对top_hits聚合的总和

时间:2018-08-06 13:56:24

标签: elasticsearch

简而言之,问题:如果我对每个存储区的top_hits进行汇总,如何在结果结构中求和特定值?

详细信息:

我有许多记录,每个商店包含一定数量的记录。我想获取每个商店的所有最新记录的总和。

要获取每个商店的最新记录,我创建以下汇总:

"latest_quantity_per_store": {
    "aggs": {
        "latest_quantity": {
            "top_hits": {
                "sort": [
                    {
                        "datetime": "desc"
                    },
                    {
                        "quantity": "asc"
                    }
                ],
                "_source": {
                    "includes": [
                        "quantity"
                    ]
                },
                "size": 1
            }
        }
    },
    "terms": {
        "field": "store",
        "size": 10000
    }
}

假设我有两个商店,每个商店有两个数量用于两个不同的时间戳。这是该聚合的结果:

"latest_quantity_per_store": {
    "doc_count_error_upper_bound": 0,
    "sum_other_doc_count": 0,
    "buckets": [
        {
            "key": "01",
            "doc_count": 2,
            "latest_quantity": {
                "hits": {
                    "total": 2,
                    "max_score": null,
                    "hits": [
                        {
                            "_index": "inventory-local",
                            "_type": "doc",
                            "_id": "O6wFD2UBG8e7nvSU8dYg",
                            "_score": null,
                            "_source": {
                                "quantity": 6
                            },
                            "sort": [
                                1532476800000,
                                6
                            ]
                        }
                    ]
                }
            }
        },
        {
            "key": "02",
            "doc_count": 2,
            "latest_quantity": {
                "hits": {
                    "total": 2,
                    "max_score": null,
                    "hits": [
                        {
                            "_index": "inventory-local",
                            "_type": "doc",
                            "_id": "pLUFD2UBHBuSGcoH0ZT4",
                            "_score": null,
                            "_source": {
                                "quantity": 11
                            },
                            "sort": [
                                1532476800000,
                                11
                            ]
                        }
                    ]
                }
            }
        }
    ]
}

我现在想在ElasticSearch中进行汇总,以汇总这些存储桶中的总和。在示例数据中,总和超过6和11。我尝试了以下聚合:

"latest_quantity": {
    "sum_bucket": {
        "buckets_path": "latest_quantity_per_store>latest_quantity>hits>hits>_source>quantity"
    }
}

但这会导致此错误:

{
  "error": {
    "root_cause": [
      {
        "type": "illegal_argument_exception",
        "reason": "No aggregation [hits] found for path [latest_quantity_per_store>latest_quantity>hits>hits>_source>quantity]"
      }
    ],
    "type": "search_phase_execution_exception",
    "reason": "all shards failed",
    "phase": "query",
    "grouped": true,
    "failed_shards": [
      {
        "shard": 0,
        "index": "inventory-local",
        "node": "3z5CqmmAQ-yT2sUCb69DzA",
        "reason": {
          "type": "illegal_argument_exception",
          "reason": "No aggregation [hits] found for path [latest_quantity_per_store>latest_quantity>hits>hits>_source>quantity]"
        }
      }
    ]
  },
  "status": 400
}

什么是正确的聚合方式以某种方式从ElasticSearch中获得数字17?

对于另一个聚合,我做了类似的事情,而不是top_hits聚合。

"average_quantity": {
    "sum_bucket": {
        "buckets_path": "average_quantity_per_store>average_quantity"
    }
},
"average_quantity_per_store": {
    "aggs": {
        "average_quantity": {
            "avg": {
                "field": "quantity"
            }
        }
    },
    "terms": {
        "field": "store",
        "size": 10000
    }
}

这可以按预期工作,这是结果:

"average_quantity_per_store": {
    "doc_count_error_upper_bound": 0,
    "sum_other_doc_count": 0,
    "buckets": [
        {
            "key": "01",
            "doc_count": 2,
            "average_quantity": {
                "value": 6
            }
        },
        {
            "key": "02",
            "doc_count": 2,
            "average_quantity": {
                "value": 11.5
            }
        }
    ]
},
"average_quantity": {
    "value": 17.5
}

1 个答案:

答案 0 :(得分:2)

有一种方法可以结合使用scripted_metric聚合和sum_bucket管道聚合来解决。脚本化的指标聚合有点复杂,但主要思想是允许您提供自己的存储算法并从中吐出一个指标指标。

在您的情况下,您要做的是找出每个商店的最新数量,然后对这些商店数量求和。解决方案如下所示,我将在下面解释一些细节:

POST inventory-local/_search
{
  "size": 0,
  "aggs": {
    "bystore": {
      "terms": {
        "field": "store.keyword",
        "size": 10000
      },
      "aggs": {
        "latest_quantity": {
          "scripted_metric": {
            "init_script": "params._agg.quantities = new TreeMap()",
            "map_script": "params._agg.quantities.put(doc.datetime.date, [doc.datetime.date.millis, doc.quantity.value])",
            "combine_script": "return params._agg.quantities.lastEntry().getValue()",
            "reduce_script": "def maxkey = 0; def qty = 0; for (a in params._aggs) {def currentKey = a[0]; if (currentKey > maxkey) {maxkey = currentKey; qty = a[1]} } return qty;"
          }
        }
      }
    },
    "sum_latest_quantities": {
      "sum_bucket": {
        "buckets_path": "bystore>latest_quantity.value"
      }
    }
  }
}

请注意,要使其正常工作,您需要在script.painless.regex.enabled: true配置文件中设置elasticsearch.yml

init_script为每个分片创建一个TreeMapmap_script用日期/数量的映射填充每个分片上的TreeMap。我们在地图中输入的值在单个字符串中包含时间戳和数量。我们稍后在reduce_script中需要该时间戳。 combine_script仅取TreeMap的最后一个值,因为这是给定分片的最新数量。 大部分工作位于reduce_script中。我们迭代每个分片的所有最新数量,并返回最新的数量。

目前,我们为每家商店提供最新数量。剩下要做的就是使用sum_bucket管道聚合来求和每个存储量。这样就得到了17的结果。

响应如下:

 "aggregations": {
    "bystore": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "01",
          "doc_count": 2,
          "latest_quantity": {
            "value": 6
          }
        },
        {
          "key": "02",
          "doc_count": 2,
          "latest_quantity": {
            "value": 11
          }
        }
      ]
    },
    "sum_latest_quantities": {
      "value": 17
    }
  }