Nested嵌套对象类型还挺实用

上一篇文章中,我们学习了Join类型的父子文档,今天继续学习一下嵌套文档,毕竟嵌套文档也是Elasticsearch推荐的,首先我们看下面这条语句

PUT word_document/_doc/1
{
  "title" : "up",
  "user" : [ 
    {
      "name" : "honghong",
      "sex" :  "female",
      "numberOfLikes":500
    },
    {
      "name" : "mingming",
      "sex" :  "male",
      "numberOfLikes":50
    },
    {
      "name" : "lanlan",
      "sex" :  "male",
      "numberOfLikes":100
    }
  ]
}

对于上面这种格式的数据,user就是嵌套对象数组,那么userElasticsearch中是怎么存储的呢?如果我们要对嵌套的子对象进行检索,怎么才能检索出我们所需要的数据呢,下面我们就一起来研究下Nested数据类型

环境

  • macos 10.14.6
  • Elasticsearch 8.1
  • Kibana 8.1

Nested

开头我们还是先了解一下,什么是Nested类型,其实就是字面意思,Nested就是嵌套,也就是文章开头user数据类型那种,所以可以看为是一种特殊的Object类型。还是以文章开头的数据为例

PUT word_document/_doc/1
{
  "title" : "up",
  "user" : [ 
    {
      "name" : "honghong",
      "sex" :  "female",
      "numberOfLikes":500
    },
    {
      "name" : "mingming",
      "sex" :  "male",
      "numberOfLikes":50
    },
    {
      "name" : "lanlan",
      "sex" :  "male",
      "numberOfLikes":100
    }
  ]
}

如果我们没有对word_document索引进行显示设置数据类型,在上面这个语句执行之后,Elasticsearch会默认推断数据类型,在Elasticsearch中内容会转换为可能如下的形式,扁平化的处理数据

{
  "title":"up",
  "user.name":["honghong","mingming","lanlan"],
  "user.sex":["male","male","female"],
  "user.numberOfLikes":[500,50,100]
}

相信大家也看出来了,如果被Elasticsearch转换成上面的这种数据结构之后,我们的搜索结果是会被影响的,假如我们使用如下这个语句进行查询,我们想搜索namehonghongsexmale,预期结果是没有匹配的文档,但是因为Elasticsearch对上述的结果进行了扁平化的处理,造成了错误的匹配

GET word_document/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "user.name": "honghong" }},
        { "match": { "user.sex":  "male" }}
      ]
    }
  }
}

如何避免上述情况的发生呢,那就是使用Elasticsearch提供的Nested数据类型,Nested 数据类型保证了嵌套对象的独立性,也就是让我们可以对嵌套对象的内容进行检索,从而不会发生上述的这种情况

  • 首先我们还是以上面文档为例,不过是这次我们优先创建索引,并指定user字段为nested

    PUT word_document
    {
      "mappings": {
        "properties": {
          "title":{
            "type": "keyword"
            },
          "user": {
            "type": "nested" 
          },
          "numberOfLikes":{
            "type": "integer"
          }
        }
      }
    }
  • 下面加入我们的测试数据,来验证我们的搜索语句

    PUT word_document/_doc/1
    {
      "title" : "up",
      "user" : [ 
        {
          "name" : "honghong",
          "sex" :  "female",
          "numberOfLikes":500
        },
        {
          "name" : "mingming",
          "sex" :  "male",
          "numberOfLikes":50
        },
        {
          "name" : "lanlan",
          "sex" :  "male",
          "numberOfLikes":100
        }
      ]
    }
    PUT word_document/_doc/2
    {
      "title" : "up",
      "user" : [ 
          {
          "name" : "honghong",
          "sex" :  "female",
          "numberOfLikes":20
        },
        {
          "name" : "mingming",
          "sex" :  "male",
          "numberOfLikes":30
        },
        {
          "name" : "lanlan",
          "sex" :  "male",
          "numberOfLikes":50
        }
      ]
    }
    PUT word_document/_doc/3
    {
      "title" : "up",
      "user" : [ 
        {
          "name" : "honghong",
          "sex" :  "female",
          "numberOfLikes":50
        },
        {
          "name" : "mingming",
          "sex" :  "male",
          "numberOfLikes":50
        },
        {
          "name" : "lanlan",
          "sex" :  "male",
          "numberOfLikes":50
        }
      ]
    }
  • 下面还是刚才那个搜索语句,此时就不会有匹配的文档返回,返回结果为空

    GET word_document/_search
    {
      "query": {
        "nested": {
          "path": "user",
          "query": {
            "bool": {
              "must": [
                { "match": { "user.name": "honghong" }},
                { "match": { "user.sex":  "male" }} 
              ]
            }
          }
        }
      }
    }
  • 那么对于嵌套文档我们可以怎么查询呢,那就是指定nested查询类型,使用普通的查询是查询不到的哦,nested查询语句如下所示,此时返回的就是我们

    GET word_document/_search
    {
      "query": {
        "nested": {
          "path": "user",
          "query": {
            "bool": {
              "must": [
                { "match": { "user.name": "honghong" }},
                { "match": { "user.sex":  "female" }} 
              ]
            }
          },
          "inner_hits": { 
            "highlight": {
              "fields": {
                "user.name": {}
              }
            }
          }
        }
      }
    }
  • 此外我们还可以根据嵌套对象中的字段进行排序,升序时获取嵌套对象中最小的值最为比较值,降序时获取嵌套对象最大的值作为比较值

    GET word_document/_search
    {
      "query": {
        "nested": {
          "path": "user",
          "query": {
            "match": {
              "user.sex": "male"
            }
          }
        }
      },
      "sort":[
        {
          "user.numberOfLikes": {
            "order": "asc", 
            "nested": {
              "path":"user"
            }
          }
        }
        ]
    }

    返回如下

    {
      "took" : 101,
      "timed_out" : false,
      "_shards" : {
        "total" : 1,
        "successful" : 1,
        "skipped" : 0,
        "failed" : 0
      },
      "hits" : {
        "total" : {
          "value" : 3,
          "relation" : "eq"
        },
        "max_score" : null,
        "hits" : [
          {
            "_index" : "word_document",
            "_id" : "2",
            "_score" : null,
            "_source" : {
              "title" : "up",
              "user" : [
                {
                  "name" : "honghong",
                  "sex" : "female",
                  "numberOfLikes" : 20
                },
                {
                  "name" : "mingming",
                  "sex" : "male",
                  "numberOfLikes" : 30
                },
                {
                  "name" : "lanlan",
                  "sex" : "male",
                  "numberOfLikes" : 50
                }
              ]
            },
            "sort" : [
              20
            ]
          },
          {
            "_index" : "word_document",
            "_id" : "1",
            "_score" : null,
            "_source" : {
              "title" : "up",
              "user" : [
                {
                  "name" : "honghong",
                  "sex" : "female",
                  "numberOfLikes" : 500
                },
                {
                  "name" : "mingming",
                  "sex" : "male",
                  "numberOfLikes" : 50
                },
                {
                  "name" : "lanlan",
                  "sex" : "male",
                  "numberOfLikes" : 100
                }
              ]
            },
            "sort" : [
              50
            ]
          },
          {
            "_index" : "word_document",
            "_id" : "3",
            "_score" : null,
            "_source" : {
              "title" : "up",
              "user" : [
                {
                  "name" : "honghong",
                  "sex" : "female",
                  "numberOfLikes" : 50
                },
                {
                  "name" : "mingming",
                  "sex" : "male",
                  "numberOfLikes" : 50
                },
                {
                  "name" : "lanlan",
                  "sex" : "male",
                  "numberOfLikes" : 50
                }
              ]
            },
            "sort" : [
              50
            ]
          }
        ]
      }
    }
    
  • 我们也可以对嵌套对象进行聚合操作,如下我们获取索引中user.name=honghong,user.sex=female的所有文档,聚合统计numberOfLikes的最小值

    GET word_document/_search
    {
      "query": {
        "nested": {
          "path": "user",
          "query": {
            "bool": {
              "must": [
                {
                  "match": {
                    "user.name": "honghong"
                  }
                },
                {
                  "match": {
                    "user.sex": "female"
                  }
                }
              ]
            }
          }
        }
      },
      "aggs": {
        "my_min_value": {
          "nested": {
            "path": "user"
          }, 
          "aggs": {
            "min_value": {
              "min": {
                "field": "user.numberOfLikes"
              }
            }
          }
        }
      }
    }
  • 上面的聚合统计只是对外部的文档过滤,那如果我们有这么一个需求,聚合统计嵌套对象user内容sex=male的最小值,那么我们可以使用如下filter,下面这个语句优先过滤title=up的文档,聚合统计user.sex=malenumberOfLikes最小值

    GET /word_document/_search?size=0
    {
      "query": {
        "match": {
          "title": "up"
        }
      },
      "aggs": {
        "my_user": {
          "nested": {
            "path": "user"
          },
          "aggs": {
            "filter_my_user": {
              "filter": {
                "bool": {
                  "filter": [
                    {
                      "match": {
                        "user.sex": "male"
                      }
                    }
                  ]
                }
              },
              "aggs": {
                "min_price": {
                  "min": {
                    "field": "user.numberOfLikes"
                  }
                }
              }
            },
            "no_filter_my_user":{
              "min": {
                "field": "user.numberOfLikes"
              }
            }
          }
        }
      }
    }
  • 最后还有一种就是反向嵌套聚合,通过嵌套对象聚合父文档,返回父文档信息

    首先我们还是先创建一个索引添加几条数据用来测试

    PUT /issues
    {
      "mappings": {
        "properties": {
          "tags": { "type": "keyword" },
          "comments": {                            
            "type": "nested",
            "properties": {
              "username": { "type": "keyword" },
              "comment": { "type": "text" }
            }
          }
        }
      }
    }
    PUT /issues/_doc/1
    {
      "tags":"跳舞",
      "comments":[{
        "username":"小李",
        "comment":"小李想学跳舞"
      },
      {
        "username":"小红",
        "comment":"小红跳舞很有天赋"
      }
      ]
    }
    PUT /issues/_doc/2
    {
      "tags":"唱歌",
      "comments":[{
        "username":"小李",
        "comment":"小李会唱歌"
      },
      {
        "username":"小李",
        "comment":"小李唱歌有天赋"
      },
      {
        "username":"小红",
        "comment":"小红是歌手"
      }
      ]
    }
    PUT /issues/_doc/3
    {
      "tags":"跳舞",
      "comments":[
      {
        "username":"小红",
        "comment":"小红会跳舞"
      },
      {
        "username":"小红",
        "comment":"小红是舞神"
      }
      ]
    }
    PUT /issues/_doc/4
    {
      "tags":"唱歌",
      "comments":[
      {
        "username":"小李",
        "comment":"小李简直就是天生歌手"
      }
      ]
    }
    PUT /issues/_doc/5
    {
      "tags":"跳舞",
      "comments":[
      {
        "username":"小红",
        "comment":"小红舞姿很美"
      }
      ]
    }
    issues 问题;tags 标签;username 名字;comment 评论;

    下面我们使用反向嵌套聚合父文档,需求如下:

    1、先聚合统计出评论最多的username

    2、在聚合统计usernamecomment最多的tag

    GET /issues/_search?size=0
    {
      "query": {
        "match_all": {}
      },
      "aggs": {
        "comments": {
          "nested": {
            "path": "comments"
          },
          "aggs": {
            "top_usernames": {
              "terms": {
                "field": "comments.username"
              },
              "aggs": {
                "comment_to_issue": {
                  "reverse_nested": {}, 
                  "aggs": {
                    "top_tags_per_comment": {
                      "terms": {
                        "field": "tags"
                      }
                    }
                  }
                }
              }
            }
          }
        }
      }
    }

    结果如下,得出结论:小红评论次数最多,评论了5次,小红评论最多的标签是跳舞,有3次

    {
      "aggregations" : {
        "comments" : {
          "doc_count" : 9,
          "top_usernames" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "小红",
                "doc_count" : 5,
                "comment_to_issue" : {
                  "doc_count" : 4,
                  "top_tags_per_comment" : {
                    "doc_count_error_upper_bound" : 0,
                    "sum_other_doc_count" : 0,
                    "buckets" : [
                      {
                        "key" : "跳舞",
                        "doc_count" : 3
                      },
                      {
                        "key" : "唱歌",
                        "doc_count" : 1
                      }
                    ]
                  }
                }
              },
              {
                "key" : "小李",
                "doc_count" : 4,
                "comment_to_issue" : {
                  "doc_count" : 3,
                  "top_tags_per_comment" : {
                    "doc_count_error_upper_bound" : 0,
                    "sum_other_doc_count" : 0,
                    "buckets" : [
                      {
                        "key" : "唱歌",
                        "doc_count" : 2
                      },
                      {
                        "key" : "跳舞",
                        "doc_count" : 1
                      }
                    ]
                  }
                }
              }
            ]
          }
        }
      }
    }
    

Nested 支持的参数有哪些

Nested也只是特殊的Object的一种,也是有支持的几种参数

  • dynamic: (可选参数) 是否允许在索引mapping文件未定义字段的情况下对新字段的处理,默认是加入到现有的嵌套对象中(true),还支持falsestrict
  • properties: (可选参数) 嵌套对象字段内容属性设置
  • include_in_parent:(可选参数) 默认false,如果为true,嵌套对象的字段也会作为普通字段的形式(flat)添加到父文档
  • include_in_root:(可选参数) 默认false,如果为true,嵌套对象的字段也会作为普通字段的形式(flat)添加到根文档

Nested 类型的约束

通过前面的学习,我们也知道了nested类型可以作为一个单独的Lucene文档进行所有,当我们有100个嵌套对象的时候我们需要101个文档来存储映射关系,一个用于父文档,一个用于嵌套文档,所以这一部分的开销,ELasticsearch来通过一下设置进行了约束

  • index.mapping.nested_fields.limit

    一个索引中,嵌套类型字段(nested)最多存在多个限制,默认50个,如我们上面的例子中,也就是只占用了一个

  • index.mapping.nested_objects.limit

    一个索引中,单个嵌套类型字段包含的嵌套JSON对象的最大数量,默认10000

总结

通过上面的学习实践,我们可以知道Nested嵌套类型是Elasticsearch推荐的相对于Join类型,并且Nested可以实现查询,聚合,排序等,基本满足了工作的需要。好了,到这就结束吧,有什么需要深入了解的,留言哦,也可以去官网查看,毕竟官网还是一手资料,博主的也只能算是入门启蒙笔记,实践起来吧,加油!

Join 字段的详解可以参考博主的这一篇文章哦

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