java - 如何按字段类型对匹配的文档字段进行分组？

Question

我正在使用术语聚合来计算字段值，但在聚合之前，我正在根据结果聚合进行过滤器搜索将起作用..最后我需要 id 和聚合计数 如果异常 id 为 1 并且它匹配，那么我需要输出为

1 -> "key": "转账" “doc_count”:2

2 -> “关键”:“股票” “doc_count”:4

我在下面突出显示了异常 ID，我希望将其作为指向每个存储桶的指针。

我如何在 Elasticsearch 中做到这一点，我已附上示例响应。

{
  "took": 250,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 0.0,
    "hits": [
      {
        "_index": "america",
        "_type": "_doc",
        "_id": "1",
        "_score": 0.0,
        "_source": {
          "clusterId": "1",
          "rank": 1,
          "events": [
            {
              "eventId": "1",
              "eventType": "Delayed",
              "metaInfo": {
                "batch_id": "batch_1"
              },
              "recommendationData": [
                {
                  ***"exceptionId": "1",***
                  "item": "Item1",
                  "location": "DC1",
                  "dueDate": "2019-01-10T05:30:00.000+0530",
                  "quantity": 100,
                  "metaInfo": {
                    "batch_id": "batch_1",
                    "dummy_id": "dummy_1"
                  },
                  "rank": 1,
                  "recommendations": [
                    {
                      "rank": 1,
                      "recommendationType": "transfer",
                      "customerName": "Walmart",
                      "stockTransfer": {
                        "primaryRecommendation": true,
                        "priority": 1,
                        "sourceLocation": "DC3",
                        "metaInfo": 40,
                        "shipDate": "2019-01-09T05:30:00.000+0530",
                        "arrivalDate": "2019-01-10T05:30:00.000+0530",
                        "transportMode": "Road",
                        "transferCost": 200.0,
                        "maxQtyAvailableForTransfer": 40,
                        "totalQtyAtSource": 40
                      },
                      "expedite": null
                    },
                    {
                      "rank": 1,
                      "recommendationType": "transfer",
                      "customerName": "Walmart",
                      "stockTransfer": {
                        "primaryRecommendation": true,
                        "priority": 2,
                        "sourceLocation": "DC2",
                        "transferQuantity": 60,
                        "shipDate": "2019-01-09T05:30:00.000+0530",
                        "arrivalDate": "2019-01-10T05:30:00.000+0530",
                        "transportMode": "Road",
                        "transferCost": 600.0,
                        "maxQtyAvailableForTransfer": 100,
                        "totalQtyAtSource": 100
                      },
                      "expedite": null
                    }
                  ]
                }
              ]
            }
          ]
        }
      }
    ]
  },
  "aggregations": {
    "recommendationTypes": {
      "doc_count": 2,
      "recommendationTypes": {
        "doc_count_error_upper_bound": 0,
        "sum_other_doc_count": 0,
        "buckets": [
          {
            "key": "transfer",
            "doc_count": 2
          }
        ]
      }
    }
  }
}

Answer 1

如果您想对任何 exceptionId 或RecommendationType 进行聚合(两者都位于嵌套对象内)，则需要使用嵌套聚合。

例如。如果您有一个文档，其中包含两个嵌套文档，其中 exceptionId 为 1 和 2。您想要聚合异常 ID 为 2 的嵌套文档，则即使您在“查询”部分中使用嵌套查询进行过滤，也需要使用嵌套聚合，因为整个文档即使嵌套对象匹配并且您必须在聚合中特别提及您想要聚合特定嵌套对象时，也会返回。 查询

{
  "aggs": {
    "recommendations": {
      "nested": {
        "path": "events.recommendationData"
      },
      "aggs": {
        "exception": {
          "filter": {
            "terms": {
              "events.recommendationData.exceptionId": [
                "2"
              ]
            }
          },
          "aggs": {
            "exceptionIds": {
              "terms": {
                "field": "events.recommendationData.exceptionId.keyword",
                "size": 10
              },
              "aggs": {
                "recommendations": {
                  "nested": {
                    "path": "events.recommendationData.recommendations"
                  },
                  "aggs": {
                    "recommendationType": {
                      "terms": {
                        "field": "events.recommendationData.recommendations.recommendationType",
                        "size": 10
                      }
                    }
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}

结果:

"aggregations" : {
    "recommendations" : {
      "doc_count" : 1,
      "exception" : {
        "doc_count" : 1,
        "exceptionIds" : {
          "doc_count_error_upper_bound" : 0,
          "sum_other_doc_count" : 0,
          "buckets" : [
            {
              "key" : "2",
              "doc_count" : 1,
              "recommendations" : {
                "doc_count" : 2,
                "recommendationType" : {
                  "doc_count_error_upper_bound" : 0,
                  "sum_other_doc_count" : 0,
                  "buckets" : [
                    {
                      "key" : "transfer",
                      "doc_count" : 2
                    }
                  ]
                }
              }
            }
          ]
        }
      }
    }
  }