Elasticsearch搜索引擎深度实战

Elasticsearch概述

Elasticsearch是一个基于Lucene的开源分布式搜索引擎，提供全文检索、结构化搜索、分析等功能。它是ELK（Elasticsearch、Logstash、Kibana）栈的核心组件。

核心概念

• Index：文档集合，类似数据库
• Type：文档类型（ES 7.x后已废弃）
• Document：JSON文档，类似记录
• Field：文档字段
• Mapping：字段类型定义
• Shard：索引分片，支持分布式
• Replica：分片副本，提高可用性

索引管理

创建索引

PUT /products
{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 1,
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "stop"]
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "my_analyzer",
        "fields": {
          "keyword": { "type": "keyword" }
        }
      },
      "description": { "type": "text" },
      "price": { "type": "float" },
      "category": { "type": "keyword" },
      "tags": { "type": "keyword" },
      "created_at": { "type": "date" }
    }
  }
}

文档操作

# 创建文档（自动生成ID）
POST /products/_doc
{
  "title": "iPhone 15 Pro",
  "description": "Latest Apple smartphone",
  "price": 999.99,
  "category": "electronics",
  "tags": ["phone", "apple", "5G"]
}

# 创建文档（指定ID）
PUT /products/_doc/1
{
  "title": "MacBook Pro",
  "description": "Professional laptop",
  "price": 2499.00,
  "category": "electronics"
}

# 批量操作
POST /_bulk
{"index": {"_index": "products"}}
{"title": "Product 1", "price": 100}
{"index": {"_index": "products"}}
{"title": "Product 2", "price": 200}
{"delete": {"_index": "products", "_id": "123"}}

查询DSL

全文检索

# match查询
GET /products/_search
{
  "query": {
    "match": {
      "title": "iPhone smartphone"
    }
  }
}

# match_phrase精确短语
GET /products/_search
{
  "query": {
    "match_phrase": {
      "title": "iPhone 15"
    }
  }
}

# multi_match多字段搜索
GET /products/_search
{
  "query": {
    "multi_match": {
      "query": "Apple phone",
      "fields": ["title^2", "description"],
      "type": "best_fields"
    }
  }
}

精确查询

# term精确匹配
GET /products/_search
{
  "query": {
    "term": {
      "category": "electronics"
    }
  }
}

# range范围查询
GET /products/_search
{
  "query": {
    "range": {
      "price": {
        "gte": 100,
        "lte": 1000
      }
    }
  }
}

# bool复合查询
GET /products/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "title": "phone" } }
      ],
      "filter": [
        { "term": { "category": "electronics" } },
        { "range": { "price": { "lte": 1500 } } }
      ],
      "should": [
        { "term": { "tags": "5G" } }
      ],
      "must_not": [
        { "term": { "tags": "discontinued" } }
      ]
    }
  }
}

聚合分析

# 统计聚合
GET /products/_search
{
  "size": 0,
  "aggs": {
    "avg_price": { "avg": { "field": "price" } },
    "max_price": { "max": { "field": "price" } },
    "price_stats": { "stats": { "field": "price" } }
  }
}

# 分组聚合
GET /products/_search
{
  "size": 0,
  "aggs": {
    "categories": {
      "terms": { "field": "category", "size": 10 },
      "aggs": {
        "avg_price": { "avg": { "field": "price" } }
      }
    }
  }
}

# 日期直方图
GET /orders/_search
{
  "size": 0,
  "aggs": {
    "sales_over_time": {
      "date_histogram": {
        "field": "created_at",
        "calendar_interval": "month"
      },
      "aggs": {
        "revenue": { "sum": { "field": "amount" } }
      }
    }
  }
}

Python客户端

from elasticsearch import Elasticsearch, helpers

# 连接
es = Elasticsearch(['http://localhost:9200'])

# 创建索引
if not es.indices.exists(index='products'):
    es.indices.create(index='products', body={
        'mappings': {
            'properties': {
                'title': {'type': 'text'},
                'price': {'type': 'float'}
            }
        }
    })

# 索引文档
es.index(index='products', body={
    'title': 'iPhone 15',
    'price': 999.99
})

# 批量索引
actions = [
    {'_index': 'products', '_source': {'title': f'Product {i}', 'price': i * 10}}
    for i in range(1000)
]
helpers.bulk(es, actions)

# 搜索
result = es.search(index='products', body={
    'query': {
        'match': {'title': 'iPhone'}
    },
    'size': 10,
    'sort': [{'price': 'desc'}]
})

for hit in result['hits']['hits']:
    print(hit['_source'])

性能优化

1. 合理设置分片数：每个分片10-50GB为宜
2. 使用批量API：减少网络开销
3. 优化映射：避免不必要的text字段
4. 使用filter缓存：filter不计算分数
5. 控制结果集：避免深度分页

Elasticsearch是构建搜索应用的强大工具，掌握其核心概念和查询语法至关重要。

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