ElasticSearch日期范围内的第一个和最后一个值以及其他聚合

Question

我有像这样的弹性搜索索引的数据,这是我希望数据在sku_id上分组的输出,我需要整个日期范围的平均排名,并且在日期范围内,last_7days_avg_rank的第一个值和last_7days_avg_rank的最后一个值将日期作为2个单独的字段,如下所示

如果弹性搜索中有可能,有人可以告诉我吗？现在我正在服务层进行这种计算,但由于响应时间已经成为UN可接受的,我想将这个逻辑移到ES本身,但是无法弄清楚如何实现这一点？

输入:

 date     sku_id last_7days_avg_rank rank 
 20180101  S1      200                200
 20180102  S1      210                200
 20180105  S1      220                200
 20180108  S1      230                200

 20180101  S2      180                300
 20180103  S2      200                300
 20180106  S2      250                300
 20180107  S2      300                300

预期产出:

sku  first_val_last7day_avg  last_val_last7days_avg  avg(rank)   
S1    200                       230                  200
S2    180                       300                  300

谢谢!

Answer 1

您可以使用聚合获得所需的结果

{

   "size": 0,
   "aggs": {
      "GROUP": {
         "terms": {
            "field": "sku_id"
         },
         "aggs": {
            "AVG_RANK": {
               "avg": {
                  "field": "rank"
               }
            },
            "FIRST_7_RANK": {
               "top_hits": {
                  "size": 1,
                  "sort": [
                     {
                        "my_date": {
                           "order": "asc"
                        }
                     }
                  ]
               }
            },
            "LAST_7_RANK": {
               "top_hits": {
                  "size": 1,
                  "sort": [
                     {
                        "my_date": {
                           "order": "desc"
                        }
                     }
                  ]
               }
            }
         }
      }
   }
}

您可以获得以下结果作为输出:

 "aggregations": {
      "GROUP": {
         "doc_count_error_upper_bound": 0,
         "sum_other_doc_count": 0,
         "buckets": [
            {
               "key": "S1",
               "doc_count": 40,
               "LAST_7_RANK": {
                  "hits": {
                     "total": 40,
                     "max_score": null,
                     "hits": [
                        {
                           "_index": "index_name",
                           "_type": "type_name",
                           "_id": "AWI9MU6JeKRzn3ttxGOr",
                           "_score": null,
                           "_source": {
                              "my_date": "2018-01-08",
                              "sku_id": "S1",
                              "last_7days_avg_rank": 230,
                              "rank": 200
                           },
                           "sort": [
                              1515369600000
                           ]
                        }
                     ]
                  }
               },
               "AVG_RANK": {
                  "value": 200
               },
               "FIRST_7_RANK": {
                  "hits": {
                     "total": 40,
                     "max_score": null,
                     "hits": [
                        {
                           "_index": "index_name",
                           "_type": "type_name",
                           "_id": "AWI9LYVpeKRzn3ttxGOQ",
                           "_score": null,
                           "_source": {
                              "my_date": "20180101",
                              "sku_id": "S1",
                              "last_7days_avg_rank": 200,
                              "rank": 200
                           },
                           "sort": [
                              20180101
                           ]
                        }
                     ]
                  }
               }
            },
            {
               "key": "S2",
               "doc_count": 40,
               "LAST_7_RANK": {
                  "hits": {
                     "total": 40,
                     "max_score": null,
                     "hits": [
                        {
                           "_index": "index_name",
                           "_type": "type_name",
                           "_id": "AWI9MU6JeKRzn3ttxGOv",
                           "_score": null,
                           "_source": {
                              "my_date": "2018-01-07",
                              "sku_id": "S2",
                              "last_7days_avg_rank": 300,
                              "rank": 300
                           },
                           "sort": [
                              1515283200000
                           ]
                        }
                     ]
                  }
               },
               "AVG_RANK": {
                  "value": 300
               },
               "FIRST_7_RANK": {
                  "hits": {
                     "total": 40,
                     "max_score": null,
                     "hits": [
                        {
                           "_index": "index_name",
                           "_type": "type_name",
                           "_id": "AWI9LYVpeKRzn3ttxGOU",
                           "_score": null,
                           "_source": {
                              "my_date": "20180101",
                              "sku_id": "S2",
                              "last_7days_avg_rank": 180,
                              "rank": 300
                           },
                           "sort": [
                              20180101
                           ]
                        }
                     ]
                  }
               }
            }
         ]
      }
   }

上面的结果为S1和S2创建了两个桶(组).在每个桶中你可以获得AVG_RANK字段中该组的平均排名,对于first_val_last7day_avg你需要追踪"FIRST_7_RANK" - >"点击" - >"命中" - >"_源" - >"排名"的值,同样如此last_val_last7days_avg你需要恍惚值"LAST_7_RANK" - >"点击" - >"命中" - >"_ source" - >"rank"我希望这可以帮到你