Use Hibernate Search in Standalone mode with Elasticsearch/OpenSearch

You have a Quarkus application? You want to provide a full-featured full-text search to your users? You’re at the right place.

With this guide, you’ll learn how to index entities into an Elasticsearch or OpenSearch cluster in a heartbeat with Hibernate Search. We will also explore how you can query your Elasticsearch or OpenSearch cluster using the Hibernate Search API.

If you want to index Hibernate ORM entities, see this dedicated guide instead.

Prerequisites

To complete this guide, you need:

Roughly 20 minutes
An IDE
JDK 17+ installed with JAVA_HOME configured appropriately
Apache Maven ${proposed-maven-version}
A working container runtime (Docker or Podman)
Optionally the Quarkus CLI if you want to use it
Optionally Mandrel or GraalVM installed and configured appropriately if you want to build a native executable (or Docker if you use a native container build)

Architecture

The application described in this guide allows to manage a (simple) library: you manage authors and their books.

The entities are stored and indexed in an Elasticsearch cluster.

Solution

We recommend that you follow the instructions in the next sections and create the application step by step. However, you can go right to the completed example.

Clone the Git repository: git clone $${quickstarts-base-url}.git, or download an $${quickstarts-base-url}/archive/main.zip[archive].

The solution is located in the hibernate-search-standalone-elasticsearch-quickstart directory.

The provided solution contains a few additional elements such as tests and testing infrastructure.

Creating the Maven project

First, we need a new project. Create a new project with the following command:

CLI

quarkus create app org.acme:hibernate-search-standalone-elasticsearch-quickstart \
    --extension='hibernate-search-standalone-elasticsearch,rest-jackson' \
    --no-code
cd hibernate-search-standalone-elasticsearch-quickstart

To create a Gradle project, add the --gradle or --gradle-kotlin-dsl option.

For more information about how to install and use the Quarkus CLI, see the Quarkus CLI guide.

Maven

mvn io.quarkus.platform:quarkus-maven-plugin:${project.version}:create \
    -DprojectGroupId=org.acme \
    -DprojectArtifactId=hibernate-search-standalone-elasticsearch-quickstart \
    -Dextensions='hibernate-search-standalone-elasticsearch,rest-jackson' \
    -DnoCode
cd hibernate-search-standalone-elasticsearch-quickstart

To create a Gradle project, add the -DbuildTool=gradle or -DbuildTool=gradle-kotlin-dsl option.

For Windows users:

If using cmd, (don’t use backward slash \ and put everything on the same line)
If using Powershell, wrap -D parameters in double quotes e.g. "-DprojectArtifactId=hibernate-search-standalone-elasticsearch-quickstart"

This command generates a Maven structure importing the following extensions:

Hibernate Search Standalone + Elasticsearch,
Quarkus REST (formerly RESTEasy Reactive) and Jackson.

If you already have your Quarkus project configured, you can add the hibernate-search-standalone-elasticsearch extension to your project by running the following command in your project base directory:

CLI

quarkus extension add hibernate-search-standalone-elasticsearch

Maven

./mvnw quarkus:add-extension -Dextensions='hibernate-search-standalone-elasticsearch'

Gradle

./gradlew addExtension --extensions='hibernate-search-standalone-elasticsearch'

This will add the following to your pom.xml:

pom.xml

<dependency>
    <groupId>io.quarkus</groupId>
    <artifactId>quarkus-hibernate-search-standalone-elasticsearch</artifactId>
</dependency>

build.gradle

implementation("io.quarkus:quarkus-hibernate-search-standalone-elasticsearch")

Creating the bare classes

First, let’s create our Book and Author classes in the model subpackage.

package org.acme.hibernate.search.elasticsearch.model;

import java.util.List;
import java.util.Objects;

public class Author {

    public UUID id; (1)

    public String firstName;

    public String lastName;

    public List<Book> books;

    public Author(UUID id, String firstName, String lastName, List<Book> books) {
        this.id = id;
        this.firstName = firstName;
        this.lastName = lastName;
        this.books = books;
    }
}

We’re using public fields here, because it’s shorter and there is no expectation of encapsulation on what is essentially a data class.

However, if you prefer using private fields with getters/setters, that’s totally fine and will work perfectly as long as the getters/setters follow the JavaBeans naming convention (getSomething()/isSomething()/setSomething(…)).

package org.acme.hibernate.search.elasticsearch.model;

import java.util.Objects;

public class Book {

    public UUID id;

    public String title;

    public Book(UUID id, String title) {
        this.id = id;
        this.title = title;
    }
}

Using Hibernate Search annotations

Enabling full text search capabilities for our classes is as simple as adding a few annotations.

Let’s edit the Author entity to include this content:

package org.acme.hibernate.search.elasticsearch.model;

import java.util.ArrayList;
import java.util.List;
import java.util.Objects;
import java.util.UUID;

import org.hibernate.search.engine.backend.types.Sortable;
import org.hibernate.search.mapper.pojo.mapping.definition.annotation.DocumentId;
import org.hibernate.search.mapper.pojo.mapping.definition.annotation.FullTextField;
import org.hibernate.search.mapper.pojo.mapping.definition.annotation.IdProjection;
import org.hibernate.search.mapper.pojo.mapping.definition.annotation.Indexed;
import org.hibernate.search.mapper.pojo.mapping.definition.annotation.IndexedEmbedded;
import org.hibernate.search.mapper.pojo.mapping.definition.annotation.KeywordField;
import org.hibernate.search.mapper.pojo.mapping.definition.annotation.ProjectionConstructor;
import org.hibernate.search.mapper.pojo.mapping.definition.annotation.SearchEntity;

@SearchEntity (1)
@Indexed (2)
public class Author {

    @DocumentId (3)
    public UUID id;

    @FullTextField(analyzer = "name") (4)
    @KeywordField(name = "firstName_sort", sortable = Sortable.YES, normalizer = "sort") (5)
    public String firstName;

    @FullTextField(analyzer = "name")
    @KeywordField(name = "lastName_sort", sortable = Sortable.YES, normalizer = "sort")
    public String lastName;

    @IndexedEmbedded (6)
    public List<Book> books = new ArrayList<>();

    public Author(UUID id, String firstName, String lastName) {
        this.id = id;
        this.firstName = firstName;
        this.lastName = lastName;
    }

    @ProjectionConstructor (7)
    public Author(@IdProjection UUID id, String firstName, String lastName, List<Book> books) {
        this( id, firstName, lastName );
        this.books = books;
    }
}

1	First, let’s mark the `Author` type as an entity type. In short, this implies the `Author` type it has its own, distinct lifecycle (not tied to another type), and that every `BookAuthor instance carries an immutable, unique identifier.
2	Then, let’s use the `@Indexed` annotation to register our `Author` entity as part of the full text index.
3	And let’s end the mandatory configuration by defining a document identifier.
4	The `@FullTextField` annotation declares a field in the index specifically tailored for full text search. In particular, we have to define an analyzer to split and analyze the tokens (~ words) - more on this later.
5	As you can see, we can define several fields for the same property. Here, we define a `@KeywordField` with a specific name. The main difference is that a keyword field is not tokenized (the string is kept as one single token) but can be normalized (i.e. filtered) - more on this later. This field is marked as sortable as our intention is to use it for sorting our authors.
6	The purpose of `@IndexedEmbedded` is to include the `Book` fields into the `Author` index. In this case, we just use the default configuration: all the fields of the associated `Book` instances are included in the index (i.e. the `title` field). `@IndexedEmbedded` also supports nested documents (using the `structure = NESTED` attribute), but we don’t need it here. You can also specify the fields you want to embed in your parent index using the `includePaths`/`excludePaths` attributes if you don’t want them all.
7	We mark a (single) constructor as a `@ProjectionConstructor`, so that an `Author` instance can be reconstructed from the content of the index.

Now that our authors are indexed, we will want to map books, so that this @IndexedEmbedded annotation actually embeds something.

Open the Book class and include the content below.

package org.acme.hibernate.search.elasticsearch.model;

import java.util.Objects;
import java.util.UUID;

import org.hibernate.search.mapper.pojo.mapping.definition.annotation.FullTextField;
import org.hibernate.search.mapper.pojo.mapping.definition.annotation.KeywordField;
import org.hibernate.search.mapper.pojo.mapping.definition.annotation.ProjectionConstructor;
import org.hibernate.search.mapper.pojo.mapping.definition.annotation.SearchEntity;

@SearchEntity (1)
public class Book {

    @KeywordField (2)
    public UUID id;

    @FullTextField(analyzer = "english") (3)
    public String title;

    @ProjectionConstructor (4)
    public Book(UUID id, String title) {
        this.id = id;
        this.title = title;
    }
}

1	We also mark the Book type as an entity type, but we don’t use `@Indexed`, because we decided we don’t need a dedicated index for books.
2	We index the book’s ID, so it can be projected (see below).
3	We use a `@FullTextField` similar to what we did for `Author` but you’ll notice that the analyzer is different - more on this later.
4	Like `Author`, we mark a constructor as a `@ProjectionConstructor`, so that a `Book` instance can be reconstructed from the content of the index.

Analyzers and normalizers

Introduction

Analysis is a big part of full text search: it defines how text will be processed when indexing or building search queries.

The role of analyzers is to split the text into tokens (~ words) and filter them (making it all lowercase and removing accents for instance).

Normalizers are a special type of analyzers that keeps the input as a single token. It is especially useful for sorting or indexing keywords.

There are a lot of bundled analyzers, but you can also develop your own for your own specific purposes.

You can learn more about the Elasticsearch analysis framework in the Analysis section of the Elasticsearch documentation.

Defining the analyzers used

When we added the Hibernate Search annotations to our entities, we defined the analyzers and normalizers used. Typically:

@FullTextField(analyzer = "english")

@FullTextField(analyzer = "name")

@KeywordField(name = "lastName_sort", sortable = Sortable.YES, normalizer = "sort")

We use:

an analyzer called name for person names,
an analyzer called english for book titles,
a normalizer called sort for our sort fields

but we haven’t set them up yet.

Let’s see how you can do it with Hibernate Search.

Setting up the analyzers

It is an easy task, we just need to create an implementation of ElasticsearchAnalysisConfigurer (and configure Quarkus to use it, more on that later).

To fulfill our requirements, let’s create the following implementation:

package org.acme.hibernate.search.elasticsearch.config;

import org.hibernate.search.backend.elasticsearch.analysis.ElasticsearchAnalysisConfigurationContext;
import org.hibernate.search.backend.elasticsearch.analysis.ElasticsearchAnalysisConfigurer;

import io.quarkus.hibernate.search.standalone.elasticsearch.SearchExtension;

@SearchExtension (1)
public class AnalysisConfigurer implements ElasticsearchAnalysisConfigurer {

    @Override
    public void configure(ElasticsearchAnalysisConfigurationContext context) {
        context.analyzer("name").custom() (2)
                .tokenizer("standard")
                .tokenFilters("asciifolding", "lowercase");

        context.analyzer("english").custom() (3)
                .tokenizer("standard")
                .tokenFilters("asciifolding", "lowercase", "porter_stem");

        context.normalizer("sort").custom() (4)
                .tokenFilters("asciifolding", "lowercase");
    }
}

1	Annotate the configurer implementation with the `@SearchExtension` qualifier to tell Quarkus it should be used in Hibernate Search Standalone, for all Elasticsearch indexes (by default). The annotation can also target a specific persistence unit (`@SearchExtension(persistenceUnit = "nameOfYourPU")`), backend (`@SearchExtension(backend = "nameOfYourBackend")`), index (`@SearchExtension(index = "nameOfYourIndex")`), or a combination of those (`@SearchExtension(persistenceUnit = "nameOfYourPU", backend = "nameOfYourBackend", index = "nameOfYourIndex")`).
2	This is a simple analyzer separating the words on spaces, removing any non-ASCII characters by its ASCII counterpart (and thus removing accents) and putting everything in lowercase. It is used in our examples for the author’s names.
3	We are a bit more aggressive with this one and we include some stemming: we will be able to search for `mystery` and get a result even if the indexed input contains `mysteries`. It is definitely too aggressive for person names, but it is perfect for the book titles.
4	Here is the normalizer used for sorting. Very similar to our first analyzer, except we don’t tokenize the words as we want one and only one token.

For more information about configuring analyzers, see this section of the reference documentation.

Implementing the REST service

Create the org.acme.hibernate.search.elasticsearch.LibraryResource class:

package org.acme.hibernate.search.elasticsearch;

import java.util.ArrayList;
import java.util.UUID;

import jakarta.inject.Inject;
import jakarta.ws.rs.Consumes;
import jakarta.ws.rs.DELETE;
import jakarta.ws.rs.GET;
import jakarta.ws.rs.NotFoundException;
import jakarta.ws.rs.POST;
import jakarta.ws.rs.PUT;
import jakarta.ws.rs.Path;
import jakarta.ws.rs.core.MediaType;

import org.acme.hibernate.search.elasticsearch.model.Author;
import org.acme.hibernate.search.elasticsearch.model.Book;

import org.hibernate.search.mapper.pojo.standalone.mapping.SearchMapping;
import org.hibernate.search.mapper.pojo.standalone.session.SearchSession;

import org.jboss.resteasy.reactive.RestForm;
import org.jboss.resteasy.reactive.RestPath;

@Path("/library")
public class LibraryResource {

    @Inject
    SearchMapping searchMapping; (1)

    @PUT
    @Path("author")
    @Consumes(MediaType.APPLICATION_FORM_URLENCODED)
    public void addAuthor(@RestForm String firstName, @RestForm String lastName) {
        try (var searchSession = searchMapping.createSession()) { (2)
            Author author = new Author(UUID.randomUUID(), firstName, lastName, new ArrayList<>());
            searchSession.indexingPlan().add(author); (3)
        }
    }

    @GET
    @Path("author/{id}")
    public Author getAuthor(@RestPath UUID id) {
        try (var searchSession = searchMapping.createSession()) {
            return getAuthor(searchSession, id);
        }
    }

    private Author getAuthor(SearchSession searchSession, UUID id) {
        return searchSession.search(Author.class) (4)
                .where(f -> f.id().matching(id))
                .fetchSingleHit()
                .orElseThrow(NotFoundException::new);
    }

    @POST
    @Path("author/{id}")
    @Consumes(MediaType.APPLICATION_FORM_URLENCODED)
    public void updateAuthor(@RestPath UUID id, @RestForm String firstName, @RestForm String lastName) {
        try (var searchSession = searchMapping.createSession()) {
            Author author = getAuthor(searchSession, id); (5)
            author.firstName = firstName;
            author.lastName = lastName;
            searchSession.indexingPlan().addOrUpdate(author); (5)
        }
    }

    @DELETE
    @Path("author/{id}")
    public void deleteAuthor(@RestPath UUID id) {
        try (var searchSession = searchMapping.createSession()) {
            searchSession.indexingPlan().purge(Author.class, id, null); (6)
        }
    }

    @PUT
    @Path("author/{authorId}/book/")
    @Consumes(MediaType.APPLICATION_FORM_URLENCODED)
    public void addBook(@RestPath UUID authorId, @RestForm String title) {
        try (var searchSession = searchMapping.createSession()) {
            Author author = getAuthor(searchSession, authorId); (7)
            author.books.add(new Book(authorId, title));
            searchSession.indexingPlan().addOrUpdate(author);
        }
    }

    @DELETE
    @Path("author/{authorId}/book/{bookId}")
    public void deleteBook(@RestPath UUID authorId, @RestPath UUID bookId) {
        try (var searchSession = searchMapping.createSession()) {
            Author author = getAuthor(searchSession, authorId); (7)
            author.books.removeIf(book -> book.id.equals(bookId));
            searchSession.indexingPlan().addOrUpdate(author);
        }
    }
}

1	Inject a Hibernate Search mapping, the main entry point to Hibernate Search APIs.
2	Create a Hibernate Search session, which allows executing operations on the indexes.
3	To index a new Author, retrieve the session’s indexing plan and call `add`, passing the author instance in argument.
4	To retrieve an Author from the index, execute a simple search — more on search later — by identifier.
5	To update an Author, retrieve it from the index, apply changes, retrieve the session’s indexing plan and call `addOrUpdate`, passing the author instance in argument.
6	To delete an Author by identifier, retrieve the session’s indexing plan and call `purge`, passing the author class and identifier in argument.
7	Since books are "owned" by authors (they are duplicated for each author and their lifecycle is bound to their author’s), adding/deleting a book is simply an update to the author.

Nothing groundbreaking here: just a few CRUD operations in a REST service, using Hibernate Search APIs.

The interesting part comes with the addition of a search endpoint. In our LibraryResource, we just need to add the following method (and a few imports):

    @GET
    @Path("author/search")
    public List<Author> searchAuthors(@RestQuery String pattern, (1)
            @RestQuery Optional<Integer> size) {
        try (var searchSession = searchMapping.createSession()) { (2)
            return searchSession.search(Author.class) (3)
                    .where(f -> pattern == null || pattern.isBlank()
                            ? f.matchAll() (4)
                            : f.simpleQueryString()
                                    .fields("firstName", "lastName", "books.title").matching(pattern)) (5)
                    .sort(f -> f.field("lastName_sort").then().field("firstName_sort")) (6)
                    .fetchHits(size.orElse(20)); (7)
        }
    }

1	Use the `org.jboss.resteasy.reactive.RestQuery` annotation type to avoid repeating the parameter name.
2	Create a Hibernate Search session, which allows executing operations on the indexes.
3	We indicate that we are searching for `Author`s.
4	We create a predicate: if the pattern is empty, we use a `matchAll()` predicate.
5	If we have a valid pattern, we create a `simpleQueryString()` predicate on the `firstName`, `lastName` and `books.title` fields matching our pattern.
6	We define the sort order of our results. Here we sort by last name, then by first name. Note that we use the specific fields we created for sorting.
7	Fetch the `size` top hits, `20` by default. Obviously, paging is also supported.

The Hibernate Search DSL supports a significant subset of the Elasticsearch predicates (match, range, nested, phrase, spatial…). Feel free to explore the DSL using autocompletion.

When that’s not enough, you can always fall back to defining a predicate using JSON directly.

Automatic data initialization

For the purpose of this demonstration, let’s import an initial dataset.

Let’s add a few methods in LibraryResource:

    void onStart(@Observes StartupEvent ev) { (1)
        // Index some test data if nothing exists
        try (var searchSession = searchMapping.createSession()) {
            if (0 < searchSession.search(Author.class) (2)
                    .where(f -> f.matchAll())
                    .fetchTotalHitCount()) {
                return;
            }
            for (Author author : initialDataSet()) { (3)
                searchSession.indexingPlan().add(author); (4)
            }
        }
    }

    private List<Author> initialDataSet() {
        return List.of(
                new Author(UUID.randomUUID(), "John", "Irving",
                        List.of(
                                new Book(UUID.randomUUID(), "The World According to Garp"),
                                new Book(UUID.randomUUID(), "The Hotel New Hampshire"),
                                new Book(UUID.randomUUID(), "The Cider House Rules"),
                                new Book(UUID.randomUUID(), "A Prayer for Owen Meany"),
                                new Book(UUID.randomUUID(), "Last Night in Twisted River"),
                                new Book(UUID.randomUUID(), "In One Person"),
                                new Book(UUID.randomUUID(), "Avenue of Mysteries"))),
                new Author(UUID.randomUUID(), "Paul", "Auster",
                        List.of(
                                new Book(UUID.randomUUID(), "The New York Trilogy"),
                                new Book(UUID.randomUUID(), "Mr. Vertigo"),
                                new Book(UUID.randomUUID(), "The Brooklyn Follies"),
                                new Book(UUID.randomUUID(), "Invisible"),
                                new Book(UUID.randomUUID(), "Sunset Park"),
                                new Book(UUID.randomUUID(), "4 3 2 1"))));
    }

1	Add a method that will get executed on application startup.
2	Check whether there already is data in the index — if not, bail out.
3	Generate the initial dataset.
4	For each author, add it to the index.

Configuring the application

As usual, we can configure everything in the Quarkus configuration file, application.properties.

Edit src/main/resources/application.properties and inject the following configuration:

quarkus.hibernate-search-standalone.mapping.structure=document (1)
quarkus.hibernate-search-standalone.elasticsearch.version=9 (2)
quarkus.hibernate-search-standalone.indexing.plan.synchronization.strategy=sync (3)

%prod.quarkus.hibernate-search-standalone.elasticsearch.hosts=localhost:9200 (4)

1	We need to tell Hibernate Search about the structure of our entities. In this application we consider an indexed entity (the author) is the root of a "document": the author "owns" books it references through associations, which cannot be updated independently of the author. See `quarkus.hibernate-search-standalone.mapping.structure` for other options and more details.
2	We need to tell Hibernate Search about the version of Elasticsearch we will use. It is important because there are significant differences between Elasticsearch mapping syntax depending on the version. Since the mapping is created at build time to reduce startup time, Hibernate Search cannot connect to the cluster to automatically detect the version. Note that, for OpenSearch, you need to prefix the version with `opensearch:`; see OpenSearch compatibility.
3	This means that we wait for the entities to be searchable before considering a write complete. On a production setup, the `write-sync` default will provide better performance. Using `sync` is especially important when testing as you need the entities to be searchable immediately.
4	For development and tests, we rely on Dev Services, which means Quarkus will start an Elasticsearch cluster automatically. In production mode, however, we will want to start an Elasticsearch cluster manually, which is why we provide Quarkus with this connection info in the `prod` profile (`%prod.` prefix).

Because we rely on Dev Services, the Elasticsearch schema will automatically be dropped and re-created on each application startup in tests and dev mode (unless quarkus.hibernate-search-standalone.schema-management.strategy is set explicitly).

If for some reason you cannot use Dev Services, you will have to set the following properties to get similar behavior:

%dev,test.quarkus.hibernate-search-standalone.schema-management.strategy=drop-and-create

For more information about configuration of the Hibernate Search Standalone extension, refer to the Configuration Reference.

Creating a frontend

Now let’s add a simple web page to interact with our LibraryResource. Quarkus automatically serves static resources located under the META-INF/resources directory. In the src/main/resources/META-INF/resources directory, overwrite the existing index.html file with the content from this $${quickstarts-base-url}/blob/main/hibernate-search-standalone-elasticsearch-quickstart/src/main/resources/META-INF/resources/index.html[index.html] file.

Time to play with your application

You can now interact with your REST service:

start your Quarkus application with:

CLI

quarkus dev

Maven

./mvnw quarkus:dev

Gradle

./gradlew --console=plain quarkusDev

open a browser to http://localhost:8080/
search for authors or book titles (we initialized some data for you)
create new authors and books and search for them too

As you can see, all your updates are automatically synchronized to the Elasticsearch cluster.

Building a native executable

You can build a native executable with the usual command:

CLI

quarkus build --native

Maven

./mvnw install -Dnative

Gradle

./gradlew build -Dquarkus.native.enabled=true

As usual with native executable compilation, this operation consumes a lot of memory.

It might be safer to stop the two containers while you are building the native executable and start them again once you are done.

Running it is as simple as executing ./target/hibernate-search-standalone-elasticsearch-quickstart-1.0.0-SNAPSHOT-runner.

You can then point your browser to http://localhost:8080/ and use your application.

The startup is a bit slower than usual: it is mostly due to us dropping and recreating the Elasticsearch mapping every time at startup. We also index some initial data.

In a real life application, it is obviously something you won’t do on every startup.

Dev Services (Configuration Free Datastores)

Quarkus supports a feature called Dev Services that allows you to start various containers without any config.

In the case of Elasticsearch this support extends to the default Elasticsearch connection. What that means practically, is that if you have not configured quarkus.hibernate-search-standalone.elasticsearch.hosts, Quarkus will automatically start an Elasticsearch container when running tests or in dev mode, and automatically configure the connection.

When running the production version of the application, the Elasticsearch connection needs to be configured as normal, so if you want to include a production database config in your application.properties and continue to use Dev Services we recommend that you use the %prod. profile to define your Elasticsearch settings.

Dev Services for Elasticsearch is currently unable to start multiple clusters concurrently, so it only works with the default backend of the default persistence unit: named persistence units or named backends won’t be able to take advantage of Dev Services for Elasticsearch.

For more information you can read the Dev Services for Elasticsearch guide.

Programmatic mapping

If, for some reason, adding Hibernate Search annotations to entities is not possible, mapping can be applied programmatically instead. Programmatic mapping is configured through the ProgrammaticMappingConfigurationContext that is exposed via a mapping configurer (HibernateOrmSearchMappingConfigurer).

A mapping configurer (StandalonePojoMappingConfigurer) allows much more than just programmatic mapping capabilities. It also allows configuring annotation mapping, bridges, and more.

Below is an example of a mapping configurer that applies programmatic mapping:

package org.acme.hibernate.search.elasticsearch.config;

import org.hibernate.search.mapper.pojo.standalone.mapping.StandalonePojoMappingConfigurationContext;
import org.hibernate.search.mapper.pojo.standalone.mapping.StandalonePojoMappingConfigurer;
import org.hibernate.search.mapper.pojo.mapping.definition.programmatic.TypeMappingStep;

import io.quarkus.hibernate.search.standalone.elasticsearch.SearchExtension;

@SearchExtension (1)
public class CustomMappingConfigurer implements StandalonePojoMappingConfigurer {

	@Override
    public void configure(StandalonePojoMappingConfigurationContext context) {
        TypeMappingStep type = context.programmaticMapping()    (2)
                .type(SomeIndexedEntity.class);                 (3)
        type.searchEntity();                                    (4)
        type.indexed()                                          (5)
                .index(SomeIndexedEntity.INDEX_NAME);           (6)
        type.property("id").documentId();                       (7)
        type.property("text").fullTextField();                  (8)
    }
}

1	Annotate the configurer implementation with the `@SearchExtension` qualifier to tell Quarkus it should be used by Hibernate Search Standalone.
2	Access the programmatic mapping context.
3	Create mapping step for the `SomeIndexedEntity` type.
4	Define `SomeIndexedEntity` as an entity type for Hibernate Search.
5	Define the `SomeIndexedEntity` entity as indexed.
6	Provide an index name to be used for the `SomeIndexedEntity` entity.
7	Define the document id property.
8	Define a full-text search field for the `text` property.

OpenSearch compatibility

Hibernate Search is compatible with both Elasticsearch and OpenSearch, but it assumes it is working with an Elasticsearch cluster by default.

To have Hibernate Search work with an OpenSearch cluster instead, prefix the configured version with opensearch:, as shown below.

quarkus.hibernate-search-standalone.elasticsearch.version=opensearch:3.1

All other configuration options and APIs are exactly the same as with Elasticsearch.

You can find more information about compatible distributions and versions of Elasticsearch in this section of Hibernate Search’s reference documentation.

CDI integration

Injecting entry points

You can inject Hibernate Search’s main entry point, SearchMapping, using CDI:

@Inject
SearchMapping searchMapping;

Plugging in custom components

The Quarkus extension for Hibernate Search Standalone will automatically inject components annotated with @SearchExtension into Hibernate Search.

The annotation can optionally target a specific backend (@SearchExtension(backend = "nameOfYourBackend")), index (@SearchExtension(index = "nameOfYourIndex")), or a combination of those (@SearchExtension(backend = "nameOfYourBackend", index = "nameOfYourIndex")), when it makes sense for the type of the component being injected.

This feature is available for the following component types:

org.hibernate.search.engine.reporting.FailureHandler: A component that should be notified of any failure occurring in a background process (mainly index operations).

Scope: one per application.

See this section of the reference documentation for more information.
org.hibernate.search.mapper.pojo.standalone.mapping.StandalonePojoMappingConfigurer: A component used to configure the Hibernate Search mapping, in particular programmatically.

Scope: one or more per persistence unit.

See this section of this guide for more information.
org.hibernate.search.mapper.pojo.work.IndexingPlanSynchronizationStrategy: A component used to configure how to synchronize between application threads and indexing.

Scope: one per application.

Can also be set to built-in implementations through quarkus.hibernate-search-standalone.indexing.plan.synchronization.strategy.

See this section of the reference documentation for more information.
org.hibernate.search.backend.elasticsearch.analysis.ElasticsearchAnalysisConfigurer: A component used to configure full text analysis (e.g. analyzers, normalizers).

Scope: one or more per backend.

See this section of this guide for more information.
org.hibernate.search.backend.elasticsearch.index.layout.IndexLayoutStrategy: A component used to configure the Elasticsearch layout: index names, index aliases, …

Scope: one per backend.

Can also be set to built-in implementations through quarkus.hibernate-search-standalone.elasticsearch.layout.strategy.

See this section of the reference documentation for more information.

Offline startup

By default, Hibernate Search sends a few requests to the Elasticsearch cluster on startup. If the Elasticsearch cluster is not necessarily up and running when Hibernate Search starts, this could cause a startup failure.

To address this, you can configure Hibernate Search to not send any request on startup:

Disable Elasticsearch version checks on startup by setting the configuration property quarkus.hibernate-search-standalone.elasticsearch.version-check.enabled to false.
Disable schema management on startup by setting the configuration property quarkus.hibernate-search-standalone.schema-management.strategy to none.

Of course, even with this configuration, Hibernate Search still won’t be able to index anything or run search queries until the Elasticsearch cluster becomes accessible.

If you disable automatic schema creation by setting quarkus.hibernate-search-standalone.schema-management.strategy to none, you will have to create the schema manually at some point before your application starts persisting/updating entities and executing search requests.

See this section of the reference documentation for more information.

Loading

As an alternative to using Elasticsearch as a primary datastore, this extension can also be used to index entities coming from another datastore.

In such a scenario, you will need to set quarkus.hibernate-search-standalone.mapping.structure to a value matching the structure of the primary datastore.

In order to do this, entities need to be loaded from that other datastore, and such loading must be implemented explicitly.

You can refer to Hibernate Search’s reference documentation for more information about configuring loading:

To load entities from an external datasource in order to reindex them, see Mass loading strategy.
To load entities from an external datasource when returning search hits, see Selection loading strategy.

In Quarkus, the entity loader mentioned in Hibernate Search’s reference documentation can be defined as a CDI bean, but will still need to be attached to particular entities using @SearchEntity(loadingBinder = …).

Management endpoint

Hibernate Search’s management endpoint is considered preview.

In preview, backward compatibility and presence in the ecosystem is not guaranteed. Specific improvements might require changing configuration or APIs, or even storage formats, and plans to become stable are under way. Feedback is welcome on our mailing list or as issues in our GitHub issue tracker.

The Hibernate Search extension provides an HTTP endpoint to reindex your data through the management interface. By default, this endpoint is not available. It can be enabled through configuration properties as shown below.

quarkus.management.enabled=true (1)
quarkus.hibernate-search-standalone.management.enabled=true (2)

1	Enable the management interface.
2	Enable Hibernate Search Standalone specific management endpoints.

Once the management endpoints are enabled, data can be re-indexed via /q/hibernate-search/standalone/reindex, where /q is the default management root path and /hibernate-search/standalone/ is the default Hibernate Search root management path. It (/hibernate-search/standalone/) can be changed via configuration property as shown below.

quarkus.hibernate-search-standalone.management.root-path=custom-root-path (1)

1	Use a custom `custom-root-path` path for Hibernate Search’s management endpoint. If the default management root path is used then the reindex path becomes `/q/custom-root-path/reindex`.

This endpoint accepts POST requests with application/json content type only. All indexed entities will be re-indexed if an empty request body is submitted.

In order to reindex an entity type, it needs to be configured for loading from an external source.

Without that configuration, reindexing through the management endpoint (or through any other API) will fail.

If only a subset of entities must be re-indexed or if there is a need to have a custom configuration of the underlying mass indexer then this information can be passed through the request body as shown below.

{
  "filter": {
    "types": ["EntityName1", "EntityName2", "EntityName3", ...], (1)
  },
  "massIndexer":{
    "typesToIndexInParallel": 1, (2)
  }
}

1	An array of entity names that should be re-indexed. If unspecified or empty, all entity types will be re-indexed.
2	Sets the number of entity types to be indexed in parallel.

The full list of possible filters and available mass indexer configurations is presented in the example below.

{
  "filter": { (1)
    "types": ["EntityName1", "EntityName2", "EntityName3", ...], (2)
    "tenants": ["tenant1", "tenant2", ...] (3)
  },
  "massIndexer":{ (4)
    "typesToIndexInParallel": 1, (5)
    "threadsToLoadObjects": 6,  (6)
    "batchSizeToLoadObjects": 10, (7)
    "cacheMode": "IGNORE", (8)
    "mergeSegmentsOnFinish": false, (9)
    "mergeSegmentsAfterPurge": true, (10)
    "dropAndCreateSchemaOnStart": false, (11)
    "purgeAllOnStart": true, (12)
    "idFetchSize": 100, (13)
    "transactionTimeout": 100000, (14)
  }
}

1	Filter object that allows to limit the scope of reindexing.
2	An array of entity names that should be re-indexed. If unspecified or empty, all entity types will be re-indexed.
3	An array of tenant ids, in case of multi-tenancy. If unspecified or empty, all tenants will be re-indexed.
4	Mass indexer configuration object.
5	Sets the number of entity types to be indexed in parallel.
6	Sets the number of threads to be used to load the root entities.
7	Sets the batch size used to load the root entities.
8	Sets the cache interaction mode for the data loading tasks.
9	Whether each index is merged into a single segment after indexing.
10	Whether each index is merged into a single segment after the initial index purge, just before indexing.
11	Whether the indexes and their schema (if they exist) should be dropped and re-created before indexing.
12	Whether all entities are removed from the indexes before indexing.
13	Specifies the fetch size to be used when loading primary keys if objects to be indexed.
14	Specifies the timeout of transactions for loading ids and entities to be re-indexed. Note all the properties in the JSON are optional, and only those that are needed should be used.

For more detailed information on mass indexer configuration see the corresponding section of the Hibernate Search reference documentation.

Submitting the reindexing request will trigger indexing in the background. Mass indexing progress will appear in the application logs. For testing purposes, it might be useful to know when the indexing finished. Adding wait_for=finished query parameter to the URL will result in the management endpoint returning a chunked response that will report when the indexing starts and then when it is finished.

Limitations

The Hibernate Search Standalone extension cannot be used in the same application as the Hibernate Search extension with Hibernate ORM

See #39517 to track progress.
AWS request signing is not available at the moment, unlike in the Hibernate Search extension with Hibernate ORM

See #26991 to track progress.
Optimistic concurrency control is not available at the moment.

See HSEARCH-5105 to track progress.
Elasticsearch/OpenSearch do not support transactions, so multi-document updates may fail partially and leave the index in an inconsistent state.

This cannot be avoided like in the Hibernate Search extension with Hibernate ORM with coordination through outbox polling, because that coordination requires Hibernate ORM and relies on the fact that data is derived from that of a (transactional) relational database.

FAQ

Why Elasticsearch only?

Hibernate Search supports both a Lucene backend and an Elasticsearch backend.

In the context of Quarkus and to build scalable applications, we thought the latter would make more sense. Thus, we focused our efforts on it.

We don’t have plans to support the Lucene backend in Quarkus for now, though there is an issue tracking progress on such an implementation in the Quarkiverse: quarkiverse/quarkus-hibernate-search-extras#180.

Configuration Reference for Hibernate Search Standalone

About bean references

First, be aware that referencing beans in configuration properties is optional and, in fact, discouraged: you can achieve the same results by annotating your beans with @SearchExtension. See this section for more information.

If you really do want to reference beans using a string value in configuration properties know that string is parsed; here are the most common formats:

bean: followed by the name of a @Named CDI bean. For example bean:myBean.
class: followed by the fully-qualified name of a class, to be instantiated through CDI if it’s a CDI bean, or through its public, no-argument constructor otherwise. For example class:com.mycompany.MyClass.
An arbitrary string referencing a built-in implementation. Available values are detailed in the documentation of each configuration property, such as async/read-sync/write-sync/sync for quarkus.hibernate-search-standalone.indexing.plan.synchronization.strategy.

Other formats are also accepted, but are only useful for advanced use cases. See this section of Hibernate Search’s reference documentation for more information.

Use Hibernate Search in Standalone mode with Elasticsearch/OpenSearch

Prerequisites

Architecture

Solution

Creating the Maven project

Creating the bare classes

Using Hibernate Search annotations

Analyzers and normalizers

Introduction

Defining the analyzers used

Setting up the analyzers

Implementing the REST service

Automatic data initialization

Configuring the application

Creating a frontend

Time to play with your application

Building a native executable

Dev Services (Configuration Free Datastores)

Programmatic mapping

OpenSearch compatibility

CDI integration

Injecting entry points

Plugging in custom components

Offline startup

Loading

Management endpoint

Limitations

Further reading

FAQ

Why Elasticsearch only?

Configuration Reference for Hibernate Search Standalone