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. Enabling Hibernate Search and automatic indexing
3.1.1. Enabling Hibernate Search
3.1.2. Automatic indexing
3.2. Directory configuration
3.3. Sharding indexes
3.4. Sharing indexes
3.5. Worker configuration
3.6. JMS Master/Slave configuration
3.6.1. Slave nodes
3.6.2. Master node
3.7. JGroups Master/Slave configuration
3.7.1. Slave nodes
3.7.2. Master node
3.7.3. JGroups channel configuration
3.8. Infinispan Directory configuration
3.8.1. Requirements
3.8.2. Architecture
3.8.3. Infinispan Configuration
3.9. Reader strategy configuration
3.10. Tuning Lucene indexing performance
3.11. LockFactory configuration
3.12. 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
4.2. 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 (experimental)
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
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. Optimizing the query process
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
7. Index Optimization
7.1. Automatic optimization
7.2. Manual optimization
7.3. Adjusting optimization
8. Monitoring
8.1. JMX
8.1.1. StatisticsInfoMBean
8.1.2. IndexControlMBean
8.1.3. IndexingProgressMonitorMBean
9. Advanced features
9.1. Accessing the SearchFactory
9.2. Accessing a Lucene Directory
9.3. Using an IndexReader
9.4. Use external services in Hibernate Search components (experimental)
9.4.1. Exposing a service
9.4.2. Using a service
9.5. Customizing Lucene's scoring formula
10. Further reading

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 models. 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):

Example 1.1. Adding the JBoss maven repository to settings.xml

          <name>JBoss Public Maven Repository Group</name>
          <name>JBoss Public Maven Repository Group</name>



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 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 3.2, “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.

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.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 ( see 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 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 4.4, “Bridges”.

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 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”.

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.

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.

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 3.6, “JMS Master/Slave configuration”, Section 3.8, “Infinispan Directory configuration”) and large index handling (Section 3.3, “Sharding indexes”).

Hibernate Search consists of an indexing 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 3.1, “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 3.2, “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 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. 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.


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


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 batched work being processed by different back ends. Three back ends are provided out of the box and you have the option to plugin in your own implementation.

Apache Lucene has a 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 DirectoryProviders” 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 3.4, “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.

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.


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 DirectoryProviders

Class or shortcut nameDescriptionProperties
ramMemory based directory, the directory will be uniquely identified (in the same deployment unit) by the @Indexed.index elementnone
filesystemFile 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 3.11, “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.


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 3.11, “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.


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 3.11, “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.

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 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 3.8, “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.


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>.

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

Possible use cases for sharding are:

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 3.3, “Enabling index sharding”. In this example 5 shards are enabled.

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.

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.getDirectoryProvidersForQuery) and thus speed up the query execution.

Each shard has an independent directory provider configuration. The DirectoryProvider index names for the Animal entity in Example 3.5, “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 3.2, “Directory configuration”).

In Example 3.5, “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)

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”.

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”.

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”.


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 BackendQueueProcessorFactory implementation.

This section describes in greater detail how to configure the Master/Slave 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 has to 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 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.

This section describes how to configure the JGroups Master/Slave back end. The configuration examples illustrated in Section 3.6, “JMS Master/Slave configuration” also apply here, only a different backend (hibernate.search.worker.backend) needs to be set.

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.worker.backend.jgroups.configurationFile property and specify a file containing the JGroups configuration or you can use the property hibernate.search.worker.backend.jgroups.configurationXml or hibernate.search.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 3.11, “JGroups transport protocol configuration”.


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

Example 3.11. JGroups transport protocol configuration

## JGroups configuration options
# OPTION 1 - udp.xml file needs to be located in the classpath
hibernate.search.worker.backend.jgroups.configurationFile = udp.xml

# OPTION 2 - protocol stack configuration provided in XML format
hibernate.search.worker.backend.jgroups.configurationXml =

<config xmlns="urn:org:jgroups"
xsi:schemaLocation="urn:org:jgroups file:schema/JGroups-2.8.xsd">
<PING timeout="1000" num_initial_members="3"/>
<MERGE2 max_interval="30000" min_interval="10000"/>
<FD timeout="3000" max_tries="3"/>
<VERIFY_SUSPECT timeout="1500"/>
<pbcast.FLUSH timeout="0"/>

# OPTION 3 - protocol stack configuration provided in "old style" jgroups format
hibernate.search.worker.backend.jgroups.configurationString =


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 3.12, “JGroups channel name configuration”.

Infinispan is a distributed scalable, highly available data grid platform which supports autodiscovery of peer nodes. It is possible to store the Lucene index in Infinispan, making it easy to setup a clustering configuration with Hibernate Search and having updates to the index available on other nodes very quickly.

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

Using an Infinispan Directory the index is stored in memory and shared across multiple nodes. It is considered a single directory across all participating nodes. If a node updates the index, all other nodes are affected 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 single centralized CacheStore, such as plain filesystem, Amazon S3, Cassandra, Berkley DB, JDBC standard databases. You can also have a CacheStore on each node or chain cachestores. See the Infinispan documentation for all options and configuration details.

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 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.

The configuration in
Example 3.14, “Example performance option configuration” will result in these settings applied on the second shard of the Animal 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. 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.


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

Table 3.7. 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 when 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. 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.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 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.LockFactoryFactory.

Configuration example:

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

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.


There is currently no xml configuration option available (see HSEARCH-210).

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

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, “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 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.


    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.

  • 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:

    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.
  • 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.


    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-tokenized fields (Index.UN_TOKENIZED).


    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()).

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

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.


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.

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 4.4, “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.

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 Example 4.4, “Indexing associations” a bit more complex by nesting @IndexEmbedded as seen in Example 4.5, “Nested usage of @IndexedEmbedded and @ContainedIn”.

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 4.5, “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.


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.


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.

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.

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.

In Example 4.7, “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.

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 4.8, “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.

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.

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

This separation of tasks - a list of char filters, and a tokenizer followed by a list of filters - allows for easy reuse of each individual component and 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.


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

Let's have a look at a concrete example now - Example 4.10, “@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.


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.

Once defined, an analyzer definition can be reused by an @Analyzer declaration as seen in Example 4.12, “Referencing an analyzer by name”.

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.

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 4.3. Examples of available filters

FactoryDescriptionParametersAdditional dependencies
StandardFilterFactoryRemove dots from acronyms and 's from wordsnonenone
LowerCaseFilterFactoryLowercases all wordsnonenone
StopFilterFactoryRemove 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

SnowballPorterFilterFactoryReduces 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 morelucene-snowball
ISOLatin1AccentFilterFactoryRemove accents for languages like Frenchnonenone
PhoneticFilterFactoryInserts 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

CollationKeyFilterFactoryConverts 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 infolucene-collation, 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.

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 4.13, “Usage of @AnalyzerDiscriminator” 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.

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.

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.

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.


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

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.

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


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, “@Field” for more information.


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


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


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;


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

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 implement the org.hibernate.search.bridge.StringBridge interface. All implementations have to be thread-safe as they are used concurrently.

Given the string bridge defined in Example 4.15, “Custom StringBridge implementation”, any property or field can use this bridge thanks to the @FieldBridge annotation:

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

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 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. You can for example store a property in two different document fields. The interface is very similar in its concept to the Hibernate UserTypes.

In Example 4.18, “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.


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 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 4.19, “Implementing a class bridge”, @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 :)

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

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 fullTextFilterDef method.

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

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.

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

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:

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

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.

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 =

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.


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:


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.

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:

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 )

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.

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.

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


public class Myth {
  @Field(index = Index.UN_TOKENIZED) 
  @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.keywork().onField("creationDate").matching(birthdate).createQuery();

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") } )
public class Myth {
  @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.

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


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()


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

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.

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:

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.


    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:

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!

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

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

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.


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.


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

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.

To define this soft limit, use the following approach

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.

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


This approach is considered experimental

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.

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.

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.


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.

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.

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.


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 3.10, “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.

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.

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.

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 inReader 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.

By components, this section means any of the pluggable contracts - DirectoryProvider being the most useful use case:

Some of these compnents 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 to store the various indexes using the same underlying infrastructure.

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.

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 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.

@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.

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!