Hibernate.orgCommunity Documentation

Hibernate Search

Apache Lucene™ Integration

Reference Guide


1. Getting started
1.1. System Requirements
1.2. Using Maven
1.3. Configuration
1.4. Indexing
1.5. Searching
1.6. Analyzer
1.7. What's next
2. Architecture
2.1. Overview
2.2. Back end
2.2.1. Back end types
2.2.2. Work execution
2.3. Reader strategy
2.3.1. Shared
2.3.2. Not-shared
2.3.3. Custom
3. Configuration
3.1. Directory configuration
3.2. Sharding indexes
3.3. Sharing indexes (two entities into the same directory)
3.4. Worker configuration
3.5. JMS Master/Slave configuration
3.5.1. Slave nodes
3.5.2. Master node
3.6. JGroups Master/Slave configuration
3.6.1. Slave nodes
3.6.2. Master node
3.6.3. JGroups channel configuration
3.7. Reader strategy configuration
3.8. Enabling Hibernate Search and automatic indexing
3.8.1. Enabling Hibernate Search
3.8.2. Automatic indexing
3.9. Tuning Lucene indexing performance
3.10. LockFactory configuration
3.11. Exception Handling Configuration
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
4.1.4. Boost factor
4.1.5. Dynamic boost factor
4.1.6. Analyzer
4.2. Property/Field Bridge
4.2.1. Built-in bridges
4.2.2. Custom Bridge
4.3. Providing your own id
4.3.1. The ProvidedId annotation
4.4. Programmatic API
4.4.1. Mapping an entity as indexable
4.4.2. Adding DocumentId to indexed entity
4.4.3. Defining analyzers
4.4.4. Defining full text filter definitions
4.4.5. Defining fields for indexing
4.4.6. Programmatically defining embedded entities
4.4.7. Contained In definition
4.4.8. Date/Calendar Bridge
4.4.9. Defining bridges
4.4.10. Mapping class bridge
4.4.11. Mapping dynamic boost
5. Querying
5.1. Building queries
5.1.1. Building a Lucene query
5.1.2. 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. Optimizing the query process
5.5. Native Lucene Queries
6. Manual index changes
6.1. Adding instances to the Index
6.2. Deleting instances from the Index: Purging
6.3. Rebuilding the whole Index
6.3.1. Using flushToIndexes()
6.3.2. Using a MassIndexer
7. Index Optimization
7.1. Automatic optimization
7.2. Manual optimization
7.3. Adjusting optimization
8. Advanced features
8.1. SearchFactory
8.2. Accessing a Lucene Directory
8.3. Using an IndexReader
8.4. Customizing Lucene's scoring formula

Full text search engines like Apache Lucene are very powerful technologies to add efficient free text search capabilities to applications. However, Lucene suffers several mismatches when dealing with object domain model. Amongst other things indexes have to be kept up to date and mismatches between index structure and domain model as well as query mismatches have to be avoided.

Hibernate Search addresses these shortcomings - it indexes your domain model with the help of a few annotations, takes care of database/index synchronization and brings back regular managed objects from free text queries. To achieve this Hibernate Search is combining the power of Hibernate and Apache Lucene.

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.

Instead of managing all dependencies manually, maven users have the possibility to use the JBoss maven repository. Add the following to your Maven settings.xml file (see also Maven Getting Started):

Then add the following dependencies to your pom.xml:

Only the hibernate-search dependency is mandatory, because it contains together with its required transitive dependencies all required classes needed to use Hibernate Search. hibernate-entitymanager is only required if you want to use Hibernate Search in conjunction with JPA.

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:

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 subclass of DirectoryProvider called FSDirectoryProvider. 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 3.1, “Directory configuration”). Next to the directory provider you also have to specify the default root 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.

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. In fact since the 3.1.0 release of Hibernate Search @DocumentId 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.TOKENIZED will ensure that the text will be tokenized using the default Lucene analyzer. Usually, tokenizing 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 second 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 (Section, “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 - Store.NO - is recommended since it returns managed objects whereas projections only return object arrays.

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 4.2, “Property/Field Bridge”.

This leaves us with @IndexedEmbedded. This annotation is used to index associated entities (@ManyToMany, @*ToOne and @Embedded) 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 wil 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”.

Now it is time to execute a first search. The general approach is to create a native Lucene query 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.

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 search process. Hibernate Search offers several ways to configure the analyzer to use (see Section 4.1.6, “Analyzer”):

  • 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.

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 4.1.6, “Analyzer”) and field bridges (Section 4.2, “Property/Field Bridge”), both important features required for more fine-grained indexing. More advanced topics cover clustering (Section 3.5, “JMS Master/Slave configuration”) and large indexes handling (Section 3.2, “Sharding indexes”).

Hibernate Search consists of an indexing component and 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 the index updates are handled without you having to use the Apache Lucene APIs (see Section 3.8, “Enabling Hibernate Search and automatic indexing”).

To interact with Apache Lucene indexes, Hibernate Search has the notion of DirectoryProviders. A directory provider will manage a given Lucene Directory type. You can configure directory providers to adjust the directory target (see Section 3.1, “Directory configuration”).

Hibernate Search uses the Lucene index to search an entity and return a list 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 way a HQL, JPA-QL or native queries would do.

To be more efficient, Hibernate Search batches the write interactions with the Lucene index. There is currently two types of batching depending on the expected scope. Outside a transaction, the index update operation is executed right after the actual database operation. This scope is really a no scoping setup and no batching is performed. However, it is recommended - for both your database and Hibernate Search - to execute your operation in a transaction be it JDBC or JTA. When in a 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 scopes (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.


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


Depending on user demand, additional scoping will be considered, the pluggability mechanism being already in place.

Hibernate Search offers the ability to let the scoped work being processed by different back ends. Two back ends are provided out of the box for two different scenarios.

Apache Lucene has a notion of Directory to store the index files. The Directory implementation can be customized, but Lucene comes bundled with a file system (FSDirectoryProvider) and an in memory (RAMDirectoryProvider) implementation. DirectoryProviders are the Hibernate Search abstraction around a Lucene Directory and handle the configuration and the initialization of the underlying Lucene resources. Table 3.1, “List of built-in Directory Providers” shows the list of the directory providers bundled with Hibernate Search.

Table 3.1. List of built-in Directory Providers

org.hibernate.search.store.RAMDirectoryProviderMemory based directory, the directory will be uniquely identified (in the same deployment unit) by the @Indexed.index elementnone
org.hibernate.search.store.FSDirectoryProviderFile 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 3.10, “LockFactory configuration”


File system based directory. Like FSDirectoryProvider. 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 3.10, “LockFactory configuration”


File system based directory. Like FSDirectoryProvider, 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 3.10, “LockFactory configuration”

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.

Each indexed entity is associated to a Lucene index (an index can be shared by several entities but this is not usually the case). You can configure the index through properties prefixed by hibernate.search.indexname . Default properties inherited to all indexes can be defined using the prefix hibernate.search.default.

To define the directory provider of a given index, you use the hibernate.search.indexname.directory_provider

applied on

will create a file system directory in /usr/lucene/indexes/Status where the Status entities will be indexed, and use an in memory directory named Rules where Rule entities will be indexed.

You can easily define common rules like the directory provider and base directory, and override those defaults later on on a per index basis.

Writing your own DirectoryProvider, you can utilize this configuration mechanism as well.

In some cases, it is necessary to split (shard) the indexing data of a given entity type into several Lucene indexes. This solution is not recommended unless there is a pressing need because by default, searches will be slower as all shards have to be opened for a single search. In other words don't do it until you have problems :)

For example, sharding may be desirable if:

Hibernate Search allows you to index a given entity type into several sub indexes. Data is sharded into the different sub indexes thanks to an IndexShardingStrategy. By default, no sharding strategy is enabled, unless the number of shards is configured. To configure the number of shards use the following property

This will use 5 different shards.

The default sharding strategy, when shards are set up, splits the data according to the hash value of the id string representation (generated by the Field Bridge). This ensures a fairly balanced sharding. You can replace the strategy by implementing IndexShardingStrategy and by setting the following property

Using a custom IndexShardingStrategy implementation, it's possible to define what shard a given entity is indexed to.

It also allows for optimizing searches by selecting which shard to run the query onto. 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.getDirectoryProvidersForQuery) and thus speed up the query execution.

Each shard has an independent directory provider configuration as described in Section 3.1, “Directory configuration”. The DirectoryProvider default name for the previous example are <indexName>.0 to <indexName>.4. In other words, each shard has the name of it's owning index followed by . (dot) and its index number.

This configuration uses the default id string hashing strategy and shards the Animal index into 5 subindexes. All subindexes are FSDirectoryProvider instances and the directory where each subindex is stored is as followed:

  • for subindex 0: /usr/lucene/indexes/Animal00 (shared indexBase but overridden indexName)

  • for subindex 1: /usr/lucene/indexes/Animal.1 (shared indexBase, default indexName)

  • for subindex 2: /usr/lucene/indexes/Animal.2 (shared indexBase, default indexName)

  • for subindex 3: /usr/lucene/shared/Animal03 (overridden indexBase, overridden indexName)

  • for subindex 4: /usr/lucene/indexes/Animal.4 (shared indexBase, default indexName)

It is possible to refine how Hibernate Search interacts with Lucene through the worker configuration. The work can be executed to the Lucene directory or sent to a JMS queue for later processing. When processed to the Lucene directory, the work can be processed synchronously or asynchronously to the transaction commit.

You can define the worker configuration using the following properties

Table 3.2. worker configuration

hibernate.search.worker.backendOut of the box support for the Apache Lucene back end and the JMS back end. Default to lucene. Supports also jms, blackhole, jgroupsMaster and jgroupsSlave.
hibernate.search.worker.executionSupports synchronous and asynchronous execution. Default to sync. Supports also async.
hibernate.search.worker.thread_pool.sizeDefines the number of threads in the pool. useful only for asynchronous execution. Default to 1.
hibernate.search.worker.buffer_queue.maxDefines 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.
hibernate.search.worker.jndi.*Defines the JNDI properties to initiate the InitialContext (if needed). JNDI is only used by the JMS back end.
hibernate.search.worker.jms.connection_factoryMandatory for the JMS back end. Defines the JNDI name to lookup the JMS connection factory from (/ConnectionFactory by default in JBoss AS)
hibernate.search.worker.jms.queueMandatory for the JMS back end. Defines the JNDI name to lookup the JMS queue from. The queue will be used to post work messages.
hibernate.search.worker.jgroups.clusterNameOptional for JGroups back end. Defines the name of JGroups channel.
hibernate.search.worker.jgroups.configurationFileOptional JGroups network stack configuration. Defines the name of a JGroups configuration file, which must exist on classpath.
hibernate.search.worker.jgroups.configurationXmlOptional JGroups network stack configuration. Defines a String representing JGroups configuration as XML.
hibernate.search.worker.jgroups.configurationStringOptional JGroups network stack configuration. Provides JGroups configuration in plain text.

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

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

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

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

This example inherits from the abstract JMS controller class available in the Hibernate Search source code and implements a JavaEE 5 MDB. This implementation is given as an example and, while most likely be more complex, 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.

Describes how to configure JGroups Master/Slave back end. Configuration examples illustrated in JMS Master/Slave configuration section (Section 3.5, “JMS Master/Slave configuration”) also apply here, only a different backend needs to be set.

Optionally configuration for JGroups transport protocols (UDP, TCP) and channel name can be defined. It can be applied to both master and slave nodes. There are several ways to configure JGroups transport details. If it is not defined explicity, configuration found in the flush-udp.xml file is used.

Master and slave nodes communicate over JGroups channel that is identified by this same name. Name of the channel can be defined explicity, if not default HSearchCluster is used.

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.reader.strategy = not-shared

Adding this property switches to the not-shared strategy.

Or if you have a custom reader strategy:

hibernate.search.reader.strategy = my.corp.myapp.CustomReaderProvider

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

Hibernate Search is enabled out of the box when using Hibernate Annotations or Hibernate EntityManager. 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.

To enable Hibernate Search in Hibernate Core (ie. if you don't use Hibernate Annotations), add the FullTextIndexEventListener for the following six Hibernate events and also add it after the default DefaultFlushEventListener, as in the following example.

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


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.

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 two sets of parameters allowing for different performance settings depending on the use case. During indexing operations triggered by database modifications, the parameters are grouped by the transaction keyword:


When indexing occurs via FullTextSession.index() or via a MassIndexer (see Section 6.3, “Rebuilding the whole Index”), the used properties are those grouped under the batch keyword:


If no value is set for a .batch value in a specific shard configuration, Hibernate Search will look at the index section, then at the default section:

hibernate.search.Animals.2.indexwriter.transaction.max_merge_docs 10
hibernate.search.Animals.2.indexwriter.transaction.merge_factor 20
hibernate.search.default.indexwriter.batch.max_merge_docs 100

This configuration will result in these settings applied to the second shard of Animals index:

  • transaction.max_merge_docs = 10

  • batch.max_merge_docs = 100

  • transaction.merge_factor = 20

  • batch.merge_factor = 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, so the listed values in the following table actually depend on the version of Lucene you are using; values shown are relative to version 2.4. For more information about Lucene indexing performances, please refer to the Lucene documentation.


Previous versions had the batch parameters inherit from transaction properties. This needs now to be explicitly set.

Table 3.3. List of indexing performance and behavior properties

PropertyDescriptionDefault Value

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 in writing changes to the index.

false (releases locks as soon as possible)

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)

Controls the amount of documents buffered in memory during indexing. The bigger the more RAM is consumed.

Disabled (flushes by RAM usage)

The maximum number of terms that will be indexed for a single field. This limits the amount of memory required for indexing so that very large data will not crash the indexing process by running out of memory. This setting refers to the number of running terms, not to the number of different terms.

This silently truncates large documents, excluding from the index all terms that occur further in the document. If you know your source documents are large, be sure to set this value high enough to accommodate the expected size. If you set it to Integer.MAX_VALUE, then the only limit is your memory, but you should anticipate an OutOfMemoryError.

If setting this value in batch differently than in transaction you may get different data (and results) in your index depending on the indexing mode.


Defines the largest number of documents allowed in a segment. Larger values are best for batched indexing and speedier searches. Small values are best for transaction indexing.

Unlimited (Integer.MAX_VALUE)

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 no be lower than 2.


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

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.

hibernate.search.[default|<indexname>].indexwriter.[transaction|batch].use_compound_fileThe 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.



When your architecture permits it, always set hibernate.search.default.exclusive_index_use=true as it greatly improves efficiency in index writing.

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 you 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. As opposite to setting the hibernate.search.indexing_strategy to manual when using blackhole it will possibly load more data to rebuild the index from associated entities.

hibernate.search.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.

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

Lucene Directories 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, or set it to the fully qualified name of an implementation of org.hibernate.search.store.LockFactoryFactory; Implementing this interface you can provide a custom org.apache.lucene.store.LockFactory.

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

All the metadata information needed to index entities is described through annotations. There is no need for xml mapping files. In fact there is currently no xml configuration option available (see HSEARCH-210). You can still use Hibernate mapping files for the basic Hibernate configuration, but the Hibernate Search specific configuration has to be expressed via annotations.

First, 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):

The index attribute tells Hibernate what the Lucene directory name is (usually a directory on your file system). It is recommended to define a base directory for all Lucene indexes using the hibernate.search.default.indexBase property in your configuration file. Alternatively you can specify a base directory per indexed entity by specifying hibernate.search.<index>.indexBase, where <index> is the fully qualified classname of the indexed entity. Each entity instance will be represented by a Lucene Document inside the given index (aka Directory).

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. When indexing an element to a Lucene document you can specify how it is indexed:

  • 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, “Projection” for more information), 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 how the element is indexed and the type of information store. The different values are Index.NO (no indexing, ie cannot be found by a query), Index.TOKENIZED (use an analyzer to process the property), Index.UN_TOKENIZED (no analyzer pre-processing), Index.NO_NORMS (do not store the normalization data). The default value is TOKENIZED.

  • termVector: describes collections of term-frequency pairs. This attribute enables term vectors being stored during indexing so they are available within documents. The default value is TermVector.NO.

    The different values of this attribute are:

    TermVector.YESStore the term vectors of each document. This produces two synchronized arrays, one contains document terms and the other contains the term's frequency.
    TermVector.NODo not store term vectors.
    TermVector.WITH_OFFSETSStore 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_POSITIONSStore 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_OFFSETSStore the term vector, token position and offset information. This is a combination of the YES, WITH_OFFSETS and WITH_POSITIONS.

Whether or not you want to store the original data in the index depends on how you wish to use the index query result. For a regular Hibernate Search usage storing is not necessary. However you might want to store some fields to subsequently project them (see Section, “Projection” for more information).

Whether or not you want to tokenize a property depends on whether you wish to search the element as is, or by the words it contains. It make sense to tokenize a text field, but probably not a date field.


Fields used for sorting must not be tokenized.

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 Hibernate Annotations and you have specified @Id you can omit @DocumentId. The chosen entity id will also be used as document id.

Example 4.2, “Adding @DocumentId ad @Field annotations to an indexed entity” define an index with three fields: id , Abstract and text . Note that by default the field name is decapitalized, following the JavaBean specification

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 the following example 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).

In this example, 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. This is enabled by the @IndexedEmbedded annotation.

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.

@ContainedIn is only useful on associations pointing to entities as opposed to embedded (collection of) objects.

Let's make our example a bit more complex:

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 the previous example, 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.


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 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.


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.

The @Boost annotation used in Section 4.1.4, “Boost factor” defines a static boost factor which is 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 4.8, “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.


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 as described in Section 4.1.4, “Boost factor”.

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).

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.


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.

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

This separation of tasks - a list of char filters, and a tokenizer followed by a list of filters - allows for easy reuse of each individual component and 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. Make sure to add solr-core.jar and solr-solrj.jar to your classpath to use analyzer definitions. In case you also want to use the snowball stemmer also include the lucene-snowball.jar. Other Solr analyzers might depend on more libraries. For example, the PhoneticFilterFactory depends on commons-codec. Your distribution of Hibernate Search provides these dependencies in its lib directory.

A char filter is defined by its factory which is responsible for building the char filter and using the optional list of parameters. 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. A tokenizer is also defined by its factory. This example use the standard tokenizer. A filter is defined by its factory which is responsible for creating the filter instance using the optional parameters. In our example, the StopFilter filter is built reading the dedicated words property file and is expected to ignore case. The list of parameters is dependent on the tokenizer or filter factory.


Filters and char filters are applied in the order they are defined in the @AnalyzerDef annotation. Make sure to think twice about this order.

Once defined, an analyzer definition can be reused by an @Analyzer declaration using the definition name rather than declaring an implementation class.

Analyzer instances declared by @AnalyzerDef are available by their name in the SearchFactory.

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

This is quite useful wen building queries. 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.

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 check a few of them.

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.

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 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. The following example demonstrates the usage of this annotation:

The prerequisite for using @AnalyzerDiscriminator is that all analyzers which are going to be used 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 if the analyzer should be set dynamically or null if the default analyzer should not be overridden. The given example assumes that the language parameter is either 'de' or 'en' which matches the specified names in the @AnalyzerDefs.

During indexing time, Hibernate Search is using analyzers under the hood for you. In some situations, retrieving analyzers can be handy. 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.

You can retrieve the scoped analyzer for a given entity used at indexing time by Hibernate Search. A scoped analyzer is an analyzer which applies the right analyzers depending on the field indexed: multiple analyzers can be defined on a given entity each one working on an individual field, a scoped analyzer unify 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.

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.

If your query targets more that one query and you wish to use your standard analyzer, make sure to describe it using an analyzer definition. You can retrieve analyzers by their definition name using searchFactory.getAnalyzer(String).

In Lucene all index fields have to be represented as Strings. For this reason all entity properties annotated with @Field have to be indexed in a String form. For most of your properties, Hibernate Search does the translation job for you thanks to a built-in set of bridges. In some cases, though you need a more fine grain control over the translation process.

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


null elements are not indexed. Lucene does not support null elements and this does not make much sense either.


String are indexed as is

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

Numbers are converted in 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


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 DateRange Query, 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.

public class Meeting {
    private Date date;
java.net.URI, java.net.URL

URI and URL are converted to their string representation


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

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.

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

Then any property or field can use this bridge thanks to the @FieldBridge annotation

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

Parameters can be passed to the Bridge implementation making it more flexible. The Bridge implementation implements a ParameterizedBridge interface, and the parameters are passed through the @FieldBridge annotation.

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.

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.

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

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. The interface is very similar in its concept to the Hibernate UserTypes.

You can for example store a given property in two different document fields:

In the previous example the fields where not added directly to Document but we where delegating this task to the LuceneOptions helper; this will apply the options you have selected on @Field, like Store or TermVector options, or apply the choosen @Boost value. It is especially useful to encapsulate the complexity of COMPRESS implementations so it's recommended to delegate to LuceneOptions to add fields to the Document, but nothing stops you from editing the Document directly and ignore the LuceneOptions in case you need to.


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.

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 and @ClassBridge annotations can be defined at the 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 this example, @ClassBridge supports the termVector attribute discussed in section Section 4.1.1, “Basic mapping”.

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.

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

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 :)

Th idea of a programmatic API was much more appealing and has now become a reality. You can programmatically and safely 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. This object is passed to Hibernate Search via a property set to the Configuration object. The property key is hibernate.search.model_mapping or it's type-safe representation Environment.MODEL_MAPPING.

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 );

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.

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 :

The programmatic API provides easy mechanism for defining full text filter definitions which is available via @FullTextFilterDef and @FullTextFilterDefs. Note that contrary to the annotation equivalent, full text filter definitions are a global construct and are not tied to an entity. The next example depicts the creation of full text filter definition using the fullTextFilterDef method.

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.

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. The is the option to add a prefix to the embedded entity definition and this can be done by calling prefix as seen in the example below:

The second most important capability of Hibernate Search is the ability to execute a Lucene query and retrieve entities managed by an Hibernate session, providing the power of Lucene without leaving the Hibernate paradigm, and 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

  • Wrapping the Lucene query using a org.hibernate.Query

  • Executing the search by calling for example list() or scroll()

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

The actual search facility is built on native Lucene queries which the following example illustrates.

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 (aka EJB 3.0 Persistence), the same extensions exist:

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.

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.

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

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), but has some constraints:

  • the properties projected must be stored in the index (@Field(store=Store.YES)), which increase 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. 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 useful for another kind of use cases. Lucene provides some metadata information to the user about the results. By using some special placeholders, the projection mechanism can retrieve them:

You can mix and match regular fields and special placeholders. Here is the list of available placeholders:

  • 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!

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().

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:

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.

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

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

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:

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:

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.

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:

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. For people familiar with JBoss Seam, this is similar to the component factory pattern, but the annotation is different!

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:

Each parameter name should have an associated setter on either the filter or filter factory of the targeted named filter definition.

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/new IndexReader instance, however, works potentially on a different set of Documents (either from a different index or simply because the index has changed), hence the cached DocIdSet has to be recomputed.

Hibernate Search also helps with this aspect of caching. Per default the cache flag of @FullTextFilterDef is set to FilterCacheModeType.INSTANCE_AND_DOCIDSETRESULTS which will automatically cache the filter instance as well as wrap the specified filter around a Hibernate specific implementation of CachingWrapperFilter (org.hibernate.search.filter.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:

FilterCacheModeType.NONENo 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_ONLYThe 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_DOCIDSETRESULTSBoth 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)

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

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 DirectoryProviders in a array indexed by customerID
	private DirectoryProvider<?>[] providers;
	public void initialize(Properties properties, DirectoryProvider<?>[] providers) {
		this.providers = providers;

	public DirectoryProvider<?>[] getDirectoryProvidersForAllShards() {
		return providers;

	public DirectoryProvider<?> getDirectoryProviderForAddition(Class<?> entity, Serializable id, String idInString, Document document) {
		Integer customerID = Integer.parseInt(document.getField("customerID").stringValue());
		return providers[customerID];

	public DirectoryProvider<?>[] getDirectoryProvidersForDeletion(Class<?> entity, Serializable id, String idInString) {
		return getDirectoryProvidersForAllShards();

	 * 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 DirectoryProvider<?>[] getDirectoryProvidersForQuery(FullTextFilterImplementor[] filters) {
		FFullTextFilter filter = getCustomerFilter(filters, "customer");
		if (filter == null) {
			return getDirectoryProvidersForAllShards();
		else {
			return new DirectoryProvider[] { providers[Integer.parseInt(filter.getParameter("customerID").toString())] };

	private FullTextFilter getFilter(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);
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.

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.

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.

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.

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


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


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.

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:

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.


hibernate.search.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.

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.


During the progress of a MassIndexer the content of the index is undefined, make sure that nobody will try to make some query during index rebuilding! If somebody should query the index it will not corrupt but most results will likely be missing.


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.


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 also affect indexing time and memory consumption are:

All .indexwriter parameters are Lucene specific and Hibernate Search is just passing these parameters through - see Section 3.9, “Tuning Lucene indexing performance” for more details.

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.

Queries in Lucene are executed on an IndexReader. Hibernate Search caches all index readers to maximize performance. Your code can access this cached resources, but you have to follow some "good citizen" rules.

The ReaderProvider (described in Reader strategy), will open an IndexReader on top of the index(es) referenced by the directory providers. 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 (you would get an exception). If you want to use a read/write index reader, open one from the Lucene Directory object.

Aside from those rules, you can use the IndexReader freely, especially to do native queries. Using the shared IndexReaders will make most queries more efficient.

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) )

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

@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.