Question

我正在尝试通过聚合嵌套列表来获取加权平均值。每个文档都包含一个学生的详细信息，并且每个学生的主题有所不同，并且每个主题的权重都不同。

我正在尝试按主题计算加权平均数。

我的文档的格式-

[{'class': '10th',
 'id': '1',
'subject': [{'marks': 60, 'name': 's1', 'weight': 30},
         {'marks': 80, 'name': 's2', 'weight': 70}]},
{'class': '11th',
 'id': '2',
'subject': [{'marks': 43, 'name': 's10', 'weight': 40},
         {'marks': 54, 'name': 's20', 'weight': 60}]},
{'class': '10th',
 'id': '3',
'subject': [{'marks': 43, 'name': 's1', 'weight': 20},
         {'marks': 54, 'name': 's20', 'weight': 80}]},
{'class': '10th',
 'id': '4',
'subject': [{'marks': 69, 'name': 's10', 'weight': 30},
         {'marks': 45, 'name': 's2', 'weight': 70}]}]

这里s1，s10，s2，s20是主题。对于给定的班级，说“第十”，我正在尝试汇总加权平均值。

我进行的查询是

GET students_try/_search
{
 "query": {
"match": {
  "class": "10th"
}
 },
"aggs": {
"subjects": {
  "nested": {
    "path": "subject"
  },
  "aggs": {
    "subjects": {
      "terms": {
        "field": "subject.name"
      },
      "aggs": {
        "avg_score": {
          "avg": {
            "field": "subject.marks"
          }
        },
        "weighted_grade": {
          "weighted_avg": {
            "value": {
              "field": "subject.marks"
            },
            "weight": {
              "field": "subject.weight"
            }
          }
        }
      }
    }
  }
}
  },
 "size": 0
}

我得到的错误是-

{u'error': {u'col': 211,
        u'line': 1,
        u'reason': u'Unknown BaseAggregationBuilder [weighted_avg]',
        u'root_cause': [{u'col': 211,
                         u'line': 1,
                         u'reason': u'Unknown BaseAggregationBuilder [weighted_avg]',
                         u'type': u'unknown_named_object_exception'}],
        u'type': u'unknown_named_object_exception'},
 u'status': 400}

我不确定是什么引起了错误。

Answer 1

是的，Nishant提到的加权平均值仅出现在此link的6.4版详细发布的A few others部分中提到的6.4之后

但是我使用Bucket Script Aggregation提出了以下脚本，该脚本计算了每个存储桶的加权平均值：

POST <your_index_name>/_search
{
  "size": 0,
  "query": {
    "match": {
      "class": "10th"
    }
  },
  "aggs": {
    "subjects": {
      "nested": {
        "path": "subject"
      },
      "aggs": {
        "subjects": {
          "terms": {
            "field": "subject.name.keyword"
          },
          "aggs": {
            "avg_score": {
              "avg": {
                "field": "subject.marks"
              }
            },
            "sum_score":{
              "sum_productOfMarksAndWeight": {
                "script": "doc['subject.marks'].value * doc['subject.weight'].value"
              }
            },
            "sum_weights": {
              "sum": {
                "field": "subject.weight"
              }
            },
            "weighted_avg":{
              "bucket_script": {
                "buckets_path": {
                  "sumScore": "sum_productOfMarksAndWeight",
                  "sumWeights": "sum_weights"
                },
                "script": "params.sumScore/params.sumWeights"
              }
            }
          }             
        }
      }
    }
  }
}

如果您仔细查看上述汇总，则对于每个存储区，我都使用Sum Aggregation计算了sum of weights和sum of product of weights and marks，然后使用了这两个汇总来计算加权聚合。

以下是您的回复显示方式。请注意，在汇总结果中还会看到sum of weights和sum of product of weights and marks。

响应

{
  "took": 12,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 3,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "subjects": {
      "doc_count": 6,
      "subjects": {
        "doc_count_error_upper_bound": 0,
        "sum_other_doc_count": 0,
        "buckets": [
          {
            "key": "s1",
            "doc_count": 2,
            "sum_weights": {
              "value": 50
            },
            "sum_productOfMarksAndWeight": {
              "value": 2660
            },
            "avg_score": {
              "value": 51.5
            },
            "weighted_avg": {
              "value": 53.2
            }
          },
          {
            "key": "s2",
            "doc_count": 2,
            "sum_weights": {
              "value": 140
            },
            "sum_productOfMarksAndWeight": {
              "value": 8750
            },
            "avg_score": {
              "value": 62.5
            },
            "weighted_avg": {
              "value": 62.5
            }
          },
          {
            "key": "s10",
            "doc_count": 1,
            "sum_weights": {
              "value": 30
            },
            "sum_productOfMarksAndWeight": {
              "value": 2070
            },
            "avg_score": {
              "value": 69
            },
            "weighted_avg": {
              "value": 69
            }
          },
          {
            "key": "s20",
            "doc_count": 1,
            "sum_weights": {
              "value": 80
            },
            "sum_productOfMarksAndWeight": {
              "value": 4320
            },
            "avg_score": {
              "value": 54
            },
            "weighted_avg": {
              "value": 54
            }
          }
        ]
      }
    }
  }
}

我希望这会有所帮助，如果没有，请告诉我，如果您认为这可以解决您的需求，请继续接受此答案;-）

弹性搜索中嵌套聚合的加权平均值

1 个答案:

响应