ElasticSearch-7.3.0 进阶语法

elasticsearch官方文档

字段类型

# Text：被分析索引的字符串类型
# Keyword：不能被分析只能被精确匹配的字符串类型
# Date：日期类型，可以配置 format 一起使用（{"type": "date", "format": "yyyy-MM-dd"}）
# 数字类型：long，integer，short，double 等
# boolean 类型：true，false
# Array：数组类型 ["one", "two"]
# Object：json 嵌套（{"property1": "value1", "property2": "value2"}）
# Ip类型：127.0.0.1
# Geo_point：地理位置
	地址的定义：
	{
		"mappings": {
			"_doc": {
				"properties": {
					"location": {
						"type": "geo_point"
					}
				}
			}
		}
	}
	建立索引的方式：
	"location": {
		"lat": 41.12,
		"lon": -71.34
	}

高级查询语法

`analyze`分析过程

# 使用 analyze api 查看分词状态
GET /movie/_analyze
{
  "field": "name",
  "text": "Eating an apple a day & keeps the doctor awawy"
}

在这里插入图片描述

# 使用结构化的方式重新创建索引（指定分词器）
PUT /movie
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 1
  },
  "mappings": {
    "properties": {
      "name": {
        "type": "text",
        "analyzer": "english"
      }
    }
  }
}

`Tmdb`实例

数据下载

在网上直接搜索kiggle tmdb即可下载相对应的数据文件

索引建立

# 建立 movie 索引
PUT /movie
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 1
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "english"
      },
      "tagline": {
        "type": "text",
        "analyzer": "english"
      },
      "release_date": {
        "type": "date",
        "format": "8yyyy/MM/dd||yyyy/M/dd||yyyy/MM/d||yyyy/M/d"
      },
      "popularity": {
        "type": "double"
      },
      "overview": {
        "type": "text",
        "analyzer": "english"
      },
      "cast": {
        "type": "object",
        "properties": {
          "character": {
            "type": "text",
            "analyzer": "standard"
          },
          "name": {
            "type": "text",
            "analyzer": "standard"
          }
        }
      }
    }
  }
}

`match`和`term`

# match 查询
GET /movie/_search
{
  "query": {
    "match": {
      "title": "steve zissou"
    }
  }
}

# term 查询
GET /movie/_search
{
  "query": {
    "term": {
      "title": {
        "value": "steve zissou"
      }
    }
  }
}

# match 查询会根据字段所指定的分词器对查询字段进行分词，而 term 并不会对查询字段进行分词，也就是说对于上面两个示例，title 指定的是 english 分词器，所以 match 查询中的 steve zissou 会被分词器解析成 steve 和 zissou 两个关键词，所以只要 title 中含有 steve 和 zissou 中任意一个关键词的都可以被命中，而 term 查询中的 steve zissou 不会被分词器解析，也就是说只有 title 中经过分词器解析后的词包含 steve zissou 时才会被命中。

分词后的`and`和`or`

# 分词后的 or 的逻辑
GET /movie/_search
{
  "query": {
    "match": {
      "title": "basketball with cartoom aliens"
    }
  }
}

# 分词后的 and 的逻辑
GET /movie/_search
{
  "query": {
    "match": {
      "title": {
        "query": "basketball with cartoom aliens",
        "operator": "and"
      }
    }
  }
}

最小词匹配项

# 最小词匹配项
GET /movie/_search
{
  "query": {
    "match": {
      "title": {
        "query": "basketball love aliens",
        "operator": "or",
        "minimum_should_match": 2
      }
    }
  }
}

短语查询

# 短语查询
GET /movie/_search
{
  "query": {
    "match_phrase": {
      "title": "steve zissou"
    }
  }
}

`score`打分

# 查看 score
GET /movie/_search
{
  "explain": true,
  "query": {
    "match": {
      "title": "steve"
    }
  }
}
======================================================
"details" : [
 {
	# 2.2 * 7.1592917 * 0.47008154 = 7.403992
    "value" : 7.403992,
    "description" : "score(freq=1.0), product of:",
    "details" : [
      {
        "value" : 2.2,
        # 可以手动指定这个 boost 放大系数，如果不指定，那么 es 将使用默认值为 2.2 的放大系数
        "description" : "boost",
        "details" : [ ]
      },
      {
        "value" : 7.1592917,
        # 逆文档频率：随着 n 的增加，整个 idf 是减少的
        "description" : "idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:",
        "details" : [
          {
            "value" : 3,
            # 一共命中了 3 篇文档
            "description" : "n, number of documents containing term",
            "details" : [ ]
          },
          {
            "value" : 4500,
            # 文档的总个数为 4500
            "description" : "N, total number of documents with field",
            "details" : [ ]
          }
        ]
      },
      {
        "value" : 0.47008154,
        "description" : "tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:",
        "details" : [
          {
            "value" : 1.0,
            # 搜索关键词在文档字段中出现的次数
            "description" : "freq, occurrences of term within document",
            "details" : [ ]
          },
          {
            "value" : 1.2,
            "description" : "k1, term saturation parameter",
            "details" : [ ]
          },
          {
            "value" : 0.75,
            "description" : "b, length normalization parameter",
            "details" : [ ]
          },
          {
            "value" : 2.0,
            # 文档字段的长度
            "description" : "dl, length of field",
            "details" : [ ]
          },
          {
            "value" : 2.1757777,
            "description" : "avgdl, average length of field",
            "details" : [ ]
          }
        ]
      }
    ]
  }
]

多字段查询

# 多字段查询：在多字段查询时，会对两个字段都进行打分，最后的打分结果取的是最大的那个分值
GET /movie/_search
{
  "query": {
    "multi_match": {
      "query": "basketball with cartoom aliens",
      "fields": ["title", "overview"]
    }
  }
}

# 优化多字段查询：让 title 字段占比加大
GET /movie/_search
{
  "query": {
    "multi_match": {
      "query": "basketball with cartoom aliens",
      "fields": ["title^10", "overview"]
    }
  }
}

# 优化多字段查询
GET /movie/_search
{
  "explain": true, 
  "query": {
    "multi_match": {
      "query": "basketball with cartoom aliens",
      "fields": ["title^10", "overview"],
      "tie_breaker": 0.3
    }
  }
}

# bool 查询
# must：必须都为 true
# must not：必须都是 false
# should：其中只要有一个为 true，即可
# 为 true 的越多则得分越高
GET /movie/_search
{
  "explain": true, 
  "query": {
    "bool": {
      "should": [
        {
          "match": {
            "title": "basketball with cartoom aliens"
          }
        },
        {
          "match": {
            "overview": "basketball with cartoom aliens"
          }
        }
      ]
    }
  }
}

# 不同的 multi_query 其实是有不同的 type，type 不同则打分方式不同
# best_fields：默认的得分方式，取得最高的分数作为对应文档的得分，“最匹配模式” -> dis_max
GET /movie/_search
{
  "query": {
    "multi_match": {
      "query": "basketball with cartoom aliens",
      "fields": ["title", "overview"],
      "type": "best_fields"
    }
  }
}

# dis_max
GET /movie/_search
{
  "explain": true, 
  "query": {
    "dis_max": {
      "queries": [
        {
          "match": {
            "title": "basketball with cartoom aliens"
          }
        },
        {
          "match": {
            "overview": "basketball with cartoom aliens"
          }
        }
      ]
    }
  }
}

# 查看打分规则：dis_max
GET /movie/_validate/query?explain
{
  "query": {
    "multi_match": {
      "query": "basketball with cartoom aliens",
      "fields": ["title^10", "overview"],
      "type": "best_fields"
    }
  }
}

# most_fields：考虑绝大多数（所有的），文档的字段得分相加获得我们想要的结果
GET /movie/_search
{
  "explain": true, 
  "query": {
    "multi_match": {
      "query": "basketball with cartoom aliens",
      "fields": ["title", "overview"],
      "type": "most_fields"
    }
  }
}

# 权重的调整是针对于 boost进行调整
GET /movie/_validate/query?explain
{
  "query": {
    "multi_match": {
      "query": "basketball with cartoom aliens",
      "fields": ["title^10", "overview^0.1"],
      "type": "most_fields"
    }
  }
}

# cross_fields：以分词为单位计算栏位的总分，适用于词导向的匹配模式
GET /movie/_search
{
  "explain": true, 
  "query": {
    "multi_match": {
      "query": "steve jobs",
      "fields": ["title", "overview"],
      "type": "cross_fields"
    }
  }
}

GET /movie/_validate/query?explain
{
  "query": {
    "multi_match": {
      "query": "steve jobs",
      "fields": ["title", "overview"],
      "type": "cross_fields"
    }
  }
}

# query string
# 方便的利用 AND OR NOT
GET /movie/_search
{
  "query": {
    "query_string": {
      "fields": ["title"],
      "query": "steve AND jobs"
    }
  }
}

过滤与排序

# filter 过滤查询
# 单条件过滤
GET /movie/_search
{
  "query": {
    "bool": {
      "filter": {
        "term": {
          "title": "steve"
        }
      }
    }
  }
}

# 多条件过滤
GET /movie/_search
{
  "query": {
    "bool": {
      "filter": [
        {
          "term": {
            "title": "steve"
          }
        },
        {
          "term": {
            "cast.name": "gaspard"
          }
        }
      ]
    }
  }
}

# 多条件过滤
GET /movie/_search
{
  "query": {
    "bool": {
      "filter": [
        {
          "term": {
            "title": "steve"
          }
        },
        {
          "term": {
            "cast.name": "gaspard"
          }
        },
        {
          "range": {
            "release_date": {
              "lte": "2015/01/01"
            }
          }
        },
        {
          "range": {
            "popularity": {
              "gte": 25
            }
          }
        }
      ]
    }
  }
}

# 多条件过滤并排序
GET /movie/_search
{
  "query": {
    "bool": {
      "filter": [
        {
          "term": {
            "title": "steve"
          }
        },
        {
          "term": {
            "cast.name": "gaspard"
          }
        },
        {
          "range": {
            "release_date": {
              "lte": "2015/01/01"
            }
          }
        },
        {
          "range": {
            "popularity": {
              "gte": 25
            }
          }
        }
      ]
    }
  },
  "sort": [
    {
      "popularity": {
        "order": "desc"
      }
    }
  ]
}

# 带 match 打分的 filter，should 控制打分，filter 控制过滤
GET /movie/_search
{
  "query": {
    "bool": {
      "should": [
        {
          "match": {
            "title": "life"
          }
        }
      ],
      "filter": [
        {
          "term": {
            "title": "steve"
          }
        },
        {
          "term": {
            "cast.name": "gaspard"
          }
        },
        {
          "range": {
            "release_date": {
              "lte": "2015/01/01"
            }
          }
        },
        {
          "range": {
            "popularity": {
              "gte": 25
            }
          }
        }
      ]
    }
  }
}

查全率查准率

查全率：正确的结果有 n 个，查询出来正确的有 m 个，所以查全率就是 m / n
查准率：查出的 n 个文档有 m 个文档是正确的，所以查准率就是 m / n
两者不可兼得，但是可以调整顺序

通常可以追求高的查全率，因为查全率高必然会导致查准率降低，保证查准率中所查询的 m 个内容排在前面，这样既可以保证用户体验，还可以保证查全率。

自定义`score`

# function-score
GET /movie/_search
{
  "explain": true, 
  "query": {
    "function_score": {
      # 原始查询得到的 oldScore
      "query": {
        "multi_match": {
          "query": "steve job",
          "fields": [
            "title",
            "overview"
          ],
          "operator": "or",
          "type": "most_fields"
        }
      },
      "functions": [
        {
          "field_value_factor": {
            # 对应要调整处理的字段
            "field": "popularity",
            "modifier": "log2p",
            "factor": 10
          }
        }
      ]
    }
  }
}

# function-score
GET /movie/_search
{
  "explain": true, 
  "query": {
    "function_score": {
      # 原始查询得到的 oldScore
      "query": {
        "multi_match": {
          "query": "steve job",
          "fields": [
            "title",
            "overview"
          ],
          "operator": "or",
          "type": "most_fields"
        }
      },
      "functions": [
        {
          "field_value_factor": {
            # 对应要调整处理的字段
            "field": "popularity",
            "modifier": "log2p",
            "factor": 10
          }
        },
        {
          "field_value_factor": {
            "field": "popularity",
            "modifier": "log2p",
            "factor": 5
          }
        }
      ],
      # 不同的 field value 之间的得分相加
      "score_mode": "sum",
      # 最后再与 old value 相加
      "boost_mode": "sum"
    }
  }
}

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ElasticSearch-7.3.0 进阶语法

ElasticSearch-7.3.0 进阶语法

字段类型

高级查询语法

analyze分析过程

Tmdb实例

数据下载

索引建立

match和term

分词后的and和or

最小词匹配项

短语查询

score打分

多字段查询

过滤与排序

查全率查准率

自定义score

猜你喜欢

`analyze`分析过程

`Tmdb`实例

`match`和`term`

分词后的`and`和`or`

`score`打分

自定义`score`