Chapter 13. Hibernate Search

Hibernate Search uses various back ends to process batches of work. The back end is not limited to the configuration option default.worker.backend. This property specifies a implementation of the BackendQueueProcessor interface which is a part of a back-end configuration. Additional settings are required to set up a back-end, for example the JMS back-end.

In the Lucene mode, all index updates for a node are executed by the same node to the Lucene directories using the directory providers. Use this mode in a non-clustered environment or in clustered environments with a shared directory store.

Figure 13.1. Lucene Back-end Configuration

Lucene mode targets non-clustered or clustered applications where the directory manages the locking strategy. The primary advantage of Lucene mode is simplicity and immediate visibility of changes in Lucene queries. The Near Real Time (NRT) back end is an alternative back end for non-clustered and non-shared index configurations.

Index updates for a node are sent to the JMS queue. A unique reader processes the queue and updates the master index. The master index is subsequently replicated regularly to slave copies, to establish the master and slave pattern. The master is responsible for Lucene index updates. The slaves accept read and write operations but process read operations on local index copies. The master is solely responsible for updating the Lucene index. Only the master applies the local changes in an update operation.

Figure 13.2. JMS Back-end Configuration

This mode targets clustered environment where throughput is critical and index update delays are affordable. The JMS provider ensures reliability and uses the slaves to change the local index copies.

Using the shared strategy, Hibernate Search shares the same IndexReader for a given Lucene index across multiple queries and threads provided that the IndexReader remains updated. If the IndexReader is not updated, a new one is opened and provided. Each IndexReader is made of several SegmentReaders. The shared strategy reopens segments that have been modified or created after the last opening and shares the already loaded segments from the previous instance. This is the default strategy.

Using the not-shared strategy, a Lucene IndexReader opens every time a query executes. Opening and starting up a IndexReader is an expensive operation. As a result, opening an IndexReader for each query execution is not an efficient strategy.

You can write a custom reader strategy using an implementation of org.hibernate.search.reader.ReaderProvider. The implementation must be thread safe.

The Directory-based implementation is the default IndexManager implementation. It is highly configurable and allows separate configurations for the reader strategy, back ends, and directory providers.

The NRTIndexManager is an extension of the default IndexManager and leverages the Lucene NRT, Near Real Time, feature for low latency index writes. However, it ignores configuration settings for alternative back ends other than lucene and acquires exclusive write locks on the Directory.

The IndexWriter does not flush every change to the disk to provide low latency. Queries can read the updated states from the unflushed index writer buffers. However, this means that if the IndexWriter is killed or the application crashes, updates can be lost so the indexes must be rebuilt.

The Near Real Time configuration is recommended for non-clustered websites with limited data due to the mentioned disadvantages and because a master node can be individually configured for improved performance as well.

Specify a fully qualified class name for the custom implementation to set up a customized IndexManager. Set up a no-argument constructor for the implementation as follows:

[default|<indexname>].indexmanager = my.corp.myapp.CustomIndexManager

The custom index manager implementation does not require the same components as the default implementations. For example, delegate to a remote indexing service which does not expose a Directory interface.

This section describes in greater detail how to configure the Master/Slave Hibernate Search architecture.

Figure 13.3. JMS Backend Configuration

Every index update operation is sent to a JMS queue. Index querying operations are executed on a local index copy.

### slave configuration

## DirectoryProvider
# (remote) master location
hibernate.search.default.sourceBase = /mnt/mastervolume/lucenedirs/mastercopy

# local copy location
hibernate.search.default.indexBase = /Users/prod/lucenedirs

# refresh every half hour
hibernate.search.default.refresh = 1800

# appropriate directory provider
hibernate.search.default.directory_provider = filesystem-slave

## Back-end configuration
hibernate.search.default.worker.backend = jms
hibernate.search.default.worker.jms.connection_factory = /ConnectionFactory
hibernate.search.default.worker.jms.queue = queue/hibernatesearch
#optional jndi configuration (check your JMS provider for more information)

## Optional asynchronous execution strategy
# hibernate.search.default.worker.execution = async
# hibernate.search.default.worker.thread_pool.size = 2
# hibernate.search.default.worker.buffer_queue.max = 50

Every index update operation is taken from a JMS queue and executed. The master index is copied on a regular basis.

Index update operations in the JMS queue are executed and the master index is copied regularly.

### master configuration

## DirectoryProvider
# (remote) master location where information is copied to
hibernate.search.default.sourceBase = /mnt/mastervolume/lucenedirs/mastercopy

# local master location
hibernate.search.default.indexBase = /Users/prod/lucenedirs

# refresh every half hour
hibernate.search.default.refresh = 1800

# appropriate directory provider
hibernate.search.default.directory_provider = filesystem-master

## Back-end configuration
#Back-end is the default for Lucene

In addition to the Hibernate Search framework configuration, a Message Driven Bean has to be written and set up to process the index works queue through JMS.

@MessageDriven(activationConfig = {
      @ActivationConfigProperty(propertyName="destinationType",
                                propertyValue="javax.jms.Queue"),
      @ActivationConfigProperty(propertyName="destination",
                                propertyValue="queue/hibernatesearch"),
      @ActivationConfigProperty(propertyName="DLQMaxResent", propertyValue="1")
   } )
public class MDBSearchController extends AbstractJMSHibernateSearchController
                                 implements MessageListener {
    @PersistenceContext EntityManager em;

    //method retrieving the appropriate session
    protected Session getSession() {
        return (Session) em.getDelegate();
    }

    //potentially close the session opened in #getSession(), not needed here
    protected void cleanSessionIfNeeded(Session session)
    }
}

This example inherits from the abstract JMS controller class available in the Hibernate Search source code and implements a JavaEE MDB. This implementation is given as an example and can be adjusted to make use of non Java EE Message Driven Beans.

Hibernate Search is used to tune the Lucene indexing performance by specifying a set of parameters which are passed through to underlying Lucene IndexWriter such as mergeFactor, maxMergeDocs, and maxBufferedDocs. Specify these parameters either as default values applying for all indexes, on a per index basis, or even per shard.

There are several low level IndexWriter settings which can be tuned for different use cases. These parameters are grouped by the indexwriter keyword:

hibernate.search.[default|<indexname>].indexwriter.<parameter_name>

If no value is set for an indexwriter value in a specific shard configuration, Hibernate Search checks the index section, then at the default section.

The configuration in the following table will result in these settings applied on the second shard of the Animal index:

All other values will use the defaults defined in Lucene.

The default for all values is to leave them at Lucene’s own default. The values listed in List of indexing performance and behavior properties depend for this reason on the version of Lucene you are using. The values shown are relative to version 2.4.

There are several low level IndexWriter settings which can be tuned for different use cases. These parameters are grouped by the indexwriter keyword:

default.<indexname>.indexwriter.<parameter_name>

If no value is set for indexwriter in a shard configuration, Hibernate Search looks at the index section and then at the default section.

The following configuration will result in these settings being applied on the second shard of the Animal index:

default.Animals.2.indexwriter.max_merge_docs = 10
default.Animals.2.indexwriter.merge_factor = 20
default.Animals.2.indexwriter.term_index_interval = default
default.indexwriter.max_merge_docs = 100
default.indexwriter.ram_buffer_size = 64

All other values will use the defaults defined in Lucene.

The Lucene default values are the default setting for Hibernate Search. Therefore, the values listed in the following table depend on the version of Lucene being used. The values shown are relative to version 2.4. For more information about Lucene indexing performance, see the Lucene documentation.

When the architecture permits it, keep default.exclusive_index_use=true for improved index writing efficiency.

When tuning indexing speed the recommended approach is to focus first on optimizing the object loading, and then use the timings you achieve as a baseline to tune the indexing process. Set the blackhole as worker back end and start your indexing routines. This back end does not disable Hibernate Search. It generates the required change sets to the index, but discards them instead of flushing them to the index. In contrast to setting the hibernate.search.indexing_strategy to manual, using blackhole will possibly load more data from the database because associated entities are re-indexed as well.

hibernate.search.[default|<indexname>].worker.backend blackhole

The following options configure the maximum size of segments created:

//to be fairly confident no files grow above 15MB, use:
hibernate.search.default.indexwriter.ram_buffer_size = 10
hibernate.search.default.indexwriter.merge_max_optimize_size = 7
hibernate.search.default.indexwriter.merge_max_size = 7

Set the max_size for merge operations to less than half of the hard limit segment size, as merging segments combines two segments into one larger segment.

A new segment may initially be a larger size than expected, however a segment is never created significantly larger than the ram_buffer_size. This threshold is checked as an estimate.

Let us start with the most commonly used annotations for mapping an entity.

The Lucene-based Query API uses the following common annotations to map entities:

Foremost we must declare a persistent class as indexable. This is done by annotating the class with @Indexed (all entities not annotated with @Indexed will be ignored by the indexing process):

@Entity
@Indexed
public class Essay {
...
}

You can optionally specify the index attribute of the @Indexed annotation to change the default name of the index.

For each property (or attribute) of your entity, you have the ability to describe how it will be indexed. The default (no annotation present) means that the property is ignored by the indexing process.

@Field does declare a property as indexed and allows to configure several aspects of the indexing process by setting one or more of the following attributes:

There is a companion annotation to @Field called @NumericField that can be specified in the same scope as @Field or @DocumentId. It can be specified for Integer, Long, Float, and Double properties. At index time the value will be indexed using a Trie structure. When a property is indexed as numeric field, it enables efficient range query and sorting, orders of magnitude faster than doing the same query on standard @Field properties. The @NumericField annotation accept the following parameters:

@NumericField supports only Double, Long, Integer and Float. It is not possible to take any advantage from similar functionality in Lucene for the other numeric types, so remaining types should use the string encoding via the default or custom TwoWayFieldBridge.

It is possible to use a custom NumericFieldBridge assuming you can deal with the approximation during type transformation:

Example: Defining a custom NumericFieldBridge

public class BigDecimalNumericFieldBridge extends NumericFieldBridge {
   private static final BigDecimal storeFactor = BigDecimal.valueOf(100);

   @Override
   public void set(String name, Object value, Document document, LuceneOptions luceneOptions) {
      if ( value != null ) {
         BigDecimal decimalValue = (BigDecimal) value;
         Long indexedValue = Long.valueOf( decimalValue.multiply( storeFactor ).longValue() );
         luceneOptions.addNumericFieldToDocument( name, indexedValue, document );
      }
   }

    @Override
    public Object get(String name, Document document) {
        String fromLucene = document.get( name );
        BigDecimal storedBigDecimal = new BigDecimal( fromLucene );
        return storedBigDecimal.divide( storeFactor );
    }

}

Finally, the id (identifier) property of an entity is a special property used by Hibernate Search to ensure index uniqueness of a given entity. By design, an id must be stored and must not be tokenized. To mark a property as an index identifier, use the @DocumentId annotation. If you are using JPA and you have specified @Id you can omit @DocumentId. The chosen entity identifier will also be used as the document identifier.

Infinispan Query uses the entity’s id property to ensure the index is uniquely identified. By design, an ID is stored and must not be converted into a token. To mark a property as index ID, use the @DocumentId annotation.

Example: Specifying indexed properties

@Entity
@Indexed
public class Essay {
    ...
    @Id
    @DocumentId
    public Long getId() { return id; }

    @Field(name="Abstract", store=Store.YES)
    public String getSummary() { return summary; }

    @Lob
    @Field
    public String getText() { return text; }

    @Field @NumericField( precisionStep = 6)
    public float getGrade() { return grade; }
}

The example above defines an index with four fields: id , Abstract, text and grade . Note that by default the field name is not capitalized, following the JavaBean specification. The grade field is annotated as numeric with a slightly larger precision step than the default.

Sometimes you need to map a property multiple times per index, with slightly different indexing strategies. For example, sorting a query by field requires the field to be un-analyzed. To search by words on this property and still sort it, it needs to be indexed - once analyzed and once un-analyzed. @Fields allows you to achieve this goal.

Example: Using @Fields to map a property multiple times

@Entity
@Indexed(index = "Book" )
public class Book {
    @Fields( {
            @Field,
            @Field(name = "summary_forSort", analyze = Analyze.NO, store = Store.YES)
            } )
    public String getSummary() {
        return summary;
    }

    ...
}

In this example the field summary is indexed twice, once as summary in a tokenized way, and once as summary_forSort in an untokenized way.

Associated objects as well as embedded objects can be indexed as part of the root entity index. This is useful if you expect to search a given entity based on properties of associated objects. The aim is to return places where the associated city is Atlanta (In the Lucene query parser language, it would translate into address.city:Atlanta). The place fields will be indexed in the Place index. The Place index documents will also contain the fields address.id, address.street, and address.city which you will be able to query.

Example: Indexing associations

@Entity
@Indexed
public class Place {
    @Id
    @GeneratedValue
    @DocumentId
    private Long id;

    @Field
    private String name;

    @OneToOne( cascade = { CascadeType.PERSIST, CascadeType.REMOVE } )
    @IndexedEmbedded
    private Address address;
    ....
}

@Entity
public class Address {
    @Id
    @GeneratedValue
    private Long id;

    @Field
    private String street;

    @Field
    private String city;

    @ContainedIn
    @OneToMany(mappedBy="address")
    private Set<Place> places;
    ...
}

Because the data is denormalized in the Lucene index when using the @IndexedEmbedded technique, Hibernate Search must be aware of any change in the Place object and any change in the Address object to keep the index up to date. To ensure the Lucene document is updated when it is Address changes, mark the other side of the bidirectional relationship with @ContainedIn.

To expand upon this, the following example demonstrates nesting @IndexedEmbedded.

@Entity
@Indexed
public class Place {
    @Id
    @GeneratedValue
    @DocumentId
    private Long id;

    @Field
    private String name;

    @OneToOne( cascade = { CascadeType.PERSIST, CascadeType.REMOVE } )
    @IndexedEmbedded
    private Address address;
    ....
}

@Entity
public class Address {
    @Id
    @GeneratedValue
    private Long id;

    @Field
    private String street;

    @Field
    private String city;

    @IndexedEmbedded(depth = 1, prefix = "ownedBy_")
    private Owner ownedBy;

    @ContainedIn
    @OneToMany(mappedBy="address")
    private Set<Place> places;
    ...
}

@Embeddable
public class Owner {
    @Field
    private String name;
   ...
}

Any @*ToMany, @*ToOne and @Embedded attribute can be annotated with @IndexedEmbedded. The attributes of the associated class will then be added to the main entity index. The index will contain the following fields:

The default prefix is propertyName., following the traditional object navigation convention. You can override it using the prefix attribute as it is shown on the ownedBy property.

The depth property is necessary when the object graph contains a cyclic dependency of classes (not instances). For example, if Owner points to Place. Hibernate Search will stop including Indexed embedded attributes after reaching the expected depth (or the object graph boundaries are reached). A class having a self reference is an example of cyclic dependency. In our example, because depth is set to 1, any @IndexedEmbedded attribute in Owner (if any) will be ignored.

Using @IndexedEmbedded for object associations allows you to express queries (using Lucene’s query syntax) such as:

This behavior mimics the relational join operation in a more efficient way (at the cost of data duplication). Remember that, out of the box, Lucene indexes have no notion of association, the join operation does not exist. It might help to keep the relational model normalized while benefiting from the full text index speed and feature richness.

When @IndexedEmbedded points to an entity, the association has to be directional and the other side has to be annotated @ContainedIn (as seen in the previous example). If not, Hibernate Search has no way to update the root index when the associated entity is updated (in our example, a Place index document has to be updated when the associated Address instance is updated).

Sometimes, the object type annotated by @IndexedEmbedded is not the object type targeted by Hibernate and Hibernate Search. This is especially the case when interfaces are used in lieu of their implementation. For this reason you can override the object type targeted by Hibernate Search using the targetElement parameter.

@Entity
@Indexed
public class Address {
    @Id
    @GeneratedValue
    @DocumentId
    private Long id;

    @Field
    private String street;

    @IndexedEmbedded(depth = 1, prefix = "ownedBy_", )
    @Target(Owner.class)
    private Person ownedBy;


    ...
}

@Embeddable
public class Owner implements Person { ... }

The @IndexedEmbedded annotation provides also an attribute includePaths which can be used as an alternative to depth, or be combined with it.

When using only depth all indexed fields of the embedded type will be added recursively at the same depth. This makes it harder to select only a specific path without adding all other fields as well, which might not be needed.

To avoid unnecessarily loading and indexing entities you can specify exactly which paths are needed. A typical application might need different depths for different paths, or in other words it might need to specify paths explicitly, as shown in the example below:

Example: Using the includePaths property of @IndexedEmbedded

@Entity
@Indexed
public class Person {

   @Id
   public int getId() {
      return id;
   }

   @Field
   public String getName() {
      return name;
   }

   @Field
   public String getSurname() {
      return surname;
   }

   @OneToMany
   @IndexedEmbedded(includePaths = { "name" })
   public Set<Person> getParents() {
      return parents;
   }

   @ContainedIn
   @ManyToOne
   public Human getChild() {
      return child;
   }

    ...//other fields omitted

Using a mapping as in the example above, you would be able to search on a Person by name and/or surname, and/or the name of the parent. It will not index the surname of the parent, so searching on parent’s surnames will not be possible but speeds up indexing, saves space and improve overall performance.

The @IndexedEmbeddedincludePaths will include the specified paths in addition to what you would index normally specifying a limited value for depth. When using includePaths, and leaving depth undefined, behavior is equivalent to setting depth=0: only the included paths are indexed.

Example: Using the includePaths property of @IndexedEmbedded

@Entity
@Indexed
public class Human {

   @Id
   public int getId() {
      return id;
   }

   @Field
   public String getName() {
      return name;
   }

   @Field
   public String getSurname() {
      return surname;
   }

   @OneToMany
   @IndexedEmbedded(depth = 2, includePaths = { "parents.parents.name" })
   public Set<Human> getParents() {
      return parents;
   }

   @ContainedIn
   @ManyToOne
   public Human getChild() {
      return child;
   }

    ...//other fields omitted

In the example above, every human will have its name and surname attributes indexed. The name and surname of parents will also be indexed, recursively up to second line because of the depth attribute. It will be possible to search by name or surname, of the person directly, his parents or of his grand parents. Beyond the second level, we will in addition index one more level but only the name, not the surname.

This results in the following fields in the index:

Having explicit control of the indexed paths might be easier if you are designing your application by defining the needed queries first, as at that point you might know exactly which fields you need, and which other fields are unnecessary to implement your use case.

To define a static boost value for an indexed class or property you can use the @Boost annotation. You can use this annotation within @Field or specify it directly on method or class level.

Example: Different ways of using @Boost

@Entity
@Indexed

public class Essay {
    ...

    @Id
    @DocumentId
    public Long getId() { return id; }

    @Field(name="Abstract", store=Store.YES, boost=@Boost(2f))
    @Boost(1.5f)
    public String getSummary() { return summary; }

    @Lob
    @Field(boost=@Boost(1.2f))
    public String getText() { return text; }

    @Field
    public String getISBN() { return isbn; }

}

In the example above, Essay’s probability to reach the top of the search list will be multiplied by 1.7. The summary field will be 3.0 (2 * 1.5, because @Field.boost and @Boost on a property are cumulative) more important than the isbn field. The text field will be 1.2 times more important than the isbn field. Note that this explanation is wrong in strictest terms, but it is simple and close enough to reality for all practical purposes.

The @Boost annotation used in Static Index Time Boosting defines a static boost factor which is independent of the state of the indexed entity at runtime. However, there are use cases in which the boost factor may depend on the actual state of the entity. In this case you can use the @DynamicBoost annotation together with an accompanying custom BoostStrategy.

Example: Dynamic boost example

public enum PersonType {
    NORMAL,
    VIP
}

@Entity
@Indexed
@DynamicBoost(impl = VIPBoostStrategy.class)
public class Person {
    private PersonType type;

    // ....
}

public class VIPBoostStrategy implements BoostStrategy {
    public float defineBoost(Object value) {
        Person person = ( Person ) value;
        if ( person.getType().equals( PersonType.VIP ) ) {
            return 2.0f;
        }
        else {
            return 1.0f;
        }
    }
}

In the example above, a dynamic boost is defined on class level specifying VIPBoostStrategy as implementation of the BoostStrategy interface to be used at indexing time. You can place the @DynamicBoost either at class or field level. Depending on the placement of the annotation either the whole entity is passed to the defineBoost method or just the annotated field/property value. It is up to you to cast the passed object to the correct type. In the example all indexed values of a VIP person would be double as important as the values of a normal person.

Of course you can mix and match @Boost and @DynamicBoost annotations in your entity. All defined boost factors are cumulative.

The default analyzer class used to index tokenized fields is configurable through the hibernate.search.analyzer property. The default value for this property is org.apache.lucene.analysis.standard.StandardAnalyzer.

You can also define the analyzer class per entity, property and even per @Field (useful when multiple fields are indexed from a single property).

Example: Different ways of using @Analyzer

@Entity
@Indexed
@Analyzer(impl = EntityAnalyzer.class)
public class MyEntity {
    @Id
    @GeneratedValue
    @DocumentId
    private Integer id;

    @Field
    private String name;

    @Field
    @Analyzer(impl = PropertyAnalyzer.class)
    private String summary;

    @Field(analyzer = @Analyzer(impl = FieldAnalyzer.class)
    private String body;

    ...
}

In this example, EntityAnalyzer is used to index tokenized property (name), except summary and body which are indexed with PropertyAnalyzer and FieldAnalyzer respectively.

Analyzers can become quite complex to deal with. For this reason introduces Hibernate Search the notion of analyzer definitions. An analyzer definition can be reused by many @Analyzer declarations and is composed of:

This separation of tasks - a list of char filters, and a tokenizer followed by a list of filters - allows for easy reuse of each individual component and lets you build your customized analyzer in a very flexible way (like Lego). Generally speaking the char filters do some pre-processing in the character input, then the Tokenizer starts the tokenizing process by turning the character input into tokens which are then further processed by the TokenFilters. Hibernate Search supports this infrastructure by utilizing the Solr analyzer framework.

Let us review a concrete example stated below. First a char filter is defined by its factory. In our example, a mapping char filter is used, and will replace characters in the input based on the rules specified in the mapping file. Next a tokenizer is defined. This example uses the standard tokenizer. Last but not least, a list of filters is defined by their factories. In our example, the StopFilter filter is built reading the dedicated words property file. The filter is also expected to ignore case.

Example: @AnalyzerDef and the Solr framework

@AnalyzerDef(name="customanalyzer",
  charFilters = {
    @CharFilterDef(factory = MappingCharFilterFactory.class, params = {
      @Parameter(name = "mapping",
        value = "org/hibernate/search/test/analyzer/solr/mapping-chars.properties")
    })
  },
  tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class),
  filters = {
    @TokenFilterDef(factory = ISOLatin1AccentFilterFactory.class),
    @TokenFilterDef(factory = LowerCaseFilterFactory.class),
    @TokenFilterDef(factory = StopFilterFactory.class, params = {
      @Parameter(name="words",
        value= "org/hibernate/search/test/analyzer/solr/stoplist.properties" ),
      @Parameter(name="ignoreCase", value="true")
    })
})
public class Team {
    ...
}

Some tokenizers, token filters or char filters load resources like a configuration or metadata file. This is the case for the stop filter and the synonym filter. If the resource charset is not using the VM default, you can explicitly specify it by adding a resource_charset parameter.

Example: Use a specific charset to load the property file

@AnalyzerDef(name="customanalyzer",
  charFilters = {
    @CharFilterDef(factory = MappingCharFilterFactory.class, params = {
      @Parameter(name = "mapping",
        value = "org/hibernate/search/test/analyzer/solr/mapping-chars.properties")
    })
  },
  tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class),
  filters = {
    @TokenFilterDef(factory = ISOLatin1AccentFilterFactory.class),
    @TokenFilterDef(factory = LowerCaseFilterFactory.class),
    @TokenFilterDef(factory = StopFilterFactory.class, params = {
      @Parameter(name="words",
        value= "org/hibernate/search/test/analyzer/solr/stoplist.properties" ),
      @Parameter(name="resource_charset", value = "UTF-16BE"),
      @Parameter(name="ignoreCase", value="true")
  })
})
public class Team {
    ...
}

Once defined, an analyzer definition can be reused by an @Analyzer declaration as seen in the following example.

Example: Referencing an analyzer by name

@Entity
@Indexed
@AnalyzerDef(name="customanalyzer", ... )
public class Team {
    @Id
    @DocumentId
    @GeneratedValue
    private Integer id;

    @Field
    private String name;

    @Field
    private String location;

    @Field
    @Analyzer(definition = "customanalyzer")
    private String description;
}

Analyzer instances declared by @AnalyzerDef are also available by their name in the SearchFactory which is quite useful when building queries.

Analyzer analyzer = fullTextSession.getSearchFactory().getAnalyzer("customanalyzer");

Fields in queries must be analyzed with the same analyzer used to index the field so that they speak a common "language": the same tokens are reused between the query and the indexing process. This rule has some exceptions but is true most of the time. Respect it unless you know what you are doing.

Solr and Lucene come with many useful default char filters, tokenizers, and filters. You can find a complete list of char filter factories, tokenizer factories and filter factories at http://wiki.apache.org/solr/AnalyzersTokenizersTokenFilters. Let us check a few of them.

We recommend to check all the implementations of org.apache.lucene.analysis.TokenizerFactory and org.apache.lucene.analysis.TokenFilterFactory in your IDE to see the implementations available.

So far all the introduced ways to specify an analyzer were static. However, there are use cases where it is useful to select an analyzer depending on the current state of the entity to be indexed, for example in a multilingual applications. For an BlogEntry class for example the analyzer could depend on the language property of the entry. Depending on this property the correct language specific stemmer should be chosen to index the actual text.

To enable this dynamic analyzer selection Hibernate Search introduces the AnalyzerDiscriminator annotation. Following example demonstrates the usage of this annotation.

Example: Usage of @AnalyzerDiscriminator

@Entity
@Indexed
@AnalyzerDefs({
  @AnalyzerDef(name = "en",
    tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class),
    filters = {
      @TokenFilterDef(factory = LowerCaseFilterFactory.class),
      @TokenFilterDef(factory = EnglishPorterFilterFactory.class
      )
    }),
  @AnalyzerDef(name = "de",
    tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class),
    filters = {
      @TokenFilterDef(factory = LowerCaseFilterFactory.class),
      @TokenFilterDef(factory = GermanStemFilterFactory.class)
    })
})
public class BlogEntry {

    @Id
    @GeneratedValue
    @DocumentId
    private Integer id;

    @Field
    @AnalyzerDiscriminator(impl = LanguageDiscriminator.class)
    private String language;

    @Field
    private String text;

    private Set<BlogEntry> references;

    // standard getter/setter
    ...
}

public class LanguageDiscriminator implements Discriminator {

    public String getAnalyzerDefinitionName(Object value, Object entity, String field) {
        if ( value == null || !( entity instanceof BlogEntry ) ) {
            return null;
        }
        return (String) value;

    }
}

The prerequisite for using @AnalyzerDiscriminator is that all analyzers which are going to be used dynamically are predefined via @AnalyzerDef definitions. If this is the case, one can place the @AnalyzerDiscriminator annotation either on the class or on a specific property of the entity for which to dynamically select an analyzer. Via the impl parameter of the AnalyzerDiscriminator you specify a concrete implementation of the Discriminator interface. It is up to you to provide an implementation for this interface. The only method you have to implement is getAnalyzerDefinitionName() which gets called for each field added to the Lucene document. The entity which is getting indexed is also passed to the interface method. The value parameter is only set if the AnalyzerDiscriminator is placed on property level instead of class level. In this case the value represents the current value of this property.

An implementation of the Discriminator interface has to return the name of an existing analyzer definition or null if the default analyzer should not be overridden. The example above assumes that the language parameter is either 'de' or 'en' which matches the specified names in the @AnalyzerDefs.

Retrieving an analyzer can be used when multiple analyzers have been used in a domain model, in order to benefit from stemming or phonetic approximation, etc. In this case, use the same analyzers to building a query. Alternatively, use the Hibernate Search query DSL, which selects the correct analyzer automatically. See

Whether you are using the Lucene programmatic API or the Lucene query parser, you can retrieve the scoped analyzer for a given entity. A scoped analyzer is an analyzer which applies the right analyzers depending on the field indexed. Remember, multiple analyzers can be defined on a given entity each one working on an individual field. A scoped analyzer unifies all these analyzers into a context-aware analyzer. While the theory seems a bit complex, using the right analyzer in a query is very easy.

Example: Using the scoped analyzer when building a full-text query

org.apache.lucene.queryParser.QueryParser parser = new QueryParser(
    "title",
    fullTextSession.getSearchFactory().getAnalyzer( Song.class )
);

org.apache.lucene.search.Query luceneQuery =
    parser.parse( "title:sky Or title_stemmed:diamond" );

org.hibernate.Query fullTextQuery =
    fullTextSession.createFullTextQuery( luceneQuery, Song.class );

List result = fullTextQuery.list(); //return a list of managed objects

In the example above, the song title is indexed in two fields: the standard analyzer is used in the field title and a stemming analyzer is used in the field title_stemmed. By using the analyzer provided by the search factory, the query uses the appropriate analyzer depending on the field targeted.

Hibernate Search comes bundled with a set of built-in bridges between a Java property type and its full text representation.

@Entity
@Indexed
public class Meeting {
    @Field(analyze=Analyze.NO)

    private Date date;
    ...

Sometimes, the built-in bridges of Hibernate Search do not cover some of your property types, or the String representation used by the bridge does not meet your requirements. The following paragraphs describe several solutions to this problem.

/**
 * Padding Integer bridge.
 * All numbers will be padded with 0 to match 5 digits
 *
 * @author Emmanuel Bernard
 */
public class PaddedIntegerBridge implements StringBridge {

    private int PADDING = 5;

    public String objectToString(Object object) {
        String rawInteger = ( (Integer) object ).toString();
        if (rawInteger.length() > PADDING)
            throw new IllegalArgumentException( "Try to pad on a number too big" );
        StringBuilder paddedInteger = new StringBuilder( );
        for ( int padIndex = rawInteger.length() ; padIndex < PADDING ; padIndex++ ) {
            paddedInteger.append('0');
        }
        return paddedInteger.append( rawInteger ).toString();
    }
}

@FieldBridge(impl = PaddedIntegerBridge.class)
private Integer length;

public class PaddedIntegerBridge implements StringBridge, ParameterizedBridge {

    public static String PADDING_PROPERTY = "padding";
    private int padding = 5; //default

    public void setParameterValues(Map<String,String> parameters) {
        String padding = parameters.get( PADDING_PROPERTY );
        if (padding != null) this.padding = Integer.parseInt( padding );
    }

    public String objectToString(Object object) {
        String rawInteger = ( (Integer) object ).toString();
        if (rawInteger.length() > padding)
            throw new IllegalArgumentException( "Try to pad on a number too big" );
        StringBuilder paddedInteger = new StringBuilder( );
        for ( int padIndex = rawInteger.length() ; padIndex < padding ; padIndex++ ) {
            paddedInteger.append('0');
        }
        return paddedInteger.append( rawInteger ).toString();
    }
}


//property
@FieldBridge(impl = PaddedIntegerBridge.class,
             params = @Parameter(name="padding", value="10")
            )
private Integer length;

public class PaddedIntegerBridge implements TwoWayStringBridge, ParameterizedBridge {

    public static String PADDING_PROPERTY = "padding";
    private int padding = 5; //default

    public void setParameterValues(Map parameters) {
        Object padding = parameters.get( PADDING_PROPERTY );
        if (padding != null) this.padding = (Integer) padding;
    }

    public String objectToString(Object object) {
        String rawInteger = ( (Integer) object ).toString();
        if (rawInteger.length() > padding)
            throw new IllegalArgumentException( "Try to pad on a number too big" );
        StringBuilder paddedInteger = new StringBuilder( );
        for ( int padIndex = rawInteger.length() ; padIndex < padding ; padIndex++ ) {
            paddedInteger.append('0');
        }
        return paddedInteger.append( rawInteger ).toString();
    }

    public Object stringToObject(String stringValue) {
        return new Integer(stringValue);
    }
}


//id property
@DocumentId
@FieldBridge(impl = PaddedIntegerBridge.class,
             params = @Parameter(name="padding", value="10")
private Integer id;

/**
 * Store the date in 3 different fields - year, month, day - to ease Range Query per
 * year, month or day (eg get all the elements of December for the last 5 years).
 * @author Emmanuel Bernard
 */
public class DateSplitBridge implements FieldBridge {
    private final static TimeZone GMT = TimeZone.getTimeZone("GMT");

    public void set(String name, Object value, Document document, LuceneOptions luceneOptions) {
        Date date = (Date) value;
        Calendar cal = GregorianCalendar.getInstance(GMT);
        cal.setTime(date);
        int year = cal.get(Calendar.YEAR);
        int month = cal.get(Calendar.MONTH) + 1;
        int day = cal.get(Calendar.DAY_OF_MONTH);

        // set year
        luceneOptions.addFieldToDocument(
            name + ".year",
            String.valueOf( year ),
            document );

        // set month and pad it if needed
        luceneOptions.addFieldToDocument(
            name + ".month",
            month < 10 ? "0" : "" + String.valueOf( month ),
            document );

        // set day and pad it if needed
        luceneOptions.addFieldToDocument(
            name + ".day",
            day < 10 ? "0" : "" + String.valueOf( day ),
            document );
    }
}

//property
@FieldBridge(impl = DateSplitBridge.class)
private Date date;

@Entity
@Indexed
(name="branchnetwork",
             store=Store.YES,
             impl = CatFieldsClassBridge.class,
             params = @Parameter( name="sepChar", value=" " ) )
public class Department {
    private int id;
    private String network;
    private String branchHead;
    private String branch;
    private Integer maxEmployees
    ...
}

public class CatFieldsClassBridge implements FieldBridge, ParameterizedBridge {
    private String sepChar;

    public void setParameterValues(Map parameters) {
        this.sepChar = (String) parameters.get( "sepChar" );
    }

    public void set( String name, Object value, Document document, LuceneOptions luceneOptions) {
        // In this particular class the name of the new field was passed
        // from the name field of the ClassBridge Annotation. This is not
        // a requirement. It just works that way in this instance. The
        // actual name could be supplied by hard coding it below.
        Department dep = (Department) value;
        String fieldValue1 = dep.getBranch();
        if ( fieldValue1 == null ) {
            fieldValue1 = "";
        }
        String fieldValue2 = dep.getNetwork();
        if ( fieldValue2 == null ) {
            fieldValue2 = "";
        }
        String fieldValue = fieldValue1 + sepChar + fieldValue2;
        Field field = new Field( name, fieldValue, luceneOptions.getStore(),
            luceneOptions.getIndex(), luceneOptions.getTermVector() );
        field.setBoost( luceneOptions.getBoost() );
        document.add( field );
   }
}

With the Lucene API, use either the query parser (simple queries) or the Lucene programmatic API (complex queries). Building a Lucene query is out of scope for the Hibernate Search documentation. For details, see the online Lucene documentation or a copy of Lucene in Action or Hibernate Search in Action.

The Lucene programmatic API enables full-text queries. However, when using the Lucene programmatic API, the parameters must be converted to their string equivalent and must also apply the correct analyzer to the right field. A ngram analyzer for example uses several ngrams as the tokens for a given word and should be searched as such. It is recommended to use the QueryBuilder for this task.

The Hibernate Search query API is fluent, with the following key characteristics:

To use the API, first create a query builder that is attached to a given indexedentitytype. This QueryBuilder knows what analyzer to use and what field bridge to apply. Several QueryBuilders (one for each entity type involved in the root of your query) can be created. The QueryBuilder is derived from the SearchFactory.

QueryBuilder mythQB = searchFactory.buildQueryBuilder().forEntity( Myth.class ).get();

The analyzer used for a given field or fields can also be overridden.

QueryBuilder mythQB = searchFactory.buildQueryBuilder()
    .forEntity( Myth.class )
        .overridesForField("history","stem_analyzer_definition")
    .get();

The query builder is now used to build Lucene queries. Customized queries generated using Lucene’s query parser or Query objects assembled using the Lucene programmatic API are used with the Hibernate Search DSL.

The following example shows how to search for a specific word:

Query luceneQuery = mythQB.keyword().onField("history").matching("storm").createQuery();

To search a property that is not of type string.

@Indexed
public class Myth {
  @Field(analyze = Analyze.NO)
  @DateBridge(resolution = Resolution.YEAR)
  public Date getCreationDate() { return creationDate; }
  public Date setCreationDate(Date creationDate) { this.creationDate = creationDate; }
  private Date creationDate;

  ...
}

Date birthdate = ...;
Query luceneQuery = mythQb.keyword().onField("creationDate").matching(birthdate).createQuery();

This conversion works for any object, provided that the FieldBridge has an objectToString method (and all built-in FieldBridge implementations do).

The next example searches a field that uses ngram analyzers. The ngram analyzers index succession of ngrams of words, which helps to avoid user typos. For example, the 3-grams of the word hibernate are hib, ibe, ber, ern, rna, nat, ate.

@AnalyzerDef(name = "ngram",
  tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class ),
  filters = {
    @TokenFilterDef(factory = StandardFilterFactory.class),
    @TokenFilterDef(factory = LowerCaseFilterFactory.class),
    @TokenFilterDef(factory = StopFilterFactory.class),
    @TokenFilterDef(factory = NGramFilterFactory.class,
      params = {
        @Parameter(name = "minGramSize", value = "3"),
        @Parameter(name = "maxGramSize", value = "3") } )
  }
)

public class Myth {
  @Field(analyzer=@Analyzer(definition="ngram")
  public String getName() { return name; }
  public String setName(String name) { this.name = name; }
  private String name;

  ...
}

Date birthdate = ...;
Query luceneQuery = mythQb.keyword().onField("name").matching("Sisiphus")
   .createQuery();

The matching word "Sisiphus" will be lower-cased and then split into 3-grams: sis, isi, sip, iph, phu, hus. Each of these ngram will be part of the query. The user is then able to find the Sysiphus myth (with a y). All that is transparently done for the user.

To search for multiple possible words in the same field, add them all in the matching clause.

//search document with storm or lightning in their history
Query luceneQuery =
    mythQB.keyword().onField("history").matching("storm lightning").createQuery();

To search the same word on multiple fields, use the onFields method.

Query luceneQuery = mythQB
    .keyword()
    .onFields("history","description","name")
    .matching("storm")
    .createQuery();

Sometimes, one field should be treated differently from another field even if searching the same term, use the andField() method for that.

Query luceneQuery = mythQB.keyword()
    .onField("history")
    .andField("name")
      .boostedTo(5)
    .andField("description")
    .matching("storm")
    .createQuery();

In the previous example, only field name is boosted to 5.

To execute a fuzzy query (based on the Levenshtein distance algorithm), start with a keyword query and add the fuzzy flag.

Query luceneQuery = mythQB
    .keyword()
      .fuzzy()
        .withThreshold( .8f )
        .withPrefixLength( 1 )
    .onField("history")
    .matching("starm")
    .createQuery();

The threshold is the limit above which two terms are considering matching. It is a decimal between 0 and 1 and the default value is 0.5. The prefixLength is the length of the prefix ignored by the "fuzzyness". While the default value is 0, a nonzero value is recommended for indexes containing a huge number of distinct terms.

Wildcard queries are useful in circumstances where only part of the word is known. The ? represents a single character and * represents multiple characters. Note that for performance purposes, it is recommended that the query does not start with either ? or *.

Query luceneQuery = mythQB
    .keyword()
      .wildcard()
    .onField("history")
    .matching("sto*")
    .createQuery();

So far we have been looking for words or sets of words, the user can also search exact or approximate sentences. Use phrase() to do so.

Query luceneQuery = mythQB
    .phrase()
    .onField("history")
    .sentence("Thou shalt not kill")
    .createQuery();

Approximate sentences can be searched by adding a slop factor. The slop factor represents the number of other words permitted in the sentence: this works like a within or near operator.

Query luceneQuery = mythQB
    .phrase()
      .withSlop(3)
    .onField("history")
    .sentence("Thou kill")
    .createQuery();

A range query searches for a value in between given boundaries (included or not) or for a value below or above a given boundary (included or not).

//look for 0 <= starred < 3
Query luceneQuery = mythQB
    .range()
    .onField("starred")
    .from(0).to(3).excludeLimit()
    .createQuery();

//look for myths strictly BC
Date beforeChrist = ...;
Query luceneQuery = mythQB
    .range()
    .onField("creationDate")
    .below(beforeChrist).excludeLimit()
    .createQuery();

Queries can be aggregated (combined) to create more complex queries. The following aggregation operators are available:

The subqueries can be any Lucene query including a boolean query itself.

Example: SHOULD Query

//look for popular myths that are preferably urban
Query luceneQuery = mythQB
    .bool()
      .should( mythQB.keyword().onField("description").matching("urban").createQuery() )
      .must( mythQB.range().onField("starred").above(4).createQuery() )
    .createQuery();

Example: MUST Query

//look for popular urban myths
Query luceneQuery = mythQB
    .bool()
      .must( mythQB.keyword().onField("description").matching("urban").createQuery() )
      .must( mythQB.range().onField("starred").above(4).createQuery() )
    .createQuery();

Example: MUST NOT Query

//look for popular modern myths that are not urban
Date twentiethCentury = ...;
Query luceneQuery = mythQB
    .bool()
      .must( mythQB.keyword().onField("description").matching("urban").createQuery() )
        .not()
      .must( mythQB.range().onField("starred").above(4).createQuery() )
      .must( mythQB
        .range()
        .onField("creationDate")
        .above(twentiethCentury)
        .createQuery() )
    .createQuery();

The Hibernate Search query DSL is an easy to use and easy to read query API. In accepting and producing Lucene queries, you can incorporate query types not yet supported by the DSL.

The following is a summary of query options for query types and fields:

Example: Combination of Query Options

Query luceneQuery = mythQB
    .bool()
      .should( mythQB.keyword().onField("description").matching("urban").createQuery() )
      .should( mythQB
        .keyword()
        .onField("name")
          .boostedTo(3)
          .ignoreAnalyzer()
        .matching("urban").createQuery() )
      .must( mythQB
        .range()
          .boostedTo(5).withConstantScore()
        .onField("starred").above(4).createQuery() )
    .createQuery();

If you expect a reasonable number of results (for example using pagination) and expect to work on all of them, list() or uniqueResult() are recommended. list() work best if the entity batch-size is set up properly. Note that Hibernate Search has to process all Lucene Hits elements (within the pagination) when using list() , uniqueResult() and iterate().

If you wish to minimize Lucene document loading, scroll() is more appropriate. Do not forget to close the ScrollableResults object when you are done, since it keeps Lucene resources. If you expect to use scroll, but wish to load objects in batch, you can use query.setFetchSize(). When an object is accessed, and if not already loaded, Hibernate Search will load the next fetchSize objects in one pass.

It is sometimes useful to know the total number of matching documents:

Of course it would be too costly to retrieve all the matching documents. Hibernate Search allows you to retrieve the total number of matching documents regardless of the pagination parameters. Even more interesting, you can retrieve the number of matching elements without triggering a single object load.

Example: Determining the Result Size of a Query

org.hibernate.search.FullTextQuery query =
    s.createFullTextQuery( luceneQuery, Book.class );
//return the number of matching books without loading a single one
assert 3245 == ;

org.hibernate.search.FullTextQuery query =
    s.createFullTextQuery( luceneQuery, Book.class );
query.setMaxResult(10);
List results = query.list();
//return the total number of matching books regardless of pagination
assert 3245 == ;

Projection results are returned as Object arrays. If the data structure used for the object does not match the requirements of the application, apply a ResultTransformer. The ResultTransformer builds the required data structure after the query execution.

Projection results are returned as Object arrays. If the data structure used for the object does not match the requirements of the application, apply a ResultTransformer. The ResultTransformer builds the required data structure after the query execution.

Exampl: Using ResultTransformer with Projections

org.hibernate.search.FullTextQuery query =
    s.createFullTextQuery( luceneQuery, Book.class );
query.setProjection( "title", "mainAuthor.name" );

query.setResultTransformer( new StaticAliasToBeanResultTransformer( BookView.class, "title", "author" ) );
List<BookView> results = (List<BookView>) query.list();
for(BookView view : results) {
    log.info( "Book: " + view.getTitle() + ", " + view.getAuthor() );
}

Examples of ResultTransformer implementations can be found in the Hibernate Core codebase.

If the results of a query are not what you expected, the Luke tool is useful in understanding the outcome. However, Hibernate Search also gives you access to the Lucene Explanation object for a given result (in a given query). This class is considered fairly advanced to Lucene users but can provide a good understanding of the scoring of an object. You have two ways to access the Explanation object for a given result:

The first approach takes a document ID as a parameter and return the Explanation object. The document ID can be retrieved using projection and the FullTextQuery.DOCUMENT_ID constant.

In the second approach you project the Explanation object using the FullTextQuery.EXPLANATION constant.

Example: Retrieving the Lucene Explanation Object Using Projection

FullTextQuery ftQuery = s.createFullTextQuery( luceneQuery, Dvd.class )
        .setProjection(
             FullTextQuery.DOCUMENT_ID,
             ,
             FullTextQuery.THIS );
@SuppressWarnings("unchecked") List<Object[]> results = ftQuery.list();
for (Object[] result : results) {
    Explanation e = (Explanation) result[1];
    display( e.toString() );
}

Use the Explanation object only when required as it is roughly as expensive as running the Lucene query again.

Apache Lucene has a powerful feature that allows you to filter query results according to a custom filtering process. This is a very powerful way to apply additional data restrictions, especially since filters can be cached and reused. Use cases include:

Hibernate Search pushes the concept further by introducing the notion of parameterizable named filters which are transparently cached. For people familiar with the notion of Hibernate Core filters, the API is very similar:

Example: Enabling Fulltext Filters for a Query

fullTextQuery = s.createFullTextQuery( query, Driver.class );
fullTextQuery.enableFullTextFilter("bestDriver");
fullTextQuery.enableFullTextFilter("security").setParameter( "login", "andre" );
fullTextQuery.list(); //returns only best drivers where andre has credentials

In this example we enabled two filters on top of the query. You can enable (or disable) as many filters as you like.

Declaring filters is done through the @FullTextFilterDef annotation. This annotation can be on any @Indexed entity regardless of the query the filter is later applied to. This implies that filter definitions are global and their names must be unique. A SearchException is thrown in case two different @FullTextFilterDef annotations with the same name are defined. Each named filter has to specify its actual filter implementation.

Example: Defining and Implementing a Filter

@FullTextFilterDefs( {
    @FullTextFilterDef(name = "bestDriver", impl = BestDriversFilter.class),
    @FullTextFilterDef(name = "security", impl = SecurityFilterFactory.class)
})
public class Driver { ... }

public class BestDriversFilter extends org.apache.lucene.search.Filter {

    public DocIdSet getDocIdSet(IndexReader reader) throws IOException {
        OpenBitSet bitSet = new OpenBitSet( reader.maxDoc() );
        TermDocs termDocs = reader.termDocs( new Term( "score", "5" ) );
        while ( termDocs.next() ) {
            bitSet.set( termDocs.doc() );
        }
        return bitSet;
    }
}

BestDriversFilter is an example of a simple Lucene filter which reduces the result set to drivers whose score is 5. In this example the specified filter implements the org.apache.lucene.search.Filter directly and contains a no-arg constructor.

If your Filter creation requires additional steps or if the filter you want to use does not have a no-arg constructor, you can use the factory pattern:

Example: Creating a filter using the factory pattern

@FullTextFilterDef(name = "bestDriver", impl = BestDriversFilterFactory.class)
public class Driver { ... }

public class BestDriversFilterFactory {

@Factory
    public Filter getFilter() {
        //some additional steps to cache the filter results per IndexReader
        Filter bestDriversFilter = new BestDriversFilter();
        return new CachingWrapperFilter(bestDriversFilter);
    }
}

Hibernate Search will look for a @Factory annotated method and use it to build the filter instance. The factory must have a no-arg constructor.

Infinispan Query uses a @Factory annotated method to build the filter instance. The factory must have a no argument constructor.

Named filters come in handy where parameters have to be passed to the filter. For example a security filter might want to know which security level you want to apply:

Example: Passing parameters to a defined filter

fullTextQuery = s.createFullTextQuery( query, Driver.class );
fullTextQuery.enableFullTextFilter("security").setParameter( "level", 5 );

Each parameter name should have an associated setter on either the filter or filter factory of the targeted named filter definition. ] Example: Using parameters in the actual filter implementation

public class SecurityFilterFactory {
    private Integer level;

    /**
     * injected parameter
     */
    public void setLevel(Integer level) {
        this.level = level;
    }

    @Key public FilterKey getKey() {
        StandardFilterKey key = new StandardFilterKey();
        key.addParameter( level );
        return key;
    }

    @Factory
    public Filter getFilter() {
        Query query = new TermQuery( new Term("level", level.toString() ) );
        return new CachingWrapperFilter( new QueryWrapperFilter(query) );
    }
}

Note the method annotated @Key returns a FilterKey object. The returned object has a special contract: the key object must implement equals() / hashCode() so that two keys are equal if and only if the given Filter types are the same and the set of parameters are the same. In other words, two filter keys are equal if and only if the filters from which the keys are generated can be interchanged. The key object is used as a key in the cache mechanism.

@Key methods are needed only if:

In most cases, using the StandardFilterKey implementation will be good enough. It delegates the equals() / hashCode() implementation to each of the parameters equals and hashcode methods.

As mentioned before the defined filters are per default cached and the cache uses a combination of hard and soft references to allow disposal of memory when needed. The hard reference cache keeps track of the most recently used filters and transforms the ones least used to SoftReferences when needed. Once the limit of the hard reference cache is reached additional filters are cached as SoftReferences. To adjust the size of the hard reference cache, use hibernate.search.filter.cache_strategy.size (defaults to 128). For advanced use of filter caching, implement your own FilterCachingStrategy. The classname is defined by hibernate.search.filter.cache_strategy.

This filter caching mechanism should not be confused with caching the actual filter results. In Lucene it is common practice to wrap filters using the IndexReader around a CachingWrapperFilter. The wrapper will cache the DocIdSet returned from the getDocIdSet(IndexReader reader) method to avoid expensive recomputation. It is important to mention that the computed DocIdSet is only cachable for the same IndexReader instance, because the reader effectively represents the state of the index at the moment it was opened. The document list cannot change within an opened IndexReader. A different/new IndexReader instance, however, works potentially on a different set of Documents (either from a different index or simply because the index has changed), hence the cached DocIdSet has to be recomputed.

Hibernate Search also helps with this aspect of caching. Per default the cache flag of @FullTextFilterDef is set to FilterCacheModeType.INSTANCE_AND_DOCIDSETRESULTS which will automatically cache the filter instance as well as wrap the specified filter around a Hibernate specific implementation of CachingWrapperFilter. In contrast to Lucene’s version of this class SoftReferences are used together with a hard reference count (see discussion about filter cache). The hard reference count can be adjusted using hibernate.search.filter.cache_docidresults.size (defaults to 5). The wrapping behaviour can be controlled using the @FullTextFilterDef.cache parameter. There are three different values for this parameter:

Last but not least - why should filters be cached? There are two areas where filter caching shines:

Filters should be cached in the following situations:

In a sharded environment it is possible to execute queries on a subset of the available shards. This can be done in two steps:

public class CustomerShardingStrategy implements IndexShardingStrategy {

     // stored IndexManagers in an array indexed by customerID
     private IndexManager[] indexManagers;

     public void initialize(Properties properties, IndexManager[] indexManagers) {
       this.indexManagers = indexManagers;
     }

     public IndexManager[] getIndexManagersForAllShards() {
       return indexManagers;
     }

     public IndexManager getIndexManagerForAddition(
         Class<?> entity, Serializable id, String idInString, Document document) {
       Integer customerID = Integer.parseInt(document.getFieldable("customerID").stringValue());
       return indexManagers[customerID];
     }

     public IndexManager[] getIndexManagersForDeletion(
         Class<?> entity, Serializable id, String idInString) {
       return getIndexManagersForAllShards();
     }

      /**
      * Optimization; don't search ALL shards and union the results; in this case, we
      * can be certain that all the data for a particular customer Filter is in a single
      * shard; simply return that shard by customerID.
      */
     public IndexManager[] getIndexManagersForQuery(
         FullTextFilterImplementor[] filters) {
       FullTextFilter filter = getCustomerFilter(filters, "customer");
       if (filter == null) {
         return getIndexManagersForAllShards();
       }
       else {
         return new IndexManager[] { indexManagers[Integer.parseInt(
           filter.getParameter("customerID").toString())] };
       }
     }

     private FullTextFilter getCustomerFilter(FullTextFilterImplementor[] filters, String name) {
       for (FullTextFilterImplementor filter: filters) {
         if (filter.getName().equals(name)) return filter;
       }
       return null;
     }
    }

In this example, if the filter named customer is present, only the shard dedicated to this customer is queried, otherwise, all shards are returned. A given Sharding strategy can react to one or more filters and depends on their parameters.

The second step is to activate the filter at query time. While the filter can be a regular filter (as defined in ) which also filters Lucene results after the query, you can make use of a special filter that will only be passed to the sharding strategy (and is otherwise ignored).

To use this feature, specify the ShardSensitiveOnlyFilter class when declaring your filter.

@Indexed
@FullTextFilterDef(name="customer", impl=ShardSensitiveOnlyFilter.class)
public class Customer {
   ...
}

FullTextQuery query = ftEm.createFullTextQuery(luceneQuery, Customer.class);
query.enableFulltextFilter("customer").setParameter("CustomerID", 5);
@SuppressWarnings("unchecked")
List<Customer> results = query.getResultList();

Note that by using the ShardSensitiveOnlyFilter, you do not have to implement any Lucene filter. Using filters and sharding strategy reacting to these filters is recommended to speed up queries in a sharded environment.

The first step towards a faceted search is to create the FacetingRequest. Currently two types of faceting requests are supported. The first type is called discrete faceting and the second type range faceting request. In the case of a discrete faceting request you specify on which index field you want to facet (categorize) and which faceting options to apply. An example for a discrete faceting request can be seen in the following example:

Example: Creating a discrete faceting request

QueryBuilder builder = fullTextSession.getSearchFactory()
    .buildQueryBuilder()
        .forEntity( Cd.class )
            .get();
FacetingRequest labelFacetingRequest = builder.facet()
    .name( "labelFaceting" )
    .onField( "label")
    .discrete()
    .orderedBy( FacetSortOrder.COUNT_DESC )
    .includeZeroCounts( false )
    .maxFacetCount( 1 )
    .createFacetingRequest();

When executing this faceting request a Facet instance will be created for each discrete value for the indexed field label. The Facet instance will record the actual field value including how often this particular field value occurs within the original query results. orderedBy, includeZeroCounts and maxFacetCount are optional parameters which can be applied on any faceting request. orderedBy allows to specify in which order the created facets will be returned. The default is FacetSortOrder.COUNT_DESC, but you can also sort on the field value or the order in which ranges were specified. includeZeroCount determines whether facets with a count of 0 will be included in the result (per default they are) and maxFacetCount allows to limit the maximum amount of facets returned.

The creation of a range faceting request is quite similar except that we have to specify ranges for the field values we are faceting on. A range faceting request can be seen below where three different price ranges are specified. The below and above can only be specified once, but you can specify as many from - to ranges as you want. For each range boundary you can also specify via excludeLimit whether it is included into the range or not.

QueryBuilder builder = fullTextSession.getSearchFactory()
    .buildQueryBuilder()
        .forEntity( Cd.class )
            .get();
FacetingRequest priceFacetingRequest = builder.facet()
    .name( "priceFaceting" )
    .onField( "price" )
    .range()
    .below( 1000 )
    .from( 1001 ).to( 1500 )
    .above( 1500 ).excludeLimit()
    .createFacetingRequest();

A faceting request is applied to a query via the FacetManager class which can be retrieved via the FullTextQuery class.

You can enable as many faceting requests as you like and retrieve them afterwards via getFacets() specifying the faceting request name. There is also a disableFaceting() method which allows you to disable a faceting request by specifying its name.

A faceting request can be applied on a query using the FacetManager, which can be retrieved via the FullTextQuery.

Example: Applying a faceting request

// create a fulltext query
Query luceneQuery = builder.all().createQuery(); // match all query
FullTextQuery fullTextQuery = fullTextSession.createFullTextQuery( luceneQuery, Cd.class );

// retrieve facet manager and apply faceting request
FacetManager facetManager = fullTextQuery.getFacetManager();
facetManager.enableFaceting( priceFacetingRequest );

// get the list of Cds
List<Cd> cds = fullTextQuery.list();
...

// retrieve the faceting results
List<Facet> facets = facetManager.getFacets( "priceFaceting" );
...

Multiple faceting requests can be retrieved using getFacets() and specifying the faceting request name.

The disableFaceting() method disables a faceting request by specifying its name.

Last but not least, you can apply any of the returned Facets as additional criteria on your original query in order to implement a "drill-down" functionality. For this purpose FacetSelection can be utilized. FacetSelections are available via the FacetManager and allow you to select a facet as query criteria (selectFacets), remove a facet restriction (deselectFacets), remove all facet restrictions (clearSelectedFacets) and retrieve all currently selected facets (getSelectedFacets). The following snippet shows an example.

// create a fulltext query
Query luceneQuery = builder.all().createQuery(); // match all query
FullTextQuery fullTextQuery = fullTextSession.createFullTextQuery( luceneQuery, clazz );

// retrieve facet manager and apply faceting request
FacetManager facetManager = fullTextQuery.getFacetManager();
facetManager.enableFaceting( priceFacetingRequest );

// get the list of Cd
List<Cd> cds = fullTextQuery.list();
assertTrue(cds.size() == 10);

// retrieve the faceting results
List<Facet> facets = facetManager.getFacets( "priceFaceting" );
assertTrue(facets.get(0).getCount() == 2)

// apply first facet as additional search criteria
facetManager.getFacetGroup( "priceFaceting" ).selectFacets( facets.get( 0 ) );

// re-execute the query
cds = fullTextQuery.list();
assertTrue(cds.size() == 2);

The primary function of a Lucene index is to identify matches to your queries. After the query is performed the results must be analyzed to extract useful information. Hibernate Search would typically need to extract the Class type and the primary key.

Extracting the needed values from the index has a performance cost, which in some cases might be very low and not noticeable, but in some other cases might be a good candidate for caching.

The requirements depend on the kind of Projections being used, as in some cases the Class type is not needed as it can be inferred from the query context or other means.

Using the @CacheFromIndex annotation you can experiment with different kinds of caching of the main metadata fields required by Hibernate Search:

import static org.hibernate.search.annotations.FieldCacheType.CLASS;
import static org.hibernate.search.annotations.FieldCacheType.ID;

@Indexed
@CacheFromIndex( { CLASS, ID } )
public class Essay {
    ...

It is possible to cache Class types and IDs using this annotation:

Using a FieldCache has two downsides to consider:

With some queries the classtype will not be needed at all, in that case even if you enabled the CLASS field cache, this might not be used; for example if you are targeting a single class, obviously all returned values will be of that type (this is evaluated at each Query execution).

For the ID FieldCache to be used, the ids of targeted entities must be using a TwoWayFieldBridge (as all builting bridges), and all types being loaded in a specific query must use the fieldname for the id, and have ids of the same type (this is evaluated at each Query execution).

This strategy consists of removing the existing index and then adding all entities back to the index using FullTextSession.purgeAll() and FullTextSession.index(), however there are some memory and efficiency constraints. For maximum efficiency Hibernate Search batches index operations and executes them at commit time. If you expect to index a lot of data you need to be careful about memory consumption since all documents are kept in a queue until the transaction commit. You can potentially face an OutOfMemoryException if you do not empty the queue periodically; to do this use fullTextSession.flushToIndexes(). Every time fullTextSession.flushToIndexes() is called (or if the transaction is committed), the batch queue is processed, applying all index changes. Be aware that, once flushed, the changes cannot be rolled back.

Example: Index rebuilding using index() and flushToIndexes()

fullTextSession.setFlushMode(FlushMode.MANUAL);
fullTextSession.setCacheMode(CacheMode.IGNORE);
transaction = fullTextSession.beginTransaction();
//Scrollable results will avoid loading too many objects in memory
ScrollableResults results = fullTextSession.createCriteria( Email.class )
    .setFetchSize(BATCH_SIZE)
    .scroll( ScrollMode.FORWARD_ONLY );
int index = 0;
while( results.next() ) {
    index++;
    fullTextSession.index( results.get(0) ); //index each element
    if (index % BATCH_SIZE == 0) {
        fullTextSession.flushToIndexes(); //apply changes to indexes
        fullTextSession.clear(); //free memory since the queue is processed
    }
}
transaction.commit();

Try to use a batch size that guarantees that your application will not be out of memory: with a bigger batch size objects are fetched faster from database but more memory is needed.

Hibernate Search’s MassIndexer uses several parallel threads to rebuild the index. You can optionally select which entities need to be reloaded or have it reindex all entities. This approach is optimized for best performance but requires to set the application in maintenance mode. Querying the index is not recommended when a MassIndexer is busy.

Example: Rebuild the Index Using a MassIndexer

fullTextSession.createIndexer().startAndWait();

This will rebuild the index, deleting it and then reloading all entities from the database. Although it is simple to use, some tweaking is recommended to speed up the process.

Example: Using a Tuned MassIndexer

fullTextSession
 .createIndexer( User.class )
 .batchSizeToLoadObjects( 25 )
 .cacheMode( CacheMode.NORMAL )
 .threadsToLoadObjects( 12 )
 .idFetchSize( 150 )
 .progressMonitor( monitor ) //a MassIndexerProgressMonitor implementation
 .startAndWait();

This will rebuild the index of all User instances (and subtypes), and will create 12 parallel threads to load the User instances using batches of 25 objects per query. These same 12 threads will also need to process indexed embedded relations and custom FieldBridges or ClassBridges to output a Lucene document. The threads trigger lazy loading of additional attributes during the conversion process. Because of this, a high number of threads working in parallel is required. The number of threads working on actual index writing is defined by the back-end configuration of each index.

It is recommended to leave cacheMode to CacheMode.IGNORE (the default), as in most reindexing situations the cache will be a useless additional overhead. It might be useful to enable some other CacheMode depending on your data as it could increase performance if the main entity is relating to enum-like data included in the index.

Other parameters which affect indexing time and memory consumption are:

Previous versions also had a max_field_length but this was removed from Lucene, it is possible to obtain a similar effect by using a LimitTokenCountAnalyzer.

All .indexwriter parameters are Lucene specific and Hibernate Search passes these parameters through.

The MassIndexer uses a forward only scrollable result to iterate on the primary keys to be loaded, but MySQL’s JDBC driver will load all values in memory. To avoid this "optimization" set idFetchSize to Integer.MIN_VALUE.