Springboot2.x整合ElasticSearch7.x的过程是怎样的

发布时间：2021-09-29 15:49:12 作者：柒染
来源：亿速云阅读：147

# Spring Boot 2.x整合Elasticsearch 7.x的全过程详解

## 一、前言

### 1.1 技术背景
Elasticsearch作为基于Lucene的分布式搜索引擎，以其**高性能、可扩展性**和**丰富的查询功能**成为大数据搜索领域的首选方案。Spring Boot作为Java生态中最流行的微服务框架，其**约定优于配置**的理念极大简化了企业级应用开发。

### 1.2 版本选择考量
- Spring Boot 2.3.x+ 官方开始支持ES 7.x客户端
- Elasticsearch 7.x 移除type概念，性能提升40%
- 注意JDK版本兼容性（ES7需要JDK11+）

## 二、环境准备

### 2.1 基础环境配置
```bash
# 开发环境要求
- JDK 11+
- Maven 3.6+
- Elasticsearch 7.9.2（测试版本）

2.2 依赖引入

<!-- pom.xml关键依赖 -->
<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-data-elasticsearch</artifactId>
    </dependency>
    <!-- 使用High Level REST Client -->
    <dependency>
        <groupId>org.elasticsearch.client</groupId>
        <artifactId>elasticsearch-rest-high-level-client</artifactId>
        <version>7.9.2</version>
    </dependency>
</dependencies>

三、核心配置详解

3.1 配置文件

# application.yml
spring:
  elasticsearch:
    rest:
      uris: http://localhost:9200
      username: elastic
      password: changeme
      connection-timeout: 5000
      read-timeout: 30000

3.2 Java配置类

@Configuration
public class ElasticsearchConfig {

    @Value("${spring.elasticsearch.rest.uris}")
    private String[] uris;

    @Bean
    public RestHighLevelClient client() {
        ClientConfiguration config = ClientConfiguration.builder()
            .connectedTo(uris)
            .withConnectTimeout(Duration.ofSeconds(5))
            .withSocketTimeout(Duration.ofSeconds(30))
            .build();
        return RestClients.create(config).rest();
    }
}

四、数据模型设计

4.1 实体类映射

@Document(indexName = "products", createIndex = false)
public class Product {
    @Id
    private String id;
    
    @Field(type = FieldType.Text, analyzer = "ik_max_word")
    private String name;
    
    @Field(type = FieldType.Double)
    private Double price;
    
    @Field(type = FieldType.Date, format = DateFormat.date_hour_minute_second)
    private Date createTime;
}

4.2 自定义映射配置

PUT /products
{
  "mappings": {
    "properties": {
      "name": {
        "type": "text",
        "analyzer": "ik_smart"
      },
      "price": {
        "type": "scaled_float",
        "scaling_factor": 100
      }
    }
  }
}

五、Repository操作

5.1 基础CRUD接口

public interface ProductRepository extends ElasticsearchRepository<Product, String> {
    
    List<Product> findByName(String name);
    
    Page<Product> findByPriceBetween(Double min, Double max, Pageable pageable);
}

5.2 自定义查询示例

@Repository
public class ProductCustomRepositoryImpl implements ProductCustomRepository {

    @Autowired
    private RestHighLevelClient client;

    public List<Product> complexQuery(String keyword, Date startDate) {
        NativeSearchQueryBuilder queryBuilder = new NativeSearchQueryBuilder();
        queryBuilder.withQuery(QueryBuilders.boolQuery()
            .must(QueryBuilders.matchQuery("name", keyword))
            .filter(QueryBuilders.rangeQuery("createTime").gte(startDate)));
        
        SearchHits<Product> hits = elasticsearchOperations.search(
            queryBuilder.build(), Product.class);
        // 结果处理...
    }
}

六、高级特性集成

6.1 异步操作

@Async
public CompletableFuture<Page<Product>> asyncSearch(String query) {
    NativeSearchQuery searchQuery = new NativeSearchQueryBuilder()
        .withQuery(QueryBuilders.queryStringQuery(query))
        .build();
    return CompletableFuture.completedFuture(
        elasticsearchOperations.queryForPage(searchQuery, Product.class));
}

6.2 批量处理

@Autowired
private ElasticsearchRestTemplate template;

public void bulkIndex(List<Product> products) {
    BulkRequest bulkRequest = new BulkRequest();
    products.forEach(p -> {
        IndexRequest request = new IndexRequest("products")
            .id(p.getId())
            .source(BeanUtil.beanToMap(p));
        bulkRequest.add(request);
    });
    client.bulk(bulkRequest, RequestOptions.DEFAULT);
}

七、性能优化方案

7.1 连接池配置

@Bean
public RestClientBuilderCustomizer restClientBuilderCustomizer() {
    return builder -> builder
        .setHttpClientConfigCallback(httpClientBuilder -> {
            httpClientBuilder.setMaxConnTotal(50);
            httpClientBuilder.setMaxConnPerRoute(10);
            return httpClientBuilder;
        });
}

7.2 查询优化技巧

// 使用source filtering减少网络传输
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder()
    .fetchSource(new String[]{"name", "price"}, null);

// 启用查询缓存
QueryBuilder queryBuilder = QueryBuilders
    .termQuery("category", "electronics")
    .queryName("cached_query");

八、异常处理

8.1 统一异常拦截

@ControllerAdvice
public class ElasticsearchExceptionHandler {

    @ExceptionHandler(ElasticsearchStatusException.class)
    public ResponseEntity<String> handleEsException(ElasticsearchStatusException e) {
        return ResponseEntity.status(e.status().getStatus())
            .body("ES服务异常: " + e.getDetailedMessage());
    }
}

8.2 重试机制

@Retryable(value = { ElasticsearchException.class }, 
           maxAttempts = 3, 
           backoff = @Backoff(delay = 1000))
public void updateProduct(Product product) {
    // 更新操作...
}

九、测试验证

9.1 集成测试

@SpringBootTest
@ExtendWith(SpringExtension.class)
public class ProductRepositoryTest {

    @Autowired
    private ProductRepository repository;

    @Test
    public void testIndexAndSearch() {
        Product product = new Product();
        product.setName("华为手机");
        repository.save(product);
        
        List<Product> result = repository.findByName("华为");
        assertThat(result).hasSize(1);
    }
}

9.2 性能测试

@Test
public void benchmarkSearch() {
    int threadCount = 50;
    ExecutorService executor = Executors.newFixedThreadPool(threadCount);
    
    LongAdder counter = new LongAdder();
    IntStream.range(0, 1000).forEach(i -> {
        executor.submit(() -> {
            repository.search(new NativeSearchQueryBuilder()
                .withQuery(QueryBuilders.matchAllQuery()).build());
            counter.increment();
        });
    });
    
    // 输出QPS...
}

十、生产实践建议

10.1 集群部署方案

推荐3个master节点（奇数原则）
数据节点根据数据量配置（建议16核64G内存）
使用Ingest节点处理数据预处理

10.2 监控方案

# 集成Prometheus监控
management:
  endpoints:
    web:
      exposure:
        include: prometheus,health,info
  metrics:
    export:
      prometheus:
        enabled: true

十一、版本升级指南

11.1 从6.x升级到7.x

重建索引的兼容性方案
类型(type)字段的迁移策略
查询语法的变化点对比

十二、总结

本文详细介绍了Spring Boot 2.x与Elasticsearch 7.x整合的全流程，涵盖从环境搭建到生产部署的完整知识体系。关键点包括： 1. 使用RestHighLevelClient替代TransportClient 2. 新版Spring Data Elasticsearch的API变化 3. 性能调优的二十个实用技巧

最佳实践建议：对于高并发场景，建议结合Spring Cache实现二级缓存，可降低ES集群压力30%以上。 “`

（注：实际文章内容会根据技术细节展开每个章节的详细说明，包含代码示例的完整实现、参数调优的具体数值分析、性能对比测试数据等，此处为保持简洁仅展示核心框架和关键代码片段）