Elasticsearch: 运用 Pinned query 来提高文档的排名 （7.5发行版新功能）

时间:2022-07-25
本文章向大家介绍Elasticsearch: 运用 Pinned query 来提高文档的排名 （7.5发行版新功能），主要内容包括其使用实例、应用技巧、基本知识点总结和需要注意事项，具有一定的参考价值，需要的朋友可以参考一下。

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Pinned 查询用来提升所选文档的排名，使其高于匹配给定查询的文档。 此功能通常用于引导搜索者查找精选的文档，这些文档在搜索的任何 “organic” 匹配项之上被提升。 使用存储在_id字段中的文档 ID 来标识升级或“固定”的文档。

下面有给一个例子来说明：

准备数据

首先我们使用如下的bulk API接口来把我们所需要的数据导入到Elasticsearch之中：

POST _bulk{ "index" : { "_index" : "twitter", "_id": 1} }{"user":"张三","message":"今儿天气不错啊，出去转转去","uid":2,"city":"北京","province":"北京","country":"中国","address":"中国北京市海淀区","location":{"lat":"39.970718","lon":"116.325747"}, "DOB":"1980-12-01"}{ "index" : { "_index" : "twitter", "_id": 2 }}{"user":"老刘","message":"出发，下一站云南！","uid":3,"city":"北京","province":"北京","country":"中国","address":"中国北京市东城区台基厂三条3号","location":{"lat":"39.904313","lon":"116.412754"}, "DOB":"1981-12-01"}{ "index" : { "_index" : "twitter", "_id": 3} }{"user":"李四","message":"happy birthday!","uid":4,"city":"北京","province":"北京","country":"中国","address":"中国北京市东城区","location":{"lat":"39.893801","lon":"116.408986"}, "DOB":"1982-12-01"}{ "index" : { "_index" : "twitter", "_id": 4} }{"user":"老贾","message":"123,gogogo","uid":5,"city":"北京","province":"北京","country":"中国","address":"中国北京市朝阳区建国门","location":{"lat":"39.718256","lon":"116.367910"}, "DOB":"1983-12-01"}{ "index" : { "_index" : "twitter", "_id": 5} }{"user":"老王","message":"Happy BirthDay My Friend!","uid":6,"city":"北京","province":"北京","country":"中国","address":"中国北京市朝阳区国贸","location":{"lat":"39.918256","lon":"116.467910"}, "DOB":"1984-12-01"}{ "index" : { "_index" : "twitter", "_id": 6} }{"user":"老吴","message":"好友来了都今天我生日，好友来了,什么 birthday happy 就成!","uid":7,"city":"上海","province":"上海","country":"中国","address":"中国上海市闵行区","location":{"lat":"31.175927","lon":"121.383328"}, "DOB":"1985-12-01"}

这样，我们就有6个数据。它们的Id分别是从1到6。

搜索

正常搜索

首先我们来做一个正常的搜索，比如寻找所有在北京的文档：

GET twitter/_search{  "query": {    "match": {      "city.keyword": "北京"    }  }}

那么查询的结果为：

    "hits" : [      {        "_index" : "twitter",        "_type" : "_doc",        "_id" : "1",        "_score" : 0.24116206,        "_source" : {          "user" : "张三",          "message" : "今儿天气不错啊，出去转转去",          "uid" : 2,          "city" : "北京",          "province" : "北京",          "country" : "中国",          "address" : "中国北京市海淀区",          "location" : {            "lat" : "39.970718",            "lon" : "116.325747"          },          "DOB" : "1980-12-01"        }      },      {        "_index" : "twitter",        "_type" : "_doc",        "_id" : "2",        "_score" : 0.24116206,        "_source" : {          "user" : "老刘",          "message" : "出发，下一站云南！",          "uid" : 3,          "city" : "北京",          "province" : "北京",          "country" : "中国",          "address" : "中国北京市东城区台基厂三条3号",          "location" : {            "lat" : "39.904313",            "lon" : "116.412754"          },          "DOB" : "1981-12-01"        }      },      {        "_index" : "twitter",        "_type" : "_doc",        "_id" : "3",        "_score" : 0.24116206,        "_source" : {          "user" : "李四",          "message" : "happy birthday!",          "uid" : 4,          "city" : "北京",          "province" : "北京",          "country" : "中国",          "address" : "中国北京市东城区",          "location" : {            "lat" : "39.893801",            "lon" : "116.408986"          },          "DOB" : "1982-12-01"        }      },      {        "_index" : "twitter",        "_type" : "_doc",        "_id" : "4",        "_score" : 0.24116206,        "_source" : {          "user" : "老贾",          "message" : "123,gogogo",          "uid" : 5,          "city" : "北京",          "province" : "北京",          "country" : "中国",          "address" : "中国北京市朝阳区建国门",          "location" : {            "lat" : "39.718256",            "lon" : "116.367910"          },          "DOB" : "1983-12-01"        }      },      {        "_index" : "twitter",        "_type" : "_doc",        "_id" : "5",        "_score" : 0.24116206,        "_source" : {          "user" : "老王",          "message" : "Happy BirthDay My Friend!",          "uid" : 6,          "city" : "北京",          "province" : "北京",          "country" : "中国",          "address" : "中国北京市朝阳区国贸",          "location" : {            "lat" : "39.918256",            "lon" : "116.467910"          },          "DOB" : "1984-12-01"        }      }    ]  }

我们可以看出来共有5条数据结果，它们的_id分别是从1到5。

那么现在的问题是：如果我想把_id为4和5的那两个文档排在查询的最前面，那么我们该如何来做呢？答案是我们是使用 pinned query。

Pinned query

我们可以使用如下的方式来进行查询：

GET twitter/_search{  "query": {    "pinned": {      "ids": [        "4",        "5"      ],      "organic": {        "match": {          "city.keyword": "北京"        }      }    }  }}

那么我们再来看一下我们的查询结果：

    "hits" : [      {        "_index" : "twitter",        "_type" : "_doc",        "_id" : "4",        "_score" : 1.7014124E38,        "_source" : {          "user" : "老贾",          "message" : "123,gogogo",          "uid" : 5,          "city" : "北京",          "province" : "北京",          "country" : "中国",          "address" : "中国北京市朝阳区建国门",          "location" : {            "lat" : "39.718256",            "lon" : "116.367910"          },          "DOB" : "1983-12-01"        }      },      {        "_index" : "twitter",        "_type" : "_doc",        "_id" : "5",        "_score" : 1.7014122E38,        "_source" : {          "user" : "老王",          "message" : "Happy BirthDay My Friend!",          "uid" : 6,          "city" : "北京",          "province" : "北京",          "country" : "中国",          "address" : "中国北京市朝阳区国贸",          "location" : {            "lat" : "39.918256",            "lon" : "116.467910"          },          "DOB" : "1984-12-01"        }      },      {        "_index" : "twitter",        "_type" : "_doc",        "_id" : "1",        "_score" : 0.24116206,        "_source" : {          "user" : "张三",          "message" : "今儿天气不错啊，出去转转去",          "uid" : 2,          "city" : "北京",          "province" : "北京",          "country" : "中国",          "address" : "中国北京市海淀区",          "location" : {            "lat" : "39.970718",            "lon" : "116.325747"          },          "DOB" : "1980-12-01"        }      },      {        "_index" : "twitter",        "_type" : "_doc",        "_id" : "2",        "_score" : 0.24116206,        "_source" : {          "user" : "老刘",          "message" : "出发，下一站云南！",          "uid" : 3,          "city" : "北京",          "province" : "北京",          "country" : "中国",          "address" : "中国北京市东城区台基厂三条3号",          "location" : {            "lat" : "39.904313",            "lon" : "116.412754"          },          "DOB" : "1981-12-01"        }      },      {        "_index" : "twitter",        "_type" : "_doc",        "_id" : "3",        "_score" : 0.24116206,        "_source" : {          "user" : "李四",          "message" : "happy birthday!",          "uid" : 4,          "city" : "北京",          "province" : "北京",          "country" : "中国",          "address" : "中国北京市东城区",          "location" : {            "lat" : "39.893801",            "lon" : "116.408986"          },          "DOB" : "1982-12-01"        }      }    ]  }

在这一次的查询结果中，我们可以看到_id为4和5的两个文档的排名是排在最前面，它们的分数被提高了。

参考：

【1】 https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-pinned-query.html

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