Elasticsearch: 运用 Pinned query 来提高文档的排名 （7.5发行版新功能）
Elasticsearch: 运用 Pinned query 来提高文档的排名 （7.5发行版新功能）
腾讯云大数据
修改于 2021-01-08 16:01:41
修改于 2021-01-08 16:01:41
【腾讯云 Elasticsearch Service】高可用，可伸缩，云端全托管。集成X-Pack高级特性，适用日志分析/企业搜索/BI分析等场景
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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