elasticsearch - 在Elasticsearch中可以计算 “distinct sum”和 “distinct average”吗？

Question

如何在Elasticsearch中计算“不同的平均值”？我有一些这样的非规范化数据:

{ "record_id" : "100", "cost" : 42 }
{ "record_id" : "200", "cost" : 67 }
{ "record_id" : "200", "cost" : 67 }
{ "record_id" : "200", "cost" : 67 }
{ "record_id" : "400", "cost" : 11 }
{ "record_id" : "400", "cost" : 11 }
{ "record_id" : "500", "cost" : 10 }
{ "record_id" : "600", "cost" : 99 }

请注意，对于给定的“record_id”，“成本”始终是相同的。

因此，根据以上数据:

如何获取“成本”字段的平均值但由“record_id”区分？
结果将是(42 + 67 + 11 + 10 + 99)/5=45.8

如何获取“费用”字段的总和值，但如何通过“record_id”区分？
结果将是42 + 67 + 11 + 10 + 99 = 229

我可以结合使用“术语”聚合，然后结合“第一”和“平均”子聚合吗？我在想这样的事情: elasticsearch calculate average of unique values

Answer 1

它不适用于terms aggs。这是使用无痛脚本的可能方式:

编制索引-您的实际映射可能与生成的默认映射不同(特别是.keyword上的rec_id部分):

POST _bulk
{"index":{"_index":"uniques","_type":"_doc"}}
{"record_id":"100","cost":42}
{"index":{"_index":"uniques","_type":"_doc"}}
{"record_id":"200","cost":67}
{"index":{"_index":"uniques","_type":"_doc"}}
{"record_id":"200","cost":67}
{"index":{"_index":"uniques","_type":"_doc"}}
{"record_id":"200","cost":67}
{"index":{"_index":"uniques","_type":"_doc"}}
{"record_id":"400","cost":11}
{"index":{"_index":"uniques","_type":"_doc"}}
{"record_id":"400","cost":11}
{"index":{"_index":"uniques","_type":"_doc"}}
{"record_id":"500","cost":10}
{"index":{"_index":"uniques","_type":"_doc"}}
{"record_id":"600","cost":99}

然后汇总

GET uniques/_search
{
  "size": 0,
  "aggs": {
    "terms": {
      "scripted_metric": {
        "init_script": "state.id_map = [:]; state.sum = 0.0; state.elem_count = 0.0;",
        "map_script": """
          def id = doc['record_id.keyword'].value;
          if (!state.id_map.containsKey(id)) {
            state.id_map[id] = true;
            state.elem_count++;
            state.sum += doc['cost'].value;
          }
        """,
        "combine_script": """
            def sum = state.sum;
            def avg = sum / state.elem_count;

            def stats = [:];
            stats.sum = sum;
            stats.avg = avg;

            return stats
        """,
        "reduce_script": "return states"
      }
    }
  }
}

并屈服

...
"aggregations" : {
    "terms" : {
      "value" : [
        {
          "avg" : 45.8,
          "sum" : 229.0
        }
      ]
    }
  }