ElasticSearch04-高级操作

李宥小哥2024-12-15 20:02

零、文章目录

ElasticSearch04-高级操作

1、文档添加

（1）生成文档ID
  • 不指定 ID，即自动生成ID，ID 是一行数据的唯一键。
  • 语法：POST /index/_doc
json 复制代码 
# 创建索引
PUT testid

# 默认情况下自动生成ID
POST /testid/_doc
{
  "test_field": "test"
}

# 返回
{
  "_index" : "testid",
  "_type" : "_doc",
  "_id" : "pyxXs5MBW--orQEFRZhH",
  "_version" : 1,
  "result" : "created",
  "_shards" : {
    "total" : 2,
    "successful" : 1,
    "failed" : 0
  },
  "_seq_no" : 0,
  "_primary_term" : 1
}
  • 场景：如果导入 ES 的数据想使用外部定义的 ID，需要在添加文档数据的时候指定 ID。
  • 语法：POST /index/_doc/id
json 复制代码 
# 指定ID
POST /testid/_doc/1
{
  "test_field": "testid"
}

# 返回 1是指定的ID
{
  "_index" : "testid",
  "_type" : "_doc",
  "_id" : "1",
  "_version" : 1,
  "result" : "created",
  "_shards" : {
    "total" : 2,
    "successful" : 1,
    "failed" : 0
  },
  "_seq_no" : 1,
  "_primary_term" : 1
}

2、文档条件查询

（1）准备数据
json 复制代码 
# 创建索引
PUT teacher

POST /teacher/_doc/001
{
  "name":"zhangsan",
  "nickname":"zhangsan",
  "sex":"男",
  "age":30
}

POST /teacher/_doc/002
{
  "name":"lisi",
  "nickname":"lisi",
  "sex":"男",
  "age":20
}

POST /teacher/_doc/003
{
  "name":"wangwu1",
  "nickname":"wangwu2",
  "sex":"女",
  "age":40
}

POST /teacher/_doc/004
{
  "name":"zhaoliu1",
  "nickname":"zhaoliu2",
  "sex":"女",
  "age":50
}

POST /teacher/_doc/005
{
  "name":"zhangsan2",
  "nickname":"zhangsan2",
  "sex":"女",
  "age":30
}

POST /teacher/_doc/006
{
  "name":"zhangsan222",
  "nickname":"zhangsan222",
  "sex":"女",
  "age":30
}
（2）查询全部(match_all)
  • ES中提供了一种强大的检索数据方式，这种检索方式称之为Query DSL,Query DSL是利用Rest API传递JSON格式的请求体与ES进行交互，这种方式的丰富查询语法让ES检索变得更强大，更简洁。
  • 使用_search 表示查询
json 复制代码 
# 请求
Get /teacher/_search
{
  "query": {
    "match_all": {}
  }
}

# 返回
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "002",
        "_score" : 1.0,
        "_source" : {
          "name" : "lisi",
          "nickname" : "lisi",
          "sex" : "男",
          "age" : 20
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "003",
        "_score" : 1.0,
        "_source" : {
          "name" : "wangwu1",
          "nickname" : "wangwu2",
          "sex" : "女",
          "age" : 40
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "004",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhaoliu1",
          "nickname" : "zhaoliu2",
          "sex" : "女",
          "age" : 50
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "005",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan2",
          "nickname" : "zhangsan2",
          "sex" : "女",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "006",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan222",
          "nickname" : "zhangsan222",
          "sex" : "女",
          "age" : 30
        }
      }
    ]
  }
}
  • 返回的结果说明：
    • took：这个字段表示查询执行所花费的毫秒数。在这个例子中，查询执行花费了0毫秒。
    • timed_out：这个字段表示查询是否超时。false表示查询没有超时。
    • _shards：这个字段包含了关于分片的信息。
      • total：查询被分发到的分片总数。
      • successful：成功处理的分片数。
      • skipped：被跳过的分片数。
      • failed：处理失败的分片数。
    • hits：这个字段包含了查询结果的相关信息。
      • total：匹配的文档总数。value表示总数，relation表示总数的类型，这里是eq，表示精确匹配。
      • max_score：所有匹配文档中最高的相关性得分，这里是1.0。
      • hits：实际匹配的文档列表，每个文档包含以下信息：
        • _index：文档所在的索引名称。
        • _type：文档的类型（在Elasticsearch 7.x之后，类型被标记为废弃）。
        • _id：文档的唯一标识符。
        • _score：文档的相关性得分。
        • _source：文档的原始数据，包含了文档的所有字段和值。
    • 在hits数组中，每个元素都是一个文档，包含了文档的元数据（如索引名、类型、ID、得分）和文档的内容（在_source字段中）。这个例子中返回了6个文档，每个文档都有name、nickname、sex和age字段。
（3）分词查询(match)
  • match查询用于在单个字段上执行全文检索。它会使用指定字段的分词器（analyzer）来处理查询文本，然后搜索与这些词元匹配的文档。
  • 只有 text 类型的字段才会进行分词查询。
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "match": {
      "name": "zhangsan"
    }
  }
}

# 返回
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.540445,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.540445,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      }
    ]
  }
}
（4）精确查询(term)
  • term查询用于精确匹配，不进行分词处理，适用于关键字（keyword）、数字、日期等精确值字段。
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "term": {
      "name": {
        "value": "zhangsan"
      }
    }
  }
}

# 返回
{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.540445,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.540445,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      }
    ]
  }
}
（5）范围查询(range)
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "range": {
      "age": {
        "gte": 30,
        "lte": 30
      }
    }
  }
}

# 返回
{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "005",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan2",
          "nickname" : "zhangsan2",
          "sex" : "女",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "006",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan222",
          "nickname" : "zhangsan222",
          "sex" : "女",
          "age" : 30
        }
      }
    ]
  }
}
（6）前缀查询(prefix)
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "prefix": {
      "name": {
        "value": "zhangsan"
      }
    }
  }
}

# 返回
{
  "took" : 7,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "005",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan2",
          "nickname" : "zhangsan2",
          "sex" : "女",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "006",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan222",
          "nickname" : "zhangsan222",
          "sex" : "女",
          "age" : 30
        }
      }
    ]
  }
}
（7）通配符查询(wildcard)
  • ?：匹配任意一个字符
  • *：匹配任意多个字符
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "wildcard": {
      "name": {
        "value": "*san*"
      }
    }
  }
}

# 返回
{
  "took" : 6,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "005",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan2",
          "nickname" : "zhangsan2",
          "sex" : "女",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "006",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan222",
          "nickname" : "zhangsan222",
          "sex" : "女",
          "age" : 30
        }
      }
    ]
  }
}
（8）多id查询(ids)
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "ids": {
      "values": ["001","003","005"]
    }
  }
}

#返回
{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "003",
        "_score" : 1.0,
        "_source" : {
          "name" : "wangwu1",
          "nickname" : "wangwu2",
          "sex" : "女",
          "age" : 40
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "005",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan2",
          "nickname" : "zhangsan2",
          "sex" : "女",
          "age" : 30
        }
      }
    ]
  }
}
（9）模糊查询(fuzzy)
  • Elasticsearch中的fuzzy查询允许用户搜索包含与指定查询词相似的文档，即使它们不完全相同。这种查询特别适用于处理近似或不精确的搜索词，例如用户可能打错字或输入同一词的不同变体的情况。
  • 以下是fuzzy查询的一些关键点：
    • 编辑距离（Edit Distance） ：fuzzy查询使用Levenshtein编辑距离来衡量搜索词之间的相似度。编辑距离是指将一个词转换成另一个词所需的单字符更改次数，包括改变一个字符、删除一个字符、插入一个字符或交换两个相邻字符。
    • 创建变体（Creating Variations） ：fuzzy查询在指定的编辑距离内创建搜索词的所有可能变体，然后返回每个变体的精确匹配。
    • 参数配置 ：
      • fuzziness：允许的最大编辑距离，默认为AUTO。可以设置为AUTO或一个整数，表示允许的编辑次数。
      • max_expansions：创建的变体的最大数量，默认为50。高值可能会导致性能问题，因为会检查大量的变体。
      • prefix_length：在创建变体时保持不变的开始字符数，默认为0。
      • transpositions：是否包括两个相邻字符的置换作为编辑，默认为true。
      • rewrite：用于重写查询的方法，如果fuzziness参数不为0，默认使用top_terms_blended_freqs_${max_expansions}。
    • 性能考虑 ：由于fuzzy查询可能会生成大量的变体，特别是当max_expansions参数值较高且prefix_length参数值为0时，可能会影响查询性能。因此，合理配置这些参数对于保持Elasticsearch查询性能至关重要。
    • 使用场景 ：fuzzy查询是term查询的模糊等价物，通常不会直接使用，但理解其工作原理有助于在更高级的match查询中使用模糊匹配。
    • 不进行分析 ：与match查询不同，fuzzy查询是一个term级别的查询，不会对查询词进行分析，它直接在词典中查找与指定fuzziness范围内的所有项。
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "fuzzy": {
      "name": "zhangsan"
    }
  }
}

# 返回
{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.540445,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.540445,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "005",
        "_score" : 1.3478894,
        "_source" : {
          "name" : "zhangsan2",
          "nickname" : "zhangsan2",
          "sex" : "女",
          "age" : 30
        }
      }
    ]
  }
}
（10）组合查询(bool)
  • must: 相当于&& 同时成立
  • should: 相当于|| 成立一个就行
  • must_not: 相当于! 不能满足任何一个
bash 复制代码 
# 请求
GET /teacher/_search
{
    "query": {
        "bool": {
            "must": [
                {
                    "match": {
                        "name": "zhangsan"
                    }
                }
            ],
            "must_not": [
                {
                    "match": {
                        "age": "40"
                    }
                }
            ],
            "should": [
                {
                    "match": {
                        "sex": "男"
                    }
                }
            ]
        }
    }
}

# 返回
{
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 2.5700645,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 2.5700645,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      }
    ]
  }
}
（11）多字段查询(multi_match)
  • multi_match查询允许你在多个字段上执行全文检索。与match查询类似，它也会进行分词，但是可以在多个字段上搜索给定的文本
json 复制代码 
# 请求
GET /teacher/_search
{
    "query": {
        "multi_match": {
            "query": "zhangsan",
            "fields": ["name","nickname"]
        }
    }
}

# 返回
{
  "took" : 6,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.540445,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.540445,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      }
    ]
  }
}
（12）多值查询(terms)
  • terms 查询和 term 查询一样，但它允许你指定多值进行匹配。类似 SQL 中的 in。
json 复制代码 
# 请求
GET /teacher/_search
{
    "query": {
        "terms": {
            "name": ["zhangsan","lisi"]
        }
    }
}

# 返回
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "002",
        "_score" : 1.0,
        "_source" : {
          "name" : "lisi",
          "nickname" : "lisi",
          "sex" : "男",
          "age" : 20
        }
      }
    ]
  }
}
（13）复杂查询(query_string)
  • Elasticsearch中的query_string查询是一个强大的全文搜索查询，它允许用户使用一个查询字符串来构造复杂的查询。这个查询字符串可以包含多个字段、逻辑操作符（如AND, OR, NOT）以及通配符等。query_string查询非常灵活，可以模拟其他查询类型，如match、bool等。
  • 以下是query_string查询的一些关键点：
    • 查询语法：
      • 支持布尔操作符：AND, OR, NOT。
      • 支持通配符查询：*（匹配任意字符）。
      • 支持短语搜索：使用双引号""包围短语。
      • 支持前缀搜索：使用问号?表示单个字符，使用星号*表示任意长度的字符。
      • 支持正则表达式：使用正斜杠/包围正则表达式。
    • 字段选择：可以在查询中指定一个或多个字段进行搜索。
    • 分析器：默认使用字段的搜索分析器（search analyzer），但也可以使用查询分析器（query analyzer）。
    • 默认操作：默认的布尔操作是AND，意味着如果查询字符串中没有指定布尔操作符，那么查询会将所有条件视为AND连接。
    • 字段名和值的转义：如果需要在查询中使用特殊字符，可以使用反斜杠\进行转义。
    • 性能：由于query_string查询的复杂性，它可能比简单的match查询更消耗资源。因此，在性能敏感的应用中需要谨慎使用。
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "query_string": {
      "query": "name:zhansan OR sex:男"
    }
  }
}

# 返回
{
  "took" : 9,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.0296195,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.0296195,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "002",
        "_score" : 1.0296195,
        "_source" : {
          "name" : "lisi",
          "nickname" : "lisi",
          "sex" : "男",
          "age" : 20
        }
      }
    ]
  }
}
  • 分析器的使用：这个查询使用standard分析器来处理查询字符串。
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "query_string": {
      "query": "name:zhangsan",
      "analyzer": "standard"
    }
  }
}

# 返回
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.540445,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.540445,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      }
    ]
  }
}

3、文档查询结果集处理

（1）查询结果高亮(highlight)
  • Elasticsearch中的高亮（highlight）功能允许你在搜索结果中突出显示与查询条件匹配的文本片段，以便用户能够快速定位到关键信息。
  • 以下是关于Elasticsearch高亮功能的详细介绍：
    • 高亮参数：
      • Elasticsearch提供了多种高亮参数，包括字段设置、自定义标签、片段大小等。
      • 默认情况下，Elasticsearch使用<em></em>标签来标记高亮的关键字。
    • 自定义高亮片段：
      • 用户可以自定义高亮标签，比如使用<b></b>标签来代替默认的<em></em>标签。
      • 通过pre_tags和post_tags参数，用户可以指定高亮前后的标签。
    • 多字段高亮：
      • 高亮功能可以应用于一个或多个字段，只需在highlight字段中添加相应的字段名称。
    • 高亮性能分析：
      • 高亮功能需要字段的实际内容，如果字段未存储（映射未设置store为true），则需要从_source中加载实际值并从中提取相关字段。
  • 高亮查询示例：
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "term": {
      "name": {
        "value": "zhangsan"
      }
    }
  },
  "highlight": {
    "fields": {
      "*":{}
    }
  }
}

# 返回
{
  "took" : 33,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.540445,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.540445,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        },
        "highlight" : {
          "name" : [
            "<em>zhangsan</em>"
          ]
        }
      }
    ]
  }
}
  • 自定义标签
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "term": {
      "name": {
        "value": "zhangsan"
      }
    }
  },
  "highlight": {
        "pre_tags": "<font color='red'>",
        "post_tags": "</font>",
        "fields": {
            "name": {}
        }
    }
}

# 返回
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.540445,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.540445,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        },
        "highlight" : {
          "name" : [
            "<font color='red'>zhangsan</font>"
          ]
        }
      }
    ]
  }
}
（2）指定返回条数(size)
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "match_all": {}
  },
  "size": 2
}

# 返回
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "002",
        "_score" : 1.0,
        "_source" : {
          "name" : "lisi",
          "nickname" : "lisi",
          "sex" : "男",
          "age" : 20
        }
      }
    ]
  }
}
（3）分页查询(from)
  • size：取多少条数据
  • from：从哪条数据开始
  • size+from 合起来实现分页效果
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "match_all": {}
  },
  "size": 2,
  "from": 2
}

# 返回
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "003",
        "_score" : 1.0,
        "_source" : {
          "name" : "wangwu1",
          "nickname" : "wangwu2",
          "sex" : "女",
          "age" : 40
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "004",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhaoliu1",
          "nickname" : "zhaoliu2",
          "sex" : "女",
          "age" : 50
        }
      }
    ]
  }
}
（4）指定字段排序(sort)
  • 文本字段（text字段）默认不适用于需要逐文档字段数据的操作，如聚合（aggregations）和排序（sorting）。这是因为text字段会被分词器分词，存储的是词元（tokens），而不是完整的原始字符串。
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "terms": {
      "name":["zhangsan","lisi"]
    }
  },
  "sort": [
    {
      "age": {
        "order": "desc"
      }
    }
  ]
}

# 返回
{
  "took" : 5,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : null,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan",
          "sex" : "男",
          "age" : 30
        },
        "sort" : [
          30
        ]
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "002",
        "_score" : null,
        "_source" : {
          "name" : "lisi",
          "nickname" : "lisi",
          "sex" : "男",
          "age" : 20
        },
        "sort" : [
          20
        ]
      }
    ]
  }
}
（5）指定返回字段(_source)
  • includes：来指定想要显示的字段
  • excludes：来指定不想要显示的字段
json 复制代码 
# 请求
GET /teacher/_search
{
  "query": {
    "terms": {
      "name":["zhangsan","lisi"]
    }
  },
  "_source": {
      "includes": ["name","nickname"]
  }
}

# 返回
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "001",
        "_score" : 1.0,
        "_source" : {
          "name" : "zhangsan",
          "nickname" : "zhangsan"
        }
      },
      {
        "_index" : "teacher",
        "_type" : "_doc",
        "_id" : "002",
        "_score" : 1.0,
        "_source" : {
          "name" : "lisi",
          "nickname" : "lisi"
        }
      }
    ]
  }
}

4、文档聚合查询

（1）返回最大值(max)
  • aggs：表示聚合
    • max_age：自定义返回聚合字段名
      • max：取最大值
        • field：聚合的字段是 age
  • size：0 表示返回聚合结果不返回详细数据
json 复制代码 
# 请求
GET /teacher/_search
{
    "aggs":{
        "max_age":{
            "max":{"field":"age"}
        }
    },
    "size":0
}

# 返回
{
  "took" : 4,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "max_age" : {
      "value" : 50.0
    }
  }
}
（2）返回最小值(min)
json 复制代码 
# 请求
GET /teacher/_search
{
    "aggs":{
        "min_age":{
            "min":{"field":"age"}
        }
    },
    "size":0
}

# 返回值
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "min_age" : {
      "value" : 20.0
    }
  }
}
（3）返回平均值(avg)
json 复制代码 
# 请求
GET /teacher/_search
{
    "aggs":{
        "avg_age":{
            "avg":{"field":"age"}
        }
    },
    "size":0
}

# 返回值
{
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "avg_age" : {
      "value" : 33.333333333333336
    }
  }
}
（4）返回和(sum)
json 复制代码 
# 请求
GET /teacher/_search
{
    "aggs":{
        "sum_age":{
            "sum":{"field":"age"}
        }
    },
    "size":0
}

# 返回值
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "sum_age" : {
      "value" : 200.0
    }
  }
}
（5）返回去重行数(cardinality)
json 复制代码 
# 请求
GET /teacher/_search
{
    "aggs":{
        "cardinality_age":{
            "cardinality":{"field":"age"}
        }
    },
    "size":0
}

# 返回值
{
  "took" : 10,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "cardinality_age" : {
      "value" : 4
    }
  }
}
（6）返回全部聚合(stats)
json 复制代码 
# 请求
GET /teacher/_search
{
    "aggs":{
        "stats_age":{
            "stats":{"field":"age"}
        }
    },
    "size":0
}

# 返回值
{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "stats_age" : {
      "count" : 6,
      "min" : 20.0,
      "max" : 50.0,
      "avg" : 33.333333333333336,
      "sum" : 200.0
    }
  }
}
（7）分组聚合(terms)
  • 默认情况下，分组统计的是每个分组的行数
json 复制代码 
# 请求
GET /teacher/_search
{
    "aggs":{
        "terms_age":{
            "terms":{"field":"age"}
        }
    },
    "size":0
}

# 返回值
{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "terms_age" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : 30,
          "doc_count" : 3
        },
        {
          "key" : 20,
          "doc_count" : 1
        },
        {
          "key" : 40,
          "doc_count" : 1
        },
        {
          "key" : 50,
          "doc_count" : 1
        }
      ]
    }
  }
}
（8）分组指定聚合(terms+aggs)
json 复制代码 
# 请求
GET /teacher/_search?size=0
{
  "aggs": {
    "group_by_field": {
      "terms": {
        "field": "age"
      },
      "aggs": {
        "average_value": {
          "avg": {
            "field": "age"
          }
        }
      }
    }
  }
}

# 返回值
{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "group_by_field" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : 30,
          "doc_count" : 3,
          "average_value" : {
            "value" : 30.0
          }
        },
        {
          "key" : 20,
          "doc_count" : 1,
          "average_value" : {
            "value" : 20.0
          }
        },
        {
          "key" : 40,
          "doc_count" : 1,
          "average_value" : {
            "value" : 40.0
          }
        },
        {
          "key" : 50,
          "doc_count" : 1,
          "average_value" : {
            "value" : 50.0
          }
        }
      ]
    }
  }
}
（9）分组聚合排序(terms+aggs+sort)
json 复制代码 
GET /teacher/_search?size=0
{
  "aggs": {
    "group_by_field": {
      "terms": {
        "field": "age",
        "order": {"average_value": "desc"}
      },
      "aggs": {
        "average_value": {
          "avg": {
            "field": "age"
          }
        }
      }
    }
  }
}

# 返回值
{
  "took" : 4,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "group_by_field" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : 50,
          "doc_count" : 1,
          "average_value" : {
            "value" : 50.0
          }
        },
        {
          "key" : 40,
          "doc_count" : 1,
          "average_value" : {
            "value" : 40.0
          }
        },
        {
          "key" : 30,
          "doc_count" : 3,
          "average_value" : {
            "value" : 30.0
          }
        },
        {
          "key" : 20,
          "doc_count" : 1,
          "average_value" : {
            "value" : 20.0
          }
        }
      ]
    }
  }
}
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