Red Hat

In Relation To Hibernate Search

In Relation To Hibernate Search

Hi, I’m Mincong, an engineering student from France. I’m glad to present my Google Summer of Code 2016 project, which provides an alternative to the current mass indexer implementation of Hibernate Search, using the Java Batch architecture (JSR 352). I’ve been working on this project for 4 months. Before getting started, I want to thank Google for sponsoring the project, the Hibernate team for accepting my proposal and my mentors Gunnar and Emmanuel for their help during this period. Now, let’s begin!

What is it about?

Hibernate Search brings full-text search capabilities to your Hibernate/JPA applications by synchronizing the state of your entities with a search index maintained by Lucene (or Elasticsearch as of Hibernate Search 5.6!). Index synchronization usually happens on the fly as entities are modified, but there may be cases where an entire index needs to be re-built, e.g. when enabling indexing for an existing entity type or after changes have been applied directly to the database, bypassing Hibernate (Search).

Hibernate Search provides the mass indexer for this purpose. It was the goal of my GSoC project to develop an alternative using the API for Java batch applications standardized by JSR 352.

What do we gain from JSR 352?

Implementing the mass indexing functionality using the standardized batching API allows you to use the existing tools of your runtime environment for starting/stopping and monitoring the status of the indexing process. E.g. in WildFly you can use the CLI to do so.

Also JSR 352 provides a way to restart specific job runs. This is very useful if re-indexing of an entity type failed mid-way, for instance due to connectivity issues with the database. Once the problem is solved, the batch job will continue where it left off, not processing again those items already processed successfully.

As JSR 352 defines common concepts of batch-oriented applications such as item readers, processors and writers, the job architecture and workflow is very easy to follow. In JSR 352, the workflow is written in an XML file (the "job XML"), which is used to specify a job, its steps and directs their execution. So you can understand the process without jumping into the code.

<job id="massIndex">
    <step id="beforeChunk" next="produceLuceneDoc">
        <batchlet ref="beforeChunkBatchlet"/>

    <step id="produceLuceneDoc" next="afterChunk">
        <chunk checkpoint-policy="custom">
            <reader ref="entityReader"/>
            <processor ref="luceneDocProducer"/>
            <writer ref="luceneDocWriter"/>
            <checkpoint-algorithm ref="checkpointAlgorithm"/>

    <step id="afterChunk">
        <batchlet ref="afterChunkBatchlet"/>

As you see, it brings a pervasive batch-processing workload. Anyone who has experience in ETL processes should have no difficulty to understand our new implementation.

Example usages

Here are the example usages of the new mass indexer under a draft version. It allows you to add one or multiple class types. If you have more than one root entity to index, then you can use the addRootEntities(Class<?>…​) method.

How to use the new MassIndexer
long executionId = new MassIndexer()
        .addRootEntity( Company.class )
Another example with a more customized configuration:
long executionId = new MassIndexer()
        .addRootEntity( Company.class, Employee.class )
        .cacheable( false )
        .checkpointFreq( 1000 )
        .rowsPerPartition( 100000 )
        .maxThreads( 10 )
        .purgeAtStart( true )
        .optimizeAfterPurge( true )
        .optimizeAtEnd( true )


In order to maximize the performance, we highly recommend you to speed up the mass indexer using parallelism. Parallelism is activated by default. Under the JSR 352 standard, the exact word is "partitioning". The indexing step may run as multiple partitions, one per thread. Each partition has its own partition ID and parameters. If there are more partitions than threads, partitions are considered as a queue to consume: each thread can only run one partition at a time and won’t consume the next partition until the previous one is finished.

massIndexer = massIndexer.rowsPerPartition( 500 );


Mass indexer supports checkpoint algorithm. If the job is interrupted for any reason, mass indexer can be restarted from the last checkpoint, stored by the batch runtime. And the entities already indexed won’t be lost, because they are already flushed to the directory provider. Assume that N is the value of checkpoint frequency, then a partition will reach at checkpoint every N items processed inside the partition. You can overwrite it to adapt your business requirements.

massIndexer = massIndexer.checkpointFreq( 1000 );


For further usage, please check my GitHub repo gsoc-hsearch. If you want to play with it, you can download the code and build it with Maven:

$ git clone -b 1.0 git://
$ cd gsoc-hsearch
$ mvn install

Current status and next steps

Currently, the new implementation accepts different types of entity as entry, provides high level of customization of the job properties and parallel indexation. The job periodically saves its current progress to enable restart from the last point of consistency. Load balancing has been considered to avoid overload of any single thread. This indexing batch job is available under Java SE and Java EE.

There are still many things to do, e.g. related to performance improvements, integration into WildFly, monitoring, more fine-grained selection of entities to be re-indexed etc. Here are some of the ideas:

  • Core: partition mapping for composite ID

  • Integration: package the batch job as a WildFly module

  • Integration: start the indexing batch job from FullTextSession and FullTextEntityManager

  • Integration: embed this project into Hibernate Search

  • Monitoring: enhance the basic monitoring, e.g. progress status for restarted job

  • Performance: Ensure a great performance of this implementation

These tasks are tracked as GitHub issues, you can check the complete TODO list here.


If you are using Hibernate Search and ever wished for a more standardized approach to mass indexing, this project clearly is for you.

We still need to apply some improvements and polishing before integrating it as a module into the Hibernate Search core code base, but any bug reports or comments on the project will be very helpful. So please give it a try and let us know about your feedback. Just drop a comment below or raise an issue on GitHub.

Looking forward to hearing from you!

We are making good progress on our next major release which focuses on Elasticsearch integration but we don’t forget our beloved users of Hibernate Search 5.5.x and here is a new stable release to prove it!

This bugfix release is entirely based on user feedback so keep it coming!

Hibernate Search version 5.5.4.Final is available now and fixes the following issues:

  • HSEARCH-2301 - CriteriaObjectInitializer is suboptimal when we query only one subtype of a hierarchy

  • HSEARCH-2286 - DistanceSortField should support reverse sorting

  • HSEARCH-2306 - Upgrade 5.5.x to Hibernate ORM 5.0.9

  • HSEARCH-2307 - Documentation shouldn’t suggest need for @Indexed of embedded association fields

Small focus on HSEARCH-2301 as it might significantly improve your performances if you index complex hierarchy of objects. Prior to this fix, when querying the database to hydrate the objects, Hibernate Search was using the root type of the hierarchy potentially leading to queries with a lot of joins. Hibernate now builds the most efficient query possible depending on the effective results.

You can see two instances of this issue on Stack Overflow here and here.


How to get this release

Everything you need is available on Hibernate Search’s web site. Download the full distribution from here, or get it from Maven Central using the above coordinates, and don’t hesitate to reach us in our forums or mailing lists.

We also monitor closely the hibernate-search tag on Stack Overflow.

After over 60 resolved tasks, we’re proud to release Hibernate Search version 5.6.0.Beta1.

The Elasticsearch integration made significant progress, and we believe it to be ready for wider usage.

Progress of the Elasticsearch integration

Improvements since the previous milestone:


significant better performance as it now uses bulk operations.

Calendar, Dates, numbers and mapping details

several corrections and improvements were made to produce a cleaner schema.

Cluster state

we now wait for a newly started Elasticsearch cluster to be "green" - or optionally "yellow" - before starting to use it.

WildFly modules

a critical bug was resolved, the modules should work fine now.

Many more

for a full list of all 63 improvements, see this JIRA query.

What is missing yet?

Performance testing

we didn’t do much performance testing, it’s probably not as efficient as it could be.

Relax the expected Elasticsearch version

it’s being tested with version 2.3.1 but we have plans to support a wider range of versions.

Explicit refresh requests

we plan to add methods to issue an indexreader refresh request, as the changes pushed to Elasticsearch are not immediately visible by default.

Your Feedback!

we think it’s in pretty good shape, it would be great for more people to try it out and let us know what is missing and how it’s working for you.

Notable differences between using embededd Lucene vs Elasticsearch

Unless you reconfigure Hibernate Search to use an async worker, by default when using the Lucene backend after you commit a transaction the changes to the index are immediately applied and any subsequent search will "see" the changes. On Elasticsearch the default is different: changes received by the cluster are only "visible" to searches after some seconds (1 by default).

You can reconfigure Hibernate Search to force a refresh of indexes after each write operation by using the configuration setting.

This setting defaults to false as that’s the recommended setting for optimal performance on Elasticsearch. You might want to set this to true to make it simpler to write unit tests, but you should take care to not rely on the synchronous behaviour for your production code.

Improvements for embedded Lucene users

While working on Elasticsearch, we also applied some performance improvements which apply to users of the traditional Lucene embedded users.

Special thanks to Andrej Golovnin, who contributed several patches to reduce allocation of objects on the hot path and improve overall performance.

How to get this release

Everything you need is available on Hibernate Search’s web site.

Get it from Maven Central using the above coordinates.

Downloads from Sourceforge are available as well.


Feedback always welcome!

Please let us know of any problem or suggestion by creating an issue on JIRA, or by sending an email to the developer’s developer’s mailing lists, or posting on the forums.

We also monitor Stack Overflow; when posting on SO please use the tag hibernate-search.

While the team has been busy implementing great new features such as the Elasticsearch integration for the next 5.6 release, some of you provided interesting feedback on our stable release.

The summary of the feedback I heard is that migrating to the new sorting requirements can be confusing, and there were some issues with our Faceting implementation.

Hibernate Search version 5.5.3.Final is available now, fixing the reported issues and improving the error messages around sorting.

The changelog is rather small, so this time I’ll post it verbatim:

  • HSEARCH-1917 - Cannot index null or empty values for faceted fields

  • HSEARCH-2082 - Documentation refers to @SortField when it should be @SortableField

  • HSEARCH-2085 - Typo in hibernate-search-engine logger

  • HSEARCH-2086 - Long and Date range faceting doesn’t honor hasZeroCountsIncluded

  • HSEARCH-2179 - Hanging during shutdown of SyncWorkProcessor

  • HSEARCH-2193 - LuceneBackendQueueTask does not release the Directory lock on update failures

  • HSEARCH-2200 - Typo in log message

  • HSEARCH-2240 - Parallel service lookup might fail to find the service

  • HSEARCH-2199 - Allows the use of CharFilter in the programmatic API of SearchMapping

  • HSEARCH-2084 - Upgrade to WildFly 10.0.0.Final

  • HSEARCH-2089 - Ensure the performance tests do not use the WildFly embedded version of Search

  • HSEARCH-1951 - Improve resulting error message when applying the wrong Sort Type

  • HSEARCH-2090 - Using the wrong header in the distribution/pom.xml

  • HSEARCH-2241 - Clarify deprecation of setFilter() method on FullTextQuery

Spot inefficient sorting operations easily in test suites

While Hibernate Search already would log a warning when forced to perform a query using a sub-optimal sorting strategy, that wasn’t making it very easy to spot mapping or usage mistakes.

Set this property: = false

and you’ll have your tests fail with an exception rather than log warnings.

This property is not new in this release, but it’s worth reminding as it makes it much easier to validate your migrations from previous versions.


What are we working on?

The Elasticsearch integration is almost feature complete, we expect to be able to release a Beta1 version in some weeks.

How to get this release

Everything you need is available on Hibernate Search’s web site. Download the full distribution from here, or get it from Maven Central using the above coordinates, and don’t hesitate to reach us in our forums or mailing lists.

Having fixed several issues and tasks since the previous milestone, it’s time to publish our third milestone towards Elasticsearch integration: Hibernate Search version 5.6.0.Alpha3 is now available!

Migration from Hibernate Search 5.5.x

Even if you’re not interested in the new Elasticsearch support, you might want to try out this version as it benefits from Apache Lucene 5.5.0.

If you ignore the new features and want to simply use Lucene in embedded mode the migration is easy, and as usual we are maintaining notes regarding relevant API changes in the Migration Guide to Hibernate Search 5.6.

Elasticsearch support progress

  • you can now use the Analyzers from Elasticsearch

  • Multiple operations will now be sent to Elasticsearch as a single batch to improve both performance and consistency

  • Spatial indexing and querying is now feature complete

  • We’ll wait for Elasticsearch to be "green" before attempting to use it at boot

  • Many improvements in the query translation

  • Error capture and reporting was improved

  • the Massindexer is working now, but is not yet using efficient bulk operations

  • the Elasticsearch extensions are now included in the WildFly modules

How to get this release

Everything you need is available on Hibernate Search’s web site.

Get it from Maven Central using the above coordinates.

Downloads from Sourceforge are available as well.


Feedback always welcome!

Please let us know of any problem or suggestion by creating an issue on JIRA, or by sending an email to the developer’s developer’s mailing lists, or posting on the forums.

We also monitor Stack Overflow; when posting on SO please use the tag hibernate-search.

In this post, I’d like you to meet Martin, who, in spite of his young age, has been very active in the Hibernate Search project development, implementing some interesting extensions or helping with pull request reviewing.

Because I’d love to see more university students getting involved with open source software, I took this opportunity and interviewed Martin about this experience.

  1. Hi, Martin. You are one of the youngest contributors we’ve ever had. Can you please introduce yourself?

    Hi, Vlad. I am a 22-year-old Master’s Degree student at University of Bayreuth, Germany and have been interested in Hibernate Search and Fulltext Search (Lucene, Solr) for quite some time now. I am also a firm believer of Open Source and have actually always wanted to become a contributor of a tool (or software) many other developers use in their projects. Knowing that a piece of code you wrote is running in other systems is quite the rewarding feeling.

  2. I understand that you took part in the Google Summer of Code event. Can you tell us a little bit about this program?

    Yes, I took part in last year Google Summer of Code program and was mentored by Sanne Grinovero while working on adapting Hibernate Search to work with any JPA provider. It gave me the opportunity to dive more deeply into the codebase as it allowed me to concentrate on nothing but my project work-wise. In general Google Summer of Code is one of the best learning experiences any student that wants to get into Open Source can have.

  3. Contributing to an open-source project is a great learning experience. Has this activity helped you improve your skills?

    Definitely. While building new features or tracking down bugs, you encounter loads of different pieces of code you have to work through. With that comes learning new technologies and APIs. Also, the general process of submitting JIRA issues, discussing them and implementing the solutions is something you can learn while working on an open source project. Trying out the process yourself is invaluable and cannot be compared to just learning them on paper. This is also something I always tell to new coders: Try it out or you will not get it 100%.

  4. Do you think the entry barrier is high for starting contributing to an open source project? How should we encourage students to getting involved with open source?

    In the case of the Hibernate team, I can only say that it was quite easy to get into contact with the other developers. I just got onto IRC and asked questions about problems I had. They helped me with every question I had, so I stuck around. Then, I started reporting issues or making feature requests and was immediately incorporated into discussions. So no, the barrier is not high (at least for me in the case of the Hibernate team).

    I think open source needs to be encouraged more at a university level. I think many students don’t realize what they are missing. Yes, open standards are encouraged and teaching uses open APIs all over the place, but universities tend to keep much of the work that is suitable for open source behind closed doors (btw: I don’t think that closed source is always a bad thing, but it sometimes is in the way of innovation).

  5. What are your plans for the future?

    Firstly, I want to finish my Masters degree at University. I haven’t fully decided yet, whether I want to stay at University or not. Time will tell, I guess. Secondly, I want to keep contributing to Hibernate Search and finish merging the features of last years Google Summer of Code into the core code base.

Thank you, Martin, and keep up the good work.

During my talk at VoxxedVienna on using Hibernate Search with Elasticsearch earlier this week, there was an interesting question which I couldn’t answer right away:

"When running a full-text query with a projection of fields, is it possible to return the result as a list of POJOs rather than as a list of arrays of Object?"

The answer is: Yes, it is possible, result transformers are the right tool for this.

Let’s assume you want to convert the result of a projection query against the VideoGame entity shown in the talk into the following DTO (data transfer object):

public static class VideoGameDto {

    private String title;
    private String publisherName;
    private Date release;

    public VideoGameDto(String title, String publisherName, Date release) {
        this.title = title;
        this.publisherName = publisherName;
        this.release = release;

    // getters...

This is how you could do it via a result transformer:

FullTextEntityManager ftem = ...;

QueryBuilder qb = ftem.getSearchFactory()
    .forEntity( VideoGame.class )

FullTextQuery query = ftem.createFullTextQuery(
        .onField( "tags" )
        .matching( "round-based" )
    .setProjection( "title", "", "release" )
    .setResultTransformer( new BasicTransformerAdapter() {
        public VideoGameDto transformTuple(Object[] tuple, String[] aliases) {
            return new VideoGameDto( (String) tuple[0], (String) tuple[1], (Date) tuple[2] );
    } );

List<VideoGameDto> results = query.getResultList();

I’ve pushed this example to the demo repo on GitHub.

There are also some ready-made implementations of the ResultTransformer interface which you might find helpful. So be sure to check out its type hierarchy. For this example I found it easiest to extend BasicTransformerAdapter and implement the transformTuple() method by hand.

To the person asking the question: Thanks, and I hope this answer is helpful to you!

Today we’re proud to announce the first Alpha release sporting experimental integration with Elasticsearch!

We also updated to use Apache Lucene 5.5 - the latest stable release of our favourite search engine - as of course we’re not abandoning our traditional users!

What is the Hibernate Search / Elasticsearch integration?

Both Hibernate Search and Elasticsearch can do much more, but for the purpose of explaining this integration at an high level I’ll simplify their definitions as:

Hibernate Search

is a popular extension of the super popular Hibernate ORM framework, which makes it easy to index and query your entities using Apache Lucene.


is a very popular REST server which wraps the capabilities of Apache Lucene and makes it easier to scale this service horizontally.

Until today when using Hibernate Search you’d be using Apache Lucene directly, in what we will now call "embedded mode". In this mode a query is executed by the same process of your application, and while indexing happens in background still the overhead of such processing is happening within the same server and within the same JVM process as your Hibernate powered application.

With the Elasticsearch integration, rather than indexing your entities directly by managing the Lucene resources, we will be sending RPCs to an Elasticsearch cluster to achieve a very similar purpose: after all it is also Lucene based, so the feature match is extremely close!

This means that we’re able to transparently map all the current features to this new alternative backend, and by so doing give you more architectural choices at minimum required changes in your applications: the goal is that for most users the differences will be mostly in configuration details.

When using Elasticsearch we will need to send RPCs over the network to run queries and index updates, but on the other hand you benefit from Microservices - style decoupling and all the nice features that Elasticsearch can provide in terms of running and managing an horizontally scalable cluster.

In a nutshell…​

Elasticsearch will manage the index for you, Hibernate ORM will manage your objects and the transactions, Hibernate Search will transparently keep Elasticsearch synchronized to your database transactions and let you query your Domain Model returning managed objects, but moving the heavy lifting to an independent Elasticsearch service.

Elasticsearch integration status

This is literally being developed right now, so do not expect this to be feature complete nor reliable enough to run in a production system. Still, we already have a great set of features working so it’s a nice time to start playing with it and hopefully provide some feedback.

The documentation sports a new Elasticsearch chapter, which is a good place to start.

Configuring the Elasticsearch backend


You’ll need a new dependency, named org.hibernate:hibernate-search-backend-elasticsearch.

The Maven dependencies for Hibernate Search with Elasticsearch:


Configuration options

Point it to your Elasticsearch node

Enable the backend to be used for all your indexed entities elasticsearch

Allow it to destroy and create indexes CREATE_DELETE

For details about these configuration options see the Reference documentation.

Updating the indexes

As with Lucene in embedded mode, the indexes are updated automatically when you create or update entities which are mapped to Hibernate Search using the same annotations already familiar from our traditional index mapping (see Mapping entities to the index structure).

Running a query on an Elasticsearch mapped entity

In many cases the existing way (see Querying) of running queries should work: we do automatically translate the most common types of Apache Lucene queries and many of the queries generated by the Hibernate Search DSL.

On top of translating Lucene queries, you can directly create Elasticsearch queries by using either its String format or a JSON format:

Creating an Elasticsearch native query from a string:

FullTextSession fullTextSession = Search.getFullTextSession(session);
QueryDescriptor query = ElasticsearchQueries.fromQueryString("title:tales");
List<?> result = fullTextSession.createFullTextQuery(query, ComicBook.class).list();

Creating an Elasticsearch native query from JSON:

FullTextSession fullTextSession = Search.getFullTextSession(session);
QueryDescriptor query = ElasticsearchQueries.fromJson(
      "{ 'query': { 'match' : { 'lastName' : 'Brand' } } }");
List<?> result = session.createFullTextQuery(query, GolfPlayer.class).list();

Remaining work ahead

This is an early preview, and while we’re proud of some of the progress there are several areas which still need much coding. On the other hand, implementing some of these is not very hard: this might be the perfect time to join the project.

Please check with JIRA and the mailing lists for updates, but at the time of writing this at least the following features are known to not work yet:

  • Analyzer definitions are not being applied

  • Spatial queries need more work

  • Filters can’t be applied yet

  • Faceting is mostly implemented

  • Scheduled index optimisation is not applied

  • Query timeouts

  • Delete by queries

  • Resolution for Date type mapping is ignored

  • Scrolling on large results won’t work yet

  • MoreLikeThis queries

  • Mixing Lucene based indexes and Elasticsearch based indexes

Any aspect related to performance and efficiency will also be looked at only at the end of basic feature development.

API Changes

In the 5.x series we will keep backward compatibility.

That might come at a cost of not perfect Hibernate Search / Elasticsearch integration API wise. This is something we will address in the 6.x series. But our focus is on offering the right set of features and get feedback in 5.x before improving the APIs.

In a nutshell, 6.x will depend on how you use this feature in 5.6.

How to get this release

Everything you need is available on Hibernate Search’s web site.

Get it from Maven Central using the above coordinates.

Downloads from Sourceforge are available as well, but these don’t contain the Elasticsearch integration components yet. Similarly the WildFly modules also are not including the new Elasticsearch extensions yet.


Feedback always welcome!

Please let us know of any problem or suggestion by creating an issue on JIRA, or by sending an email to the developer’s developer’s mailing lists, or posting on the forums.

We also monitor Stack Overflow; when posting on SO please use the tag hibernate-search.

Eclipse tools for Hibernate Search

Posted by    |       |    Tagged as Hibernate Search JBoss Tools

I’m glad to announce the first release of the Eclipse plugin for Hibernate Search. In this post I want to describe its features and ask you for any comments, positive or (even more important for me) negative.


The plugin was made as a feature of jbosstools-hibernate plugin, which can be downloaded and installed on its own or together with the full JBoss Tools distribution. After that you can install the Hibernate Search plugin via the Eclipse Marketplace.

All requirements, such as eclipse version and platform support, are listed in the link.

In order to work with Hibernate Search you have to set the Hibernate configuration properties and


The plugin was thought to be some kind of a Luke tool inside Eclipse. It was thought to be more convenient than launching a separate application, and picks up the configuration directly from your Hibernate configuration.

Three options were added to the console configurations: Index rebuild, Explore documents and Try analysers.

Index Rebuild Action screenshot

Index rebuild

When introducing Hibernate Search in an existing application, you have to create an initial Lucene index for the data already present in your database.

The option "Rebuild index" will do so by re-creating the Lucene index in the directory specified by the property.

Screenshot of Hibernate Search indexed entities
Screenshot for Hibernate Search configuration properties

Explore Documents

After creating the initial index you can now inspect the Lucene Documents it contains.

All entities annotated as @Indexed are displayed in the Lucene Documents tab. Tick the checkboxes as needed and load the documents. Iterate through the documents using arrows.

Screenshot of Lucene Documents inspection

Try the Analyzers

The "try analyzers" instrument allows you to view the result of work of different Lucene Analyzers. The combo-box contains all classes in the workspace which extend org.apache.lucene.analysis.Analyzer, including custom implementations created by the user. While you type the text you want to analyse, the result immediately appears in the AnalysisResultTab view.

Screenshot of Try Analyzers

Possible issues

One problem which you might have is that "Index rebuild" option seems to do nothing. As a temporary workaround try to set the Hibernate configuration property "" to "true" explicitly.

If you have any other problems, such as unexpected behaviour, strange windows with exceptions or any errors in the Error log view feel free to contact me directly anywhere or just create an issue in the plugin github page.


  • Make options "Index rebuild" and "Explore documents" available not only for configurations, but also for concrete entities under session factory.

  • Make Lucene Documents view more comfortable to use and add there more features from Luke tool, for example, the ability to search over documents.

  • Increasing stability of the plugin and implementing your suggestions!

Version 5.5.2.Final is now available, our latest stable version sporting integration with Hibernate ORM 5 and Apache Lucene 5.3 - the state of the art.

Creating this version to be compatible with these two great OSS projects kept us busy for a good deal of this past year; I remember discussing this option with superstar OSS contributors Uwe Schindler (Apache Lucene developer) and Gustavo Nalle (Infinispan developer) at FOSDEM in January 2015! I am grateful to both for their guidance and suggestions, as driving progress forward is sometimes challenging when we strive to keep backwards compatibility as best as we can.

On top of that, our same small but amazing team as been working hard on Hibernate OGM 5, a bit of Hibernate Validator, incredible performance improvements on Hibernate ORM "classic" and is still tinkering on the internals of Hibernate Search to make an Elasticsearch backend an alternative to plain Lucene.

Feel like helping with the Elasticsearch integration?

Version 5.6 will feature an experimental integration with Elasticsearch. Early feedback would be very welcome! If you feel like helping, quite some code is integrated in the master branch already and there are clear TODO and FIXME comments to be found in the code.

Feel free to join the hacking! But best to let us know which area you feel like working on to avoid duplication of efforts and conflicting patches.

The 5.5.2.Final release and our great community

Technically I pushed the release button the 24th, but I couldn’t publish the blog until now because of travels.

On top of the improvements from previous 5.5.1.Final release which sported significant performance improvements, this version now incorporates several important corrections by Yoann Rodière and Guillaume Smet around metadata which was producing incorrect range queries for embedded numeric types.

Yoann also contributed another great performance improving patch, by limiting the recursion triggered by @ContainedIn more strictly, by considering the indexed paths and not just the recursion depth. You might not notice this if you have very simple flat models, but this can provide a very significant boost for all those of you using Hibernate Search to index rich models with extensive usage of @ContainedIn annotations.

Many thanks to Yoann and Guillaume, they are "power users" who do the right thing: their hands-on regular feedback is invaluable, not to mention they send such great patches for everybody else to benefit from. Not least, this is very encouraging for us: let us know which great things you’re building with these libraries!


How to get this release

Everything you need is available on Hibernate Search’s web site. Download the full distribution from here, or get it from Maven Central using the above coordinates, and don’t hesitate to reach us in our forums or mailing lists.

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