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1. Overview

In this tutorial, we'll explore the basics of Spring Data Elasticsearch in a code-focused and practical manner.

We'll show how to index, search, and query Elasticsearch in a Spring application using Spring Data Elasticsearch. Spring Data Elasticseach is a Spring module that implements Spring Data thus offering a way to interact with the popular open-source, Lucene-based search engine.

While Elasticsearch can work with no hardly defined schema, it's a common practice to design one and create mappings specifying the type of data to be expected in certain fields. When a document is indexed, its fields are processed according to their types. For example, a text field will be tokenized and filtered according to mapping rules. We could also create filters and tokenizers of our own.

For the sake of simplicity, we'll use a docker image for our Elasticsearch instance, though any Elasticsearch instance listening on port 9200 will do.

We start by firing up our Elasticsearch instance:

docker run -d --name es762 -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2

2. Spring Data

Spring Data helps avoid boilerplate code. For example, if we define a repository interface that extends the ElasticsearchRepository interface provided by Spring Data Elasticsearch, CRUD operations for the corresponding document class will be made available by default.

Additionally, simply by declaring methods with names in a predefined format, method implementations are generated for us – there is no need to write an implementation of the repository interface.

The Baeldung guides on Spring Data provide the essentials to get started on the topic.

2.1. Maven Dependency

Spring Data Elasticsearch provides a Java API for the search engine. In order to use it we need to add a new dependency to the pom.xml:


2.2. Defining Repository Interfaces

In order to define new repositories, we extend one of the provided repository interfaces, replacing the generic types with our actual document and primary key types.

It's important to note that ElasticsearchRepository extends from PagingAndSortingRepository. This allows built-in support for pagination and sorting.

In our example, we will use the paging feature in our custom search methods:

public interface ArticleRepository extends ElasticsearchRepository<Article, String> {

    Page<Article> findByAuthorsName(String name, Pageable pageable);

    @Query("{\"bool\": {\"must\": [{\"match\": {\"\": \"?0\"}}]}}")
    Page<Article> findByAuthorsNameUsingCustomQuery(String name, Pageable pageable);

With the findByAuthorsName method, the repository proxy will create an implementation based on the method name. The resolution algorithm will determine that it needs to access the authors property and then search the name property of each item.

The second method, findByAuthorsNameUsingCustomQuery, uses a custom Elasticsearch boolean query, defined using the @Query annotation, which requires strict matching between the author's name and the provided name argument.

2.3. Java Configuration

When configuring Elasticsearch in our Java application, we need to define how we connect to the Elasticsearch instance. For that, we use a RestHighLevelClient that is offered by the Elasticsearch dependency:

@EnableElasticsearchRepositories(basePackages = "")
@ComponentScan(basePackages = { "" })
public class Config {

    public RestHighLevelClient client() {
        ClientConfiguration clientConfiguration 
            = ClientConfiguration.builder()

        return RestClients.create(clientConfiguration).rest();

    public ElasticsearchOperations elasticsearchTemplate() {
        return new ElasticsearchRestTemplate(client());

We're using a standard Spring-enabled style annotation. @EnableElasticsearchRepositories will make Spring Data Elasticsearch scan the provided package for Spring Data repositories.

In order to communicate with our Elasticsearch server, we use a simple RestHighLevelClient. While Elasticsearch provides multiple types of clients, using the RestHighLevelClient is a good way to future-proof the communication with the server.

Finally, we set up an ElasticsearchOperations bean to execute operations on our server. In this case, we instantiate an ElasticsearchRestTemplate.

3. Mappings

Mappings are used to define a schema for our documents. By defining a schema to our documents, we protect them from undesired outcomes such as mapping to a type we wouldn't want to.

Our entity is a simple document named Article where the id is of the type String. We also specify that such documents must be stored in an index named blog within the article type.

@Document(indexName = "blog", type = "article")
public class Article {

    private String id;
    private String title;
    @Field(type = FieldType.Nested, includeInParent = true)
    private List<Author> authors;
    // standard getters and setters

Indexes can have several types. We can use that feature to implement hierarchies.

The authors field is marked as FieldType.Nested. This allows us to define the Author class separately, but have the individual instances of author embedded in an Article document when it is indexed in Elasticsearch.

4. Indexing Documents

Spring Data Elasticsearch generally auto-creates indexes based on the entities in the project. However, we can also create an index programmatically via the client template:


Then, we can add documents to the index:

Article article = new Article("Spring Data Elasticsearch");
article.setAuthors(asList(new Author("John Smith"), new Author("John Doe")));;

5. Querying

5.1. Method Name-Based Query

When we use the method name-based query, we write methods that define the query we want to perform. During the setup, Spring Data will parse the method signature and create the queries accordingly:

String nameToFind = "John Smith";
Page<Article> articleByAuthorName
  = articleRepository.findByAuthorsName(nameToFind, PageRequest.of(0, 10));

By calling findByAuthorsName with a PageRequest object, we obtain the first page of results (page numbering is zero-based), with that page containing at most 10 articles. The page object also provides the total number of hits for the query along with other handy pagination information.

5.2. A Custom Query

There are a couple of ways to define custom queries for Spring Data Elasticsearch repositories. One way is to use the @Query annotation, as demonstrated in section 2.2.

Another option is to use the query builder to create our custom query.

Having to search for articles that have the word “data” in the title, we could just create a NativeSearchQueryBuilder with a Filter on the title:

Query searchQuery = new NativeSearchQueryBuilder()
   .withFilter(regexpQuery("title", ".*data.*"))
SearchHits<Article> articles =, Article.class, IndexCoordinates.of("blog");

6. Updating and Deleting

In order to update a document, we first must retrieve it:

String articleTitle = "Spring Data Elasticsearch";
Query searchQuery = new NativeSearchQueryBuilder()
  .withQuery(matchQuery("title", articleTitle).minimumShouldMatch("75%"))

SearchHits<Article> articles =, Article.class, IndexCoordinates.of("blog");
Article article = articles.getSearchHit(0).getContent();

Then, we can make changes to the document just by editing the content of the object using its assessors:

article.setTitle("Getting started with Search Engines");;

As for deleting, there are several options. We can retrieve the document and delete it using the delete method:


We can also delete it by id when it's known:


It is also possible to create custom deleteBy queries and make use of the bulk delete feature offered by Elasticsearch:


7. Conclusion

In this tutorial, we explored how to connect and make use of Spring Data Elasticsearch. We discussed how we can query, update and delete documents. Moreover, we also discussed how to create custom queries and they don't fit what's offered by Spring Data Elasticsearch.

As usual, the source code used throughout this tutorial can be found over on GitHub.

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