Welcome to Hibernate Search. The following chapter will guide you through the initial steps required to integrate Hibernate Search into an existing Hibernate enabled application. In case you are a Hibernate new timer we recommend you start here.

1.1. System Requirements

Table 1.1. System requirements

Java Runtime	A JDK or JRE version 6 or greater. You can download a Java Runtime for Windows/Linux/Solaris here. If using Java version 7 make sure you avoid builds 0 and 1: those versions contained an optimisation bug which would be triggered by Lucene. Hibernate Search 3.x was compatible with Java version 5.
Hibernate Search	`hibernate-search-4.0.0.Final.jar` and all runtime dependencies. You can get the jar artifacts either from the `dist/lib` directory of the Hibernate Search distribution or you can download them from the JBoss maven repository.
Hibernate Core	This instructions have been tested against Hibernate 4.0. You will need `hibernate-core-4.0.0.CR6.jar` and its transitive dependencies (either from the distribution bundle or the maven repository).
JPA 2	Even though Hibernate Search can be used without JPA annotations the following instructions will use them for basic entity configuration (@Entity, @Id, @OneToMany,...). This part of the configuration could also be expressed in xml or code. Hibernate Search, however, has also its own set of annotations (@Indexed, @DocumentId, @Field,...) for which there exists so far no XML based alternative; a better option is the Section 4.6, “Programmatic API”.

1.2. Using Maven

The Hibernate Search artifacts can be found in Maven's central repository but are released first in the JBoss maven repository. So it's not a requirement but we recommend to add this repository to your settings.xml file (see also Maven Getting Started for more details).

This is all you need to add to your pom.xml to get started:

Example 1.1. Maven artifact identifier for Hibernate Search


<dependency>

   <groupId>org.hibernate</groupId>

   <artifactId>hibernate-search</artifactId>

   <version>4.0.0.Final</version>

</dependency>

Example 1.2. Optional Maven dependencies for Hibernate Search


<dependency>

<!-- If using JPA (2), add: -->

<dependency>

   <groupId>org.hibernate</groupId>

   <artifactId>hibernate-entitymanager</artifactId>

   <version>4.0.0.CR6</version>

</dependency>

<!-- Additional Analyzers: -->

<dependency>

   <groupId>org.hibernate</groupId>

   <artifactId>hibernate-search-analyzers</artifactId>

   <version>4.0.0.Final</version>

</dependency>

<!-- Infinispan integration: -->

<dependency>

   <groupId>org.hibernate</groupId>

   <artifactId>hibernate-search-infinispan</artifactId>

   <version>4.0.0.Final</version>

</dependency>

Only the hibernate-search dependency is mandatory. hibernate-entitymanager is only required if you want to use Hibernate Search in conjunction with JPA.

To use hibernate-search-infinispan, adding the JBoss Maven repository is mandatory, because it contains the needed Infinispan dependencies which are currently not mirrored by central.

1.2.1. Using a Maven Archetype

Using a Maven Archetype it is possible to generate a Maven project from scratch, already containing the basic configuration files and some sample code and tests. Most development environments integrating maven support a graphical option to do this; the groupId is org.hibernate and the artifactId is hibernate-search-quickstart

Example 1.3. Generating a sample project using the Maven Archetype

mvn archetype:generate                                 \
   -DarchetypeGroupId=org.hibernate                    \
   -DarchetypeArtifactId=hibernate-search-quickstart   \
   -DarchetypeVersion=4.0.0.Final                      \
   -DarchetypeRepository=http://repository.jboss.org/nexus/content/groups/public-jboss/

1.3. Configuration

Once you have downloaded and added all required dependencies to your application you have to add a couple of properties to your hibernate configuration file. If you are using Hibernate directly this can be done in hibernate.properties or hibernate.cfg.xml. If you are using Hibernate via JPA you can also add the properties to persistence.xml. The good news is that for standard use most properties offer a sensible default. An example persistence.xml configuration could look like this:

Example 1.4. Basic configuration options to be added to hibernate.properties, hibernate.cfg.xml or persistence.xml


...

<property name="hibernate.search.default.directory_provider" 

          value="filesystem"/> 



<property name="hibernate.search.default.indexBase" 

          value="/var/lucene/indexes"/> 

...

First you have to tell Hibernate Search which DirectoryProvider to use. This can be achieved by setting the hibernate.search.default.directory_provider property. Apache Lucene has the notion of a Directory to store the index files. Hibernate Search handles the initialization and configuration of a Lucene Directory instance via a DirectoryProvider. In this tutorial we will use a a directory provider storing the index in the file system. This will give us the ability to physically inspect the Lucene indexes created by Hibernate Search (eg via Luke). Once you have a working configuration you can start experimenting with other directory providers (see Section 3.3, “Directory configuration”). Next to the directory provider you also have to specify the default base directory for all indexes via hibernate.search.default.indexBase.

Lets assume that your application contains the Hibernate managed classes example.Book and example.Author and you want to add free text search capabilities to your application in order to search the books contained in your database.

Example 1.5. Example entities Book and Author before adding Hibernate Search specific annotations

package example;

...

@Entity

public class Book {


  @Id

  @GeneratedValue

  private Integer id; 


  private String title;  


  private String subtitle; 


  @ManyToMany 

  private Set<Author> authors = new HashSet<Author>();


  private Date publicationDate;

  

  public Book() {} 

  

  // standard getters/setters follow here

  ...

}

package example;

...

@Entity

public class Author {


  @Id

  @GeneratedValue

  private Integer id;


  private String name;


  public Author() {} 

 

  // standard getters/setters follow here

  ...

}

To achieve this you have to add a few annotations to the Book and Author class. The first annotation @Indexed marks Book as indexable. By design Hibernate Search needs to store an untokenized id in the index to ensure index unicity for a given entity. @DocumentId marks the property to use for this purpose and is in most cases the same as the database primary key. The @DocumentId annotation is optional in the case where an @Id annotation exists.

Next you have to mark the fields you want to make searchable. Let's start with title and subtitle and annotate both with @Field. The parameter index=Index.YES will ensure that the text will be indexed, while analyze=analyze.YES ensures that the text will be analyzed using the default Lucene analyzer. Usually, analyzing means chunking a sentence into individual words and potentially excluding common words like 'a' or 'the'. We will talk more about analyzers a little later on. The third parameter we specify within @Field, store=Store.NO, ensures that the actual data will not be stored in the index. Whether this data is stored in the index or not has nothing to do with the ability to search for it. From Lucene's perspective it is not necessary to keep the data once the index is created. The benefit of storing it is the ability to retrieve it via projections ( see Section 5.1.3.5, “Projection”).

Without projections, Hibernate Search will per default execute a Lucene query in order to find the database identifiers of the entities matching the query critera and use these identifiers to retrieve managed objects from the database. The decision for or against projection has to be made on a case to case basis. The default behaviour is recommended since it returns managed objects whereas projections only return object arrays.

Note that index=Index.YES, analyze=analyze.YES and store=Store.NO are the default values for these paramters and could be ommited.

After this short look under the hood let's go back to annotating the Book class. Another annotation we have not yet discussed is @DateBridge. This annotation is one of the built-in field bridges in Hibernate Search. The Lucene index is purely string based. For this reason Hibernate Search must convert the data types of the indexed fields to strings and vice versa. A range of predefined bridges are provided, including the DateBridge which will convert a java.util.Date into a String with the specified resolution. For more details see Section 4.4, “Bridges”.

This leaves us with @IndexedEmbedded. This annotation is used to index associated entities (@ManyToMany, @*ToOne, @Embedded and @ElementCollection) as part of the owning entity. This is needed since a Lucene index document is a flat data structure which does not know anything about object relations. To ensure that the authors' name will be searchable you have to make sure that the names are indexed as part of the book itself. On top of @IndexedEmbedded you will also have to mark all fields of the associated entity you want to have included in the index with @Indexed. For more details see Section 4.1.3, “Embedded and associated objects”.

These settings should be sufficient for now. For more details on entity mapping refer to Section 4.1, “Mapping an entity”.

Example 1.6. Example entities after adding Hibernate Search annotations

package example;

...

@Entity
@Indexed

public class Book {


  @Id

  @GeneratedValue

  private Integer id;

  

  @Field(index=Index.YES, analyze=Analyze.YES, store=Store.NO)

  private String title;

  

  @Field(index=Index.YES, analyze=Analyze.YES, store=Store.NO)

  private String subtitle; 
  @Field(index = Index.YES, analyze=Analyze.NO, store = Store.YES)
  @DateBridge(resolution = Resolution.DAY)

  private Date publicationDate;


  @IndexedEmbedded

  @ManyToMany 

  private Set<Author> authors = new HashSet<Author>();


  public Book() {

  } 

  

  // standard getters/setters follow here

  ... 

}

package example;

...

@Entity

public class Author {


  @Id

  @GeneratedValue

  private Integer id;


  @Field

  private String name;


  public Author() {

  } 

 

  // standard getters/setters follow here

  ...

}

1.4. Indexing

Hibernate Search will transparently index every entity persisted, updated or removed through Hibernate Core. However, you have to create an initial Lucene index for the data already present in your database. Once you have added the above properties and annotations it is time to trigger an initial batch index of your books. You can achieve this by using one of the following code snippets (see also Section 6.3, “Rebuilding the whole index”):

Example 1.7. Using Hibernate Session to index data

FullTextSession fullTextSession = Search.getFullTextSession(session);

fullTextSession.createIndexer().startAndWait();

Example 1.8. Using JPA to index data

EntityManager em = entityManagerFactory.createEntityManager();

FullTextEntityManager fullTextEntityManager = Search.getFullTextEntityManager(em);

fullTextEntityManager.createIndexer().startAndWait();

After executing the above code, you should be able to see a Lucene index under /var/lucene/indexes/example.Book. Go ahead an inspect this index with Luke. It will help you to understand how Hibernate Search works.

1.5. Searching

Now it is time to execute a first search. The general approach is to create a Lucene query (either via the Lucene API (Section 5.1.1, “Building a Lucene query using the Lucene API”) or via the Hibernate Search query DSL (Section 5.1.2, “Building a Lucene query with the Hibernate Search query DSL”)) and then wrap this query into a org.hibernate.Query in order to get all the functionality one is used to from the Hibernate API. The following code will prepare a query against the indexed fields, execute it and return a list of Books.

Example 1.9. Using Hibernate Session to create and execute a search

FullTextSession fullTextSession = Search.getFullTextSession(session);

Transaction tx = fullTextSession.beginTransaction();


// create native Lucene query unsing the query DSL

// alternatively you can write the Lucene query using the Lucene query parser

// or the Lucene programmatic API. The Hibernate Search DSL is recommended though

QueryBuilder qb = fullTextSession.getSearchFactory()

    .buildQueryBuilder().forEntity( Book.class ).get();

org.apache.lucene.search.Query query = qb

  .keyword()

  .onFields("title", "subtitle", "authors.name", "publicationDate")

  .matching("Java rocks!");

  .createQuery();


// wrap Lucene query in a org.hibernate.Query

org.hibernate.Query hibQuery = 

    fullTextSession.createFullTextQuery(query, Book.class);


// execute search

List result = hibQuery.list();

  

tx.commit();

session.close();

Example 1.10. Using JPA to create and execute a search

EntityManager em = entityManagerFactory.createEntityManager();

FullTextEntityManager fullTextEntityManager = 

    org.hibernate.search.jpa.Search.getFullTextEntityManager(em);

em.getTransaction().begin();


// create native Lucene query unsing the query DSL

// alternatively you can write the Lucene query using the Lucene query parser

// or the Lucene programmatic API. The Hibernate Search DSL is recommended though

QueryBuilder qb = fullTextEntityManager.getSearchFactory()

    .buildQueryBuilder().forEntity( Book.class ).get();

org.apache.lucene.search.Query query = qb

  .keyword()

  .onFields("title", "subtitle", "authors.name", "publicationDate")

  .matching("Java rocks!")

  .createQuery();


// wrap Lucene query in a javax.persistence.Query

javax.persistence.Query persistenceQuery = 

    fullTextEntityManager.createFullTextQuery(query, Book.class);


// execute search

List result = persistenceQuery.getResultList();


em.getTransaction().commit();

em.close();

1.6. Analyzer

Let's make things a little more interesting now. Assume that one of your indexed book entities has the title "Refactoring: Improving the Design of Existing Code" and you want to get hits for all of the following queries: "refactor", "refactors", "refactored" and "refactoring". In Lucene this can be achieved by choosing an analyzer class which applies word stemming during the indexing as well as the search process. Hibernate Search offers several ways to configure the analyzer to be used (see Section 4.3.1, “Default analyzer and analyzer by class”):

Setting the hibernate.search.analyzer property in the configuration file. The specified class will then be the default analyzer.
Setting the @Analyzer annotation at the entity level.
Setting the @Analyzer annotation at the field level.

When using the @Analyzer annotation one can either specify the fully qualified classname of the analyzer to use or one can refer to an analyzer definition defined by the @AnalyzerDef annotation. In the latter case the Solr analyzer framework with its factories approach is utilized. To find out more about the factory classes available you can either browse the Solr JavaDoc or read the corresponding section on the Solr Wiki.

In the example below a StandardTokenizerFactory is used followed by two filter factories, LowerCaseFilterFactory and SnowballPorterFilterFactory. The standard tokenizer splits words at punctuation characters and hyphens while keeping email addresses and internet hostnames intact. It is a good general purpose tokenizer. The lowercase filter lowercases the letters in each token whereas the snowball filter finally applies language specific stemming.

Generally, when using the Solr framework you have to start with a tokenizer followed by an arbitrary number of filters.

Example 1.11. Using @AnalyzerDef and the Solr framework to define and use an analyzer

@Entity

@Indexed
@AnalyzerDef(name = "customanalyzer",
  tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class),
  filters = {
    @TokenFilterDef(factory = LowerCaseFilterFactory.class),
    @TokenFilterDef(factory = SnowballPorterFilterFactory.class, params = {
      @Parameter(name = "language", value = "English")
    })
  })

public class Book {


  @Id

  @GeneratedValue

  @DocumentId

  private Integer id;

  

  @Field

  @Analyzer(definition = "customanalyzer")

  private String title;

  

  @Field

  @Analyzer(definition = "customanalyzer")

  private String subtitle; 


  @IndexedEmbedded

  @ManyToMany 

  private Set<Author> authors = new HashSet<Author>();
  @Field(index = Index.YES, analyze = Analyze.NO, store = Store.YES)

  @DateBridge(resolution = Resolution.DAY)

  private Date publicationDate;

  

  public Book() {

  } 

  

  // standard getters/setters follow here

  ... 

}

Using @AnalyzerDef only defines an Analyzer, you still have to apply it to entities and or properties using @Analyzer. Like in the above example the customanalyzer is defined but not applied on the entity: it's applied on the title and subtitle properties only. An analyzer definition is global, so you can define it on any entity and reuse the definition on other entities.

1.7. What's next

The above paragraphs helped you getting an overview of Hibernate Search. The next step after this tutorial is to get more familiar with the overall architecture of Hibernate Search (Chapter 2, Architecture) and explore the basic features in more detail. Two topics which were only briefly touched in this tutorial were analyzer configuration (Section 4.3.1, “Default analyzer and analyzer by class”) and field bridges (Section 4.4, “Bridges”). Both are important features required for more fine-grained indexing. More advanced topics cover clustering (Section 3.4.1, “JMS Master/Slave back end”, Section 3.3.1, “Infinispan Directory configuration”) and large index handling (Section 9.4, “Sharding indexes”).

Chapter 2. Architecture

2.1. Overview

2.2. Back end

2.2.1. Lucene
2.2.2. JMS
2.2.3. JGroups

2.3. Reader strategy

2.3.1. shared
2.3.2. not-shared
2.3.3. Custom

2.1. Overview

Hibernate Search consists of an indexing component as well as an index search component. Both are backed by Apache Lucene.

Each time an entity is inserted, updated or removed in/from the database, Hibernate Search keeps track of this event (through the Hibernate event system) and schedules an index update. All these updates are handled without you having to interact with the Apache Lucene APIs directly (see Section 3.1, “Enabling Hibernate Search and automatic indexing”). Instead, the interaction with the underlying Lucene indexes is handled via so called IndexManagers.

Each Lucene index is managed by one index manager which is uniquely identified by name. In most cases there is also a one to one relationship between an indexed entity and a single IndexManager. The exceptions are the use cases of index sharding and index sharing. The former can be applied when the index for a single entity becomes too big and indexing operations are slowing down the application. In this case a single entity is indexed into multiple indexes each with its own index manager (see Section 9.4, “Sharding indexes”). The latter, index sharing, is the ability to index multiple entities into the same Lucene index (see Section 9.5, “Sharing indexes”).

The index manager abstracts from the specific index configuration. In the case of the default index manager this includes details about the selected backend, the configured reader strategy and the chosen DirectoryProvider. These components will be discussed in greater detail later on. It is recommended that you start with the default index manager which uses different Lucene Directory types to manage the indexes (see Section 3.3, “Directory configuration”). You can, however, also provide your own IndexManager implementation (see Section 3.2, “Configuring the IndexManager”).

Once the index is created, you can search for entities and return lists of managed entities saving you the tedious object to Lucene Document mapping. The same persistence context is shared between Hibernate and Hibernate Search. As a matter of fact, the FullTextSession is built on top of the Hibernate Session so that the application code can use the unified org.hibernate.Query or javax.persistence.Query APIs exactly the same way a HQL, JPA-QL or native query would do.

To be more efficient Hibernate Search batches the write interactions with the Lucene index. This batching is the responsibility of the Worker. There are currently two types of batching. Outside a transaction, the index update operation is executed right after the actual database operation. This is really a no batching setup. In the case of an ongoing transaction, the index update operation is scheduled for the transaction commit phase and discarded in case of transaction rollback. The batching scope is the transaction. There are two immediate benefits:

Performance: Lucene indexing works better when operation are executed in batch.
ACIDity: The work executed has the same scoping as the one executed by the database transaction and is executed if and only if the transaction is committed. This is not ACID in the strict sense of it, but ACID behavior is rarely useful for full text search indexes since they can be rebuilt from the source at any time.

You can think of those two batch modes (no scope vs transactional) as the equivalent of the (infamous) autocommit vs transactional behavior. From a performance perspective, the in transaction mode is recommended. The scoping choice is made transparently. Hibernate Search detects the presence of a transaction and adjust the scoping (see Section 3.4, “Worker configuration”).

Tip

It is recommended - for both your database and Hibernate Search - to execute your operations in a transaction, be it JDBC or JTA.

Note

Hibernate Search works perfectly fine in the Hibernate / EntityManager long conversation pattern aka. atomic conversation

2.2. Back end

Hibernate Search offers the ability to let the batched work being processed by different back ends. Several back ends are provided out of the box and you have the option to plugin your own. It is important to understand that in this context back end encompasses more than just the configuration option hibernate.search.default.worker.backend. This property just specifies a implementation of the BackendQueueProcessor interface which is a part of a back end configuration. In most cases, however, additional configuration settings are needed to successfully configure a specific backend setup, like for example the JMS back end.

2.2.1. Lucene

In this mode, all index update operations applied on a given node (JVM) will be executed to the Lucene directories (through the directory providers) by the same node. This mode is typically used in non clustered environment or in clustered environments where the directory store is shared.

Lucene back end configuration.

This mode targets non clustered applications, or clustered applications where the Directory is taking care of the locking strategy.

The main advantage is simplicity and immediate visibility of the changes in Lucene queries (a requirement in some applications).

An alternative back end viable for non-clustered and non-shared index configurations is the near-real-time backend.

2.2.2. JMS

All index update operations applied on a given node are sent to a JMS queue. A unique reader will then process the queue and update the master index. The master index is then replicated on a regular basis to the slave copies. This is known as the master/slaves pattern. The master is the sole responsible for updating the Lucene index. The slaves can accept read as well as write operations. However, they only process the read operation on their local index copy and delegate the update operations to the master.

JMS back end configuration.

This mode targets clustered environments where throughput is critical, and index update delays are affordable. Reliability is ensured by the JMS provider and by having the slaves working on a local copy of the index.

2.2.3. JGroups

The JGroups based back end works similar to the JMS one and is designed after the same master/slave pattern. However, instead of JMS the JGroups toolkit is used as a replication mechanism. This back end can be used as an alternative to JMS when response time is critical, but i.e. JNDI service is not available.

2.3. Reader strategy

When executing a query, Hibernate Search interacts with the Apache Lucene indexes through a reader strategy. Choosing a reader strategy will depend on the profile of the application (frequent updates, read mostly, asynchronous index update etc). See also Section 3.5, “Reader strategy configuration”

Chapter 3. Configuration

3.1. Enabling Hibernate Search and automatic indexing

3.1.1. Enabling Hibernate Search
3.1.2. Automatic indexing

3.2. Configuring the IndexManager

3.2.1. directory-based
3.2.2. near-real-time
3.2.3. Custom

3.3. Directory configuration

3.3.1. Infinispan Directory configuration

3.4. Worker configuration

3.4.1. JMS Master/Slave back end
3.4.2. JGroups Master/Slave back end

3.5. Reader strategy configuration

3.6. Tuning Lucene indexing performance

3.6.1. Control segment size

3.7. LockFactory configuration

3.8. Exception Handling Configuration

3.9. Index format compatibility

3.1. Enabling Hibernate Search and automatic indexing

Let's start with the most basic configuration question - how do I enable Hibernate Search?

3.1.1. Enabling Hibernate Search

The good news is that Hibernate Search is enabled out of the box when detected on the classpath by Hibernate Core. If, for some reason you need to disable it, set hibernate.search.autoregister_listeners to false. Note that there is no performance penalty when the listeners are enabled but no entities are annotated as indexed.

3.1.2. Automatic indexing

By default, every time an object is inserted, updated or deleted through Hibernate, Hibernate Search updates the according Lucene index. It is sometimes desirable to disable that features if either your index is read-only or if index updates are done in a batch way (see Section 6.3, “Rebuilding the whole index”).

To disable event based indexing, set

hibernate.search.indexing_strategy = manual

Note

In most case, the JMS backend provides the best of both world, a lightweight event based system keeps track of all changes in the system, and the heavyweight indexing process is done by a separate process or machine.

3.2. Configuring the `IndexManager`

The role of the index manager component is described in Chapter 2, Architecture. Hibernate Search provides two possible implementations for this interface to choose from.

directory-based: the default implementation which uses the Lucene Directory abstraction to mange index files.
near-real-time: avoid flushing writes to disk at each commit. This index manager is also Directory based, but also makes uses of Lucene's NRT functionallity.

To select an alternative you specify the property:

hibernate.search.[default|<indexname>].indexmanager = near-real-time

3.2.1. `directory-based`

The default IndexManager implementation. This is the one mostly referred to in this documentation. It is highly configurable and allows you to select different settings for the reader strategy, back ends and directory providers. Refer to Section 3.3, “Directory configuration”, Section 3.4, “Worker configuration” and Section 3.5, “Reader strategy configuration” for more details.

3.2.2. `near-real-time`

The NRTIndexManager is an extension of the default IndexManager, leveraging the Lucene NRT (Near Real Time) features for extreme low latency index writes. As a tradeoff it requires a non-clustered and non-shared index. In other words, it will ignore configuration settings for alternative back ends other than lucene and will acquire exclusive write locks on the Directory.

To achieve this low latency writes, the IndexWriter will not flush every change to disk. Queries will be allowed to read updated state from the unflushed index writer buffers; the downside of this strategy is that if the application crashes or the IndexWriter is otherwise killed you'll have to rebuild the indexes as some updates might be lost.

Because of these downsides, and because a master node in cluster can be configured for good performance as well, the NRT configuration is only recommended for non clustered websites with a limited amount of data.

3.2.3. Custom

It is also possible to configure a custom IndexManager implementation by specifying the fully qualified class name of your custom implementation. This implementation must have a no-argument constructor:

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

Tip

Your custom index manager implementation doesn't need to use the same components as the default implementations. For example, you can delegate to a remote indexing service which doesn't expose a Directory interface.

3.3. Directory configuration

As we have seen in Section 3.2, “Configuring the IndexManager” the default index manager uses Lucene's notion of a Directory to store the index files. The Directory implementation can be customized and Lucene comes bundled with a file system and an in-memory implementation. DirectoryProvider is the Hibernate Search abstraction around a Lucene Directory and handles the configuration and the initialization of the underlying Lucene resources. Table 3.1, “List of built-in DirectoryProvider” shows the list of the directory providers available in Hibernate Search together with their corresponding options.

To configure your DirectoryProvider you have to understand that each indexed entity is associated to a Lucene index (except of the case where multiple entities share the same index - Section 9.5, “Sharing indexes”). The name of the index is given by the index property of the @Indexed annotation. If the index property is not specified the fully qualified name of the indexed class will be used as name (recommended).

Knowing the index name, you can configure the directory provider and any additional options by using the prefix hibernate.search.<indexname>. The name default (hibernate.search.default) is reserved and can be used to define properties which apply to all indexes. Example 3.2, “Configuring directory providers” shows how hibernate.search.default.directory_provider is used to set the default directory provider to be the filesystem one. hibernate.search.default.indexBase sets then the default base directory for the indexes. As a result the index for the entity Status is created in /usr/lucene/indexes/org.hibernate.example.Status.

The index for the Rule entity, however, is using an in-memory directory, because the default directory provider for this entity is overriden by the property hibernate.search.Rules.directory_provider.

Finally the Action entity uses a custom directory provider CustomDirectoryProvider specified via hibernate.search.Actions.directory_provider.

Example 3.1. Specifying the index name

package org.hibernate.example;


@Indexed

public class Status { ... }


@Indexed(index="Rules")

public class Rule { ... }


@Indexed(index="Actions")

public class Action { ... }

Example 3.2. Configuring directory providers

hibernate.search.default.directory_provider filesystem
hibernate.search.default.indexBase=/usr/lucene/indexes
hibernate.search.Rules.directory_provider ram
hibernate.search.Actions.directory_provider com.acme.hibernate.CustomDirectoryProvider

Tip

Using the described configuration scheme you can easily define common rules like the directory provider and base directory, and override those defaults later on on a per index basis.

Table 3.1. List of built-in DirectoryProvider

Name and description	Properties
ram: Memory based directory, the directory will be uniquely identified (in the same deployment unit) by the `@Indexed.index` element	none
filesystem: File system based directory. The directory used will be <indexBase>/< indexName >	`indexBase` : Base directory `indexName`: override @Indexed.index (useful for sharded indexes) `locking_strategy` : optional, see Section 3.7, “LockFactory configuration” `filesystem_access_type`: allows to determine the exact type of `FSDirectory` implementation used by this `DirectoryProvider`. Allowed values are `auto` (the default value, selects `NIOFSDirectory` on non Windows systems, `SimpleFSDirectory` on Windows), `simple` (`SimpleFSDirectory`), `nio` (`NIOFSDirectory`), `mmap` (`MMapDirectory`). Make sure to refer to Javadocs of these `Directory` implementations before changing this setting. Even though `NIOFSDirectory` or `MMapDirectory` can bring substantial performace boosts they also have their issues.
filesystem-master: File system based directory. Like `filesystem`. It also copies the index to a source directory (aka copy directory) on a regular basis. The recommended value for the refresh period is (at least) 50% higher that the time to copy the information (default 3600 seconds - 60 minutes). Note that the copy is based on an incremental copy mechanism reducing the average copy time. DirectoryProvider typically used on the master node in a JMS back end cluster. The `buffer_size_on_copy` optimum depends on your operating system and available RAM; most people reported good results using values between 16 and 64MB.	`indexBase`: Base directory `indexName`: override @Indexed.index (useful for sharded indexes) `sourceBase`: Source (copy) base directory. `source`: Source directory suffix (default to `@Indexed.index`). The actual source directory name being `<sourceBase>/<source>` `refresh`: refresh period in second (the copy will take place every refresh seconds). `buffer_size_on_copy`: The amount of MegaBytes to move in a single low level copy instruction; defaults to 16MB. `locking_strategy` : optional, see Section 3.7, “LockFactory configuration” `filesystem_access_type`: allows to determine the exact type of `FSDirectory` implementation used by this `DirectoryProvider`. Allowed values are `auto` (the default value, selects `NIOFSDirectory` on non Windows systems, `SimpleFSDirectory` on Windows), `simple` (`SimpleFSDirectory`), `nio` (`NIOFSDirectory`), `mmap` (`MMapDirectory`). Make sure to refer to Javadocs of these `Directory` implementations before changing this setting. Even though `NIOFSDirectory` or `MMapDirectory` can bring substantial performace boosts they also have their issues.
filesystem-slave: File system based directory. Like `filesystem`, but retrieves a master version (source) on a regular basis. To avoid locking and inconsistent search results, 2 local copies are kept. The recommended value for the refresh period is (at least) 50% higher that the time to copy the information (default 3600 seconds - 60 minutes). Note that the copy is based on an incremental copy mechanism reducing the average copy time. DirectoryProvider typically used on slave nodes using a JMS back end. The `buffer_size_on_copy` optimum depends on your operating system and available RAM; most people reported good results using values between 16 and 64MB.	`indexBase`: Base directory `indexName`: override @Indexed.index (useful for sharded indexes) `sourceBase`: Source (copy) base directory. `source`: Source directory suffix (default to `@Indexed.index`). The actual source directory name being `<sourceBase>/<source>` `refresh`: refresh period in second (the copy will take place every refresh seconds). `buffer_size_on_copy`: The amount of MegaBytes to move in a single low level copy instruction; defaults to 16MB. `locking_strategy` : optional, see Section 3.7, “LockFactory configuration” `retry_marker_lookup` : optional, default to 0. Defines how many times we look for the marker files in the source directory before failing. Waiting 5 seconds between each try. `retry_initialize_period` : optional, set an integer value in seconds to enable the retry initialize feature: if the slave can't find the master index it will try again until it's found in background, without preventing the application to start: fullText queries performed before the index is initialized are not blocked but will return empty results. When not enabling the option or explicitly setting it to zero it will fail with an exception instead of scheduling a retry timer. To prevent the application from starting without an invalid index but still control an initialization timeout, see `retry_marker_lookup` instead. `filesystem_access_type`: allows to determine the exact type of `FSDirectory` implementation used by this `DirectoryProvider`. Allowed values are `auto` (the default value, selects `NIOFSDirectory` on non Windows systems, `SimpleFSDirectory` on Windows), `simple` (`SimpleFSDirectory`), `nio` (`NIOFSDirectory`), `mmap` (`MMapDirectory`). Make sure to refer to Javadocs of these `Directory` implementations before changing this setting. Even though `NIOFSDirectory` or `MMapDirectory` can bring substantial performace boosts they also have their issues.
infinispan: Infinispan based directory. Use it to store the index in a distributed grid, making index changes visible to all elements of the cluster very quickly. Also see Section 3.3.1, “Infinispan Directory configuration” for additional requirements and configuration settings. Infinispan needs a global configuration and additional dependencies; the settings defined here apply to each different index.	`locking_cachename`: name of the Infinispan cache to use to store locks. `data_cachename` : name of the Infinispan cache to use to store the largest data chunks; this area will contain the largest objects, use replication if you have enough memory or switch to distribution. `metadata_cachename`: name of the Infinispan cache to use to store the metadata relating to the index; this data is rather small and read very often, it's recommended to have this cache setup using replication. `chunk_size`: large files of the index are split in smaller chunks, you might want to set the highest value efficiently handled by your network. Networking tuning might be useful.

Tip

If the built-in directory providers do not fit your needs, you can write your own directory provider by implementing the org.hibernate.store.DirectoryProvider interface. In this case, pass the fully qualified class name of your provider into the directory_provider property. You can pass any additional properties using the prefix hibernate.search.<indexname>.

3.3.1. Infinispan Directory configuration

Infinispan is a distributed, scalable, highly available data grid platform which supports autodiscovery of peer nodes. Using Infinispan and Hibernate Search in combination, it is possible to store the Lucene index in a distributed environment where index updates are quickly available on all nodes.

This section describes in greater detail how to configure Hibernate Search to use an Infinispan Lucene Directory.

When using an Infinispan Directory the index is stored in memory and shared across multiple nodes. It is considered a single directory distributed across all participating nodes. If a node updates the index, all other nodes are updated as well. Updates on one node can be immediately searched for in the whole cluster.

The default configuration replicates all data defining the index across all nodes, thus consuming a significant amount of memory. For large indexes it's suggested to enable data distribution, so that each piece of information is replicated to a subset of all cluster members.

It is also possible to offload part or most information to a CacheStore, such as plain filesystem, Amazon S3, Cassandra, Berkley DB or standard relational databases. You can configure it to have a CacheStore on each node or have a single centralized one shared by each node.

See the Infinispan documentation for all Infinispan configuration options.

3.3.1.1. Requirements

To use the Infinispan directory via Maven, add the following dependencies:

Example 3.3. Maven dependencies for Hibernate Search


<dependency>

   <groupId>org.hibernate</groupId>

   <artifactId>hibernate-search</artifactId>

   <version>4.0.0.Final</version>

</dependency>

<dependency>

   <groupId>org.hibernate</groupId>

   <artifactId>hibernate-search-infinispan</artifactId>

   <version>4.0.0.Final</version>

</dependency>

For the non-maven users, add hibernate-search-infinispan.jar, infinispan-lucene-directory.jar and infinispan-core.jar to your application classpath. These last two jars are distributed by Infinispan.

3.3.1.2. Architecture

Even when using an Infinispan directory it's still recommended to use the JMS Master/Slave or JGroups backend, because in Infinispan all nodes will share the same index and it is likely that IndexWriters being active on different nodes will try to acquire the lock on the same index. So instead of sending updates directly to the index, send it to a JMS queue or JGroups channel and have a single node apply all changes on behalf of all other nodes.

Configuring a non-default backend is not a requirement but a performance optimization as locks are enabled to have a single node writing.

To configure a JMS slave only the backend must be replaced, the directory provider must be set to infinispan; set the same directory provider on the master, they will connect without the need to setup the copy job across nodes. Using the JGroups backend is very similar - just combine the backend configuration with the infinispan directory provider.

3.3.1.3. Infinispan Configuration

The most simple configuration only requires to enable the backend:

hibernate.search.[default|<indexname>].directory_provider infinispan

That's all what is needed to get a cluster-replicated index, but the default configuration does not enable any form of permanent persistence for the index; to enable such a feature an Infinispan configuration file should be provided.

To use Infinispan, Hibernate Search requirest a CacheManager; it can lookup and reuse an existing CacheManager, via JNDI, or start and manage a new one. In the latter case Hibernate Search will start and stop it ( closing occurs when the Hibernate SessionFactory is closed).

To use and existing CacheManager via JNDI (optional parameter):

hibernate.search.[default|<indexname>].infinispan.cachemanager_jndiname = [jndiname]

To start a new CacheManager from a configuration file (optional parameter):

hibernate.search.[default|<indexname>].infinispan.configuration_resourcename = [infinispan configuration filename]

If both parameters are defined, JNDI will have priority. If none of these is defined, Hibernate Search will use the default Infinispan configuration included in hibernate-search-infinispan.jar. This configuration should work fine in most cases but does not store the index in a persistent cache store.

As mentioned in Table 3.1, “List of built-in DirectoryProvider”, each index makes use of three caches, so three different caches should be configured as shown in the default-hibernatesearch-infinispan.xml provided in the hibernate-search-infinispan.jar. Several indexes can share the same caches.

3.4. Worker configuration

It is possible to refine how Hibernate Search interacts with Lucene through the worker configuration. There exist several architectural components and possible extension points. Let's have a closer look.

First there is a Worker. An implementation of the Worker interface is reponsible for receiving all entity changes, queuing them by context and applying them once a context ends. The most intuative context, especially in connection with ORM, is the transaction. For this reason Hibernate Search will per default use the TransactionalWorker to scope all changes per transaction. One can, however, imagine a scenario where the context depends for example on the number of entity changes or some other application (lifecycle) events. For this reason the Worker implementation is configurable as shown in Table 3.2, “Scope configuration”.

Table 3.2. Scope configuration

Property	Description
hibernate.search.worker.scope	The fully qualifed class name of the `Worker` implementation to use. If this property is not set, empty or `transaction` the default `TransactionalWorker` is used.
hibernate.search.worker.*	All configuration properties prefixed with `hibernate.search.worker` are passed to the Worker during initialization. This allows adding custom, worker specific parameters.
hibernate.search.worker.batch_size	Defines the maximum number of indexing operation batched per context. Once the limit is reached indexing will be triggered even though the context has not ended yet. This property only works if the `Worker` implementation delegates the queued work to BatchedQueueingProcessor (which is what the `TransactionalWorker` does)

Once a context ends it is time to prepare and apply the index changes. This can be done synchronously or asynchronously from within a new thread. Synchronous updates have the advantage that the index is at all times in sync with the databases. Asynchronous updates, on the other hand, can help to minimize the user response time. The drawback is potential discrepancies between database and index states. Lets look at the configuration options shown in Table 3.3, “Execution configuration”.

Note

The following options can be different on each index; in fact they need the indexName prefix or use default to set the default value for all indexes.

Table 3.3. Execution configuration

Property	Description
hibernate.search.<indexName>.worker.execution	`sync`: synchronous execution (default) `async`: asynchronous execution
hibernate.search.<indexName>.worker.thread_pool.size	The backend can apply updates from the same transaction context (or batch) in parallel, using a threadpool. The default value is 1. You can experiment with larger values if you have many operations per transaction.
hibernate.search.<indexName>.worker.buffer_queue.max	Defines the maximal number of work queue if the thread poll is starved. Useful only for asynchronous execution. Default to infinite. If the limit is reached, the work is done by the main thread.

So far all work is done within the same Virtual Machine (VM), no matter which execution mode. The total amount of work has not changed for the single VM. Luckily there is a better approach, namely delegation. It is possible to send the indexing work to a different server by configuring hibernate.search.worker.backend - see Table 3.4, “Backend configuration”. Again this option can be configured differently for each index.

Table 3.4. Backend configuration

Property Description

hibernate.search.<indexName>.worker.backend

lucene: The default backend which runs index updates in the same VM. Also used when the property is undefined or empty.

jms: JMS backend. Index updates are send to a JMS queue to be processed by an indexing master. See Table 3.5, “JMS backend configuration” for additional configuration options and Section 3.4.1, “JMS Master/Slave back end” for a more detailed descripton of this setup.

jgroupsMaster or jgroupsSlave: Backend using JGroups as communication layer. See Table 3.6, “JGroups backend configuration” for additional configuration options and Section 3.4.2, “JGroups Master/Slave back end” for a more detailed description of this setup.

blackhole: Mainly a test/developer setting which ignores all indexing work

You can also specify the fully qualified name of a class implementing BackendQueueProcessor. This way you can implement your own communication layer. The implementation is responsilbe for returning a Runnable instance which on execution will process the index work.

Table 3.5. JMS backend configuration

Property	Description
hibernate.search.<indexName>.worker.jndi.*	Defines the JNDI properties to initiate the InitialContext (if needed). JNDI is only used by the JMS back end.
hibernate.search.<indexName>.worker.jms.connection_factory	Mandatory for the JMS back end. Defines the JNDI name to lookup the JMS connection factory from (`/ConnectionFactory` by default in JBoss AS)
hibernate.search.<indexName>.worker.jms.queue	Mandatory for the JMS back end. Defines the JNDI name to lookup the JMS queue from. The queue will be used to post work messages.

Table 3.6. JGroups backend configuration

Property	Description
hibernate.search.<indexName>.worker.jgroups.clusterName	Optional for JGroups back end. Defines the name of JGroups channel.
hibernate.search.<indexName>.worker.jgroups.configurationFile	Optional JGroups network stack configuration. Defines the name of a JGroups configuration file, which must exist on classpath.
hibernate.search.<indexName>.worker.jgroups.configurationXml	Optional JGroups network stack configuration. Defines a String representing JGroups configuration as XML.
hibernate.search.<indexName>.worker.jgroups.configurationString	Optional JGroups network stack configuration. Provides JGroups configuration in plain text.

Warning

As you probably noticed, some of the shown properties are correlated which means that not all combinations of property values make sense. In fact you can end up with a non-functional configuration. This is especially true for the case that you provide your own implementations of some of the shown interfaces. Make sure to study the existing code before you write your own Worker or BackendQueueProcessor implementation.

3.4.1. JMS Master/Slave back end

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

JMS back end configuration.

3.4.1.1. Slave nodes

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

Example 3.4. JMS Slave configuration

### 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

## Backend 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

Tip

A file system local copy is recommended for faster search results.

Tip

The refresh period should be higher that the expected copy time.

3.4.1.2. Master node

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

Example 3.5. JMS Master configuration

### 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

## Backend configuration
#Backend is the default lucene one

Tip

The refresh period should be higher that the expected time copy.

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.

Example 3.6. Message Driven Bean processing the indexing queue

@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. For more information about the getSession() and cleanSessionIfNeeded(), please check AbstractJMSHibernateSearchController's javadoc.

3.4.2. JGroups Master/Slave back end

This section describes how to configure the JGroups Master/Slave back end. The configuration examples illustrated in Section 3.4.1, “JMS Master/Slave back end” also apply here, only a different backend (hibernate.search.worker.backend) needs to be set.

3.4.2.1. Slave nodes

Every index update operation is sent through a JGroups channel to the master node. Index querying operations are executed on a local index copy.

Example 3.7. JGroups Slave configuration

### slave configuration
hibernate.search.default.worker.backend = jgroupsSlave

3.4.2.2. Master node

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

Example 3.8. JGroups Master configuration

### master configuration
hibernate.search.default.worker.backend = jgroupsMaster

3.4.2.3. JGroups channel configuration

Optionally the configuration for the JGroups transport protocols and channel name can be defined and applied to master and slave nodes. There are several ways to configure the JGroups transport details. You can either set the hibernate.search.default.worker.backend.jgroups.configurationFile property and specify a file containing the JGroups configuration or you can use the property hibernate.search.default.worker.backend.jgroups.configurationXml or hibernate.search.default.worker.backend.jgroups.configurationString to directly embed either the xml or string JGroups configuration into your Hibernate configuration file. All three options are shown in Example 3.9, “JGroups transport protocol configuration”.

Tip

If no property is explicitly specified it is assumed that the JGroups default configuration file flush-udp.xml is used.

Example 3.9. JGroups transport protocol configuration


## JGroups configuration options

# OPTION 1 - udp.xml file needs to be located in the classpath

hibernate.search.default.worker.backend.jgroups.configurationFile = udp.xml



# OPTION 2 - protocol stack configuration provided in XML format

hibernate.search.default.worker.backend.jgroups.configurationXml =



<config xmlns="urn:org:jgroups"

xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

xsi:schemaLocation="urn:org:jgroups file:schema/JGroups-2.8.xsd">

<UDP

mcast_addr="${jgroups.udp.mcast_addr:228.10.10.10}"

mcast_port="${jgroups.udp.mcast_port:45588}"

tos="8"

thread_naming_pattern="pl"

thread_pool.enabled="true"

thread_pool.min_threads="2"

thread_pool.max_threads="8"

thread_pool.keep_alive_time="5000"

thread_pool.queue_enabled="false"

thread_pool.queue_max_size="100"

thread_pool.rejection_policy="Run"/>

<PING timeout="1000" num_initial_members="3"/>

<MERGE2 max_interval="30000" min_interval="10000"/>

<FD_SOCK/>

<FD timeout="3000" max_tries="3"/>

<VERIFY_SUSPECT timeout="1500"/>

<pbcast.STREAMING_STATE_TRANSFER/>

<pbcast.FLUSH timeout="0"/>

</config>



# OPTION 3 - protocol stack configuration provided in "old style" jgroups format

hibernate.search.default.worker.backend.jgroups.configurationString =



UDP(mcast_addr=228.1.2.3;mcast_port=45566;ip_ttl=32):PING(timeout=3000;

num_initial_members=6):FD(timeout=5000):VERIFY_SUSPECT(timeout=1500):

pbcast.NAKACK(gc_lag=10;retransmit_timeout=3000):UNICAST(timeout=5000):

FRAG:pbcast.GMS(join_timeout=3000;shun=false;print_local_addr=true)

In this JGroups master/slave configuration nodes communicate over a JGroups channel. The default channel name is HSearchCluster which can be configured as seen in Example 3.10, “JGroups channel name configuration”.

Example 3.10. JGroups channel name configuration

hibernate.search.default.worker.backend.jgroups.clusterName = Hibernate-Search-Cluster

3.5. Reader strategy configuration

The different reader strategies are described in Reader strategy. Out of the box strategies are:

shared: share index readers across several queries. This strategy is the most efficient.
not-shared: create an index reader for each individual query

The default reader strategy is shared. This can be adjusted:

hibernate.search.[default|<indexname>].reader.strategy = not-shared

Adding this property switches to the not-shared strategy.

Or if you have a custom reader strategy:

hibernate.search.[default|<indexname>].reader.strategy = my.corp.myapp.CustomReaderProvider

where my.corp.myapp.CustomReaderProvider is the custom strategy implementation.

3.6. Tuning Lucene indexing performance

Hibernate Search allows you 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. You can 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 will look at the index section, then at the default section.

Example 3.11. Example performance option configuration

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

The configuration in Example 3.11, “Example performance option configuration” will result in these settings applied on the second shard of the Animal index:

max_merge_docs = 10
merge_factor = 20
ram_buffer_size = 64MB
term_index_interval = Lucene default

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 Table 3.7, “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. For more information about Lucene indexing performance, please refer to the Lucene documentation.

Table 3.7. List of indexing performance and behavior properties

Property	Description	Default Value
hibernate.search.[default\|<indexname>].exclusive_index_use	Set to `true` when no other process will need to write to the same index. This will enable Hibernate Search to work in exlusive mode on the index and improve performance when writing changes to the index.	`true` (improved performance, releases locks only at shutdown)
hibernate.search.[default\|<indexname>].max_queue_length	Each index has a separate "pipeline" which contains the updates to be applied to the index. When this queue is full adding more operations to the queue becomes a blocking operation. Configuring this setting doesn't make much sense unless the `worker.execution` is configured as `async`.	`1000`
hibernate.search.[default\|<indexname>].indexwriter.max_buffered_delete_terms	Determines the minimal number of delete terms required before the buffered in-memory delete terms are applied and flushed. If there are documents buffered in memory at the time, they are merged and a new segment is created.	Disabled (flushes by RAM usage)
hibernate.search.[default\|<indexname>].indexwriter.max_buffered_docs	Controls the amount of documents buffered in memory during indexing. The bigger the more RAM is consumed.	Disabled (flushes by RAM usage)
hibernate.search.[default\|<indexname>].indexwriter.max_merge_docs	Defines the largest number of documents allowed in a segment. Smaller values perform better on frequently changing indexes, larger values provide better search performance if the index does not change often.	Unlimited (Integer.MAX_VALUE)
hibernate.search.[default\|<indexname>].indexwriter.merge_factor	Controls segment merge frequency and size. Determines how often segment indexes are merged when insertion occurs. With smaller values, less RAM is used while indexing, and searches on unoptimized indexes are faster, but indexing speed is slower. With larger values, more RAM is used during indexing, and while searches on unoptimized indexes are slower, indexing is faster. Thus larger values (> 10) are best for batch index creation, and smaller values (< 10) for indexes that are interactively maintained. The value must not be lower than 2.	10
hibernate.search.[default\|<indexname>].indexwriter.merge_min_size	Controls segment merge frequency and size. Segments smaller than this size (in MB) are always considered for the next segment merge operation. Setting this too large might result in expensive merge operations, even tough they are less frequent. See also `org.apache.lucene.index.LogDocMergePolicy`. `minMergeSize`.	0 MB (actually ~1K)
hibernate.search.[default\|<indexname>].indexwriter.merge_max_size	Controls segment merge frequency and size. Segments larger than this size (in MB) are never merged in bigger segments. This helps reduce memory requirements and avoids some merging operations at the cost of optimal search speed. When optimizing an index this value is ignored. See also `org.apache.lucene.index.LogDocMergePolicy`. `maxMergeSize`.	Unlimited
hibernate.search.[default\|<indexname>].indexwriter.merge_max_optimize_size	Controls segment merge frequency and size. Segments larger than this size (in MB) are not merged in bigger segments even when optimizing the index (see `merge_max_size` setting as well). Applied to `org.apache.lucene.index.LogDocMergePolicy`. `maxMergeSizeForOptimize`.	Unlimited
hibernate.search.[default\|<indexname>].indexwriter.merge_calibrate_by_deletes	Controls segment merge frequency and size. Set to `false` to not consider deleted documents when estimating the merge policy. Applied to `org.apache.lucene.index.LogMergePolicy`. `calibrateSizeByDeletes`.	`true`
hibernate.search.[default\|<indexname>].indexwriter.ram_buffer_size	Controls the amount of RAM in MB dedicated to document buffers. When used together max_buffered_docs a flush occurs for whichever event happens first. Generally for faster indexing performance it's best to flush by RAM usage instead of document count and use as large a RAM buffer as you can.	16 MB
hibernate.search.[default\|<indexname>].indexwriter.term_index_interval	Expert: Set the interval between indexed terms. Large values cause less memory to be used by IndexReader, but slow random-access to terms. Small values cause more memory to be used by an IndexReader, and speed random-access to terms. See Lucene documentation for more details.	128
hibernate.search.[default\|<indexname>].indexwriter.use_compound_file	The advantage of using the compound file format is that less file descriptors are used. The disadvantage is that indexing takes more time and temporary disk space. You can set this parameter to `false` in an attempt to improve the indexing time, but you could run out of file descriptors if `mergeFactor` is also large. Boolean parameter, use "`true`" or "`false`". The default value for this option is `true`.	true
hibernate.search.enable_dirty_check	Not all entity changes require an update of the Lucene index. If all of the updated entity properties (dirty properties) are not indexed Hibernate Search will skip the re-indexing work. Disable this option if you use custom `FieldBridge`s which need to be invoked at each update event (even though the property for which the field bridge is configured has not changed). This optimization will not be applied on classes using a `@ClassBridge` or a `@DynamicBoost`. Boolean parameter, use "`true`" or "`false`". The default value for this option is `true`.	true

Tip

When your architecture permits it, always keep hibernate.search.default.exclusive_index_use=true as it greatly improves efficiency in index writing. This is the default since Hibernate Search version 4.

Tip

To tune the indexing speed it might be useful to time the object loading from database in isolation from the writes to the index. To achieve this set the blackhole as worker backend and start your indexing routines. This backend does not disable Hibernate Search: it will still generate the needed changesets to the index, but will discard 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 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.

Warning

The blackhole backend is not meant to be used in production, only as a tool to identify indexing bottlenecks.

3.6.1. Control segment size

The options merge_max_size, merge_max_optimize_size, merge_calibrate_by_deletes give you control on the maximum size of the segments being created, but you need to understand how they affect file sizes. If you need to hard limit the size, consider that merging a segment is about adding it together with another existing segment to form a larger one, so you might want to set the max_size for merge operations to less than half of your hard limit. Also segments might initially be generated larger than your expected size at first creation time: before they are ever merged. A segment is never created much larger than ram_buffer_size, but the threshold is checked as an estimate.

Example:

//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

Tip

When using the Infinispan Directory to cluster indexes make sure that your segments are smaller than the chunk_size so that you avoid fragmenting segments in the grid. Note that the chunk_size of the Infinispan Directory is expressed in bytes, while the index tuning options are in MB.

3.7. LockFactory configuration

Lucene Directorys have default locking strategies which work well for most cases, but it's possible to specify for each index managed by Hibernate Search which LockingFactory you want to use.

Some of these locking strategies require a filesystem level lock and may be used even on RAM based indexes, but this is not recommended and of no practical use.

To select a locking factory, set the hibernate.search.<index>.locking_strategy option to one of simple, native, single or none. Alternatively set it to the fully qualified name of an implementation of org.hibernate.search.store.LockFactoryProvider.

Table 3.8. List of available LockFactory implementations

name	Class	Description
simple	org.apache.lucene.store.SimpleFSLockFactory	Safe implementation based on Java's File API, it marks the usage of the index by creating a marker file. If for some reason you had to kill your application, you will need to remove this file before restarting it. This is the default implementation for the `filesystem`, `filesystem-master` and `filesystem-slave` directory providers.
native	org.apache.lucene.store.NativeFSLockFactory	As does `simple` this also marks the usage of the index by creating a marker file, but this one is using native OS file locks so that even if your application crashes the locks will be cleaned up. This implementation has known problems on NFS.
single	org.apache.lucene.store.SingleInstanceLockFactory	This LockFactory doesn't use a file marker but is a Java object lock held in memory; therefore it's possible to use it only when you are sure the index is not going to be shared by any other process. This is the default implementation for the `ram` directory provider.
none	org.apache.lucene.store.NoLockFactory	All changes to this index are not coordinated by any lock; test your application carefully and make sure you know what it means.

Configuration example:

hibernate.search.default.locking_strategy simple
hibernate.search.Animals.locking_strategy native
hibernate.search.Books.locking_strategy org.custom.components.MyLockingFactory

3.8. Exception Handling Configuration

Hibernate Search allows you to configure how exceptions are handled during the indexing process. If no configuration is provided then exceptions are logged to the log output by default. It is possible to explicitly declare the exception logging mechanism as seen below:

hibernate.search.error_handler log

The default exception handling occurs for both synchronous and asynchronous indexing. Hibernate Search provides an easy mechanism to override the default error handling implementation.

In order to provide your own implementation you must implement the ErrorHandler interface, which provides the handle(ErrorContext context) method. ErrorContext provides a reference to the primary LuceneWork instance, the underlying exception and any subsequent LuceneWork instances that could not be processed due to the primary exception.

public interface ErrorContext  {
   List<LuceneWork> getFailingOperations();
   LuceneWork getOperationAtFault();
   Throwable getThrowable();
   boolean hasErrors();
}

To register this error handler with Hibernate Search you must declare the fully qualified classname of your ErrorHandler implementation in the configuration properties:

hibernate.search.error_handler CustomerErrorHandler

3.9. Index format compatibility

While Hibernate Search strives to offer a backwards compatible API to make it easy to port your application to newer versions, it delegates to Apache Lucene to handle the index writing and searching. The Lucene developers too attempt to keep a stable index format, but sometimes an update in the index format can not be avoided; in those rare cases you either have to reindex all your data, or use an index upgrade tool, or sometimes Lucene is able to read the old format so you don't need to take specific actions (besides making backup of your index).

While an index format incompatibility is an exceptional event, more often when upgrading Lucene the Analyzer implementations might slightly change behaviour, and this could lead to a poor recall score, possibly missing many hits from the results.

Hibernate Search exposes a configuration property hibernate.search.lucene_version which instructs the Analyzers and other Lucene classes to conform to their behaviour as defined in an (older) specific version of Lucene. See also org.apache.lucene.util.Version contained in the lucene-core.jar, depending on the specific version of Lucene you're using you might have different options available. When this option is not specified, Hibernate Search will instruct Lucene to use the default of it's current version, which is usually the best option for new projects. Still it's recommended to define the version you're using explicitly in the configuration so that when you happen to upgrade Lucene the Analyzers will not change behaviour; you can then choose to update this value in a second time, maybe when you have the chance to rebuild the index from scratch.

Example 3.12. Force Analyzers to be compatible with a Lucene 3.0 created index

hibernate.search.lucene_version LUCENE_30

This option is global for the configured SearchFactory and affects all Lucene APIs having such a parameter, as this should be applied consistently. So if you are also making use of Lucene bypassing Hibernate Search, make sure to apply the same value too.

Chapter 4. Mapping entities to the index structure

4.1. Mapping an entity

4.1.1. Basic mapping
4.1.2. Mapping properties multiple times
4.1.3. Embedded and associated objects

4.2. Boosting

4.2.1. Static index time boosting
4.2.2. Dynamic index time boosting

4.3. Analysis

4.3.1. Default analyzer and analyzer by class
4.3.2. Named analyzers
4.3.3. Dynamic analyzer selection
4.3.4. Retrieving an analyzer

4.4. Bridges

4.4.1. Built-in bridges
4.4.2. Custom bridges

4.5. Providing your own id

4.5.1. The ProvidedId annotation

4.6. Programmatic API

4.6.1. Mapping an entity as indexable
4.6.2. Adding DocumentId to indexed entity
4.6.3. Defining analyzers
4.6.4. Defining full text filter definitions
4.6.5. Defining fields for indexing
4.6.6. Programmatically defining embedded entities
4.6.7. Contained In definition
4.6.8. Date/Calendar Bridge
4.6.9. Defining bridges
4.6.10. Mapping class bridge
4.6.11. Mapping dynamic boost

4.1. Mapping an entity

In Chapter 1, Getting started you have already learned that all the metadata information needed to index entities is described through annotations. There is no need for xml mapping files. You can still use Hibernate mapping files for the basic Hibernate configuration, but the Hibernate Search specific configuration has to be expressed via annotations.

Note

There is no XML configuration available for Hibernate Search but we provide a powerful programmatic mapping API that elegantly replaces this kind of deployment form (see Section 4.6, “Programmatic API” for more information).

If you want to contribute the XML mapping implementation, see HSEARCH-210.

4.1.1. Basic mapping

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

4.1.1.1. @Indexed

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):

Example 4.1. Making a class indexable with @Indexed

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

You can optionially specify the index attribute of the @Indexed annotation to change the default name of the index. For more information see Section 3.3, “Directory configuration”.

4.1.1.2. @Field

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:

name : describe under which name, the property should be stored in the Lucene Document. The default value is the property name (following the JavaBeans convention)
store : describe whether or not the property is stored in the Lucene index. You can store the value Store.YES (consuming more space in the index but allowing projection, see Section 5.1.3.5, “Projection”), store it in a compressed way Store.COMPRESS (this does consume more CPU), or avoid any storage Store.NO (this is the default value). When a property is stored, you can retrieve its original value from the Lucene Document. This is not related to whether the element is indexed or not.
index: describe whether the property is indexed or not. The different values are Index.NO (no indexing, ie cannot be found by a query), Index.YES (the element gets indexed and is searchable). The default value is Index.YES. Index.NO can be useful for cases where a property does is not required to be searchable, but should be available for projection.
Tip
Index.NO in combination with Analyze.YES or Norms.YES is not useful, since analyze and norms require the property to be indexed
analyze: determines whether the property is analyzed (Analyze.YES) or not (Analyze.NO). The default value is Analyze.YES.
Tip
Whether or not you want to analyze a property depends on whether you wish to search the element as is, or by the words it contains. It make sense to analyze a text field, but probably not a date field.
Tip
Fields used for sorting must not be analyzed.
norms: describes whether index time boosting information should be stored (Norms.YES) or not (Norms.NO). Not storing it can save a considerable amount of memory, but there won't be any index time boosting information available. The default value is Norms.YES.

termVector: describes collections of term-frequency pairs. This attribute enables the storing of the term vectors within the documents during indexing. The default value is TermVector.NO.

The different values of this attribute are:

Value	Definition
TermVector.YES	Store the term vectors of each document. This produces two synchronized arrays, one contains document terms and the other contains the term's frequency.
TermVector.NO	Do not store term vectors.
TermVector.WITH_OFFSETS	Store the term vector and token offset information. This is the same as TermVector.YES plus it contains the starting and ending offset position information for the terms.
TermVector.WITH_POSITIONS	Store the term vector and token position information. This is the same as TermVector.YES plus it contains the ordinal positions of each occurrence of a term in a document.
TermVector.WITH_POSITION_OFFSETS	Store the term vector, token position and offset information. This is a combination of the YES, WITH_OFFSETS and WITH_POSITIONS.

indexNullAs : Per default null values are ignored and not indexed. However, using indexNullAs you can specify a string which will be inserted as token for the null value. Per default this value is set to Field.DO_NOT_INDEX_NULL indicating that null values should not be indexed. You can set this value to Field.DEFAULT_NULL_TOKEN to indicate that a default null token should be used. This default null token can be specified in the configuration using hibernate.search.default_null_token. If this property is not set and you specify Field.DEFAULT_NULL_TOKEN the string "_null_" will be used as default.
Note
When the indexNullAs parameter is used it is important to use the same token in the search query (see Querying) to search for null values. It is also advisable to use this feature only with un-analyzed fields (analyze=Analyze.NO).
Warning
When implementing a custom FieldBridge or TwoWayFieldBridge it is up to the developer to handle the indexing of null values (see JavaDocs of LuceneOptions.indexNullAs()).

4.1.1.3. @NumericField

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 @Fieldproperties. The @NumericField annotation accept the following parameters:

Value	Definition
forField	(Optional) Specify the name of of the related @Field that will be indexed as numeric. It's only mandatory when the property contains more than a @Field declaration
precisionStep	(Optional) Change the way that the Trie structure is stored in the index. Smaller precisionSteps lead to more disk space usage and faster range and sort queries. Larger values lead to less space used and range query performance more close to the range query in normal @Fields. Default value is 4.

@NumericField supports only Double, Long, Integer and Float. It is not possible to take any advantage from a 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 4.2. 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 );

    }


}

Note

Lucene marks the numeric field API still as experimental and warns for incompatible changes in coming releases. Using Hibernate Search will hopefully shield you from any underlying API changes, but that is not guaranteed.

4.1.1.4. @Id

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

Example 4.3. 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; }

}

Example 4.3, “Specifying indexed properties” defines an index with four fields: id , Abstract, text and grade . Note that by default the field name is decapitalized, following the JavaBean specification. The grade field is annotated as Numeric with a slightly larger precisionStep than the default.

4.1.2. Mapping properties multiple times

Sometimes one has 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. If one wants to search by words in this property and still sort it, one need to index it twice - once analyzed and once un-analyzed. @Fields allows to achieve this goal.

Example 4.4. 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 Example 4.4, “Using @Fields to map a property multiple times” the field summary is indexed twice, once as summary in a tokenized way, and once as summary_forSort in an untokenized way. @Field supports 2 attributes useful when @Fields is used:

analyzer: defines a @Analyzer annotation per field rather than per property
bridge: defines a @FieldBridge annotation per field rather than per property

See below for more information about analyzers and field bridges.

4.1.3. Embedded and associated objects

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. In Example 4.5, “Indexing associations”t 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 4.5. 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;

    ...

}

Be careful. Because the data is denormalized in the Lucene index when using the @IndexedEmbedded technique, Hibernate Search needs to be aware of any change in the Place object and any change in the Address object to keep the index up to date. To make sure the Place Lucene document is updated when it's Address changes, you need to mark the other side of the bidirectional relationship with @ContainedIn.

Tip

@ContainedIn is useful on both associations pointing to entities and on embedded (collection of) objects.

Let's make Example 4.5, “Indexing associations” a bit more complex by nesting @IndexEmbedded as seen in Example 4.6, “Nested usage of @IndexedEmbedded and @ContainedIn”.

Example 4.6. Nested usage of @IndexedEmbedded and @ContainedIn

@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;

   ...

}

As you can see, 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. In Example 4.6, “Nested usage of @IndexedEmbedded and @ContainedIn” the index will contain the following fields

id
name
address.street
address.city
address.ownedBy_name

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.

Note

The prefix cannot be set to the empty string.

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:

Return places where name contains JBoss and where address city is Atlanta. In Lucene query this would be
```
+name:jboss +address.city:atlanta  
```
Return places where name contains JBoss and where owner's name contain Joe. In Lucene query this would be
```
+name:jboss +address.orderBy_name:joe  
```

In a way it 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 is simply non-existent. It might help to keep the relational model normalized while benefiting from the full text index speed and feature richness.

Note

An associated object can itself (but does not have to) be @Indexed

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.

Example 4.7. Using the targetElement property of @IndexedEmbedded

@Entity

@Indexed

public class Address {

    @Id

    @GeneratedValue

    @DocumentId

    private Long id;


    @Field

    private String street;


    @IndexedEmbedded(depth = 1, prefix = "ownedBy_", targetElement = Owner.class)

    @Target(Owner.class)

    private Person ownedBy;



    ...

}


@Embeddable

public class Owner implements Person { ... }

4.2. Boosting

Lucene has the notion of boosting which allows you to give certain documents or fields more or less importance than others. Lucene differentiates between index and search time boosting. The following sections show you how you can achieve index time boosting using Hibernate Search.

4.2.1. Static index time boosting

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 4.8. Different ways of using @Boost

@Entity
@Indexed
@Boost(1.7f)
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 Example 4.8, “Different ways of using @Boost”, 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. Please check the Lucene documentation or the excellent Lucene In Action from Otis Gospodnetic and Erik Hatcher.

4.2.2. Dynamic index time boosting

The @Boost annotation used in Section 4.2.1, “Static index time boosting” defines a static boost factor which is independent of the state of of the indexed entity at runtime. However, there are usecases in which the boost factor may depends on the actual state of the entity. In this case you can use the @DynamicBoost annotation together with an accompanying custom BoostStrategy.

Example 4.9. Dynamic boost examle

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 Example 4.9, “Dynamic boost examle” 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's 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.

Note

The specified BoostStrategy implementation must define a public no-arg constructor.

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

4.3. Analysis

Analysis is the process of converting text into single terms (words) and can be considered as one of the key features of a fulltext search engine. Lucene uses the concept of Analyzers to control this process. In the following section we cover the multiple ways Hibernate Search offers to configure the analyzers.

4.3.1. Default analyzer and analyzer by class

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 4.10. 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 all tokenized properties (eg. name), except summary and body which are indexed with PropertyAnalyzer and FieldAnalyzer respectively.

Caution

Mixing different analyzers in the same entity is most of the time a bad practice. It makes query building more complex and results less predictable (for the novice), especially if you are using a QueryParser (which uses the same analyzer for the whole query). As a rule of thumb, for any given field the same analyzer should be used for indexing and querying.

4.3.2. Named analyzers

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:

a name: the unique string used to refer to the definition
a list of char filters: each char filter is responsible to pre-process input characters before the tokenization. Char filters can add, change or remove characters; one common usage is for characters normalization
a tokenizer: responsible for tokenizing the input stream into individual words
a list of filters: each filter is responsible to remove, modify or sometimes even add words into the stream provided by the tokenizer

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 let you build your customized analyzer in a very flexible way (just 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.

Note

Some of the analyzers and filters will require additional dependencies. For example to use the snowball stemmer you have to also include the lucene-snowball jar and for the PhoneticFilterFactory you need the commons-codec jar. Your distribution of Hibernate Search provides these dependencies in its lib/optional directory. Have a look at Table 4.2, “Example of available tokenizers” and Table 4.3, “Examples of available filters” to see which anaylzers and filters have additional dependencies

Prior to Search version 3.3.0.Beta2 it was required to add the Solr dependency org.apache.solr:solr-core when you wanted to use the analyzer definition framework. In case you are using Maven this is no longer needed: all required Solr dependencies are now defined as dependencies of the artifact org.hibernate:hibernate-search-analyzers; just add the following dependency :

<dependency>
   <groupId>org.hibernate</groupId>
   <artifactId>hibernate-search-analyzers</artifactId>
   <version>4.0.0.Final</version>
<dependency>

Let's have a look at a concrete example now - Example 4.11, “@AnalyzerDef and the Solr framework”. 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 4.11. @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 {

    ...

}

Tip

Filters and char filters are applied in the order they are defined in the @AnalyzerDef annotation. Order matters!

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 4.12. 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 Example 4.13, “Referencing an analyzer by name”.

Example 4.13. 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 wen building queries.

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

Fields in queries should 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.

4.3.2.1. Available analyzers

Solr and Lucene come with a lot of 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's check a few of them.

Table 4.1. Example of available char filters

Factory Description Parameters Additional dependencies

Factory	Description	Parameters	Additional dependencies
`MappingCharFilterFactory`	Replaces one or more characters with one or more characters, based on mappings specified in the resource file	`mapping`: points to a resource file containing the mappings using the format: "á" => "a" "ñ" => "n" "ø" => "o"	none
`HTMLStripCharFilterFactory`	Remove HTML standard tags, keeping the text	none	none

MappingCharFilterFactory

Replaces one or more characters with one or more characters, based on mappings specified in the resource file

mapping: points to a resource file containing the mappings using the format:

                    "á" => "a"
                    "ñ" => "n"
                    "ø" => "o"

none

HTMLStripCharFilterFactory Remove HTML standard tags, keeping the text none none

Table 4.2. Example of available tokenizers

Factory Description Parameters Additional dependencies

StandardTokenizerFactory Use the Lucene StandardTokenizer none none

HTMLStripCharFilterFactory Remove HTML tags, keep the text and pass it to a StandardTokenizer. none solr-core

Factory	Description	Parameters	Additional dependencies
`StandardTokenizerFactory`	Use the Lucene StandardTokenizer	none	none
`HTMLStripCharFilterFactory`	Remove HTML tags, keep the text and pass it to a StandardTokenizer.	none	`solr-core`
`PatternTokenizerFactory`	Breaks text at the specified regular expression pattern.	`pattern`: the regular expression to use for tokenizing group: says which pattern group to extract into tokens	`solr-core`

PatternTokenizerFactory

Breaks text at the specified regular expression pattern.

pattern: the regular expression to use for tokenizing

group: says which pattern group to extract into tokens

solr-core

Table 4.3. Examples of available filters

Factory	Description	Parameters	Additional dependencies
`StandardFilterFactory`	Remove dots from acronyms and 's from words	none	`solr-core`
`LowerCaseFilterFactory`	Lowercases all words	none	`solr-core`
`StopFilterFactory`	Remove words (tokens) matching a list of stop words	`words`: points to a resource file containing the stop words ignoreCase: true if `case` should be ignore when comparing stop words, `false` otherwise	`solr-core`
`SnowballPorterFilterFactory`	Reduces a word to it's root in a given language. (eg. protect, protects, protection share the same root). Using such a filter allows searches matching related words.	`language`: Danish, Dutch, English, Finnish, French, German, Italian, Norwegian, Portuguese, Russian, Spanish, Swedish and a few more	`solr-core`
`ISOLatin1AccentFilterFactory`	Remove accents for languages like French	none	`solr-core`
`PhoneticFilterFactory`	Inserts phonetically similar tokens into the token stream	`encoder`: One of DoubleMetaphone, Metaphone, Soundex or RefinedSoundex inject: `true` will add tokens to the stream, `false` will replace the existing token `maxCodeLength`: sets the maximum length of the code to be generated. Supported only for Metaphone and DoubleMetaphone encodings	`solr-core` and `commons-codec`
`CollationKeyFilterFactory`	Converts each token into its `java.text.CollationKey`, and then encodes the `CollationKey` with `IndexableBinaryStringTools`, to allow it to be stored as an index term.	`custom`, `language`, `country`, `variant`, `strength`, `decomposition`see Lucene's `CollationKeyFilter` javadocs for more info	`solr-core` and `commons-io`

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

4.3.3. Dynamic analyzer selection

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. Example 4.14, “Usage of @AnalyzerDiscriminator” demonstrates the usage of this annotation.

Example 4.14. 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 Article ) ) {

            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 implemention of the Discriminator interface has to return the name of an existing analyzer definition or null if the default analyzer should not be overridden. Example 4.14, “Usage of @AnalyzerDiscriminator” assumes that the language parameter is either 'de' or 'en' which matches the specified names in the @AnalyzerDefs.

4.3.4. Retrieving an analyzer

In some situations retrieving analyzers can be handy. For example, if your domain model makes use of multiple analyzers (maybe to benefit from stemming, use phonetic approximation and so on), you need to make sure to use the same analyzers when you build your query.

Note

This rule can be broken but you need a good reason for it. If you are unsure, use the same analyzers. If you use the Hibernate Search query DSL (see Section 5.1.2, “Building a Lucene query with the Hibernate Search query DSL”), you don't have to think about it. The query DSL does use the right analyzer transparently for you.

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 4.15. 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.

Tip

You can also retrieve analyzers defined via @AnalyzerDef by their definition name using searchFactory.getAnalyzer(String).

4.4. Bridges

When discussing the basic mapping for an entity one important fact was so far disregarded. In Lucene all index fields have to be represented as strings. All entity properties annotated with @Field have to be converted to strings to be indexed. The reason we have not mentioned it so far is, that for most of your properties Hibernate Search does the translation job for you thanks to set of built-in bridges. However, in some cases you need a more fine grained control over the translation process.

4.4.1. Built-in bridges

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

null

Per default null elements are not indexed. Lucene does not support null elements. However, in some situation it can be useful to insert a custom token representing the null value. See Section 4.1.1.2, “@Field” for more information.

java.lang.String

Strings are indexed as are

short, Short, integer, Integer, long, Long, float, Float, double, Double, BigInteger, BigDecimal

Numbers are converted into their string representation. Note that numbers cannot be compared by Lucene (ie used in ranged queries) out of the box: they have to be padded

Note

Using a Range query is debatable and has drawbacks, an alternative approach is to use a Filter query which will filter the result query to the appropriate range.

Hibernate Search will support a padding mechanism

java.util.Date

Dates are stored as yyyyMMddHHmmssSSS in GMT time (200611072203012 for Nov 7th of 2006 4:03PM and 12ms EST). You shouldn't really bother with the internal format. What is important is that when using a TermRangeQuery, you should know that the dates have to be expressed in GMT time.

Usually, storing the date up to the millisecond is not necessary. @DateBridge defines the appropriate resolution you are willing to store in the index (@DateBridge(resolution=Resolution.DAY)). The date pattern will then be truncated accordingly.

@Entity 

@Indexed

public class Meeting {

    @Field(analyze=Analyze.NO)

    @DateBridge(resolution=Resolution.MINUTE)

    private Date date;

    ...

Warning

A Date whose resolution is lower than MILLISECOND cannot be a @DocumentId

Important

The default Date bridge uses Lucene's DateTools to convert from and to String. This means that all dates are expressed in GMT time. If your requirements are to store dates in a fixed time zone you have to implement a custom date bridge. Make sure you understand the requirements of your applications regarding to date indexing and searching.

java.net.URI, java.net.URL

URI and URL are converted to their string representation

java.lang.Class

Class are converted to their fully qualified class name. The thread context classloader is used when the class is rehydrated

4.4.2. Custom bridges

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.

4.4.2.1. StringBridge

The simplest custom solution is to give Hibernate Search an implementation of your expected Object to String bridge. To do so you need to implement the org.hibernate.search.bridge.StringBridge interface. All implementations have to be thread-safe as they are used concurrently.

Example 4.16. Custom StringBridge implementation

/**

 * 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();

    }

}

Given the string bridge defined in Example 4.16, “Custom StringBridge implementation”, any property or field can use this bridge thanks to the @FieldBridge annotation:

@FieldBridge(impl = PaddedIntegerBridge.class)

private Integer length;

4.4.2.1.1. Parameterized bridge

Parameters can also be passed to the bridge implementation making it more flexible. Example 4.17, “Passing parameters to your bridge implementation” implements a ParameterizedBridge interface and parameters are passed through the @FieldBridge annotation.

Example 4.17. Passing parameters to your bridge implementation

public class PaddedIntegerBridge implements StringBridge, 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();

    }

}



//property

@FieldBridge(impl = PaddedIntegerBridge.class,

             params = @Parameter(name="padding", value="10")

            )

private Integer length;

The ParameterizedBridge interface can be implemented by StringBridge, TwoWayStringBridge, FieldBridge implementations.

All implementations have to be thread-safe, but the parameters are set during initialization and no special care is required at this stage.

4.4.2.1.2. Type aware bridge

It is sometimes useful to get the type the bridge is applied on:

the return type of the property for field/getter-level bridges
the class type for class-level bridges

An example is a bridge that deals with enums in a custom fashion but needs to access the actual enum type. Any bridge implementing AppliedOnTypeAwareBridge will get the type the bridge is applied on injected. Like parameters, the type injected needs no particular care with regard to thread-safety.

4.4.2.1.3. Two-way bridge

If you expect to use your bridge implementation on an id property (ie annotated with @DocumentId ), you need to use a slightly extended version of StringBridge named TwoWayStringBridge. Hibernate Search needs to read the string representation of the identifier and generate the object out of it. There is no difference in the way the @FieldBridge annotation is used.

Example 4.18. Implementing a TwoWayStringBridge usable for id properties

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;

Important

It is important for the two-way process to be idempotent (ie object = stringToObject( objectToString( object ) ) ).

4.4.2.2. FieldBridge

Some use cases require more than a simple object to string translation when mapping a property to a Lucene index. To give you the greatest possible flexibility you can also implement a bridge as a FieldBridge. This interface gives you a property value and let you map it the way you want in your Lucene Document. You can for example store a property in two different document fields. The interface is very similar in its concept to the Hibernate UserTypes.

Example 4.19. Implementing the FieldBridge interface

/**

 * 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;

In Example 4.19, “Implementing the FieldBridge interface” the fields are not added directly to Document. Instead the addition is delegated to the LuceneOptions helper; this helper will apply the options you have selected on @Field, like Store or TermVector, or apply the choosen @Boost value. It is especially useful to encapsulate the complexity of COMPRESS implementations. Even though it is recommended to delegate to LuceneOptions to add fields to the Document, nothing stops you from editing the Document directly and ignore the LuceneOptions in case you need to.

Tip

Classes like LuceneOptions are created to shield your application from changes in Lucene API and simplify your code. Use them if you can, but if you need more flexibility you're not required to.

4.4.2.3. ClassBridge

It is sometimes useful to combine more than one property of a given entity and index this combination in a specific way into the Lucene index. The @ClassBridge respectively @ClassBridges annotations can be defined at class level (as opposed to the property level). In this case the custom field bridge implementation receives the entity instance as the value parameter instead of a particular property. Though not shown in Example 4.20, “Implementing a class bridge”, @ClassBridge supports the termVector attribute discussed in section Section 4.1.1, “Basic mapping”.

Example 4.20. Implementing a class bridge

@Entity

@Indexed
@ClassBridge(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 );

   }

}

In this example, the particular CatFieldsClassBridge is applied to the department instance, the field bridge then concatenate both branch and network and index the concatenation.

4.5. Providing your own id

Warning

This part of the documentation is a work in progress.

You can provide your own id for Hibernate Search if you are extending the internals. You will have to generate a unique value so it can be given to Lucene to be indexed. This will have to be given to Hibernate Search when you create an org.hibernate.search.Work object - the document id is required in the constructor.

4.5.1. The ProvidedId annotation

Unlike @DocumentIdwhich is applied on field level, @ProvidedId is used on the class level. Optionally you can specify your own bridge implementation using the bridge property. Also, if you annotate a class with @ProvidedId, your subclasses will also get the annotation - but it is not done by using the java.lang.annotations.@Inherited. Be sure however, to not use this annotation with @DocumentId as your system will break.

Example 4.21. Providing your own id

@ProvidedId (bridge = org.my.own.package.MyCustomBridge)

@Indexed

public class MyClass{

    @Field

    String MyString;

    ...

}

4.6. Programmatic API

Warning

This feature is considered experimental. While stable code-wise, the API is subject to change in the future.

Although the recommended approach for mapping indexed entities is to use annotations, it is sometimes more convenient to use a different approach:

the same entity is mapped differently depending on deployment needs (customization for clients)
some automatization process requires the dynamic mapping of many entities sharing common traits

While it has been a popular demand in the past, the Hibernate team never found the idea of an XML alternative to annotations appealing due to it's heavy duplication, lack of code refactoring safety, because it did not cover all the use case spectrum and because we are in the 21st century :)

The idea of a programmatic API was much more appealing and has now become a reality. You can programmatically define your mapping using a programmatic API: you define entities and fields as indexable by using mapping classes which effectively mirror the annotation concepts in Hibernate Search. Note that fan(s) of XML approach can design their own schema and use the programmatic API to create the mapping while parsing the XML stream.

In order to use the programmatic model you must first construct a SearchMapping object which you can do in two ways:

directly
via a factory

You can pass the SearchMapping object directly via the property key hibernate.search.model_mapping or its type-safe representation Environment.MODEL_MAPPING. Use the Configuration API or the Map passed to the JPA Persistence bootstrap methods.

SearchMapping mapping = new SearchMapping();

[...]

configuration.setProperty( Environment.MODEL_MAPPING, mapping );


//or in JPA

SearchMapping mapping = new SearchMapping();

[...]

Map<String,String> properties = new HashMap<String,String)(1);

properties.put( Environment.MODEL_MAPPING, mapping );

EntityManagerFactory emf = Persistence.createEntityManagerFactory( "userPU", properties );

Alternatively, you can create a factory class (ie. hosting a method annotated with @Factory) whose factory method returns the SearchMapping object. The factory class must have a no-arg constructor and its fully qualified class name is passed to the property key hibernate.search.model_mapping or its type-safe representation Environment.MODEL_MAPPING. This approach is useful when you do not necessarily control the bootstrap process like in a Java EE, CDI or Spring Framework container.

Example 4.22. Use a mapping factory

public class MyAppSearchMappingFactory {

    @Factory

    public SearchMapping getSearchMapping() {

        SearchMapping mapping = new SearchMapping();

        mapping

                .analyzerDef( "ngram", StandardTokenizerFactory.class )

                    .filter( LowerCaseFilterFactory.class )

                    .filter( NGramFilterFactory.class )

                        .param( "minGramSize", "3" )

                        .param( "maxGramSize", "3" );

        return mapping;

    }

}


<persistence ...>

    <persistence-unit name="users">

        ...

        <properties>

            <property name="hibernate.search.model_mapping"

                      value="com.acme.MyAppSearchMappingFactory"/>

        </properties>

    </persistence-unit>

</persistence>

The SearchMapping is the root object which contains all the necessary indexable entities and fields. From there, the SearchMapping object exposes a fluent (and thus intuitive) API to express your mappings: it contextually exposes the relevant mapping options in a type-safe way. Just let your IDE autocompletion feature guide you through.

Today, the programmatic API cannot be used on a class annotated with Hibernate Search annotations, chose one approach or the other. Also note that the same default values apply in annotations and the programmatic API. For example, the @Field.name is defaulted to the property name and does not have to be set.

Each core concept of the programmatic API has a corresponding example to depict how the same definition would look using annotation. Therefore seeing an annotation example of the programmatic approach should give you a clear picture of what Hibernate Search will build with the marked entities and associated properties.

4.6.1. Mapping an entity as indexable

The first concept of the programmatic API is to define an entity as indexable. Using the annotation approach a user would mark the entity as @Indexed, the following example demonstrates how to programmatically achieve this.

Example 4.23. Marking an entity indexable

SearchMapping mapping = new SearchMapping();


mapping.entity(Address.class)

           .indexed()

               .indexName("Address_Index"); //optional


cfg.getProperties().put( "hibernate.search.model_mapping", mapping );

As you can see you must first create a SearchMapping object which is the root object that is then passed to the Configuration object as property. You must declare an entity and if you wish to make that entity as indexable then you must call the indexed() method. The indexed()method has an optional indexName(String indexName) which can be used to change the default index name that is created by Hibernate Search. Using the annotation model the above can be achieved as:

Example 4.24. Annotation example of indexing entity

@Entity

@Indexed(index="Address_Index")

public class Address {

....

}

4.6.2. Adding DocumentId to indexed entity

To set a property as a document id:

Example 4.25. Enabling document id with programmatic model

SearchMapping mapping = new SearchMapping();


mapping.entity(Address.class).indexed()

           .property("addressId", ElementType.FIELD) //field access

               .documentId()

                   .name("id");


cfg.getProperties().put( "hibernate.search.model_mapping", mapping);

The above is equivalent to annotating a property in the entity as @DocumentId as seen in the following example:

Example 4.26. DocumentId annotation definition

@Entity

@Indexed

public class Address {

 @Id

 @GeneratedValue

 @DocumentId(name="id")

 private Long addressId;


 ....

}

The next section demonstrates how to programmatically define analyzers.

4.6.3. Defining analyzers

Analyzers can be programmatically defined using the analyzerDef(String analyzerDef, Class<? extends TokenizerFactory> tokenizerFactory)method. This method also enables you to define filters for the analyzer definition. Each filter that you define can optionally take in parameters as seen in the following example :

Example 4.27. Defining analyzers using programmatic model

SearchMapping mapping = new SearchMapping();


mapping

    .analyzerDef( "ngram", StandardTokenizerFactory.class )
        .filter( LowerCaseFilterFactory.class )
        .filter( NGramFilterFactory.class )
            .param( "minGramSize", "3" )
            .param( "maxGramSize", "3" )
    .analyzerDef( "en", StandardTokenizerFactory.class )
        .filter( LowerCaseFilterFactory.class )
        .filter( EnglishPorterFilterFactory.class )
    .analyzerDef( "de", StandardTokenizerFactory.class )
        .filter( LowerCaseFilterFactory.class )
        .filter( GermanStemFilterFactory.class )

    .entity(Address.class).indexed()

        .property("addressId", ElementType.METHOD) //getter access

            .documentId()

                .name("id");


cfg.getProperties().put( "hibernate.search.model_mapping", mapping );

The analyzer mapping defined above is equivalent to the annotation model using @AnalyzerDef in conjunction with @AnalyzerDefs:

Example 4.28. Analyzer definition using annotation

@Indexed

@Entity

@AnalyzerDefs({

  @AnalyzerDef(name = "ngram",

    tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class),

    filters = {

      @TokenFilterDef(factory = LowerCaseFilterFactory.class),

      @TokenFilterDef(factory = NGramFilterFactory.class, 

        params = {

          @Parameter(name = "minGramSize",value = "3"),

          @Parameter(name = "maxGramSize",value = "3") 

       })

   }),

  @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 Address {

...

}

4.6.4. Defining full text filter definitions

The programmatic API provides easy mechanism for defining full text filter definitions which is available via @FullTextFilterDef and @FullTextFilterDefs (see Section 5.3, “Filters”). The next example depicts the creation of full text filter definition using the fullTextFilterDefmethod.

Example 4.29. Defining full text definition programmatically

SearchMapping mapping = new SearchMapping();


mapping

    .analyzerDef( "en", StandardTokenizerFactory.class )

        .filter( LowerCaseFilterFactory.class )

        .filter( EnglishPorterFilterFactory.class )

    .fullTextFilterDef("security", SecurityFilterFactory.class)
            .cache(FilterCacheModeType.INSTANCE_ONLY)

    .entity(Address.class)

        .indexed()

        .property("addressId", ElementType.METHOD)

            .documentId()

                .name("id")

        .property("street1", ElementType.METHOD)

            .field()

                .analyzer("en")

                .store(Store.YES)

            .field()

                .name("address_data")

                .analyzer("en")

                .store(Store.NO);


cfg.getProperties().put( "hibernate.search.model_mapping", mapping );

The previous example can effectively been seen as annotating your entity with @FullTextFilterDef like below:

Example 4.30. Using annotation to define full text filter definition

@Entity

@Indexed

@AnalyzerDefs({

  @AnalyzerDef(name = "en",

    tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class),

    filters = {

      @TokenFilterDef(factory = LowerCaseFilterFactory.class),

      @TokenFilterDef(factory = EnglishPorterFilterFactory.class)

   })

})

@FullTextFilterDefs({

 @FullTextFilterDef(name = "security", impl = SecurityFilterFactory.class, cache = FilterCacheModeType.INSTANCE_ONLY)

})

public class Address {

 

 @Id

 @GeneratedValue

 @DocumentId(name="id")

 pubblic Long getAddressId() {...};


 @Fields({

      @Field(store=Store.YES, analyzer=@Analyzer(definition="en")),

      @Field(name="address_data", analyzer=@Analyzer(definition="en"))

 })

 public String getAddress1() {...};


 ......

 

}

4.6.5. Defining fields for indexing

When defining fields for indexing using the programmatic API, call field() on the property(String propertyName, ElementType elementType) method. From field() you can specify the name, index, store, bridge and analyzer definitions.

Example 4.31. Indexing fields using programmatic API

SearchMapping mapping = new SearchMapping();


mapping

    .analyzerDef( "en", StandardTokenizerFactory.class )

        .filter( LowerCaseFilterFactory.class )

        .filter( EnglishPorterFilterFactory.class )

    .entity(Address.class).indexed()

        .property("addressId", ElementType.METHOD)

            .documentId()

                .name("id")

        .property("street1", ElementType.METHOD)

            .field()
                .analyzer("en")
                .store(Store.YES)
            .field()
                .name("address_data")
                .analyzer("en");


cfg.getProperties().put( "hibernate.search.model_mapping", mapping );

The above example of marking fields as indexable is equivalent to defining fields using @Field as seen below:

Example 4.32. Indexing fields using annotation

@Entity

@Indexed

@AnalyzerDefs({

  @AnalyzerDef(name = "en",

    tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class),

    filters = {

      @TokenFilterDef(factory = LowerCaseFilterFactory.class),

      @TokenFilterDef(factory = EnglishPorterFilterFactory.class)

   })

})

public class Address {

 

 @Id

 @GeneratedValue

 @DocumentId(name="id")

 private Long getAddressId() {...};


 @Fields({

      @Field(store=Store.YES, analyzer=@Analyzer(definition="en")),

      @Field(name="address_data", analyzer=@Analyzer(definition="en"))

 })

 public String getAddress1() {...}


 ......

}

4.6.6. Programmatically defining embedded entities

In this section you will see how to programmatically define entities to be embedded into the indexed entity similar to using the @IndexEmbedded model. In order to define this you must mark the property as indexEmbedded.There is the option to add a prefix to the embedded entity definition which can be done by calling prefix as seen in the example below:

Example 4.33. Programmatically defining embedded entites

SearchMapping mapping = new SearchMapping();


mappping

    .entity(ProductCatalog.class)

        .indexed()

        .property("catalogId", ElementType.METHOD)

            .documentId()

                .name("id")

        .property("title", ElementType.METHOD)

            .field()

                .index(Index.YES)

                .store(Store.NO)

        .property("description", ElementType.METHOD)

             .field()

                 .index(Index.YES)

                 .store(Store.NO)

        .property("items", ElementType.METHOD)

            .indexEmbedded()
                .prefix("catalog.items"); //optional


cfg.getProperties().put( "hibernate.search.model_mapping", mapping );

The next example shows the same definition using annotation (@IndexEmbedded):

Example 4.34. Using @IndexEmbedded

@Entity

@Indexed

public class ProductCatalog {

 @Id

 @GeneratedValue

 @DocumentId(name="id")

 public Long getCatalogId() {...}

 

 @Field

 public String getTitle() {...}


 @Field

 public String getDescription();

 

 @OneToMany(fetch = FetchType.LAZY)

 @IndexColumn(name = "list_position")

 @Cascade(org.hibernate.annotations.CascadeType.ALL)

 @IndexEmbedded(prefix="catalog.items")

 public List<Item> getItems() {...}


 ...


}

4.6.7. Contained In definition

@ContainedIn can be define as seen in the example below:

Example 4.35. Programmatically defining ContainedIn

SearchMapping mapping = new SearchMapping();


mappping

    .entity(ProductCatalog.class)

        .indexed()

        .property("catalogId", ElementType.METHOD)

            .documentId()

        .property("title", ElementType.METHOD)

            .field()

        .property("description", ElementType.METHOD)

            .field()

        .property("items", ElementType.METHOD)

            .indexEmbedded()


    .entity(Item.class)

        .property("description", ElementType.METHOD)

            .field()

        .property("productCatalog", ElementType.METHOD)

            .containedIn();


cfg.getProperties().put( "hibernate.search.model_mapping", mapping );

This is equivalent to defining @ContainedIn in your entity:

Example 4.36. Annotation approach for ContainedIn

@Entity

@Indexed

public class ProductCatalog {

 

 @Id

 @GeneratedValue

 @DocumentId

 public Long getCatalogId() {...}

 

 @Field

 public String getTitle() {...}


 @Field

 public String getDescription() {...}

 

 @OneToMany(fetch = FetchType.LAZY)

 @IndexColumn(name = "list_position")

 @Cascade(org.hibernate.annotations.CascadeType.ALL)

 @IndexEmbedded

 private List<Item> getItems() {...}


 ...

}


@Entity

public class Item {

 

 @Id

 @GeneratedValue

 private Long itemId;


 @Field

 public String getDescription() {...}

 

 @ManyToOne( cascade = { CascadeType.PERSIST, CascadeType.REMOVE } )

 @ContainedIn

 public ProductCatalog getProductCatalog() {...}


 ...

}

4.6.8. Date/Calendar Bridge

In order to define a calendar or date bridge mapping, call the dateBridge(Resolution resolution) or calendarBridge(Resolution resolution) methods after you have defined a field() in the SearchMapping hierarchy.

Example 4.37. Programmatic model for defining calendar/date bridge

SearchMapping mapping = new SearchMapping();


mapping

    .entity(Address.class)

        .indexed()

        .property("addressId", ElementType.FIELD)

            .documentId()

    .property("street1", ElementType.FIELD()

        .field()

    .property("createdOn", ElementType.FIELD)

        .field()

        .dateBridge(Resolution.DAY)

    .property("lastUpdated", ElementType.FIELD)

        .calendarBridge(Resolution.DAY);


cfg.getProperties().put( "hibernate.search.model_mapping", mapping );

See below for defining the above using @CalendarBridge and @DateBridge:

Example 4.38. @CalendarBridge and @DateBridge definition

@Entity

@Indexed

public class Address {

 

 @Id

 @GeneratedValue

 @DocumentId

 private Long addressId;


 @Field

 private String address1;


 @Field

 @DateBridge(resolution=Resolution.DAY)

 private Date createdOn;


 @CalendarBridge(resolution=Resolution.DAY)

 private Calendar lastUpdated;


 ...

}

4.6.9. Defining bridges

It is possible to associate bridges to programmatically defined fields. When you define a field() programmatically you can use the bridge(Class<?> impl) to associate a FieldBridgeimplementation class. The bridge method also provides optional methods to include any parameters required for the bridge class. The below shows an example of programmatically defining a bridge:

Example 4.39. Defining field bridges programmatically

SearchMapping mapping = new SearchMapping();


mapping

    .entity(Address.class)

        .indexed()

        .property("addressId", ElementType.FIELD)

            .documentId()

        .property("street1", ElementType.FIELD)

            .field()

            .field()

                .name("street1_abridged")

                .bridge( ConcatStringBridge.class )
                    .param( "size", "4" );

 

cfg.getProperties().put( "hibernate.search.model_mapping", mapping );

The above can equally be defined using annotations, as seen in the next example.

Example 4.40. Defining field bridges using annotation

@Entity

@Indexed

public class Address {

 

 @Id

 @GeneratedValue

 @DocumentId(name="id")

 private Long addressId;


 @Fields({

      @Field,

      @Field(name="street1_abridged", 

             bridge = @FieldBridge( impl = ConcatStringBridge.class, 

             params = @Parameter( name="size", value="4" ))

 })

 private String address1;


 ...

}

4.6.10. Mapping class bridge

You can define class bridges on entities programmatically. This is shown in the next example:

Example 4.41. Defining class briges using API

SearchMapping mapping = new SearchMapping();


mapping

    .entity(Departments.class)
       .classBridge(CatDeptsFieldsClassBridge.class)
         .name("branchnetwork")
         .index(Index.YES)
         .store(Store.YES)
         .param("sepChar", " ")
      .classBridge(EquipmentType.class)
         .name("equiptype")
         .index(Index.YES)
         .store(Store.YES)
         .param("C", "Cisco")
         .param("D", "D-Link")
         .param("K", "Kingston")
         .param("3", "3Com")

      .indexed();


cfg.getProperties().put( "hibernate.search.model_mapping", mapping );

The above is similar to using @ClassBridgeas seen in the next example:

Example 4.42. Using @ClassBridge

@Entity

@Indexed

@ClassBridges ( {

  @ClassBridge(name="branchnetwork",

     store= Store.YES,

     impl = CatDeptsFieldsClassBridge.class,

     params = @Parameter( name="sepChar", value=" " ) ),

  @ClassBridge(name="equiptype",

     store= Store.YES,

     impl = EquipmentType.class,

     params = {@Parameter( name="C", value="Cisco" ),

        @Parameter( name="D", value="D-Link" ),

        @Parameter( name="K", value="Kingston" ),

        @Parameter( name="3", value="3Com" )

   })

})

public class Departments {


....

}

4.6.11. Mapping dynamic boost

You can apply a dynamic boost factor on either a field or a whole entity:

Example 4.43. DynamicBoost mapping using programmatic model

SearchMapping mapping = new SearchMapping();


mapping

  .entity(DynamicBoostedDescLibrary.class)

   .indexed()

   .dynamicBoost(CustomBoostStrategy.class)

  .property("libraryId", ElementType.FIELD)

    .documentId().name("id")

  .property("name", ElementType.FIELD)

    .dynamicBoost(CustomFieldBoostStrategy.class);

    .field()

      .store(Store.YES)


cfg.getProperties().put( "hibernate.search.model_mapping", mapping );

The next example shows the equivalent mapping using the @DynamicBoost annotation:

Example 4.44. Using the @DynamicBoost

@Entity

@Indexed

@DynamicBoost(impl = CustomBoostStrategy.class)

public class DynamicBoostedDescriptionLibrary {


 @Id

 @GeneratedValue

 @DocumentId

 private int id;


 private float dynScore;

 

 @Field(store = Store.YES)

 @DynamicBoost(impl = CustomFieldBoostStrategy.class)

 private String name;


 public DynamicBoostedDescriptionLibrary() {

  dynScore = 1.0f;

 }

  

 .......

}

Chapter 5. Querying

5.1. Building queries

5.1.1. Building a Lucene query using the Lucene API
5.1.2. Building a Lucene query with the Hibernate Search query DSL
5.1.3. Building a Hibernate Search query

5.2. Retrieving the results

5.2.1. Performance considerations
5.2.2. Result size
5.2.3. ResultTransformer
5.2.4. Understanding results

5.3. Filters

5.3.1. Using filters in a sharded environment

5.4. Faceting

5.4.1. Creating a faceting request
5.4.2. Applying a faceting request
5.4.3. Restricting query results

5.5. Optimizing the query process

5.5.1. Caching index values: FieldCache

The second most important capability of Hibernate Search is the ability to execute Lucene queries and retrieve entities managed by a Hibernate session. The search provides the power of Lucene without leaving the Hibernate paradigm, giving another dimension to the Hibernate classic search mechanisms (HQL, Criteria query, native SQL query).

Preparing and executing a query consists of four simple steps:

Creating a FullTextSession
Creating a Lucene query either via the Hibernate Search query DSL (recommended) or by utilizing the Lucene query API
Wrapping the Lucene query using an org.hibernate.Query
Executing the search by calling for example list() or scroll()

To access the querying facilities, you have to use a FullTextSession. This Search specific session wraps a regular org.hibernate.Session in order to provide query and indexing capabilities.

Example 5.1. Creating a FullTextSession

Session session = sessionFactory.openSession();

...

FullTextSession fullTextSession = Search.getFullTextSession(session);

Once you have a FullTextSession you have two options to build the full-text query: the Hibernate Search query DSL or the native Lucene query.

If you use the Hibernate Search query DSL, it will look like this:

final QueryBuilder b = fullTextSession.getSearchFactory()
    .buildQueryBuilder().forEntity( Myth.class ).get();

org.apache.lucene.search.Query luceneQuery =
    b.keyword()
        .onField("history").boostedTo(3)
        .matching("storm")
        .createQuery();


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

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

You can alternatively write your Lucene query either using the Lucene query parser or Lucene programmatic API.

Example 5.2. Creating a Lucene query via the QueryParser

SearchFactory searchFactory = fullTextSession.getSearchFactory();
org.apache.lucene.queryParser.QueryParser parser = 
    new QueryParser("title", searchFactory.getAnalyzer(Myth.class) );
try {
    org.apache.lucene.search.Query luceneQuery = parser.parse( "history:storm^3" );
}
catch (ParseException e) {
    //handle parsing failure
}


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

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

Note

The Hibernate query built on top of the Lucene query is a regular org.hibernate.Query, which means you are in the same paradigm as the other Hibernate query facilities (HQL, Native or Criteria). The regular list() , uniqueResult(), iterate() and scroll() methods can be used.

In case you are using the Java Persistence APIs of Hibernate, the same extensions exist:

Example 5.3. Creating a Search query using the JPA API

EntityManager em = entityManagerFactory.createEntityManager();


FullTextEntityManager fullTextEntityManager = 

    org.hibernate.search.jpa.Search.getFullTextEntityManager(em);


...

final QueryBuilder b = fullTextEntityManager.getSearchFactory()

    .buildQueryBuilder().forEntity( Myth.class ).get();


org.apache.lucene.search.Query luceneQuery =

    b.keyword()

        .onField("history").boostedTo(3)

        .matching("storm")

        .createQuery();
javax.persistence.Query fullTextQuery = 
    fullTextEntityManager.createFullTextQuery( luceneQuery );


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

Note

The following examples we will use the Hibernate APIs but the same example can be easily rewritten with the Java Persistence API by just adjusting the way the FullTextQuery is retrieved.

5.1. Building queries

Hibernate Search queries are built on top of Lucene queries which gives you total freedom on the type of Lucene query you want to execute. However, once built, Hibernate Search wraps further query processing using org.hibernate.Query as your primary query manipulation API.

5.1.1. Building a Lucene query using the Lucene API

Using the Lucene API, you have several options. You can use the query parser (fine for simple queries) or the Lucene programmatic API (for more complex use cases). It is out of the scope of this documentation on how to exactly build a Lucene query. Please refer to the online Lucene documentation or get hold of a copy of Lucene In Action or Hibernate Search in Action.

5.1.2. Building a Lucene query with the Hibernate Search query DSL

Writing full-text queries with the Lucene programmatic API is quite complex. It's even more complex to understand the code once written. Besides the inherent API complexity, you have to remember to convert your parameters to their string equivalent as well as make sure to apply the correct analyzer to the right field (a ngram analyzer will for example use several ngrams as the tokens for a given word and should be searched as such).

The Hibernate Search query DSL makes use of a style of API called a fluent API. This API has a few key characteristics:

it has meaningful method names making a succession of operations reads almost like English
it limits the options offered to what makes sense in a given context (thanks to strong typing and IDE autocompletion).
It often uses the chaining method pattern
it's easy to use and even easier to read

Let's see how to use the API. You first need to create a query builder that is attached to a given indexed entity type. This QueryBuilder will know what analyzer to use and what field bridge to apply. You can create several QueryBuilders (one for each entity type involved in the root of your query). You get the QueryBuilder from the SearchFactory.

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

You can also override the analyzer used for a given field or fields. This is rarely needed and should be avoided unless you know what you are doing.

QueryBuilder mythQB = searchFactory.buildQueryBuilder()

    .forEntity( Myth.class )

        .overridesForField("history","stem_analyzer_definition")

    .get();

Using the query builder, you can then build queries. It is important to realize that the end result of a QueryBuilder is a Lucene query. For this reason you can easily mix and match queries generated via Lucene's query parser or Query objects you have assembled with the Lucene programmatic API and use them with the Hibernate Search DSL. Just in case the DSL is missing some features.

5.1.2.1. Keyword queries

Let's start with the most basic use case - searching for a specific word:

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

keyword() means that you are trying to find a specific word. onField() specifies in which Lucene field to look. matching() tells what to look for. And finally createQuery() creates the Lucene query object. A lot is going on with this line of code.

The value storm is passed through the history FieldBridge: it does not matter here but you will see that it's quite handy when dealing with numbers or dates.
The field bridge value is then passed to the analyzer used to index the field history. This ensures that the query uses the same term transformation than the indexing (lower case, n-gram, stemming and so on). If the analyzing process generates several terms for a given word, a boolean query is used with the SHOULD logic (roughly an OR logic).

Let's see how you can search a property that is not of type string.

@Entity 

@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();

Note

In plain Lucene, you would have had to convert the Date object to its string representation (in this case the year).

This conversion works for any object, not just Date, provided that the FieldBridge has an objectToString method (and all built-in FieldBridge implementations do).

We make the example a little more advanced now and have a look at how to search a field that uses ngram analyzers. ngram analyzers index succession of ngrams of your words which helps to recover from user typos. For example the 3-grams of the word hibernate are hib, ibe, ber, 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") } )

  }

)

@Entity 

@Indexed 

public class Myth {

  @Field(analyzer=@Analyzer(definition="ngram") 

  @DateBridge(resolution = Resolution.YEAR)

  public String getName() { return name; }

  public String setName(Date 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, phu, hus. Each of these n-gram will be part of the query. We will then be able to find the Sysiphus myth (with a y). All that is transparently done for you.

Note

If for some reason you do not want a specific field to use the field bridge or the analyzer you can call the ignoreAnalyzer() or ignoreFieldBridge() functions

To search for multiple possible words in the same field, simply 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, you can 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.

5.1.2.2. Fuzzy queries

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

Query luceneQuery = mythQB

    .keyword()

      .fuzzy()

        .withThreshold( .8f )

        .withPrefixLength( 1 )

    .onField("history")

    .matching("starm")

    .createQuery();

threshold is the limit above which two terms are considering matching. It's a decimal between 0 and 1 and defaults to 0.5. prefixLength is the length of the prefix ignored by the "fuzzyness": while it defaults to 0, a non zero value is recommended for indexes containing a huge amount of distinct terms.

5.1.2.3. Wildcard queries

You can also execute wildcard queries (queries where some of parts of the word are unknown). ? represents a single character and * represents any character sequence. 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();

Note

Wildcard queries do not apply the analyzer on the matching terms. Otherwise the risk of * or ? being mangled is too high.

5.1.2.4. Phrase queries

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

Query luceneQuery = mythQB

    .phrase()

    .onField("history")

    .sentence("Thou shalt not kill")

    .createQuery();

You can search approximate sentences 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();

5.1.2.5. Range queries

After looking at all these query examples for searching for to a given word, it is time to introduce range queries (on numbers, dates, strings etc). 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();

5.1.2.6. Combining queries

Finally, you can aggregate (combine) queries to create more complex queries. The following aggregation operators are available:

SHOULD: the query query should contain the matching elements of the subquery
MUST: the query must contain the matching elements of the subquery
MUST NOT: the query must not contain the matching elements of the subquery

The subqueries can be any Lucene query including a boolean query itself. Let's look at a few examples:

//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();


//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();


//look for all myths except religious ones

Query luceneQuery = mythQB

    .all()

      .except( monthQb

        .keyword()

        .onField( "description_stem" )

        .matching( "religion" )

        .createQuery() 

      )

    .createQuery();

5.1.2.7. Query options

We already have seen several query options in the previous example, but lets summarize again the options for query types and fields:

boostedTo (on query type and on field): boost the whole query or the specific field to a given factor
withConstantScore (on query): all results matching the query have a constant score equals to the boost
filteredBy(Filter) (on query): filter query results using the Filter instance
ignoreAnalyzer (on field): ignore the analyzer when processing this field
ignoreFieldBridge (on field): ignore field bridge when processing this field

Let's check out an example using some of these 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();

As you can see, the Hibernate Search query DSL is an easy to use and easy to read query API and by accepting and producing Lucene queries, you can easily incorporate query types not (yet) supported by the DSL. Please give us feedback!

5.1.3. Building a Hibernate Search query

So far we only covered the process of how to create your Lucene query (see Section 5.1, “Building queries”). However, this is only the first step in the chain of actions. Let's now see how to build the Hibernate Search query from the Lucene query.

5.1.3.1. Generality

Once the Lucene query is built, it needs to be wrapped into an Hibernate Query. If not specified otherwise, the query will be executed against all indexed entities, potentially returning all types of indexed classes.

Example 5.4. Wrapping a Lucene query into a Hibernate Query

FullTextSession fullTextSession = Search.getFullTextSession( session );

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

It is advised, from a performance point of view, to restrict the returned types:

Example 5.5. Filtering the search result by entity type

fullTextQuery = fullTextSession

    .createFullTextQuery( luceneQuery, Customer.class );


// or


fullTextQuery = fullTextSession

    .createFullTextQuery( luceneQuery, Item.class, Actor.class );

In Example 5.5, “Filtering the search result by entity type” the first example returns only matching Customers, the second returns matching Actors and Items. The type restriction is fully polymorphic which means that if there are two indexed subclasses Salesman and Customer of the baseclass Person, it is possible to just specify Person.class in order to filter on result types.

5.1.3.2. Pagination

Out of performance reasons it is recommended to restrict the number of returned objects per query. In fact is a very common use case anyway that the user navigates from one page to an other. The way to define pagination is exactly the way you would define pagination in a plain HQL or Criteria query.

Example 5.6. Defining pagination for a search query

org.hibernate.Query fullTextQuery = 

    fullTextSession.createFullTextQuery( luceneQuery, Customer.class );

fullTextQuery.setFirstResult(15); //start from the 15th element

fullTextQuery.setMaxResults(10); //return 10 elements

Tip

It is still possible to get the total number of matching elements regardless of the pagination via fulltextQuery.getResultSize()

5.1.3.3. Sorting

Apache Lucene provides a very flexible and powerful way to sort results. While the default sorting (by relevance) is appropriate most of the time, it can be interesting to sort by one or several other properties. In order to do so set the Lucene Sort object to apply a Lucene sorting strategy.

Example 5.7. Specifying a Lucene Sort in order to sort the results



org.hibernate.search.FullTextQuery query = s.createFullTextQuery( query, Book.class );

org.apache.lucene.search.Sort sort = new Sort(

    new SortField("title", SortField.STRING));
query.setSort(sort);

List results = query.list();

Tip

Be aware that fields used for sorting must not be tokenized (see Section 4.1.1.2, “@Field”).

5.1.3.4. Fetching strategy

When you restrict the return types to one class, Hibernate Search loads the objects using a single query. It also respects the static fetching strategy defined in your domain model.

It is often useful, however, to refine the fetching strategy for a specific use case.

Example 5.8. Specifying FetchMode on a query

Criteria criteria = 

    s.createCriteria( Book.class ).setFetchMode( "authors", FetchMode.JOIN );

s.createFullTextQuery( luceneQuery ).setCriteriaQuery( criteria );

In this example, the query will return all Books matching the luceneQuery. The authors collection will be loaded from the same query using an SQL outer join.

When defining a criteria query, it is not necessary to restrict the returned entity types when creating the Hibernate Search query from the full text session: the type is guessed from the criteria query itself.

Important

Only fetch mode can be adjusted, refrain from applying any other restriction. While it is known to work as of Hibernate Search 4, using restriction (ie a where clause) on your Criteria query should be avoided when possible. getResultSize() will return a SearchException if used in conjunction with a Criteria with restriction.

Important

You cannot use setCriteriaQuery if more than one entity type is expected to be returned.

5.1.3.5. Projection

For some use cases, returning the domain object (including its associations) is overkill. Only a small subset of the properties is necessary. Hibernate Search allows you to return a subset of properties:

Example 5.9. Using projection instead of returning the full domain object

org.hibernate.search.FullTextQuery query = 

    s.createFullTextQuery( luceneQuery, Book.class );

query.setProjection( "id", "summary", "body", "mainAuthor.name" );

List results = query.list();

Object[] firstResult = (Object[]) results.get(0);

Integer id = firstResult[0];

String summary = firstResult[1];

String body = firstResult[2];

String authorName = firstResult[3];

Hibernate Search extracts the properties from the Lucene index and convert them back to their object representation, returning a list of Object[]. Projections avoid a potential database round trip (useful if the query response time is critical). However, it also has several constraints:

the properties projected must be stored in the index (@Field(store=Store.YES)), which increases the index size
the properties projected must use a FieldBridge implementing org.hibernate.search.bridge.TwoWayFieldBridge or org.hibernate.search.bridge.TwoWayStringBridge, the latter being the simpler version.
Note
All Hibernate Search built-in types are two-way.
you can only project simple properties of the indexed entity or its embedded associations. This means you cannot project a whole embedded entity.
projection does not work on collections or maps which are indexed via @IndexedEmbedded

Projection is also useful for another kind of use case. Lucene can provide metadata information about the results. By using some special projection constants, the projection mechanism can retrieve this metadata:

Example 5.10. Using projection in order to retrieve meta data

org.hibernate.search.FullTextQuery query = 

    s.createFullTextQuery( luceneQuery, Book.class );

query.setProjection( 
    FullTextQuery.SCORE, 
    FullTextQuery.THIS, 
    "mainAuthor.name" );

List results = query.list();

Object[] firstResult = (Object[]) results.get(0);

float score = firstResult[0];

Book book = firstResult[1];

String authorName = firstResult[2];

You can mix and match regular fields and projection constants. Here is the list of the available constants:

FullTextQuery.THIS: returns the initialized and managed entity (as a non projected query would have done).
FullTextQuery.DOCUMENT: returns the Lucene Document related to the object projected.
FullTextQuery.OBJECT_CLASS: returns the class of the indexed entity.
FullTextQuery.SCORE: returns the document score in the query. Scores are handy to compare one result against an other for a given query but are useless when comparing the result of different queries.
FullTextQuery.ID: the id property value of the projected object.
FullTextQuery.DOCUMENT_ID: the Lucene document id. Careful, Lucene document id can change overtime between two different IndexReader opening (this feature is experimental).
FullTextQuery.EXPLANATION: returns the Lucene Explanation object for the matching object/document in the given query. Do not use if you retrieve a lot of data. Running explanation typically is as costly as running the whole Lucene query per matching element. Make sure you use projection!

5.1.3.6. Customizing object initialization strategies

By default, Hibernate Search uses the most appropriate strategy to initialize entities matching your full text query. It executes one (or several) queries to retrieve the required entities. This is the best approach to minimize database round trips in a scenario where none / few of the retrieved entities are present in the persistence context (ie the session) or the second level cache.

If most of your entities are present in the second level cache, you can force Hibernate Search to look into the cache before retrieving an object from the database.

Example 5.11. Check the second-level cache before using a query

FullTextQuery query = session.createFullTextQuery(luceneQuery, User.class);

query.initializeObjectWith(

    ObjectLookupMethod.SECOND_LEVEL_CACHE,

    DatabaseRetrievalMethod.QUERY

);

ObjectLookupMethod defines the strategy used to check if an object is easily accessible (without database round trip). Other options are:

ObjectLookupMethod.PERSISTENCE_CONTEXT: useful if most of the matching entities are already in the persistence context (ie loaded in the Session or EntityManager)
ObjectLookupMethod.SECOND_LEVEL_CACHE: check first the persistence context and then the second-level cache.

Note

Note that to search in the second-level cache, several settings must be in place:

the second level cache must be properly configured and active
the entity must have enabled second-level cache (eg via @Cacheable)
the Session, EntityManager or Query must allow access to the second-level cache for read access (ie CacheMode.NORMAL in Hibernate native APIs or CacheRetrieveMode.USE in JPA 2 APIs).

Warning

Avoid using ObjectLookupMethod.SECOND_LEVEL_CACHE unless your second level cache implementation is either EHCache or Infinispan; other second level cache providers don't currently implement this operation efficiently.

You can also customize how objects are loaded from the database (if not found before). Use DatabaseRetrievalMethod for that:

QUERY (default): use a (set of) queries to load several objects in batch. This is usually the best approach.
FIND_BY_ID: load objects one by one using the Session.get or EntityManager.find semantic. This might be useful if batch-size is set on the entity (in which case, entities will be loaded in batch by Hibernate Core). QUERY should be preferred almost all the time.

5.1.3.7. Limiting the time of a query

You can limit the time a query takes in Hibernate Search in two ways:

raise an exception when the limit is reached
limit to the number of results retrieved when the time limit is raised

5.1.3.7.1. Raise an exception on time limit

You can decide to stop a query if when it takes more than a predefined amount of time. Note that this is a best effort basis but if Hibernate Search still has significant work to do and if we are beyond the time limit, a QueryTimeoutException will be raised (org.hibernate.QueryTimeoutException or javax.persistence.QueryTimeoutException depending on your programmatic API).

To define the limit when using the native Hibernate APIs, use one of the following approaches

Example 5.12. Defining a timeout in query execution

Query luceneQuery = ...;

FullTextQuery query = fullTextSession.createFullTextQuery(luceneQuery, User.class);


//define the timeout in seconds

query.setTimeout(5);


//alternatively, define the timeout in any given time unit

query.setTimeout(450, TimeUnit.MILLISECONDS);


try {

    query.list();

}

catch (org.hibernate.QueryTimeoutException e) {

    //do something, too slow

}

Likewise getResultSize(), iterate() and scroll() honor the timeout but only until the end of the method call. That simply means that the methods of Iterable or the ScrollableResults ignore the timeout.

Note

explain() does not honor the timeout: this method is used for debug purposes and in particular to find out why a query is slow

When using JPA, simply use the standard way of limiting query execution time.

Example 5.13. Defining a timeout in query execution

Query luceneQuery = ...;

FullTextQuery query = fullTextEM.createFullTextQuery(luceneQuery, User.class);


//define the timeout in milliseconds

query.setHint( "javax.persistence.query.timeout", 450 );


try {

    query.getResultList();

}

catch (javax.persistence.QueryTimeoutException e) {

    //do something, too slow

}

Important

Remember, this is a best effort approach and does not guarantee to stop exactly on the specified timeout.

5.1.3.7.2. Limit the number of results when the time limit is reached (EXPERIMENTAL)

Alternatively, you can return the number of results which have already been fetched by the time the limit is reached. Note that only the Lucene part of the query is influenced by this limit. It is possible that, if you retrieve managed object, it takes longer to fetch these objects.

Warning

This approach is not compatible with the setTimeout approach.

To define this soft limit, use the following approach

Example 5.14. Defining a time limit in query execution

Query luceneQuery = ...;

FullTextQuery query = fullTextSession.createFullTextQuery(luceneQuery, User.class);


//define the timeout in seconds

query.limitExecutionTimeTo(500, TimeUnit.MILLISECONDS);

List results = query.list();

Likewise getResultSize(), iterate() and scroll() honor the time limit but only until the end of the method call. That simply means that the methods of Iterable or the ScrollableResults ignore the timeout.

You can determine if the results have been partially loaded by invoking the hasPartialResults method.

Example 5.15. Determines when a query returns partial results

Query luceneQuery = ...;

FullTextQuery query = fullTextSession.createFullTextQuery(luceneQuery, User.class);


//define the timeout in seconds

query.limitExecutionTimeTo(500, TimeUnit.MILLISECONDS);

List results = query.list();


if ( query.hasPartialResults() ) {

    displayWarningToUser();

}

If you use the JPA API, limitExecutionTimeTo and hasPartialResults are also available to you.

Warning

This approach is considered experimental

5.2. Retrieving the results

Once the Hibernate Search query is built, executing it is in no way different than executing a HQL or Criteria query. The same paradigm and object semantic applies. All the common operations are available: list(), uniqueResult(), iterate(), scroll().

5.2.1. Performance considerations

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. Don't forget to close the ScrollableResults object when you're 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.

Important

Pagination is preferred over scrolling.

5.2.2. Result size

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

for the Google-like feature "1-10 of about 888,000,000"
to implement a fast pagination navigation
to implement a multi step search engine (adding approximation if the restricted query return no or not enough results)

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 5.16. 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 == query.getResultSize(); 


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 == query.getResultSize();

Note

Like Google, the number of results is approximative if the index is not fully up-to-date with the database (asynchronous cluster for example).

5.2.3. ResultTransformer

As seen in Section 5.1.3.5, “Projection” projection results are returns as Object arrays. This data structure is not always matching the application needs. In this cases It is possible to apply a ResultTransformer which post query execution can build the needed data structure:

Example 5.17. Using ResultTransformer in conjunction 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.

5.2.4. Understanding results

You will find yourself sometimes puzzled by a result showing up in a query or a result not showing up in a query. Luke is a great tool to understand those mysteries. 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:

Use the fullTextQuery.explain(int) method
Use projection

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.

Warning

The Document id has nothing to do with the entity id. Do not mess up these two notions.

The second approach let's you project the Explanation object using the FullTextQuery.EXPLANATION constant.

Example 5.18. Retrieving the Lucene Explanation object using projection

FullTextQuery ftQuery = s.createFullTextQuery( luceneQuery, Dvd.class )

        .setProjection( 

             FullTextQuery.DOCUMENT_ID, 

             FullTextQuery.EXPLANATION, 

             FullTextQuery.THIS );

@SuppressWarnings("unchecked") List<Object[]> results = ftQuery.list();

for (Object[] result : results) {

    Explanation e = (Explanation) result[1];

    display( e.toString() );

}

Be careful, building the explanation object is quite expensive, it is roughly as expensive as running the Lucene query again. Don't do it if you don't need the object

5.3. Filters

Apache Lucene has a powerful feature that allows 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. Some interesting use cases are:

security
temporal data (eg. view only last month's data)
population filter (eg. search limited to a given category)
and many more

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 5.19. Enabling fulltext filters for a given 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 5.20. Defining and implementing a Filter

@Entity
@Indexed
@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 5.21. Creating a filter using the factory pattern

@Entity

@Indexed

@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.

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 5.22. 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 5.23. 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 returning a FilterKey object. The returned object has a special contract: the key object must implement equals() / hashCode() so that 2 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, 2 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:

you enabled the filter caching system (enabled by default)
your filter has parameters

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, you can 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/newIndexReader 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 @FullTextFilterDefis 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:

Value	Definition
FilterCacheModeType.NONE	No filter instance and no result is cached by Hibernate Search. For every filter call, a new filter instance is created. This setting might be useful for rapidly changing data sets or heavily memory constrained environments.
FilterCacheModeType.INSTANCE_ONLY	The filter instance is cached and reused across concurrent `Filter.getDocIdSet()` calls. `DocIdSet` results are not cached. This setting is useful when a filter uses its own specific caching mechanism or the filter results change dynamically due to application specific events making `DocIdSet` caching in both cases unnecessary.
FilterCacheModeType.INSTANCE_AND_DOCIDSETRESULTS	Both the filter instance and the `DocIdSet` results are cached. This is the default value.

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

the system does not update the targeted entity index often (in other words, the IndexReader is reused a lot)
the Filter's DocIdSet is expensive to compute (compared to the time spent to execute the query)

5.3.1. Using filters in a sharded environment

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

create a sharding strategy that does select a subset of IndexManagers depending on some filter configuration
activate the proper filter at query time

Let's first look at an example of sharding strategy that query on a specific customer shard if the customer filter is activated.

public class CustomerShardingStrategy implements IndexShardingStrategy {


 // stored IndexManagers in a 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) {

   FFullTextFilter 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, we make sure to only use the shard dedicated to this customer. Otherwise, we return all shards. A given Sharding strategy can react to one or more filters and depends on their parameters.

The second step is simply to activate the filter at query time. While the filter can be a regular filter (as defined in Section 5.3, “Filters”) 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 otherwise ignored for the rest of the query. Simply use the ShardSensitiveOnlyFilter class when declaring your filter.

@Entity @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.

5.4. Faceting

Faceted search is a technique which allows to divide the results of a query into multiple categories. This categorisation includes the calculation of hit counts for each category and the ability to further restrict search results based on these facets (categories). Example 5.24, “Search for 'Hibernate Search' on Amazon” shows a faceting example. The search results in fifteen hits which are displayed on the main part of the page. The navigation bar on the left, however, shows the category Computers & Internet with its subcategories Programming, Computer Science, Databases, Software, Web Development, Networking and Home Computing. For each of these subcategories the number of books is shown matching the main search criteria and belonging to the respective subcategory. This division of the category Computers & Internet is one concrete search facet. Another one is for example the average customer review.

In Hibernate Search the classes QueryBuilder and FullTextQuery are the entry point into the faceting API. The former allows to create faceting requests whereas the latter gives access to the so called FacetManager. With the help of the FacetManager faceting requests can be applied on a query and selected facets can be added to an existing query in order to refine search results. The following sections will describe the faceting process in more detail. The examples will use the entity Cd as shown in Example 5.25, “Entity Cd”:

Example 5.25. Entity Cd

@Entity

@Indexed

public class Cd {


    @Id

    @GeneratedValue

    private int id;


    @Fields( {

        @Field,

        @Field(name = "name_un_analyzed", analyze = Analyze.NO)

    })

    private String name;


    @Field(analyze = Analyze.NO)

    @NumericField

    private int price;


    Field(analyze = Analyze.NO)

    @DateBridge(resolution = Resolution.YEAR)

    private Date releaseYear;


    @Field(analyze = Analyze.NO)

    private String label;



// setter/getter

...

5.4.1. Creating a faceting request

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 Example 5.26, “Creating a discrete faceting request”:

Example 5.26. 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 instace 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 orginial 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.

Tip

At the moment there are several preconditions an indexed field has to meet in order to apply faceting on it. The indexed property must be of type String, Date or a subtype of Number and null values should be avoided. Furthermore the property has to be indexed with Analyze.NO and in case of a numeric property @NumericField needs to be specified.

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 in Example 5.27, “Creating a range faceting request” where three different price ranges are specified. 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.

Example 5.27. Creating a range faceting request

QueryBuilder builder = fullTextSession.getSearchFactory()

    .buildQueryBuilder()

        .forEntity( Cd.class )

            .get();

FacetingRequest priceacetingRequest = queryBuilder( Cd.class ).facet()

    .name( "priceFaceting" )

    .onField( "price" )

    .range()

    .below( 1000 )

    .from( 1001 ).to( 1500 )

    .above( 1500 ).excludeLimit()

    .createFacetingRequest();

5.4.2. Applying a faceting request

In Section 5.4.1, “Creating a faceting request” we have seen how to create a faceting request. Now it is time to apply it on a query. The key is the FacetManager which can be retrieved via the FullTextQuery (see Example 5.28, “Applying a faceting request”).

Example 5.28. Applying a faceting request

// create a fulltext query

QueryBuilder builder = queryBuilder( Cd.class );

Query luceneQuery = builder.all().createQuery(); // match all query

FullTextQuery fullTextQuery = fullTextSession.createFullTextQuery( luceneQuery, Cd.class );


// retrieve facet manager and apply faceting request

FacetManager facetManager = query.getFacetManager();

facetManager.enableFaceting( priceFacetingRequest );


// get the list of Cds 

List<Cd> cds = fullTextQuery.list();

...


// retrieve the faceting results

List<Facet> facets = facetManager.getFacets( "priceFaceting" );

...

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

5.4.3. Restricting query results

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). Example 5.29, “Restricting query results via the application of a FacetSelection” shows an example.

Example 5.29. Restricting query results via the application of a FacetSelection

// create a fulltext query

QueryBuilder builder = queryBuilder( Cd.class );

Query luceneQuery = builder.all().createQuery(); // match all query

FullTextQuery fullTextQuery = fullTextSession.createFullTextQuery( luceneQuery, clazz );


// retrieve facet manager and apply faceting request

FacetManager facetManager = query.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);

5.5. Optimizing the query process

Query performance depends on several criteria:

the Lucene query itself: read the literature on this subject
the number of object loaded: use pagination (always ;-) ) or index projection (if needed)
the way Hibernate Search interacts with the Lucene readers: defines the appropriate Reader strategy.
caching frequently extracted values from the index: see Section 5.5.1, “Caching index values: FieldCache”.

5.5.1. Caching index values: FieldCache

The primary function of a Lucene index is to identify matches to your queries, still after the query is performed the results must be analyzed to extract useful information: typically Hibernate Search might 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.

What is exactly needed depends on the kind of Projections being used (see Section 5.1.3.5, “Projection”), and 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 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 currently possible to cache Class types and IDs using this annotation:

CLASS: Hibernate Search will use a Lucene FieldCache to improve peformance of the Class type extraction from the index.
This value is enabled by default, and is what Hibernate Search will apply if you don't specify the @CacheFromIndex annotation.
ID: Extracting the primary identifier will use a cache. This is likely providing the best performing queries, but will consume much more memory which in turn might reduce performance.

Note

Measure the performance and memory consumption impact after warmup (executing some queries): enabling Field Caches is likely to improve performance but this is not always the case.

Using a FieldCache has two downsides to consider:

Memory usage: these caches can be quite memory hungry. Typically the CLASS cache has lower requirements than the ID cache.
Index warmup: when using field caches, the first query on a new index or segment will be slower than when you don't have caching enabled.

With some queries the classtype won't 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).

Chapter 6. Manual index changes

6.1. Adding instances to the index

6.2. Deleting instances from the index

6.3. Rebuilding the whole index

6.3.1. Using flushToIndexes()
6.3.2. Using a MassIndexer

As Hibernate core applies changes to the Database, Hibernate Search detects these changes and will update the index automatically (unless the EventListeners are disabled). Sometimes changes are made to the database without using Hibernate, as when backup is restored or your data is otherwise affected; for these cases Hibernate Search exposes the Manual Index APIs to explicitly update or remove a single entity from the index, or rebuild the index for the whole database, or remove all references to a specific type.

All these methods affect the Lucene Index only, no changes are applied to the Database.

6.1. Adding instances to the index

Using FullTextSession.index(T entity) you can directly add or update a specific object instance to the index. If this entity was already indexed, then the index will be updated. Changes to the index are only applied at transaction commit.

Example 6.1. Indexing an entity via FullTextSession.index(T entity)

FullTextSession fullTextSession = Search.getFullTextSession(session);

Transaction tx = fullTextSession.beginTransaction();

Object customer = fullTextSession.load( Customer.class, 8 );
fullTextSession.index(customer);

tx.commit(); //index only updated at commit time

In case you want to add all instances for a type, or for all indexed types, the recommended approach is to use a MassIndexer: see Section 6.3.2, “Using a MassIndexer” for more details.

6.2. Deleting instances from the index

It is equally possible to remove an entity or all entities of a given type from a Lucene index without the need to physically remove them from the database. This operation is named purging and is also done through the FullTextSession.

Example 6.2. Purging a specific instance of an entity from the index

FullTextSession fullTextSession = Search.getFullTextSession(session);

Transaction tx = fullTextSession.beginTransaction();

for (Customer customer : customers) {

    fullTextSession.purge( Customer.class, customer.getId() );

}

tx.commit(); //index is updated at commit time

Purging will remove the entity with the given id from the Lucene index but will not touch the database.

If you need to remove all entities of a given type, you can use the purgeAll method. This operation removes all entities of the type passed as a parameter as well as all its subtypes.

Example 6.3. Purging all instances of an entity from the index

FullTextSession fullTextSession = Search.getFullTextSession(session);

Transaction tx = fullTextSession.beginTransaction();
fullTextSession.purgeAll( Customer.class );

//optionally optimize the index

//fullTextSession.getSearchFactory().optimize( Customer.class );

tx.commit(); //index changes are applied at commit time

It is recommended to optimize the index after such an operation.

Note

Methods index, purge and purgeAll are available on FullTextEntityManager as well.

Note

All manual indexing methods (index, purge and purgeAll) only affect the index, not the database, nevertheless they are transactional and as such they won't be applied until the transaction is successfully committed, or you make use of flushToIndexes.

6.3. Rebuilding the whole index

If you change the entity mapping to the index, chances are that the whole Index needs to be updated; For example if you decide to index a an existing field using a different analyzer you'll need to rebuild the index for affected types. Also if the Database is replaced (like restored from a backup, imported from a legacy system) you'll want to be able to rebuild the index from existing data. Hibernate Search provides two main strategies to choose from:

Using FullTextSession.flushToIndexes() periodically, while using FullTextSession.index() on all entities.
Use a MassIndexer.

6.3.1. Using flushToIndexes()

This strategy consists in 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 contraints. 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 don't empty the queue periodically: to do this you can 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 6.4. 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();

Note

hibernate.search.default.worker.batch_size has been deprecated in favor of this explicit API which provides better control

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

6.3.2. Using a MassIndexer

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: making queries to the index is not recommended when a MassIndexer is busy.

Example 6.5. Index rebuilding using a MassIndexer

fullTextSession.createIndexer().startAndWait();

This will rebuild the index, deleting it and then reloading all entities from the database. Although it's simple to use, some tweaking is recommended to speed up the process: there are several parameters configurable.

Warning

During the progress of a MassIndexer the content of the index is undefined! If a query is performed while the MassIndexer is working most likely some results will be missing.

Example 6.6. Using a tuned MassIndexer

fullTextSession

 .createIndexer( User.class )

 .batchSizeToLoadObjects( 25 )

 .cacheMode( CacheMode.NORMAL )

 .threadsToLoadObjects( 5 )

 .threadsForIndexWriter( 3 )

 .idFetchSize( 150 )

 .threadsForSubsequentFetching( 20 )

 .progressMonitor( monitor ) //a MassIndexerProgressMonitor implementation

 .startAndWait();

This will rebuild the index of all User instances (and subtypes), and will create 5 parallel threads to load the User instances using batches of 25 objects per query; these loaded User instances are then pipelined to 20 parallel threads to load the attached lazy collections of User containing some information needed for the index. Finally, 3 parallel threads are being used to Analyze the text and write to the 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: it might increase performance if the main entity is relating to enum-like data included in the index.

Tip

The "sweet spot" of number of threads to achieve best performance is highly dependent on your overall architecture, database design and even data values. To find out the best number of threads for your application it is recommended to use a profiler: all internal thread groups have meaningful names to be easily identified with most tools.

Note

The MassIndexer was designed for speed and is unaware of transactions, so there is no need to begin one or committing. Also because it is not transactional it is not recommended to let users use the system during it's processing, as it is unlikely people will be able to find results and the system load might be too high anyway.

Other parameters which affect indexing time and memory consumption are:

hibernate.search.[default|<indexname>].exclusive_index_use
hibernate.search.[default|<indexname>].indexwriter.max_buffered_docs
hibernate.search.[default|<indexname>].indexwriter.max_merge_docs
hibernate.search.[default|<indexname>].indexwriter.merge_factor
hibernate.search.[default|<indexname>].indexwriter.merge_min_size
hibernate.search.[default|<indexname>].indexwriter.merge_max_size
hibernate.search.[default|<indexname>].indexwriter.merge_max_optimize_size
hibernate.search.[default|<indexname>].indexwriter.merge_calibrate_by_deletes
hibernate.search.[default|<indexname>].indexwriter.ram_buffer_size
hibernate.search.[default|<indexname>].indexwriter.term_index_interval

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

All .indexwriter parameters are Lucene specific and Hibernate Search is just passing these parameters through - see Section 3.6, “Tuning Lucene indexing performance” for more details.

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 "optimisation" set idFetchSize to Integer.MIN_VALUE.

From time to time, the Lucene index needs to be optimized. The process is essentially a defragmentation. Until an optimization is triggered Lucene only marks deleted documents as such, no physical deletions are applied. During the optimization process the deletions will be applied which also effects the number of files in the Lucene Directory.

Optimizing the Lucene index speeds up searches but has no effect on the indexation (update) performance. During an optimization, searches can be performed, but will most likely be slowed down. All index updates will be stopped. It is recommended to schedule optimization:

on an idle system or when the searches are less frequent
after a lot of index modifications

When using a MassIndexer (see Section 6.3.2, “Using a MassIndexer”) it will optimize involved indexes by default at the start and at the end of processing; you can change this behavior by using respectively MassIndexer.optimizeAfterPurge and MassIndexer.optimizeOnFinish.

7.1. Automatic optimization

Hibernate Search can automatically optimize an index after:

a certain amount of operations (insertion, deletion)
or a certain amount of transactions

The configuration for automatic index optimization can be defined on a global level or per index:

Example 7.1. Defining automatic optimization parameters

hibernate.search.default.optimizer.operation_limit.max = 1000
hibernate.search.default.optimizer.transaction_limit.max = 100
hibernate.search.Animal.optimizer.transaction_limit.max = 50

An optimization will be triggered to the Animal index as soon as either:

the number of additions and deletions reaches 1000
the number of transactions reaches 50 (hibernate.search.Animal.optimizer.transaction_limit.max having priority over hibernate.search.default.optimizer.transaction_limit.max)

If none of these parameters are defined, no optimization is processed automatically.

7.2. Manual optimization

You can programmatically optimize (defragment) a Lucene index from Hibernate Search through the SearchFactory:

Example 7.2. Programmatic index optimization

FullTextSession fullTextSession = Search.getFullTextSession(regularSession);

SearchFactory searchFactory = fullTextSession.getSearchFactory();


searchFactory.optimize(Order.class);

// or

searchFactory.optimize();

The first example optimizes the Lucene index holding Orders; the second, optimizes all indexes.

Note

searchFactory.optimize() has no effect on a JMS backend. You must apply the optimize operation on the Master node.

7.3. Adjusting optimization

Apache Lucene has a few parameters to influence how optimization is performed. Hibernate Search exposes those parameters.

Further index optimization parameters include:

hibernate.search.[default|<indexname>].indexwriter.max_buffered_docs
hibernate.search.[default|<indexname>].indexwriter.max_merge_docs
hibernate.search.[default|<indexname>].indexwriter.merge_factor
hibernate.search.[default|<indexname>].indexwriter.ram_buffer_size
hibernate.search.[default|<indexname>].indexwriter.term_index_interval

See Section 3.6, “Tuning Lucene indexing performance” for more details.

Chapter 8. Monitoring

8.1. JMX

8.1.1. StatisticsInfoMBean
8.1.2. IndexControlMBean
8.1.3. IndexingProgressMonitorMBean

Hibernate Search offers access to a Statistics object via SearchFactory.getStatistics(). It allows you for example to determine which classes are indexed and how many entities are in the index. This information is always available. However, by specifying the hibernate.search.generate_statistics property in your configuration you can also collect total and average Lucene query and object loading timings.

8.1. JMX

You can also enable access to the statistics via JMX. Setting the property hibernate.search.jmx_enabled will automatically register the StatisticsInfoMBean. Depending on your the configuration the IndexControlMBean and IndexingProgressMonitorMBean will also be registered. Lets have a closer look at the different MBeans.

Tip

If you want to access your JMX beans remotely via JConsole make sure to set the system property com.sun.management.jmxremote to true.

8.1.1. StatisticsInfoMBean

This MBean gives you access to Statistics object as desribed in the previous section.

8.1.2. IndexControlMBean

This MBean allows to build, optimize and purge the index for a given entity. Indexing occurs via the mass indexing API (see Section 6.3.2, “Using a MassIndexer”). A requirement for this bean to be registered in JMX is, that the Hibernate SessionFactory is bound to JNDI via the hibernate.session_factory_name property. Refer to the Hibernate Core manual for more information on how to configure JNDI. The IndexControlMBean and its API are for now experimental.

8.1.3. IndexingProgressMonitorMBean

This MBean is an implementation MassIndexerProgressMonitor interface. If hibernate.search.jmx_enabled is enabled and the mass indexer API is used the indexing progress can be followed via this bean. The bean will only be bound to JMX while indexing is in progress. Once indexing is completed the MBean is not longer available.

Chapter 9. Advanced features

9.1. Accessing the SearchFactory

9.2. Using an IndexReader

9.3. Accessing a Lucene Directory

9.4. Sharding indexes

9.5. Sharing indexes

9.6. Using external services

9.6.1. Exposing a service
9.6.2. Using a service

9.7. Customizing Lucene's scoring formula

In this final chapter we are offering a smorgasbord of tips and tricks which might become useful as you dive deeper and deeper into Hibernate Search.

9.1. Accessing the SearchFactory

The SearchFactory object keeps track of the underlying Lucene resources for Hibernate Search. It is a convenient way to access Lucene natively. The SearchFactory can be accessed from a FullTextSession:

Example 9.1. Accessing the SearchFactory

FullTextSession fullTextSession = Search.getFullTextSession(regularSession);

SearchFactory searchFactory = fullTextSession.getSearchFactory();

9.2. Using an IndexReader

Queries in Lucene are executed on an IndexReader. Hibernate Search might cache index readers to maximize performance, or provide other efficient strategies to retrieve an updated IndexReader minimizing IO operations. Your code can access these cached resources, but you have to follow some "good citizen" rules.

Example 9.2. Accessing an IndexReader

IndexReader reader = searchFactory.getIndexReaderAccessor().open(Order.class);

try {

   //perform read-only operations on the reader

}

finally {

   searchFactory.getIndexReaderAccessor().close(reader);

}

In this example the SearchFactory figures out which indexes are needed to query this entity (considering a Sharding strategy). Using the configured ReaderProvider (described in Reader strategy) on each index, it returns a compound IndexReader on top of all involved indexes. Because this IndexReader is shared amongst several clients, you must adhere to the following rules:

Never call indexReader.close(), but always call readerProvider.closeReader(reader), preferably in a finally block.
Don't use this IndexReader for modification operations (it's a readonly IndexReader, you would get an exception).

Aside from those rules, you can use the IndexReader freely, especially to do native Lucene queries. Using the shared IndexReaders will make most queries more efficient than by opening one directly from - for example - the filesystem.

As an alternative to the method open(Class... types) you can use open(String... indexNames); in this case you pass in one or more index names; using this strategy you can also select a subset of the indexes for any indexed type if sharding is used.

Example 9.3. Accessing an IndexReader by index names

IndexReader reader = searchFactory.getIndexReaderAccessor().open("Products.1", "Products.3");

9.3. Accessing a Lucene Directory

A Directory is the most common abstraction used by Lucene to represent the index storage; Hibernate Search doesn't interact directly with a Lucene Directory but abstracts these interactions via an IndexManager: an index does not necessarily need to be implemented by a Directory.

If you know your index is represented as a Directory and need to access it, you can get a reference to the Directory via the IndexManager. Cast the IndexManager to a DirectoryBasedIndexManager and then use getDirectoryProvider().getDirectory() to get a reference to the underlying Directory. This is not recommended, we would encourage to use the IndexReader instead.

9.4. Sharding indexes

In some cases it can be useful to split (shard) the indexed data of a given entity into several Lucene indexes.

Warning

This solution is not recommended unless there is a pressing need. Searches will be slower as all shards have to be opened for a single search. Don't do it until you have a real use case!

Possible use cases for sharding are:

A single index is so huge that index update times are slowing the application down.
A typical search will only hit a sub-set of the index, such as when data is naturally segmented by customer, region or application.

By default sharding is not enabled unless the number of shards is configured. To do this use the hibernate.search.<indexName>.sharding_strategy.nbr_of_shards property as seen in Example 9.4, “Enabling index sharding”. In this example 5 shards are enabled.

Example 9.4. Enabling index sharding

hibernate.search.<indexName>.sharding_strategy.nbr_of_shards 5

Responsible for splitting the data into sub-indexes is the IndexShardingStrategy. The default sharding strategy splits the data according to the hash value of the id string representation (generated by the FieldBridge). This ensures a fairly balanced sharding. You can replace the default strategy by implementing a custom IndexShardingStrategy. To use your custom strategy you have to set the hibernate.search.<indexName>.sharding_strategy property.

Example 9.5. Specifying a custom sharding strategy

hibernate.search.<indexName>.sharding_strategy my.shardingstrategy.Implementation

The IndexShardingStrategy also allows for optimizing searches by selecting which shard to run the query against. By activating a filter (see Section 5.3.1, “Using filters in a sharded environment”), a sharding strategy can select a subset of the shards used to answer a query (IndexShardingStrategy.getIndexManagersForQuery) and thus speed up the query execution.

Each shard has an independent IndexManager and so can be configured to use a different directory provider and backend configurations. The IndexManager index names for the Animal entity in Example 9.6, “Sharding configuration for entity Animal” are Animal.0 to Animal.4. In other words, each shard has the name of it's owning index followed by . (dot) and its index number (see also Section 3.3, “Directory configuration”).

Example 9.6. Sharding configuration for entity Animal

hibernate.search.default.indexBase /usr/lucene/indexes

hibernate.search.Animal.sharding_strategy.nbr_of_shards 5
hibernate.search.Animal.directory_provider filesystem
hibernate.search.Animal.0.indexName Animal00
hibernate.search.Animal.3.indexBase /usr/lucene/sharded
hibernate.search.Animal.3.indexName Animal03

In Example 9.6, “Sharding configuration for entity Animal”, the configuration uses the default id string hashing strategy and shards the Animal index into 5 sub-indexes. All sub-indexes are filesystem instances and the directory where each sub-index is stored is as followed:

for sub-index 0: /usr/lucene/indexes/Animal00 (shared indexBase but overridden indexName)
for sub-index 1: /usr/lucene/indexes/Animal.1 (shared indexBase, default indexName)
for sub-index 2: /usr/lucene/indexes/Animal.2 (shared indexBase, default indexName)
for sub-index 3: /usr/lucene/shared/Animal03 (overridden indexBase, overridden indexName)
for sub-index 4: /usr/lucene/indexes/Animal.4 (shared indexBase, default indexName)

When implementing a IndexShardingStrategy any field can be used to determine the sharding selection. Consider that to handle deletions, purge and purgeAll operations, the implementation might need to return one or more indexes without being able to read all the field values or the primary identifier; in case the information is not enough to pick a single index, all indexes should be returned, so that the delete operation will be propagated to all indexes potentially containing the documents to be deleted.

9.5. Sharing indexes

It is technically possible to store the information of more than one entity into a single Lucene index. There are two ways to accomplish this:

Configuring the underlying directory providers to point to the same physical index directory. In practice, you set the property hibernate.search.[fully qualified entity name].indexName to the same value. As an example let’s use the same index (directory) for the Furniture and Animal entity. We just set indexName for both entities to for example “Animal”. Both entities will then be stored in the Animal directory.
```
hibernate.search.org.hibernate.search.test.shards.Furniture.indexName = Animal
hibernate.search.org.hibernate.search.test.shards.Animal.indexName = Animal
```
Setting the @Indexed annotation’s index attribute of the entities you want to merge to the same value. If we again wanted all Furniture instances to be indexed in the Animal index along with all instances of Animal we would specify @Indexed(index="Animal") on both Animal and Furniture classes.
Note
This is only presented here so that you know the option is available. There is really not much benefit in sharing indexes.

9.6. Using external services

Any of the pluggable contracts we have seen so far allows for the injection of a service. The most notable example being the DirectoryProvider. The full list is:

DirectoryProvider
ReaderProvider
OptimizerStrategy
BackendQueueProcessor
Worker
ErrorHandler
MassIndexerProgressMonitor

Some of these components need to access a service which is either available in the environment or whose lifecycle is bound to the SearchFactory. Sometimes, you even want the same service to be shared amongst several instances of these contract. One example is the ability the share an Infinispan cache instance between several directory providers running in different JVMs to store the various indexes using the same underlying infrastructure; this provides real-time replication of indexes across nodes.

9.6.1. Exposing a service

To expose a service, you need to implement org.hibernate.search.spi.ServiceProvider<T>. T is the type of the service you want to use. Services are retrieved by components via their ServiceProvider class implementation.

9.6.1.1. Managed services

If your service ought to be started when Hibernate Search starts and stopped when Hibernate Search stops, you can use a managed service. Make sure to properly implement the start and stop methods of ServiceProvider. When the service is requested, the getService method is called.

Example 9.7. Example of ServiceProvider implementation

public class CacheServiceProvider implements ServiceProvider<Cache> {

    private CacheManager manager;


    public void start(Properties properties) {

        //read configuration

        manager = new CacheManager(properties);

    }


    public Cache getService() {

        return manager.getCache(DEFAULT);

    }


    void stop() {

        manager.close();

    }

}

Note

The ServiceProvider implementation must have a no-arg constructor.

To be transparently discoverable, such service should have an accompanying META-INF/services/org.hibernate.search.spi.ServiceProvider whose content list the (various) service provider implementation(s).

Example 9.8. Content of META-INF/services/org.hibernate.search.spi.ServiceProvider

com.acme.infra.hibernate.CacheServiceProvider

9.6.1.2. Provided services

Alternatively, the service can be provided by the environment bootstrapping Hibernate Search. For example, Infinispan which uses Hibernate Search as its internal search engine can pass the CacheContainer to Hibernate Search. In this case, the CacheContainer instance is not managed by Hibernate Search and the start/stop methods of its corresponding service provider will not be used.

Note

Provided services have priority over managed services. If a provider service is registered with the same ServiceProvider class as a managed service, the provided service will be used.

The provided services are passed to Hibernate Search via the SearchConfiguration interface (getProvidedServices).

Important

Provided services are used by frameworks controlling the lifecycle of Hibernate Search and not by traditional users.

If, as a user, you want to retrieve a service instance from the environment, use registry services like JNDI and look the service up in the provider.

9.6.2. Using a service

Many of of the pluggable contracts of Hibernate Search can use services. Services are accessible via the BuildContext interface.

Example 9.9. Example of a directory provider using a cache service

public CustomDirectoryProvider implements DirectoryProvider<RAMDirectory> {

    private BuildContext context;


    public void initialize(

        String directoryProviderName, 

        Properties properties, 

        BuildContext context) {

        //initialize

        this.context = context;

    }


    public void start() {

        Cache cache = context.requestService( CacheServiceProvider.class );

        //use cache

    }


    public RAMDirectory getDirectory() {

        // use cache

    }


    public stop() {

        //stop services

        context.releaseService( CacheServiceProvider.class );

    } 

}

When you request a service, an instance of the service is served to you. Make sure to then release the service. This is fundamental. Note that the service can be released in the DirectoryProvider.stop method if the DirectoryProvider uses the service during its lifetime or could be released right away of the service is simply used at initialization time.

9.7. Customizing Lucene's scoring formula

Lucene allows the user to customize its scoring formula by extending org.apache.lucene.search.Similarity. The abstract methods defined in this class match the factors of the following formula calculating the score of query q for document d:

score(q,d) = coord(q,d) · queryNorm(q) · ∑ _{t in q} ( tf(t in d) · idf(t) ² · t.getBoost() · norm(t,d) )

Factor	Description
tf(t ind)	Term frequency factor for the term (t) in the document (d).
idf(t)	Inverse document frequency of the term.
coord(q,d)	Score factor based on how many of the query terms are found in the specified document.
queryNorm(q)	Normalizing factor used to make scores between queries comparable.
t.getBoost()	Field boost.
norm(t,d)	Encapsulates a few (indexing time) boost and length factors.

It is beyond the scope of this manual to explain this formula in more detail. Please refer to Similarity's Javadocs for more information.

Hibernate Search provides three ways to modify Lucene's similarity calculation.

First you can set the default similarity by specifying the fully specified classname of your Similarity implementation using the property hibernate.search.similarity. The default value is org.apache.lucene.search.DefaultSimilarity.

You can also override the similarity used for a specific index by setting the similarity property

hibernate.search.default.similarity my.custom.Similarity

Finally you can override the default similarity on class level using the @Similarity annotation.

@Entity
@Indexed
@Similarity(impl = DummySimilarity.class)
public class Book {
...
}

As an example, let's assume it is not important how often a term appears in a document. Documents with a single occurrence of the term should be scored the same as documents with multiple occurrences. In this case your custom implementation of the method tf(float freq) should return 1.0.

Warning

When two entities share the same index they must declare the same Similarity implementation. Classes in the same class hierarchy always share the index, so it's not allowed to override the Similarity implementation in a subtype.

Likewise, it does not make sense to define the similarity via the index setting and the class-level setting as they would conflict. Such a configuration will be rejected.

Chapter 10. Further reading

Last but not least, a few pointers to further information. He highly recommend you to get a copy Hibernate Search in Action. This excellent book covers Hibernate Search in much more depth than this online documentation can and has a great range of additional examples. If you want to increase your knowledge in Lucene we recommend Lucene in Action (Second Edition). Because Hibernate Search's functionality is tightly coupled to Hibernate Core is it a good idea to understand Hibernate in more detail. Start with the online documentation or get hold of a copy of Java Persistence with Hibernate.

If you have any further questions regarding Hibernate Search or want to share some of your use cases have a look at the Hibernate Search Wiki and the Hibernate Search Forum. We are looking forward hearing from you.

In case you would like to report a bug use the Hibernate Search JIRA instance. Feedback is always welcome!