Using Elasticsearch for data search in Java API development

With the rapid development of Internet technology, the scale and complexity of modern applications are increasing, and these applications usually involve a large amount of data search and analysis. In order to solve these problems, more and more developers are beginning to use full-text search engines to help them process massive data.

In this article, we will introduce how to use Elasticsearch for data search, and for the application in the Java API development process, we will introduce the API interface provided by Elasticsearch, as well as the implementation process and techniques.

1. Elasticsearch Introduction

Elasticsearch is an open source full-text search engine built on the Apache Lucene search engine text search library. Elasticsearch has the characteristics of distributed, high availability, real-time search, and analysis. It can quickly store, retrieve, and analyze large amounts of data. In data search scenarios, Elasticsearch is a very popular and widely used solution as it provides a powerful and easy-to-use API interface that can be quickly integrated into Java applications.

2. Basic concepts of Elasticsearch

When using Elasticsearch for data search, there are some basic concepts and terms that need to be understood:

(1) Index

In Elasticsearch, an index is a logical container used to store data, which is similar to a table in a relational database. Each index can contain multiple documents, and each document is a data structure in JSON format.

(2) Type

In Elasticsearch, each index can contain multiple types (types), and each type can define its own fields. Types have been deprecated in recent versions of Elasticsearch, but are still used as selectors in some APIs.

(3) Sharding and replicas

In Elasticsearch, an index can be divided into multiple shards (shards), and each shard is an independent Lucene index. Sharding distributes and stores indexed data on multiple servers to achieve distributed storage and query. In addition, Elasticsearch also supports replicas. Each shard can have multiple replicas to improve search performance and data availability.

(4) Nodes and clusters

Elasticsearch is a distributed search engine that can run on multiple nodes. A node is a single Elasticsearch instance, and the entire cluster is composed of multiple nodes. Nodes can communicate with each other and work together to complete search tasks.

3. Elasticsearch Java API

Elasticsearch provides a rich Java API interface that can be easily integrated with Java applications. Java developers can use the following APIs for data indexing, query and management:

(1) Index API

The Index API is used to index documents in JSON format into the specified Elasticsearch index. It supports batch indexing, which can index multiple documents into the same index at one time.

(2)Search API

Search API is used to perform search operations. It supports a variety of search methods, including full-text search, field search, fuzzy search, etc.

(3) Delete API

Delete API is used to delete the specified Elasticsearch index.

(4) Get API

Get API is used to obtain documents based on the specified index, type and ID.

(5)Update API

Update API is used to update the specified document.

In addition, Elasticsearch also provides many other API interfaces, including geographical location search, text highlighting, aggregation, etc.

4. Using Elasticsearch for data search

Before using Elasticsearch for data search, you need to install Elasticsearch and start it. Then use the Java API to connect to the Elasticsearch server, create the index and add data to the index. Next, you can search using the Search API. The following is a basic Java code example:

import java.net.InetAddress;

import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.client.transport.TransportClient;
import org.elasticsearch.common.settings.Settings;
import org.elasticsearch.common.transport.TransportAddress;
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.search.SearchHit;
import org.elasticsearch.transport.client.PreBuiltTransportClient;

public class ElasticsearchSearchDemo {

    public static void main(String[] args)
            throws Exception {

        // 设置集群名称
        Settings settings = Settings.builder().put("cluster.name", "elasticsearch").build();

        // 创建客户端
        TransportClient client = new PreBuiltTransportClient(settings)
                .addTransportAddress(new TransportAddress(InetAddress.getByName("localhost"), 9300));

        // 搜索数据
        SearchResponse response = client.prepareSearch("books")
                .setQuery(QueryBuilders.matchQuery("title", "Java"))
                .get();

        // 打印结果
        for (SearchHit hit : response.getHits().getHits()) {
            System.out.println(hit.getSourceAsString());
        }

        // 关闭客户端
        client.close();
    }

}

The above code creates a TransportClient object, connects to the local Elasticsearch service, and uses the prepareSearch method to perform query operations. This query operation performs a fuzzy search on the title field of the books index and outputs the search results.

5. Summary

In today’s data era, the demand for data search and analysis is increasing. As a powerful full-text search engine, Elasticsearch has the advantages of distribution, high availability, real-time search, and analysis. In the Java API development scenario, Elasticsearch provides rich and easy-to-use API interfaces, providing developers with convenient data search capabilities. I hope this article can help Java developers better use Elasticsearch for data search.

The above is the detailed content of Using Elasticsearch for data search in Java API development. For more information, please follow other related articles on the PHP Chinese website!