作者：刘晓国
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索引的 mapping 设置成如下的项（这里假设我们使用的是一个叫做 twitter 的索引)。
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 Kibana 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:
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。
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”
