实战Elasticsearch6的join类型

本文涉及的产品
检索分析服务 Elasticsearch 版,2核4GB开发者规格 1个月
简介: elasticsearch6版本新增了join类型,本文通过实战来熟悉和了解该类型

欢迎访问我的GitHub

这里分类和汇总了欣宸的全部原创(含配套源码): https://github.com/zq2599/blog_demos

本篇概览

  • 《Elasticsearch实战》(英文名Elasticsearch IN ACTION)是经典es教程,对应demo源码地址为:https://github.com/dakrone/elasticsearch-in-action ,最新分支6.x,在使用源码时,发现索引_doc的静态映射脚本增加了一个类型为join的字段,如下所示,:
"mappings" : {
    "_doc" : {
      "_source" : {
        "enabled" : true
      },
      "properties" : {
        "relationship_type": {
          "type": "join",
          "relations" : {
            "group": "event"
          }
        },
        ...
  • 这是es6新增的类型,一起来通过实战学习这个join;

环境信息

  1. 操作系统:Ubuntu 18.04.2 LTS
  2. elasticsearch:6.7.1
  3. kibana:6.7.1

《Elasticsearch实战》demo源码下载地址

  • 本文用到的源码一共两个文件,一个是创建静态映射的mapping.json, 另一个是创建文档的populate.sh , 地址分别如下:
  1. https://github.com/dakrone/elasticsearch-in-action/blob/6.x/mapping.json
  2. https://github.com/dakrone/elasticsearch-in-action/blob/6.x/populate.sh
  • 上述文件的用法:下载到同一个目录,执行命令./populate.sh 192.168.1.101:9200,"192.168.1.101:9200"是es6的http地址和端口;

官方说法

  • 官方对join类型的说明如下:

在这里插入图片描述

  • 我的理解:
  1. join类型用于建立索引内文档的父子关系;
  2. 用父子文档的名字来表示关系;
  • 接下来看看《Elasticsearch实战》的demo中是怎么使用这个字段的;

《Elasticsearch实战》的demo

  • demo中部分文档的创建脚本如下所示:
curl -s -XPOST "$ADDRESS/get-together/_doc/1" -H'Content-Type: application/json' -d'{
  "relationship_type": "group",
  "name": "Denver Clojure",
  "organizer": ["Daniel", "Lee"],
  "description": "Group of Clojure enthusiasts from Denver who want to hack on code together and learn more about Clojure",
  "created_on": "2012-06-15",
  "tags": ["clojure", "denver", "functional programming", "jvm", "java"],
  "members": ["Lee", "Daniel", "Mike"],
  "location_group": "Denver, Colorado, USA"
}'

curl -s -XPOST "$ADDRESS/get-together/_doc/100?routing=1" -H'Content-Type: application/json' -d'{
  "relationship_type": {
    "name": "event",
    "parent": "1"
  },
  "host": ["Lee", "Troy"],
  "title": "Liberator and Immutant",
  "description": "We will discuss two different frameworks in Clojure for doing different things. Liberator is a ring-compatible web framework based on Erlang Webmachine. Immutant is an all-in-one enterprise application based on JBoss.",
  "attendees": ["Lee", "Troy", "Daniel", "Tom"],
  "date": "2013-09-05T18:00",
  "location_event": {
    "name": "Stoneys Full Steam Tavern",
    "geolocation": "39.752337,-105.00083"
  },
  "reviews": 4
}'
  • 如上所示,id为1的记录,其relationship_type字段的值为"group",id为2的记录,relationship_type字段的值不是字符串,而是对象,parent为1表示父文档id为1,name为"event"表示父子关系是"group:event"类型;
  • 注意:上述第二个文档的地址中携带了routing参数,以保持父子在同一个分片,这是在使用join类型是要格外注意的地方;
  • 接下来,确保前面提到的populate.sh脚本已经执行,使得_doc索引及其文档数据在es环境中准备好,就可以实战了,实战环境是Kibana的Det Tools:

查找所有父类型为"group"的文档(结果是子文档):

  • 执行如下脚本:
GET get-together/_search
{
  "query": {
    "has_parent": {
      "parent_type": "group",
      "query": {
        "match_all": {}
      }
    }
  }
}
  • 可以得到所有父类型为"group"的子文档:
{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 2,
    "successful" : 2,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 15,
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "get-together",
        "_type" : "_doc",
        "_id" : "106",
        "_score" : 1.0,
        "_routing" : "3",
        "_source" : {
          "relationship_type" : {
            "name" : "event",
            "parent" : "3"
          },
          "host" : "Mik",
          "title" : "Social management and monitoring tools",
          "description" : "Shay Banon will be there to answer questions and we can talk about management tools.",
          "attendees" : [
            "Shay",
            "Mik",
            "John",
            "Chris"
          ],
          "date" : "2013-03-06T18:00",
          "location_event" : {
            "name" : "Quid Inc",
            "geolocation" : "37.798442,-122.399801"
          },
          "reviews" : 5
        }
      },
      {
        "_index" : "get-together",
        "_type" : "_doc",
        "_id" : "107",
        "_score" : 1.0,
        "_routing" : "3",
        "_source" : {
          "relationship_type" : {
            "name" : "event",
            "parent" : "3"
          },
          "host" : "Mik",
          "title" : "Logging and Elasticsearch",
          "description" : "Get a deep dive for what Elasticsearch is and how it can be used for logging with Logstash as well as Kibana!",
          "attendees" : [
            "Shay",
            "Rashid",
            "Erik",
            "Grant",
            "Mik"
          ],
          "date" : "2013-04-08T18:00",
          "location_event" : {
            "name" : "Salesforce headquarters",
            "geolocation" : "37.793592,-122.397033"
          },
          "reviews" : 3
        }
      },
     ...

查找所有子类型为"event"的文档(结果是父文档)

  • 执行如下脚本:
GET get-together/_search
{
  "query": {
    "has_child": {
      "type": "event",
      "query": {
        "match_all": {}
      }
    }
  }
}
  • 可以得到所有子类型为"event"的文档:
{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 2,
    "successful" : 2,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 5,
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "get-together",
        "_type" : "_doc",
        "_id" : "3",
        "_score" : 1.0,
        "_source" : {
          "relationship_type" : "group",
          "name" : "Elasticsearch San Francisco",
          "organizer" : "Mik",
          "description" : "Elasticsearch group for ES users of all knowledge levels",
          "created_on" : "2012-08-07",
          "tags" : [
            "elasticsearch",
            "big data",
            "lucene",
            "open source"
          ],
          "members" : [
            "Lee",
            "Igor"
          ],
          "location_group" : "San Francisco, California, USA"
        }
      },
      {
        "_index" : "get-together",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "relationship_type" : "group",
          "name" : "Denver Clojure",
          "organizer" : [
            "Daniel",
            "Lee"
          ],
          "description" : "Group of Clojure enthusiasts from Denver who want to hack on code together and learn more about Clojure",
          "created_on" : "2012-06-15",
          "tags" : [
            "clojure",
            "denver",
            "functional programming",
            "jvm",
            "java"
          ],
          "members" : [
            "Lee",
            "Daniel",
            "Mike"
          ],
          "location_group" : "Denver, Colorado, USA"
        }
      },
     ...

查找parent的id等于1的子文档

  • 执行如下脚本:
GET get-together/_search
{
  "query": {
    "parent_id": {
      "type": "event",
      "id": "1"
    }
  }
}
  • 可以得到所有parent的id等于1的子文档:
{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 2,
    "successful" : 2,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 3,
    "max_score" : 1.3291359,
    "hits" : [
      {
        "_index" : "get-together",
        "_type" : "_doc",
        "_id" : "100",
        "_score" : 1.3291359,
        "_routing" : "1",
        "_source" : {
          "relationship_type" : {
            "name" : "event",
            "parent" : "1"
          },
          "host" : [
            "Lee",
            "Troy"
          ],
          "title" : "Liberator and Immutant",
          "description" : "We will discuss two different frameworks in Clojure for doing different things. Liberator is a ring-compatible web framework based on Erlang Webmachine. Immutant is an all-in-one enterprise application based on JBoss.",
          "attendees" : [
            "Lee",
            "Troy",
            "Daniel",
            "Tom"
          ],
          "date" : "2013-09-05T18:00",
          "location_event" : {
            "name" : "Stoneys Full Steam Tavern",
            "geolocation" : "39.752337,-105.00083"
          },
          "reviews" : 4
        }
      },
      ...

用script_fields简化返回内容

  • 前面的查询,返回的内容是整个_source,如果不需要全部内容,可以用script_fields来简化;
  • 查找所有父文档ID等1的的子文档,并且返回内容只有三个字段:父文档ID、子文档ID、子文档title字段:
GET get-together/_search
{
   "query": {
    "parent_id": {
      "type": "event",
      "id": "1"
    }
  },
  "script_fields":{
      "group_id":{
        "script":{
          "source":"doc['relationship_type#group']"
        }
      },"event_id":{
        "script":{
          "source":"doc['_id']"
        }
      },
      "title":{
        "script":"params['_source']['title']"
      }
    }
}
  • 得到结果如下:
{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 2,
    "successful" : 2,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 3,
    "max_score" : 1.3291359,
    "hits" : [
      {
        "_index" : "get-together",
        "_type" : "_doc",
        "_id" : "100",
        "_score" : 1.3291359,
        "_routing" : "1",
        "fields" : {
          "event_id" : [
            "100"
          ],
          "title" : [
            "Liberator and Immutant"
          ],
          "group_id" : [
            "1"
          ]
        }
      },
      {
        "_index" : "get-together",
        "_type" : "_doc",
        "_id" : "101",
        "_score" : 1.3291359,
        "_routing" : "1",
        "fields" : {
          "event_id" : [
            "101"
          ],
          "title" : [
            "Sunday, Surly Sunday"
          ],
          "group_id" : [
            "1"
          ]
        }
      },
      {
        "_index" : "get-together",
        "_type" : "_doc",
        "_id" : "102",
        "_score" : 1.3291359,
        "_routing" : "1",
        "fields" : {
          "event_id" : [
            "102"
          ],
          "title" : [
            "10 Clojure coding techniques you should know, and project openbike"
          ],
          "group_id" : [
            "1"
          ]
        }
      }
    ]
  }
}

聚合

  • 执行以下查询,会将所有父文档为group的子文档做桶聚合聚合:
GET get-together/_search
{
  "query": {
    "has_parent": {
      "parent_type": "group",
      "query": {
        "match_all": {}
      }
    }
  },
   "aggs":{
      "parents":{
        "terms":{
          "field":"relationship_type#group"
        }
      }
    }
}
  • 得到的结果如下,按照父文档ID得到聚合结果:
"aggregations" : {
    "parents" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "1",
          "doc_count" : 3
        },
        {
          "key" : "2",
          "doc_count" : 3
        },
        {
          "key" : "3",
          "doc_count" : 3
        },
        {
          "key" : "4",
          "doc_count" : 3
        },
        {
          "key" : "5",
          "doc_count" : 3
        }
      ]
    }
  }
}
  • 以上就是join类型的主要实战内容了,希望能帮助您理解这个新的类型;

欢迎关注阿里云开发者社区博客:程序员欣宸

学习路上,你不孤单,欣宸原创一路相伴...
相关实践学习
使用阿里云Elasticsearch体验信息检索加速
通过创建登录阿里云Elasticsearch集群,使用DataWorks将MySQL数据同步至Elasticsearch,体验多条件检索效果,简单展示数据同步和信息检索加速的过程和操作。
ElasticSearch 入门精讲
ElasticSearch是一个开源的、基于Lucene的、分布式、高扩展、高实时的搜索与数据分析引擎。根据DB-Engines的排名显示,Elasticsearch是最受欢迎的企业搜索引擎,其次是Apache Solr(也是基于Lucene)。 ElasticSearch的实现原理主要分为以下几个步骤: 用户将数据提交到Elastic Search 数据库中 通过分词控制器去将对应的语句分词,将其权重和分词结果一并存入数据 当用户搜索数据时候,再根据权重将结果排名、打分 将返回结果呈现给用户 Elasticsearch可以用于搜索各种文档。它提供可扩展的搜索,具有接近实时的搜索,并支持多租户。
相关文章
|
4月前
|
自然语言处理 关系型数据库 数据库
ElasticSearch 映射类型及数据类型区分
ElasticSearch 映射类型及数据类型区分
50 0
|
4月前
|
存储 数据采集 数据处理
数据处理神器Elasticsearch_Pipeline:原理、配置与实战指南
数据处理神器Elasticsearch_Pipeline:原理、配置与实战指南
171 12
|
5月前
|
缓存 数据处理 数据安全/隐私保护
Elasticsearch索引状态管理实战指南
Elasticsearch索引状态管理实战指南
|
5月前
|
存储 索引
Elasticsearch中父子文档的关联:利用Join类型赋予文档的层级关系
Elasticsearch中父子文档的关联:利用Join类型赋予文档的层级关系
|
5月前
|
存储 索引
Elasticsearch索引之嵌套类型:深度剖析与实战应用
Elasticsearch索引之嵌套类型:深度剖析与实战应用
|
6月前
|
人工智能 自然语言处理 开发者
Langchain 与 Elasticsearch:创新数据检索的融合实战
Langchain 与 Elasticsearch:创新数据检索的融合实战
181 10
|
5月前
|
存储 JSON 搜索推荐
Springboot2.x整合ElasticSearch7.x实战(三)
Springboot2.x整合ElasticSearch7.x实战(三)
47 0
|
5月前
|
存储 自然语言处理 关系型数据库
Springboot2.x整合ElasticSearch7.x实战(二)
Springboot2.x整合ElasticSearch7.x实战(二)
49 0
|
5月前
|
搜索推荐 数据可视化 Java
Springboot2.x整合ElasticSearch7.x实战(一)
Springboot2.x整合ElasticSearch7.x实战(一)
46 0
|
6月前
|
搜索推荐 JavaScript Java
Elasticsearch 8.X 如何依据 Nested 嵌套类型的某个字段进行排序?
Elasticsearch 8.X 如何依据 Nested 嵌套类型的某个字段进行排序?
89 0