Enable indexing in your cache configuration and specify which entities Data Grid should include when creating indexes.

You should always configure Data Grid to index caches when using queries. Indexing provides a significant performance boost to your queries, allowing you to get faster insights into your data.

Protobuf messages

Java objects

<distributed-cache>
  <indexing>
    <indexed-entities>
      <indexed-entity>book_sample.Book</indexed-entity>
    </indexed-entities>
  </indexing>
</distributed-cache>

import org.infinispan.configuration.cache.*;

ConfigurationBuilder config=new ConfigurationBuilder();
config.indexing().enable().storage(FILESYSTEM).path("/some/folder").addIndexedEntity(Book.class);

<distributed-cache>
  <indexing storage="filesystem" path="${java.io.tmpdir}/baseDir">
    <!-- Indexing configuration goes here. -->
  </indexing>
</distributed-cache>

<distributed-cache>
  <indexing storage="local-heap">
    <!-- Additional indexing configuration goes here. -->
  </indexing>
</distributed-cache>

<distributed-cache>
  <indexing storage="filesystem" path="${java.io.tmpdir}/baseDir">
    <!-- Sets an interval of one second for the index reader. -->
    <index-reader refresh-interval="1000"/>
    <!-- Additional indexing configuration goes here. -->
  </indexing>
</distributed-cache>

<distributed-cache>
  <indexing storage="filesystem" path="${java.io.tmpdir}/baseDir">
    <index-writer commit-interval="2000"
                  low-level-trace="false"
                  max-buffered-entries="32"
                  queue-count="1"
                  queue-size="10000"
                  ram-buffer-size="400"
                  thread-pool-size="2">
      <index-merge calibrate-by-deletes="true"
                   factor="3"
                   max-entries="2000"
                   min-size="10"
                   max-size="20"/>
    </index-writer>
    <!-- Additional indexing configuration goes here. -->
  </indexing>
</distributed-cache>

When you enable indexing in caches, you configure Data Grid to create indexes. You also need to provide Data Grid with a structured representation of the entities in your caches so it can actually index them.

There are two annotations that control the entities and fields that Data Grid indexes:

Remote caches

You can provide Data Grid with indexing annotations for remote caches in two ways:

Embedded caches

For embedded caches, you add indexing annotations to your Java classes according to how Data Grid stores your entries.

Use the @Indexed and @Field annotations, along with other Hibernate Search annotations such as @FullTextField.

Rebuilding an index reconstructs it from the data stored in the cache. You should rebuild indexes when you change things like the definitions of indexed types or analyzers. Likewise, you can rebuild indexes after you delete them for whatever reason.

Data Grid recommends indexing caches for the best performance for queries. However you can query caches that are non-indexed.

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. Each QueryFactory instance is bound to the same Cache instance as the Search, but it is otherwise a stateless and thread-safe object that can be used for creating multiple queries in parallel.

For instance:

// Remote Query, using protobuf
QueryFactory qf = org.infinispan.client.hotrod.Search.getQueryFactory(remoteCache);
Query<Transaction> q = qf.create("from sample_bank_account.Transaction where amount > 20");

// Embedded Query using Java Objects
QueryFactory qf = org.infinispan.query.Search.getQueryFactory(cache);
Query<Transaction> q = qf.create("from org.infinispan.sample.Book where price > 20");

// Execute the query
QueryResult<Book> queryResult = q.execute();

Executing the query and fetching the results is as simple as invoking the execute() method of the Query object. Once executed, calling execute() on the same instance will re-execute the query.

// sorted by year and match all books that have "clustering" in their title
// and return the third page of 10 results
Query<Book> query = queryFactory.create("FROM org.infinispan.sample.Book WHERE title like '%clustering%' ORDER BY year").startOffset(20).maxResults(10)

QueryFactory queryFactory = Search.getQueryFactory(cache);
// Defining a query to search for various authors and publication years
Query<Book> query = queryFactory.create("SELECT title FROM org.infinispan.sample.Book WHERE author = :authorName AND publicationYear = :publicationYear").build();

// Set actual parameter values
query.setParameter("authorName", "Doe");
query.setParameter("publicationYear", 2010);

// Execute the query
List<Book> found = query.execute().list();

Map<String, Object> parameterMap = new HashMap<>();
parameterMap.put("authorName", "Doe");
parameterMap.put("publicationYear", 2010);

query.setParameters(parameterMap);

The Ickle query language is subset of the JPQL query language, with some extensions for full-text.

The parser syntax has some notable rules:

# match all books that have "Data Grid" in their title
# or have an author named "Manik" and their description contains "clustering"

FROM org.infinispan.sample.Book WHERE title LIKE '%Data Grid%' OR author.name = 'Manik' AND description like '%clustering%'

# match all books that do not have "Data Grid" in their title and are authored by "Manik"
FROM org.infinispan.sample.Book WHERE title != 'Data Grid' AND author.name = 'Manik'

# match all books that have an author named "Manik" and their title contains
# "Data Grid" or their description contains "clustering"
FROM org.infinispan.sample.Book WHERE author.name = 'Manik' AND ( title like '%Data Grid%' OR description like '% clustering%')

# match all books that have "Data Grid" in their title or description
# and return only their title and publication year
SELECT title, publicationYear FROM org.infinispan.sample.Book WHERE title like '%Data Grid%' OR description like '%Data Grid%'

# match all books that have "Data Grid" in their title or description
# and return them sorted by the publication year and title
FROM org.infinispan.sample.Book WHERE title like '%Data Grid%' ORDER BY publicationYear DESC, title ASC

SELECT author, COUNT(title) FROM org.infinispan.sample.Book WHERE title LIKE '%engine%' GROUP BY author

DELETE FROM <entityName> [WHERE condition]

You can perform full-text searches with the Ickle query language.

FROM sample_bank_account.Transaction WHERE description : 'cofee'~2

FROM sample_bank_account.Transaction WHERE amount : [20 to 50]

FROM sample_bank_account.Transaction WHERE description : 'bus fare'

FROM sample_bank_account.Transaction WHERE description : 'canceling fee'~3

FROM sample_bank_account.Transaction where description : 'te?t'

FROM sample_bank_account.Transaction where description : 'test*'

FROM sample_library.Book  where title : /[mb]oat/

FROM sample_library.Book WHERE title : beer^3 OR wine

Data Grid lets you programmatically query remote caches from Java clients through the Hot Rod endpoint. This procedure explains how to index query a remote cache that stores Book instances.

Data Grid provides this processor for the @ProtoField and @ProtoDoc annotations so you can generate Protobuf schemas and perform queries.

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

<dependencies>
  <dependency>
    <groupId>org.infinispan.protostream</groupId>
    <artifactId>protostream-processor</artifactId>
    <scope>provided</scope>
  </dependency>
</dependencies>

<replicated-cache name="books">
  <indexing>
    <indexed-entities>
      <indexed-entity>book_sample.Book</indexed-entity>
    </indexed-entities>
  </indexing>
</replicated-cache>

The following RemoteQuery class does the following:

package org.infinispan;

import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.List;

import org.infinispan.client.hotrod.RemoteCache;
import org.infinispan.client.hotrod.RemoteCacheManager;
import org.infinispan.client.hotrod.Search;
import org.infinispan.client.hotrod.configuration.ConfigurationBuilder;
import org.infinispan.query.dsl.Query;
import org.infinispan.query.dsl.QueryFactory;
import org.infinispan.query.remote.client.ProtobufMetadataManagerConstants;

public class RemoteQuery {

   public static void main(String[] args) throws Exception {
      ConfigurationBuilder clientBuilder = new ConfigurationBuilder();
      // RemoteQueryInitializerImpl is generated
      clientBuilder.addServer().host("127.0.0.1").port(11222)
            .security().authentication().username("user").password("user")
            .addContextInitializers(new RemoteQueryInitializerImpl());

      RemoteCacheManager remoteCacheManager = new RemoteCacheManager(clientBuilder.build());

      // Grab the generated protobuf schema and registers in the server.
      Path proto = Paths.get(RemoteQuery.class.getClassLoader()
            .getResource("proto/book.proto").toURI());
      String protoBufCacheName = ProtobufMetadataManagerConstants.PROTOBUF_METADATA_CACHE_NAME;
      remoteCacheManager.getCache(protoBufCacheName).put("book.proto", Files.readString(proto));

      // Obtain the 'books' remote cache
      RemoteCache<Object, Object> remoteCache = remoteCacheManager.getCache("books");

      // Add some Books
      Book book1 = new Book("Infinispan in Action", "Learn Infinispan with using it", 2015);
      Book book2 = new Book("Cloud-Native Applications with Java and Quarkus", "Build robust and reliable cloud applications", 2019);

      remoteCache.put(1, book1);
      remoteCache.put(2, book2);

      // Execute a full-text query
      QueryFactory queryFactory = Search.getQueryFactory(remoteCache);
      Query<Book> query = queryFactory.create("FROM book_sample.Book WHERE title:'java'");

      List<Book> list = query.execute().list(); // Voila! We have our book back from the cache!
   }
}

Data Grid Console and the Data Grid Command Line Interface (CLI) let you query indexed and non-indexed remote caches. You can also use any HTTP client to index and query caches via the REST API.

This procedure explains how to index and query a remote cache that stores Person instances.

Analyzers convert input data into terms that you can index and query. You specify analyzer definitions with the @Field annotation in your Java classes or directly in Protobuf schema.

/* @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;
}

@ProtoDoc("@Field(store = Store.YES, analyze = Analyze.YES, analyzer = @Analyzer(definition = \"keyword\"))")
@ProtoField(1)
final String id;

@ProtoDoc("@Field(store = Store.YES, analyze = Analyze.YES, analyzer = @Analyzer(definition = \"simple\"))")
@ProtoField(2)
final String description;

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 {

   @Override
   public void defineMappings(Cache cache, SearchMapping searchMapping) {
      searchMapping
            .analyzerDef("standard-with-stop", StandardTokenizerFactory.class)
               .filter(StandardFilterFactory.class)
               .filter(LowerCaseFilterFactory.class)
               .filter(StopFilterFactory.class);
   }
}

This section explains how to query an embedded cache using an example cache named "books" that stores indexed Book instances.

In this example, each Book instance defines which properties are indexed and specifies some advanced indexing options with Hibernate Search annotations as follows:

package org.infinispan.sample;

import java.time.LocalDate;
import java.util.HashSet;
import java.util.Set;

import org.hibernate.search.mapper.pojo.mapping.definition.annotation.*;

// Annotate values with @Indexed to add them to indexes
// Annotate each fields according to how you want to index it
@Indexed
public class Book {
   @FullTextField
   String title;

   @FullTextField
   String description;

   @KeywordField
   String isbn;

   @GenericField
   LocalDate publicationDate;

   @IndexedEmbedded
   Set<Author> authors = new HashSet<Author>();
}

package org.infinispan.sample;

import org.hibernate.search.mapper.pojo.mapping.definition.annotation.FullTextField;

public class Author {
   @FullTextField
   String name;

   @FullTextField
   String surname;
}

Add annotations to your Java classes to map your entities to indexes.

The following sections provide information that applies to entity mapping annotations for use with Data Grid.

For complete detail about these annotations, you should refer to the Hibernate Search manual.

@DocumentId

Unlike Hibernate Search, using @DocumentId to mark a field as identifier does not apply to Data Grid values; in Data Grid 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. Data Grid 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 .

@Transformable(transformer = CustomTransformer.class)
public class CustomKey {
   ...
}

public class CustomTransformer implements Transformer {
   @Override
   public Object fromString(String s) {
      ...
      return new CustomKey(...);
   }

   @Override
   public String toString(Object customType) {
      CustomKey ck = (CustomKey) customType;
      return ...
   }
}

<replicated-cache name="test">
  <indexing auto-config="true">
    <key-transformers>
      <key-transformer key="com.mycompany.CustomKey"
                       transformer="com.mycompany.CustomTransformer"/>
    </key-transformers>
  </indexing>
</replicated-cache>

Alternatively, use the Java configuration API (embedded mode):

   ConfigurationBuilder builder = ...
   builder.indexing().enable()
         .addKeyTransformer(CustomKey.class, CustomTransformer.class);

You can programmatically map entities to the index as an alternative to annotating Java classes.

In the following example we map an object Author which is to be stored in the grid and made searchable on two properties:

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();
mapping.entity(Author.class).indexed()
       .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()
        .indexing().index(Index.NONE)
        .withProperties(properties)
        .build();

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;

Continuous queries provide applications with real-time notifications about data in Data Grid caches that are filtered by queries. When entries match the query Data Grid sends the updated data to any listeners, which provides a stream of events instead of applications having to execute the query.

Continuous queries can notify applications about 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.

For example, continuous queries can notify applications about all:

How continuous queries work

Continuous queries notify client listeners with the following events:

When a client registers a continuous query listener it immediately receives Join events for any entries that match the query. Client listeners receive subsequent events each time a cache operation modifies entries that match the query.

Data Grid determines when to send Join, Update, or Leave events to client listeners as follows:

SELECT field1, field2 FROM Entity WHERE x AND y

You can create continuous queries for remote and embedded caches.

Continuous query example

The following code example demonstrates a simple continuous query with an embedded cache.

In this example, the listener receives notifications when any Person instances under the age of 21 are added to the cache. Those Person instances are also added to the "matches" map. When the entries are removed from the cache or their age becomes greater than or equal to 21, they are removed from "matches" map. ⁠

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 Person objects.
Cache<Integer, Person> cache = ...

// Create a ContinuousQuery instance on the cache.
ContinuousQuery<Integer, Person> continuousQuery = Search.getContinuousQuery(cache);

// Define a query.
// In this example, we search for Person instances under 21 years of age.
QueryFactory queryFactory = Search.getQueryFactory(cache);
Query query = queryFactory.create("FROM Person p WHERE p.age < 21");

final Map<Integer, Person> matches = new ConcurrentHashMap<Integer, Person>();

// Define the ContinuousQueryListener.
ContinuousQueryListener<Integer, Person> listener = new ContinuousQueryListener<Integer, Person>() {
    @Override
    public void resultJoining(Integer key, Person value) {
        matches.put(key, value);
    }

    @Override
    public void resultUpdated(Integer key, Person value) {
        // We do not process this event.
    }

    @Override
    public void resultLeaving(Integer key) {
        matches.remove(key);
    }
};

// Add the listener and the query.
continuousQuery.addContinuousQueryListener(query, listener);

[...]

// Remove the listener to stop receiving notifications.
continuousQuery.removeContinuousQueryListener(listener);

Collect statistics to gather information about performance of your indexes and queries, including information such as the types of indexes and average time for queries to complete.

// Statistics for the local cluster member
SearchStatistics statistics = Search.getSearchStatistics(cache);

// Consolidated statistics for the whole cluster
CompletionStage<SearchStatisticsSnapshot> statistics = Search.getClusteredSearchStatistics(cache)

GET /v2/caches/{cacheName}/search/stats

Use the following guidelines to help you improve the performance of indexing operations and queries.

Start tuning query performance by checking the time it takes for each type of query to run. If your queries seem to be slow, you should make sure that queries are using the indexes for caches and that all entities and field mappings are indexed.

This operation is costly so you should avoid configuring an interval that is too small. The default is 1000 ms (1 second).

The default value is 0, which returns data in queries as soon as it is written to a cache.

A value greater than 0 results in some stale query results but substantially increases throughput, especially in write-heavy scenarios. If you do not need data returned in queries as soon as it is written, you should adjust the refresh interval to improve query performance.