ElasticSearch多项聚合顺序

时间:2015-12-14 14:41:15

标签: elasticsearch

我有一个描述容器的文档结构,其中一些字段是:

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

我想运行一个搜索聚合,它在两个权重字段上有两个级别的聚合,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"}}
              }
            }
          }
        }
      }
    }
  }
}

示例文件:

{"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(未完成):

{
  "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,我无法通过嵌套聚合进行排序。 (错误:术语存储区只能在子聚合器路径上排序,该路径由路径中的零个或多个单桶聚合构建,并且是最终的单桶或度量聚合...)

例如,对于上面的示例输出,如果我在术语聚合上引入一个大小(如果我的数据很大,我将不得不这样做),我无法控制生成的桶,所以我想只得到每个术语聚合的前N个权重。

有办法做到这一点吗?

1 个答案:

答案 0 :(得分:1)

如果我正确理解您的问题,您希望按照其容器估计重量的降序排列制造商术语,然后按照实际重量的降序排列每个“估计重量”桶。

{
  "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"
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}