout as a default timeout. You can set the timeout in your task by doing the following:

CacheStream<Object, String> stream = cache.entrySet().stream();
stream.timeout(1, TimeUnit.MINUTES);

For more information about this, please check the java doc in timeout javadoc.

16.9. Injection

The Stream has a terminal operation called forEach which allows for running some sort of side effect operation on the data. In this case it may be desirable to get a reference to the Cache that is backing this Stream. If your Consumer implements the CacheAware interface the injectCache method be invoked before the accept method from the Consumer interface.

16.10. Distributed Stream execution

Distributed streams execution works in a fashion very similiar to map reduce. Except in this case we are sending zero to many intermediate operations (map, filter etc.) and a single terminal operation to the various nodes. The operation basically comes down to the following:

  1. The desired segments are grouped by which node is the primary owner of the given segment

  2. A request is generated to send to each remote node that contains the intermediate and terminal operations including which segments it should process

    1. The terminal operation will be performed locally if necessary

    2. Each remote node will receive this request and run the operations and subsequently send the response back

  3. The local node will then gather the local response and remote responses together performing any kind of reduction required by the operations themselves.

  4. Final reduced response is then returned to the user

In most cases all operations are fully distributed, as in the operations are all fully applied on each remote node and usually only the last operation or something related may be reapplied to reduce the results from multiple nodes. One important note is that intermediate values do not actually have to be serializable, it is the last value sent back that is the part desired (exceptions for various operations will be highlighted below).

Terminal operator distributed result reductions The following paragraphs describe how the distributed reductions work for the various terminal operators. Some of these are special in that an intermediate value may be required to be serializable instead of the final result.

allMatch noneMatch anyMatch

The allMatch operation is ran on each node and then all the results are logically anded together locally to get the appropriate value. The noneMatch and anyMatch operations use a logical or instead. These methods also have early termination support, stopping remote and local operations once the final result is known.


The collect method is interesting in that it can do a few extra steps. The remote node performs everything as normal except it doesn’t perform the final finisher upon the result and instead sends back the fully combined results. The local thread then combines the remote and local result into a value which is then finally finished. The key here to remember is that the final value doesn’t have to be serializable but rather the values produced from the supplier and combiner methods.


The count method just adds the numbers together from each node.

findAny findFirst

The findAny operation returns just the first value they find, whether it was from a remote node or locally. Note this supports early termination in that once a value is found it will not process others. Note the findFirst method is special since it requires a sorted intermediate operation, which is detailed in the exceptions section.

max min

The max and min methods find the respective min or max value on each node then a final reduction is performed locally to ensure only the min or max across all nodes is returned.


The various reduce methods 1 , 2 , 3 will end up serializing the result as much as the accumulator can do. Then it will accumulate the local and remote results together locally, before combining if you have provided that. Note this means a value coming from the combiner doesn’t have to be Serializable.

16.11. Key based rehash aware operators

The iterator, spliterator and forEach are unlike the other terminal operators in that the rehash awareness has to keep track of what keys per segment have been processed instead of just segments. This is to guarantee an exactly once (iterator & spliterator) or at least once behavior (forEach) even under cluster membership changes.

The iterator and spliterator operators when invoked on a remote node will return back batches of entries, where the next batch is only sent back after the last has been fully consumed. This batching is done to limit how many entries are in memory at a given time. The user node will hold onto which keys it has processed and when a given segment is completed it will release those keys from memory. This is why sequential processing is preferred for the iterator method, so only a subset of segment keys are held in memory at once, instead of from all nodes.

The forEach() method also returns batches, but it returns a batch of keys after it has finished processing at least a batch worth of keys. This way the originating node can know what keys have been processed already to reduce chances of processing the same entry again. Unfortunately this means it is possible to have an at least once behavior when a node goes down unexpectedly. In this case that node could have been processing a batch and not yet completed one and those entries that were processed but not in a completed batch will be ran again when the rehash failure operation occurs. Note that adding a node will not cause this issue as the rehash failover doesn’t occur until all responses are received.

These operations batch sizes are both controlled by the same value which can be configured by invoking distributedBatchSize method on the CacheStream. This value will default to the chunkSize configured in state transfer. Unfortunately this value is a tradeoff with memory usage vs performance vs at least once and your mileage may vary.

Using iterator with replicated and distributed caches

When a node is the primary or backup owner of all requested segments for a distributed stream, Infinispan performs the iterator or spliterator terminal operations locally, which optimizes performance as remote iterations are more resource intensive.

This optimization applies to both replicated and distributed caches. However, Infinispan performs iterations remotely when using cache stores that are both shared and have write-behind enabled. In this case performing the iterations remotely ensures consistency.

16.12. Intermediate operation exceptions

There are some intermediate operations that have special exceptions, these are skip, peek, sorted 1 2. & distinct. All of these methods have some sort of artificial iterator implanted in the stream processing to guarantee correctness, they are documented as below. Note this means these operations may cause possibly severe performance degradation.


An artificial iterator is implanted up to the intermediate skip operation. Then results are brought locally so it can skip the appropriate amount of elements.


WARNING: This operation requires having all entries in memory on the local node. An artificial iterator is implanted up to the intermediate sorted operation. All results are sorted locally. There are possible plans to have a distributed sort which returns batches of elements, but this is not yet implemented.


WARNING: This operation requires having all or nearly all entries in memory on the local node. Distinct is performed on each remote node and then an artificial iterator returns those distinct values. Then finally all of those results have a distinct operation performed upon them.

The rest of the intermediate operations are fully distributed as one would expect.

16.13. Examples

Word Count

Word count is a classic, if overused, example of map/reduce paradigm. Assume we have a mapping of key → sentence stored on Infinispan nodes. Key is a String, each sentence is also a String, and we have to count occurrence of all words in all sentences available. The implementation of such a distributed task could be defined as follows:

public class WordCountExample {

    * In this example replace c1 and c2 with
    * real Cache references
    * @param args
   public static void main(String[] args) {
      Cache<String, String> c1 = ...;
      Cache<String, String> c2 = ...;

      c1.put("1", "Hello world here I am");
      c2.put("2", "Infinispan rules the world");
      c1.put("3", "JUDCon is in Boston");
      c2.put("4", "JBoss World is in Boston as well");
      c1.put("12","JBoss Application Server");
      c2.put("15", "Hello world");
      c1.put("14", "Infinispan community");
      c2.put("15", "Hello world");

      c1.put("111", "Infinispan open source");
      c2.put("112", "Boston is close to Toronto");
      c1.put("113", "Toronto is a capital of Ontario");
      c2.put("114", "JUDCon is cool");
      c1.put("211", "JBoss World is awesome");
      c2.put("212", "JBoss rules");
      c1.put("213", "JBoss division of RedHat ");
      c2.put("214", "RedHat community");

      Map<String, Long> wordCountMap = c1.entrySet().parallelStream()
         .map(e -> e.getValue().split("\\s"))
         .collect(() -> Collectors.groupingBy(Function.identity(), Collectors.counting()));

In this case it is pretty simple to do the word count from the previous example.

However what if we want to find the most frequent word in the example? If you take a second to think about this case you will realize you need to have all words counted and available locally first. Thus we actually have a few options.

We could use a finisher on the collector, which is invoked on the user thread after all the results have been collected. Some redundant lines have been removed from the previous example.

public class WordCountExample {
   public static void main(String[] args) {
      // Lines removed

      String mostFrequentWord = c1.entrySet().parallelStream()
         .map(e -> e.getValue().split("\\s"))
         .collect(() -> Collectors.collectingAndThen(
            Collectors.groupingBy(Function.identity(), Collectors.counting()),
               wordCountMap -> {
                  String mostFrequent = null;
                  long maxCount = 0;
                     for (Map.Entry<String, Long> e : wordCountMap.entrySet()) {
                        int count = e.getValue().intValue();
                        if (count > maxCount) {
                           maxCount = count;
                           mostFrequent = e.getKey();
                     return mostFrequent;


Unfortunately the last step is only going to be ran in a single thread, which if we have a lot of words could be quite slow. Maybe there is another way to parallelize this with Streams.

We mentioned before we are in the local node after processing, so we could actually use a stream on the map results. We can therefore use a parallel stream on the results.

public class WordFrequencyExample {
   public static void main(String[] args) {
      // Lines removed

      Map<String, Long> wordCount = c1.entrySet().parallelStream()
              .map(e -> e.getValue().split("\\s"))
              .collect(() -> Collectors.groupingBy(Function.identity(), Collectors.counting()));
      Optional<Map.Entry<String, Long>> mostFrequent = wordCount.entrySet().parallelStream().reduce(
              (e1, e2) -> e1.getValue() > e2.getValue() ? e1 : e2);

This way you can still utilize all of the cores locally when calculating the most frequent element.

Remove specific entries

Distributed streams can also be used as a way to modify data where it lives. For example you may want to remove all entries in your cache that contain a specific word.

public class RemoveBadWords {
   public static void main(String[] args) {
      // Lines removed
      String word = ..

         .filter(e -> e.getValue().contains(word))
         .forEach((c, e) -> c.remove(e.getKey());

If we carefully note what is serialized and what is not, we notice that only the word along with the operations are serialized across to other nods as it is captured by the lambda. However the real saving piece is that the cache operation is performed on the primary owner thus reducing the amount of network traffic required to remove these values from the cache. The cache is not captured by the lambda as we provide a special BiConsumer method override that when invoked on each node passes the cache to the BiConsumer

One thing to keep in mind using the forEach command in this manner is that the underlying stream obtains no locks. The cache remove operation will still obtain locks naturally, but the value could have changed from what the stream saw. That means that the entry could have been changed after the stream read it but the remove actually removed it.

We have specifically added a new variant which is called LockedStream.

Plenty of other examples

The Streams API is a JRE tool and there are lots of examples for using it. Just remember that your operations need to be Serializable in some way.

17. Extending Infinispan

Infinispan can be extended to provide the ability for an end user to add additional configurations, operations and components outside of the scope of the ones normally provided by Infinispan.

17.1. Custom Commands

Infinispan makes use of a command/visitor pattern to implement the various top-level methods you see on the public-facing API.

While the core commands - and their corresponding visitors - are hard-coded as a part of Infinispan’s core module, module authors can extend and enhance Infinispan by creating new custom commands.

As a module author (such as infinispan-query, etc.) you can define your own commands.

You do so by:

  1. Create a META-INF/services/org.infinispan.commands.module.ModuleCommandExtensions file and ensure this is packaged in your jar.

  2. Implementing ModuleCommandFactory, ModuleCommandInitializer and ModuleCommandExtensions

  3. Specifying the fully-qualified class name of the ModuleCommandExtensions implementation in META-INF/services/org.infinispan.commands.module.ModuleCommandExtensions.

  4. Implement your custom commands and visitors for these commands

17.1.1. An Example

Here is an example of an META-INF/services/org.infinispan.commands.module.ModuleCommandExtensions file, configured accordingly:


For a full, working example of a sample module that makes use of custom commands and visitors, check out Infinispan Sample Module .

17.1.2. Preassigned Custom Command Id Ranges

This is the list of Command identifiers that are used by Infinispan based modules or frameworks. Infinispan users should avoid using ids within these ranges. (RANGES to be finalised yet!) Being this a single byte, ranges can’t be too large.

Infinispan Query:

100 - 119

Hibernate Search:

120 - 139

Hot Rod Server:

140 - 141

17.2. Extending the configuration builders and parsers

If your custom module requires configuration, it is possible to enhance Infinispan’s configuration builders and parsers. Look at the custom module tests for a detail example on how to implement this.

18. Custom Interceptors

It is possible to add custom interceptors to Infinispan, both declaratively and programatically. Custom interceptors are a way of extending Infinispan by being able to influence or respond to any modifications to cache. Example of such modifications are: elements are added/removed/updated or transactions are committed. For a detailed list refer to CommandInterceptor API.

18.1. Adding custom interceptors declaratively

Custom interceptors can be added on a per named cache basis. This is because each named cache have its own interceptor stack. Following xml snippet depicts the ways in which a custom interceptor can be added.

<local-cache name="cacheWithCustomInterceptors">
      Define custom interceptors.  All custom interceptors need to extend org.jboss.cache.interceptors.base.CommandInterceptor
         <interceptor position="FIRST" class="com.mycompany.CustomInterceptor1">
               <property name="attributeOne">value1</property>
               <property name="attributeTwo">value2</property>
         <interceptor position="LAST" class="com.mycompany.CustomInterceptor2"/>
         <interceptor index="3" class="com.mycompany.CustomInterceptor1"/>
         <interceptor before="org.infinispanpan.interceptors.CallInterceptor" class="com.mycompany.CustomInterceptor2"/>
         <interceptor after="org.infinispanpan.interceptors.CallInterceptor" class="com.mycompany.CustomInterceptor1"/>

18.2. Adding custom interceptors programatically

In order to do that one needs to obtain a reference to the AdvancedCache . This can be done as follows:

CacheManager cm = getCacheManager();//magic
Cache aCache = cm.getCache("aName");
AdvancedCache advCache = aCache.getAdvancedCache();

Then one of the addInterceptor() methods should be used to add the actual interceptor. For further documentation refer to AdvancedCache javadoc.

18.3. Custom interceptor design

When writing a custom interceptor, you need to abide by the following rules.

  • Custom interceptors must extend BaseCustomInterceptor

  • Custom interceptors must declare a public, empty constructor to enable construction.

  • Custom interceptors will have setters for any property defined through property tags used in the XML configuration.