Elasticsearch实战：从入门到精通

Elasticsearch 核心概念与架构

Elasticsearch 是一个基于 Lucene 的分布式搜索和分析引擎，采用 JSON 文档格式存储数据。核心概念包括索引（Index）、类型（Type，7.x 后已废弃）、文档（Document）、分片（Shard）和副本（Replica）。其分布式架构通过集群（Cluster）管理多个节点（Node），自动处理数据分片和负载均衡。

Java 集成环境搭建

引入 Maven 依赖：

<dependency>
    <groupId>org.elasticsearch.client</groupId>
    <artifactId>elasticsearch-rest-high-level-client</artifactId>
    <version>7.15.2</version>
</dependency>

初始化客户端：

RestHighLevelClient client = new RestHighLevelClient(
    RestClient.builder(new HttpHost("localhost", 9200, "http"))
);

索引与文档操作

创建索引：

CreateIndexRequest request = new CreateIndexRequest("products");
client.indices().create(request, RequestOptions.DEFAULT);

索引文档：

IndexRequest request = new IndexRequest("products")
    .id("1")
    .source(
        "name", "智能手机",
        "price", 3999,
        "brand", "华为"
    );
IndexResponse response = client.index(request, RequestOptions.DEFAULT);

搜索查询实战

匹配查询：

SearchRequest request = new SearchRequest("products");
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
sourceBuilder.query(QueryBuilders.matchQuery("name", "手机"));
request.source(sourceBuilder);
SearchResponse response = client.search(request, RequestOptions.DEFAULT);

复合查询：

BoolQueryBuilder boolQuery = QueryBuilders.boolQuery()
    .must(QueryBuilders.matchQuery("brand", "华为"))
    .filter(QueryBuilders.rangeQuery("price").gte(3000));

聚合分析示例

价格区间统计：

TermsAggregationBuilder aggregation = AggregationBuilders
    .terms("price_ranges")
    .field("price")
    .subAggregation(AggregationBuilders.avg("avg_price").field("price"));

性能优化策略

分片策略建议：

单个分片大小控制在 30-50GB
副本数根据集群节点数量设置
使用 _rollover API 管理时序数据

查询优化技巧：

// 启用查询缓存
sourceBuilder.requestCache(true);
// 分页深度优化
sourceBuilder.from(10000).size(10);

生产环境实践

安全配置：

启用 X-Pack 基础安全功能
配置 TLS 加密通信
实现基于角色的访问控制

监控方案：

使用 Elastic Stack 自带监控功能
配置关键指标告警（CPU、堆内存、磁盘空间）
定期检查集群健康状态 API

典型应用场景

日志分析系统：

Filebeat 采集日志
Logstash 数据处理管道
Kibana 可视化分析

商品搜索实现：

中文分词器（IK Analyzer）配置
多字段匹配权重设置
搜索结果高亮显示

故障排查指南

常见问题处理：

分片未分配：检查磁盘空间和节点配置
查询超时：优化复杂聚合或增加 timeout 参数
内存不足：调整 JVM 堆大小和字段数据缓存

通过以上技术模块的完整实现，可以构建高性能的搜索服务。实际开发中建议结合具体业务需求调整数据建模和查询方式，定期进行集群健康检查和性能测试。

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