elasticsearch - ElasticSearch多个词的聚合顺序

Question

我有一个描述容器的文档结构，它的一些字段是:

containerId -> Unique Id,String
containerManufacturer -> String
containerValue -> Double
estContainerWeight ->Double
actualContainerWeight -> Double

我想运行一个搜索聚合，在两个权重字段上具有两个级别的术语聚合，但按权重字段的降序排列，如下所示:

{
  "size": 0,
  "aggs": {
    "by_manufacturer": {
      "terms": {
        "field": "containerManufacturer",
        "size": 10,
        "order": {"estContainerWeight": "desc"} //Cannot do this
      },
      "aggs": {
        "by_est_weight": {
          "terms": {
            "field": "estContainerWeight",
            "size": 10,
            "order": { "actualContainerWeight": "desc"} //Cannot do this
          },
          "aggs": {
            "by_actual_weight": {
              "terms": {
                "field": "actualContainerWeight",
                "size": 10
              },
              "aggs" : {
                "container_value_sum" : {"sum" : {"field" : "containerValue"}}
              }
            }
          }
        }
      }
    }
  }
}

样本文件:

{"containerId":1,"containerManufacturer":"A","containerValue":12,"estContainerWeight":5.0,"actualContainerWeight":5.1}
{"containerId":2,"containerManufacturer":"A","containerValue":24,"estContainerWeight":5.0,"actualContainerWeight":5.2}
{"containerId":3,"containerManufacturer":"A","containerValue":23,"estContainerWeight":5.0,"actualContainerWeight":5.2}
{"containerId":4,"containerManufacturer":"A","containerValue":32,"estContainerWeight":6.0,"actualContainerWeight":6.2}
{"containerId":5,"containerManufacturer":"A","containerValue":26,"estContainerWeight":6.0,"actualContainerWeight":6.3}
{"containerId":6,"containerManufacturer":"A","containerValue":23,"estContainerWeight":6.0,"actualContainerWeight":6.2}

预期输出(不完整):

{
  "by_manufacturer": {
    "buckets": [
      {
        "key": "A",
        "by_est_weight": {
          "buckets": [
            {
              "key" : 5.0,
              "by_actual_weight" : {
                "buckets" : [
                  {
                    "key" : 5.2,
                    "container_value_sum" : {
                      "value" : 1234 //Not actual sum
                    }
                  },
                  {
                    "key" : 5.1,
                    "container_value_sum" : {
                      "value" : 1234 //Not actual sum
                    }
                  }
                ]
              }
            },
            {
              "key" : 6.0,
              "by_actual_weight" : {
                "buckets" : [
                  {
                    "key" : 6.2,
                    "container_value_sum" : {
                      "value" : 1234 //Not actual sum
                    }
                  },
                  {
                    "key" : 6.3,
                    "container_value_sum" : {
                      "value" : 1234 //Not actual sum
                    }
                  }
                ]
              }
            }
          ]
        }
      }
    ]
  }
}

但是，我无法按嵌套聚合排序。 (错误:术语存储桶只能在子聚合器路径上排序，该路径是由路径中的零个或多个单存储桶聚合以及最终的单个存储桶或指标聚合构成的。)

例如，对于上面的示例输出，如果对术语聚合引入大小(如果我的数据很大，则必须这样做)，我无法控制生成的存储桶，所以我只想获得前N个每个术语聚合的权重。

有没有办法做到这一点 ？

Answer 1

如果我正确理解了您的问题，则希望按制造商的容器的估计重量的降序对它们进行排序，然后按“实际重量”的降序对每个“估计重量”桶进行排序。

{
  "size": 0,
  "aggs": {
    "by_manufacturer": {
      "terms": {
        "field": "containerManufacturer",
        "size": 10
      },
        "by_est_weight": {
          "terms": {
            "field": "estContainerWeight",
            "size": 10,
            "order": {
              "_term": "desc"       <--- change to this
            }
          },
            "by_actual_weight": {
              "terms": {
                "field": "actualContainerWeight",
                "size": 10,
                "order" : {"_term" : "desc"}   <----- Change to this
              },
              "aggs": {
                "container_value_sum": {
                  "sum": {
                    "field": "containerValue"
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}