第 202 章:Elasticsearch 入门

学习目标

理解 ES 核心概念
掌握索引与文档操作
学会基础搜索
了解 Spring Data Elasticsearch

一、Elasticsearch 简介

ES 是基于 Lucene 的分布式搜索与分析引擎,常用于全文搜索、日志分析、APM。

特点:

✅ 全文搜索快(倒排索引)
✅ 近实时(NRT)
✅ 分布式、自动容错
✅ RESTful API
✅ 强大的聚合

二、核心概念

ES	关系型数据库
Index	Database
Type(7.x 废弃)	Table
Document	Row
Field	Column
Mapping	Schema
Query DSL	SQL

三、安装

yaml

# docker-compose.yml
services:
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.11.0
    environment:
      discovery.type: single-node
      ES_JAVA_OPTS: "-Xms512m -Xmx512m"
      xpack.security.enabled: "false"
    ports:
      - "9200:9200"

  kibana:
    image: docker.elastic.co/kibana/kibana:8.11.0
    environment:
      ELASTICSEARCH_HOSTS: http://elasticsearch:9200
    ports:
      - "5601:5601"

bash

# 健康检查
curl http://localhost:9200/_cluster/health

四、基础操作

4.1 创建索引

bash

PUT /articles
{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 1
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "ik_max_word"      # 中文分词
      },
      "content": {
        "type": "text",
        "analyzer": "ik_max_word"
      },
      "author": {
        "type": "keyword"             # 不分词,用于精确匹配
      },
      "tags": {
        "type": "keyword"
      },
      "viewCount": {
        "type": "integer"
      },
      "createdAt": {
        "type": "date"
      }
    }
  }
}

4.2 文档 CRUD

bash

# 索引(创建/全量替换)
PUT /articles/_doc/1
{
  "title": "NestJS 教程",
  "content": "...",
  "author": "Tom",
  "tags": ["nestjs", "node"],
  "viewCount": 100,
  "createdAt": "2024-01-01"
}

# 局部更新
POST /articles/_update/1
{
  "doc": {
    "viewCount": 101
  }
}

# 查
GET /articles/_doc/1

# 删
DELETE /articles/_doc/1

4.3 Bulk 批量

bash

POST /_bulk
{"index":{"_index":"articles","_id":"2"}}
{"title":"Kafka","author":"Jerry"}
{"index":{"_index":"articles","_id":"3"}}
{"title":"Redis","author":"Bob"}

五、搜索

5.1 match(全文搜索)

bash

POST /articles/_search
{
  "query": {
    "match": {
      "title": "NestJS 教程"
    }
  }
}

5.2 term(精确)

bash

POST /articles/_search
{
  "query": {
    "term": {
      "author": "Tom"
    }
  }
}

5.3 terms(多值)

bash

POST /articles/_search
{
  "query": {
    "terms": {
      "author": ["Tom", "Jerry"]
    }
  }
}

5.4 bool(组合)

bash

POST /articles/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "title": "Redis" } }
      ],
      "filter": [
        { "term": { "author": "Tom" } },
        { "range": { "viewCount": { "gte": 100 } } }
      ],
      "must_not": [
        { "term": { "tags": "draft" } }
      ]
    }
  }
}

5.5 highlight(高亮)

bash

POST /articles/_search
{
  "query": { "match": { "title": "NestJS" } },
  "highlight": {
    "fields": {
      "title": {
        "pre_tags": ["<em>"],
        "post_tags": ["</em>"]
      }
    }
  }
}

5.6 分页 + 排序

bash

POST /articles/_search
{
  "query": { "match_all": {} },
  "from": 0,
  "size": 10,
  "sort": [
    { "viewCount": "desc" },
    { "createdAt": "desc" }
  ]
}

深分页

from + size > 10000 会报错,改用 search_after 或 scroll。

六、聚合

6.1 指标聚合

bash

POST /articles/_search
{
  "aggs": {
    "total_views": {
      "sum": { "field": "viewCount" }
    },
    "avg_views": {
      "avg": { "field": "viewCount" }
    },
    "max_views": {
      "max": { "field": "viewCount" }
    }
  },
  "size": 0
}

6.2 桶聚合

bash

POST /articles/_search
{
  "aggs": {
    "by_author": {
      "terms": {
        "field": "author",
        "size": 10
      },
      "aggs": {
        "avg_views": { "avg": { "field": "viewCount" } }
      }
    }
  },
  "size": 0
}

6.3 日期直方图

bash

POST /articles/_search
{
  "aggs": {
    "sales_over_time": {
      "date_histogram": {
        "field": "createdAt",
        "calendar_interval": "day"
      }
    }
  },
  "size": 0
}

七、Spring Data Elasticsearch

7.1 引入

xml

<dependency>
  <groupId>org.springframework.boot</groupId>
  <artifactId>spring-boot-starter-data-elasticsearch</artifactId>
</dependency>

7.2 配置

yaml

spring:
  elasticsearch:
    uris: http://localhost:9200
    connection-timeout: 5s

7.3 实体

java

@Document(indexName = "articles")
public class Article {

    @Id
    private String id;

    @Field(type = FieldType.Text, analyzer = "ik_max_word")
    private String title;

    @Field(type = FieldType.Text, analyzer = "ik_max_word")
    private String content;

    @Field(type = FieldType.Keyword)
    private String author;

    @Field(type = FieldType.Integer)
    private Integer viewCount;

    @Field(type = FieldType.Date)
    private LocalDateTime createdAt;
}

7.4 Repository

java

public interface ArticleRepository extends ElasticsearchRepository<Article, String> {

    List<Article> findByAuthor(String author);

    List<Article> findByTitleContaining(String keyword);

    @Query("{\"match\": {\"title\": \"?0\"}}")
    List<Article> searchByTitle(String keyword);
}

7.5 复杂查询

java

@Service
public class ArticleService {

    @Autowired
    private ElasticsearchOperations elasticsearchOperations;

    public SearchHits<Article> search(SearchRequest req) {
        NativeQuery query = NativeQuery.builder()
            .withQuery(q -> q.bool(b -> b
                .must(m -> m.match(mq -> mq.field("title").query(req.getKeyword())))
                .filter(f -> f.term(t -> t.field("author").value(req.getAuthor())))
            ))
            .withPageable(PageRequest.of(req.getPage(), req.getSize()))
            .withSort(Sort.by(Sort.Direction.DESC, "viewCount"))
            .build();

        return elasticsearchOperations.search(query, Article.class);
    }
}

八、中文分词

8.1 IK 分词器

bash

# 安装
./bin/elasticsearch-plugin install https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v8.11.0/elasticsearch-analysis-ik-8.11.0.zip

8.2 使用

json

{
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "ik_max_word"      // 最细粒度
      },
      "content": {
        "type": "text",
        "analyzer": "ik_smart"         // 粗粒度
      }
    }
  }
}

ik_max_word:拆最多词,适合索引 ik_smart:智能拆,适合搜索

九、ES 集群

9.1 分片与副本

Index: articles (3 primary + 1 replica)

Shard 0: [P0, R0]
Shard 1: [P1, R1]
Shard 2: [P2, R2]

分布:
Node 1: P0, R1
Node 2: P1, R2
Node 3: P2, R0

9.2 文档路由

bash

# 默认 hash(_id) % shards
# 自定义:routing key
PUT /articles/_doc/1?routing=user:123

十、本章小结

概念	用途
Index	文档集合
Document	JSON 文档
Mapping	字段类型定义
Query DSL	搜索语言
Aggregation	聚合分析

查询	用途
match	全文搜索
term	精确
bool	组合
range	范围

动手练习

创建 articles 索引,定义 mapping
用 bulk 导入 100 篇文档
实现标题全文搜索 + 高亮
按作者分组统计文章数

第 202 章:Elasticsearch 入门 ​

一、Elasticsearch 简介 ​

二、核心概念 ​

三、安装 ​

四、基础操作 ​

4.1 创建索引 ​

4.2 文档 CRUD ​

4.3 Bulk 批量 ​

五、搜索 ​

5.1 match(全文搜索) ​

5.2 term(精确) ​

5.3 terms(多值) ​

5.4 bool(组合) ​

5.5 highlight(高亮) ​

5.6 分页 + 排序 ​

六、聚合 ​

6.1 指标聚合 ​

6.2 桶聚合 ​

6.3 日期直方图 ​

七、Spring Data Elasticsearch ​

7.1 引入 ​

7.2 配置 ​

7.3 实体 ​

7.4 Repository ​

7.5 复杂查询 ​

八、中文分词 ​

8.1 IK 分词器 ​

8.2 使用 ​

九、ES 集群 ​

9.1 分片与副本 ​

9.2 文档路由 ​

十、本章小结 ​

动手练习 ​

推荐阅读 ​

第 202 章:Elasticsearch 入门

一、Elasticsearch 简介

二、核心概念

三、安装

四、基础操作

4.1 创建索引

4.2 文档 CRUD

4.3 Bulk 批量

五、搜索

5.1 match(全文搜索)

5.2 term(精确)

5.3 terms(多值)

5.4 bool(组合)

5.5 highlight(高亮)

5.6 分页 + 排序

六、聚合

6.1 指标聚合

6.2 桶聚合

6.3 日期直方图

七、Spring Data Elasticsearch

7.1 引入

7.2 配置

7.3 实体

7.4 Repository

7.5 复杂查询

八、中文分词

8.1 IK 分词器

8.2 使用

九、ES 集群

9.1 分片与副本

9.2 文档路由

十、本章小结

动手练习

推荐阅读