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问题:

Elasticsearch-使用function_score查询的不同文档

阎彬炳
2023-03-14

我在ElasticSearch有索引。其中的文档具有重复的字段值。在查询结果中,我需要删除所有重复项,并且只获得不同的值。例如:

PUT localhost:9200/人

{
    "mappings" : {
        "person" : {
            "properties" : {
                "name" : { "type" : "keyword" }
            }
        }
    }
}

POST localhost:9200/人/人

{
    "name": "John"
}

{
    "name": "John"
}

{
    "name": "Marry"
}

{
    "name": "Tomas"
}

我试图通过字段“name”删除重复的术语聚合,但它不起作用。

获取localhost:9200/person/person/_search

{
  "size": 3,
  "query": {
    "function_score": {
      "functions": [
        {
          "random_score": {
            "seed": "dasdfdLBpnM0"
          }
        }
      ]
    }
  },
  "aggs": {
    "top-names": {
      "terms": {
        "field": "name",
        "size": 3
      },
      "aggs": {
        "top_names_hits": {
          "top_hits": {
            "size": 1
          }
        }
      }
    }
  }
}

结果:

{
    "took": 5,
    "timed_out": false,
    "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": 10,
        "max_score": 0.9506482,
        "hits": [
            {
                "_index": "person",
                "_type": "person",
                "_id": "H-5D8GoB8pRyckNSVUeN",
                "_score": 0.9506482,
                "_source": {
                    "name": "Tomas"
                }
            },
            {
                "_index": "person",
                "_type": "person",
                "_id": "He5D8GoB8pRyckNSPEfa",
                "_score": 0.7700638,
                "_source": {
                    "name": "John"
                }
            },
            {
                "_index": "person",
                "_type": "person",
                "_id": "HO5D8GoB8pRyckNSN0fo",
                "_score": 0.71723765,
                "_source": {
                    "name": "John"
                }
            }
        ]
    },
    "aggregations": {
        "top-names": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
                {
                    "key": "John",
                    "doc_count": 2,
                    "top_names_hits": {
                        "hits": {
                            "total": 2,
                            "max_score": 0.7700638,
                            "hits": [
                                {
                                    "_index": "person",
                                    "_type": "person",
                                    "_id": "He5D8GoB8pRyckNSPEfa",
                                    "_score": 0.7700638,
                                    "_source": {
                                        "name": "John"
                                    }
                                }
                            ]
                        }
                    }
                },
                {
                    "key": "Marry",
                    "doc_count": 1,
                    "top_names_hits": {
                        "hits": {
                            "total": 1,
                            "max_score": 0.66815424,
                            "hits": [
                                {
                                    "_index": "person",
                                    "_type": "person",
                                    "_id": "Iu5D8GoB8pRyckNScUdv",
                                    "_score": 0.66815424,
                                    "_source": {
                                        "name": "Marry"
                                    }
                                }
                            ]
                        }
                    }
                },
                {
                    "key": "Tomas",
                    "doc_count": 1,
                    "top_names_hits": {
                        "hits": {
                            "total": 1,
                            "max_score": 0.9506482,
                            "hits": [
                                {
                                    "_index": "person",
                                    "_type": "person",
                                    "_id": "H-5D8GoB8pRyckNSVUeN",
                                    "_score": 0.9506482,
                                    "_source": {
                                        "name": "Tomas"
                                    }
                                }
                            ]
                        }
                    }
                }
            ]
        }
    }
}

聚合应用于name=“marry”的文档,但我不明白为什么,以及如何才能将聚合仅应用于查询结果。

共有1个答案

蓟清野
2023-03-14

下面是或多或少的Elasticsearch查询蓝图....

{
  "size": n, // Return the n documents based on "query" section (to frontend)

  "query": {
          //  Here is where you are supposed to mention what documents you want
          //  Any filter/bool/match query condition
          //  In your case, you haven't specified any correct condition. 
          //  So basically, it would return all the documents or documents based on size parameter. In your case it returns 3. 
  },

  "aggs":{
      //  This aggregation query would only be applied on documents 
      //  based on documents filtered/matched by the "query" section. 
      //  In your case it is applying aggregation on all documents of that index as per the comment I've mentioned in the above query section.
   }
}

要获得您要查找的内容,只需使用下面的简化查询,它与Terms Gaggregation(Top Hits)一起使用,作为子Gaggregation(子Gaggregation)。

POST person/_search
{
  "size": 0,                          <------- This is to say, I don't want "query" results to be returned and that I only want below aggregation results. 
  "aggs": {
    "top-names": {
      "terms": {
        "field": "name",
        "size": 10
      },
      "aggs": {
        "top_hits_documents": {       <------- Top hits would return the actual documents
          "top_hits": {
            "size": 1
          }
        }
      }
    }
  }
}

通过在顶部指定“size”:0,您基本上是在所有文档上应用聚合,并且不会返回任何查询结果。

您只需返回聚合结果。

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 4,
    "max_score" : 0.0,
    "hits" : [ ]                    <------ Notice this. No query results returned
  },
  "aggregations" : {                <------ Aggregation Result starts
    "top-names" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "John",           <------- This is to say there's a value called John 
          "doc_count" : 2,          <------- John occurs in two documents.
          "top_hits_documents" : {
            "hits" : {
              "total" : 2,
              "max_score" : 1.0,
              "hits" : [
                {
                  "_index" : "person",
                  "_type" : "person",
                  "_id" : "2",
                  "_score" : 1.0,
                  "_source" : {
                    "name" : "John"
                  }
                }
              ]
            }
          }
        },
        {
          "key" : "Marry",
          "doc_count" : 1,
          "top_hits_documents" : {
            "hits" : {
              "total" : 1,
              "max_score" : 1.0,
              "hits" : [
                {
                  "_index" : "person",
                  "_type" : "person",
                  "_id" : "3",
                  "_score" : 1.0,
                  "_source" : {
                    "name" : "Marry"
                  }
                }
              ]
            }
          }
        },
        {
          "key" : "Thomas",
          "doc_count" : 1,
          "top_hits_documents" : {
            "hits" : {
              "total" : 1,
              "max_score" : 1.0,
              "hits" : [
                {
                  "_index" : "person",
                  "_type" : "person",
                  "_id" : "4",
                  "_score" : 1.0,
                  "_source" : {
                    "name" : "Thomas"
                  }
                }
              ]
            }
          }
        }
      ]
    }
  }
}

希望有帮助!

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