1. 准备数据

PUT h1/doc/1
{
  "name": "rose",
  "gender": "female",
  "age": 18,
  "tags": ["白", "漂亮", "高"]
}

PUT h1/doc/2
{
  "name": "lila",
  "gender": "female",
  "age": 18,
  "tags": ["黑", "漂亮", "高"]
}

PUT h1/doc/3
{
  "name": "john",
  "gender": "male",
  "age": 18,
  "tags": ["黑", "帅", "高"]
}

运行结果：

{
  "_index" : "h1",
  "_type" : "doc",
  "_id" : "1",
  "_version" : 1,
  "result" : "created",
  "_shards" : {
    "total" : 2,
    "successful" : 1,
    "failed" : 0
  },
  "_seq_no" : 0,
  "_primary_term" : 1
}

2. match 查询

2.1 match 按条件查询

# 查询性别是男性的结果
GET h1/doc/_search
{
  "query": {
    "match": {
      "gender": "male"
    }
  }
}

查询结果：

{
  "took" : 59,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 1,
    "max_score" : 0.2876821,
    "hits" : [
      {
        "_index" : "h1",		# 索引
        "_type" : "doc",		# 文档类型
        "_id" : "3",			# 文档唯一 id
        "_score" : 0.2876821,	# 打分机制打出来的分数
        "_source" : {			# 查询结果
          "name" : "john",
          "gender" : "male",
          "age" : 18,
          "tags" : [
            "黑",
            "帅",
            "高"
          ]
        }
      }
    ]
  }
}

2.2 match_all 查询全部

# 查询 h1 中所有文档
GET h1/doc/_search
{
  "query": {
    "match_all": {}
  }
}

match_all的值为空，表示没有查询条件，那就是查询全部。就像select * from table_name 一样。

查询结果：

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 3,
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "h1",
        "_type" : "doc",
        "_id" : "2",
        "_score" : 1.0,
        "_source" : {
          "name" : "lila",
          "gender" : "female",
          "age" : 18,
          "tags" : [
            "黑",
            "漂亮",
            "高"
          ]
        }
      },
      {
        "_index" : "h1",
        "_type" : "doc",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "name" : "rose",
          "gender" : "female",
          "age" : 18,
          "tags" : [
            "白",
            "漂亮",
            "高"
          ]
        }
      },
      {
        "_index" : "h1",
        "_type" : "doc",
        "_id" : "3",
        "_score" : 1.0,
        "_source" : {
          "name" : "john",
          "gender" : "male",
          "age" : 18,
          "tags" : [
            "黑",
            "帅",
            "高"
          ]
        }
      }
    ]
  }
}

2.3 match_phrase 短语查询

match 查询时散列映射，包含了我们希望搜索的字段和字符串，即只要文档中有我们希望的那个关键字，但也会带来一些问题。

es 会将文档中的内容进行拆分，对于英文来说可能没有太大的影响，但是中文短语就不太适用，一旦拆分就会失去原有的含义，比如以下：

1、准备数据：

PUT t1/doc/1
{
  "title": "中国是世界上人口最多的国家"
}

PUT t1/doc/2
{
  "title": "美国是世界上军事实力最强大的国家"
}

PUT t1/doc/3
{
  "title": "北京是中国的首都"
}

2、先使用 match 查询含有中国的文档：

GET t1/doc/_search
{
  "query": {
    "match": {
      "title": "中国"
    }
  }
}

查询结果：

{
  "took" : 5,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 3,
    "max_score" : 0.68324494,
    "hits" : [
      {
        "_index" : "t1",
        "_type" : "doc",
        "_id" : "1",
        "_score" : 0.68324494,
        "_source" : {
          "title" : "中国是世界上人口最多的国家"
        }
      },
      {
        "_index" : "t1",
        "_type" : "doc",
        "_id" : "3",
        "_score" : 0.5753642,
        "_source" : {
          "title" : "北京是中国的首都"
        }
      },
      {
        "_index" : "t1",
        "_type" : "doc",
        "_id" : "2",
        "_score" : 0.39556286,
        "_source" : {
          "title" : "美国是世界上军事实力最强大的国家"
        }
      }
    ]
  }
}

发现三篇文档都被返回，与我们的预期有偏差；这是因为 title 中的内容被拆分成一个个单独的字，而 id=2 的文档包含了 国 字也符合，所以也被返回了。es 自带的中文分词处理不太好用，后面可以使用 ik 中文分词器来处理。

3、match_phrase 查询短语

不过可以使用 match_phrase 来匹配短语，将上面的 match 换成 match_phrase 试试：

# 短语查询
GET t1/doc/_search
{
  "query": {
    "match_phrase": {
      "title": "中国"
    }
  }
}

查询结果：

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 2,
    "max_score" : 0.5753642,
    "hits" : [
      {
        "_index" : "t1",
        "_type" : "doc",
        "_id" : "1",
        "_score" : 0.5753642,
        "_source" : {
          "title" : "中国是世界上人口最多的国家"
        }
      },
      {
        "_index" : "t1",
        "_type" : "doc",
        "_id" : "3",
        "_score" : 0.5753642,
        "_source" : {
          "title" : "北京是中国的首都"
        }
      }
    ]
  }
}

4、slop 间隔查询

当我们要查询的短语，中间有别的词时，可以使用 slop 来跳过。比如上述要查询 中国世界，这个短语中间被 是 隔开了，这时可以使用 slop 来跳过，相当于正则中的中国.*?世界：

# 短语查询，查询中国世界，加 slop 
GET t1/doc/_search
{
  "query": {
    "match_phrase": {
      "title": {
        "query": "中国世界",
        "slop": 1
      }
    }
  }
}

查询结果：

{
  "took" : 4,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 1,
    "max_score" : 0.7445889,
    "hits" : [
      {
        "_index" : "t1",
        "_type" : "doc",
        "_id" : "1",
        "_score" : 0.7445889,
        "_source" : {
          "title" : "中国是世界上人口最多的国家"
        }
      }
    ]
  }
}

2.4 match_phrase_prefix 最左前缀查询

场景：当我们要查询的词只能想起前几个字符时

# 最左前缀查询，查询名字为 rose 的文档
GET h1/doc/_search
{
  "query": {
    "match_phrase_prefix": {
      "name": "ro"
    }
  }
}

查询结果：

{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 1,
    "max_score" : 0.2876821,
    "hits" : [
      {
        "_index" : "h1",
        "_type" : "doc",
        "_id" : "1",
        "_score" : 0.2876821,
        "_source" : {
          "name" : "rose",
          "gender" : "female",
          "age" : 18,
          "tags" : [
            "白",
            "漂亮",
            "高"
          ]
        }
      }
    ]
  }
}

---

限制结果集

最左前缀查询很费性能，返回的是一个很大的集合，一般很少使用，使用的时候最好对结果集进行限制，max_expansions 参数可以设置最大的前缀扩展数量：

# 最左前缀查询
GET h1/doc/_search
{
  "query": {
    "match_phrase_prefix": {
      "gender": {
        "query": "fe",
        "max_expansions": 1
      }
    }
  }
}

查询结果：

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 2,
    "max_score" : 0.2876821,
    "hits" : [
      {
        "_index" : "h1",
        "_type" : "doc",
        "_id" : "2",
        "_score" : 0.2876821,
        "_source" : {
          "name" : "lila",
          "gender" : "female",
          "age" : 18,
          "tags" : [
            "黑",
            "漂亮",
            "高"
          ]
        }
      },
      {
        "_index" : "h1",
        "_type" : "doc",
        "_id" : "1",
        "_score" : 0.2876821,
        "_source" : {
          "name" : "rose",
          "gender" : "female",
          "age" : 18,
          "tags" : [
            "白",
            "漂亮",
            "高"
          ]
        }
      }
    ]
  }
}

2.5 multi_match 多字段查询

1、准备数据：

# 多字段查询
PUT t3/doc/1
{
  "title": "maggie is beautiful girl",
  "desc": "beautiful girl you are beautiful so"
}

PUT t3/doc/2
{
  "title": "beautiful beach",
  "desc": "I like basking on the beach,and you? beautiful girl"
}

2、查询包含 beautiful 字段的文档：

GET t3/doc/_search
{
  "query": {
    "multi_match": {
      "query": "beautiful",				# 要查询的词
      "fields": ["desc", "title"]		# 要查询的字段
    }
  }
}

还可以当做 match_phrase和match_phrase_prefix使用，只需要指定type类型即可：

GET t3/doc/_search
{
  "query": {
    "multi_match": {
      "query": "gi",
      "fields": ["title"],
      "type": "phrase_prefix"
    }
  }
}

GET t3/doc/_search
{
  "query": {
    "multi_match": {
      "query": "girl",
      "fields": ["title"],
      "type": "phrase"
    }
  }
}

3. term 查询

3.1 初始 es 的分析器

term 查询用于精确查询，但是不适用于 text 类型的字段查询。

在此之前我们先了解 es 的分析机制，默认的标准分析器会对文档进行：

• 删除大多数的标点符号
• 将文档拆分为单个词条，称为 token
• 将 token 转换为小写

最后保存到倒排序索引上，而倒排序索引用来查询，如 Beautiful girl 经过分析后是这样的：

POST _analyze
{
  "analyzer": "standard",
  "text": "Beautiful girl"
}


# 结果，转换为小写了
{
  "tokens" : [
    {
      "token" : "beautiful",
      "start_offset" : 0,
      "end_offset" : 9,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "girl",
      "start_offset" : 10,
      "end_offset" : 14,
      "type" : "<ALPHANUM>",
      "position" : 1
    }
  ]
}

3.2 term 查询

1、准备数据：

# 创建索引，自定义 mapping，后面会讲到
PUT t4
{
  "mappings": {
    "doc":{
      "properties":{
        "t1":{
          "type": "text"    # 定义字段类型为 text
        }
      }
    }
  }
}

PUT t4/doc/1
{
  "t1": "Beautiful girl!"
}

PUT t4/doc/2
{
  "t1": "sexy girl!"
}

2、match 查询：

GET t4/doc/_search
{
  "query": {
    "match": {
      "t1": "Beautiful girl!"
    }
  }
}

经过分析后，会得到 beautiful、girl 两个 token，然后再去 t4 索引上去查询，会返回两篇文档：

{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 2,
    "max_score" : 0.5753642,
    "hits" : [
      {
        "_index" : "t4",
        "_type" : "doc",
        "_id" : "1",
        "_score" : 0.5753642,
        "_source" : {
          "title" : "Beautiful girl"
        }
      },
      {
        "_index" : "t4",
        "_type" : "doc",
        "_id" : "2",
        "_score" : 0.2876821,
        "_source" : {
          "title" : "sex girl"
        }
      }
    ]
  }
}

3、但是我们只想精确查询包含 Beautiful girl 的文档，这时就需要使用 term 来精确查询：

GET t4/doc/_search
{
  "query": {
    "term": {
      "title": "beautiful"
    }
  }
}

查询结果：

{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 1,
    "max_score" : 0.2876821,
    "hits" : [
      {
        "_index" : "t4",
        "_type" : "doc",
        "_id" : "1",
        "_score" : 0.2876821,
        "_source" : {
          "title" : "Beautiful girl"
        }
      }
    ]
  }
}

注意：term 查询不适用于类型是 text 的字段，可以使用 match 查询；另外 Beautiful 经过分析后变为 beautiful，查询时使用 Beautiful 是查询不到的~

3.3 查询多个

精确查询多个字段：

GET t4/doc/_search
{
  "query": {
    "terms": {
      "title": ["beautiful", "sex"]
    }
  }
}