【Elasticsearch】使用Elasticsearch中的copy_to来提高搜索效率

女爷i 2022-08-30 04:15 165阅读 0赞

![在这里插入图片描述][1d26597b211b398f7f9a6b19fcce7e39.png]

# 1.概述 #

转载：[使用Elasticsearch中的copy\_to来提高搜索效率][Elasticsearch_copy_to]

在今天的这个教程中，我们来着重讲解一下如何使用Elasticsearch中的copy来提高搜索的效率。比如在我们的搜索中，经常我们会遇到如下的文档：

{ 
            "user" : "双榆树-张三",
            "message" : "今儿天气不错啊，出去转转去",
            "uid" : 2,
            "age" : 20,
            "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"
            }
        }

在这里，我们可以看到在这个文档中，我们有这样的几个字段：

"city" : "北京",
    "province" : "北京",
    "country" : "中国",

它们是非常相关的。我们在想是不是可以把它们综合成一个字段，这样可以方便我们的搜索。假如我们要经常对这三个字段进行搜索，那么一种方法我们可以在must子句中使用should子句运行bool查询。这种方法写起来比较麻烦。有没有一种更好的方法呢？

我们其实可以使用Elasticsearch所提供的copy\_to来提高我们的搜索效率。我们可以首先把我们的index的mapping设置成如下的项（这里假设我们使用的是一个叫做twitter的index)。

PUT twitter
        { 
          "mappings": { 
            "properties": { 
              "address": { 
                "type": "text",
                "fields": { 
                  "keyword": { 
                    "type": "keyword",
                    "ignore_above": 256
                  }
                }
              },
              "age": { 
                "type": "long"
              },
              "city": { 
                "type": "keyword",
                "copy_to": "region"
              },
              "country": { 
                "type": "keyword",
                "copy_to": "region"
              },
              "province": { 
                "type": "keyword",
                "copy_to": "region"
              },
              "region": { 
                "type": "text",
                "store": true
              },
              "location": { 
                "type": "geo_point"
              },
              "message": { 
                "type": "text",
                "fields": { 
                  "keyword": { 
                    "type": "keyword",
                    "ignore_above": 256
                  }
                }
              },
              "uid": { 
                "type": "long"
              },
              "user": { 
                "type": "text",
                "fields": { 
                  "keyword": { 
                    "type": "keyword",
                    "ignore_above": 256
                  }
                }
              }
            }
          }
        }

在这里，我们特别注意如下的这个部分：

"city": { 
      "type": "keyword",
      "copy_to": "region"
    },
    "country": { 
      "type": "keyword",
      "copy_to": "region"      
    },
    "province": { 
      "type": "keyword",
      "copy_to": "region"
    },
    "region": { 
      "type": "text"
    }

我们把city, country及province三个项合并成为一个项region，但是这个region并不存在于我们文档的source里。当我们这么定义我们的mapping的话，在文档被索引之后，有一个新的region项可以供我们进行搜索。

我们可以采用如下的数据来进行展示：

POST _bulk
        {  "index" : {  "_index" : "twitter", "_id": 1} }
        { "user":"双榆树-张三","message":"今儿天气不错啊，出去转转去","uid":2,"age":20,"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"}}
        {  "index" : {  "_index" : "twitter", "_id": 2 }}
        { "user":"东城区-老刘","message":"出发，下一站云南！","uid":3,"age":30,"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"}}
        {  "index" : {  "_index" : "twitter", "_id": 3} }
        { "user":"东城区-李四","message":"happy birthday!","uid":4,"age":30,"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"}}
        {  "index" : {  "_index" : "twitter", "_id": 4} }
        { "user":"朝阳区-老贾","message":"123,gogogo","uid":5,"age":35,"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"}}
        {  "index" : {  "_index" : "twitter", "_id": 5} }
        { "user":"朝阳区-老王","message":"Happy BirthDay My Friend!","uid":6,"age":50,"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"}}
        {  "index" : {  "_index" : "twitter", "_id": 6} }
        { "user":"虹桥-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,"age":90,"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"}}

在Kibnana中执行上面的语句，它将为我们生产我们的twitter索引。同时我们可以通过如下的语句来查询我们的mapping:

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIxMzgzNDM1_size_16_color_FFFFFF_t_70]  
我们可以看到twitter的mapping中有一个新的被称作为region的项。它将为我们的搜索带来方便。

那么假如我们想搜索country:中国，province:北京 这样的记录的话，我们可以只写如下的一条语句就可以了：

GET twitter/_search 
        { 
          "query": { 
            "match": { 
              "region": { 
                "query": "中国 北京",
                "minimum_should_match": 4
              }
            }
          }
        }

下面显示的是搜索的结果：

{ 
          "took" : 0,
          "timed_out" : false,
          "_shards" : { 
            "total" : 1,
            "successful" : 1,
            "skipped" : 0,
            "failed" : 0
          },
          "hits" : { 
            "total" : { 
              "value" : 5,
              "relation" : "eq"
            },
            "max_score" : 0.8114117,
            "hits" : [
              { 
                "_index" : "twitter",
                "_type" : "_doc",
                "_id" : "1",
                "_score" : 0.8114117,
                "_source" : { 
                  "user" : "双榆树-张三",
                  "message" : "今儿天气不错啊，出去转转去",
                  "uid" : 2,
                  "age" : 20,
                  "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"
                  }
                }
              },
              { 
                "_index" : "twitter",
                "_type" : "_doc",
                "_id" : "2",
                "_score" : 0.8114117,
                "_source" : { 
                  "user" : "东城区-老刘",
                  "message" : "出发，下一站云南！",
                  "uid" : 3,
                  "age" : 30,
                  "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"
                  }
                }
              },
              { 
                "_index" : "twitter",
                "_type" : "_doc",
                "_id" : "3",
                "_score" : 0.8114117,
                "_source" : { 
                  "user" : "东城区-李四",
                  "message" : "happy birthday!",
                  "uid" : 4,
                  "age" : 30,
                  "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"
                  }
                }
              },
              { 
                "_index" : "twitter",
                "_type" : "_doc",
                "_id" : "4",
                "_score" : 0.8114117,
                "_source" : { 
                  "user" : "朝阳区-老贾",
                  "message" : "123,gogogo",
                  "uid" : 5,
                  "age" : 35,
                  "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"
                  }
                }
              },
              { 
                "_index" : "twitter",
                "_type" : "_doc",
                "_id" : "5",
                "_score" : 0.8114117,
                "_source" : { 
                  "user" : "朝阳区-老王",
                  "message" : "Happy BirthDay My Friend!",
                  "uid" : 6,
                  "age" : 50,
                  "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"
                  }
                }
              }
            ]
          }
        }

这样我们只对一个region进行操作就可以了，否则我们需要针对country, city及province分别进行搜索。

# 2.如何查看copy\_to的内容 #

在之前的mapping中，我们对region字段加入了如下的一个属性：

"region": {
        "type": "text",
        "store": true
      }

这里的store属性为true，那么我们可以通过如下的命令来查看文档的region的内容：

GET twitter/_doc/1?stored_fields=region

那么它显示的内容如下：

{ 
          "_index" : "twitter",
          "_type" : "_doc",
          "_id" : "1",
          "_version" : 1,
          "_seq_no" : 0,
          "_primary_term" : 1,
          "found" : true,
          "fields" : { 
            "region" : [
              "北京",
              "北京",
              "中国"
            ]
          }
        }

如果你想了解更多关于Elastic Stack，请参阅文章“Elasticsearch简介”

[1d26597b211b398f7f9a6b19fcce7e39.png]: /images/20220829/f9a5275ac4e64709a84c2f5d3a131724.png
[Elasticsearch_copy_to]: http://www.mamicode.com/info-detail-2874997.html
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIxMzgzNDM1_size_16_color_FFFFFF_t_70]: /images/20220829/41fe3aec6fbc4aa6a14c73253c4e0515.png