Elasticsearch 使用terms对long类型日期统计按月销售

最新推荐文章于 2024-07-29 10:40:23 发布

原创最新推荐文章于 2024-07-29 10:40:23 发布 · 574 阅读

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#elasticsearch #terms #script #aggs #统计

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索引mapping：

{
      "properties" : {
        "_class" : {
          "type" : "text",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "ignore_above" : 256
            }
          }
        },
        "createDate" : {
          "type" : "long"
        },
        "goodsCode" : {
          "type" : "keyword"
        },
        "id" : {
          "type" : "text",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "ignore_above" : 256
            }
          }
        },
        "orderNumber" : {
          "type" : "keyword"
        },
        "payDate" : {
          "type" : "long"
        },
        "saleQty" : {
          "type" : "double"
        }
      }
    }

payDate字段映射为long类型，在使用date_histogram统计时，统计不出想要的效果。

{
    ...
    "aggs": {
       "agg_name": {
            "date_histogram": {
                "field": "payDate",
                "interval": "1m",
                "format": "yyyy-MM",
                "min_doc_count": 0
            }
        }
    }
}

结果输出的是"yyyy-MM1577836800000"，并不是期待的2023-06.

{
    "key_as_string": "yyyy-MM1577836800000",
    "key": 1577836800000,
    "doc_count": 3
}

改用terms+script+子聚合方式统计：

{
  "query": {
    "range": {
      "payDate": {
        "gte": 1685548800000
      }
    }
  },
  "size":0, 
  "aggs": {
    "month_sales":{
      "terms": {
        "script": {
          "source": """
        DateTimeFormatter df = DateTimeFormatter.ofPattern("yyyy-MM").withZone(ZoneId.systemDefault());
           return df.format(Instant.ofEpochMilli(doc['payDate'].value));
        """,
        "lang":"painless"
        },
        "order": {
          "_key": "asc"
        }
      },
      "aggs": {
        "sale_sum": {
          "sum": {
            "field": "saleQty"
          }
        }
      }
    }
  }
}

script：使用脚本把pay格式化成yyyy-MM的字符串。
order：根据日期排序
sale_sum：统计每个桶里面商品销售数量

输出结果：

{
    "month_sales": {
        "doc_count_error_upper_bound": 0,
        "sum_other_doc_count": 9723368,
        "buckets": [
            {
                "key": "2023-06",
                "doc_count": 49,
                "sale_sum": {
                    "value": 77.0
                }
            },
            {
                "key": "2023-07",
                "doc_count": 217,
                "sale_sum": {
                    "value": 40012.0
                }
            },
            {
                "key": "2023-08",
                "doc_count": 2187911,
                "sale_sum": {
                    "value": 3486633.0
                }
            },
            {
                "key": "2023-09",
                "doc_count": 2746235,
                "sale_sum": {
                    "value": 4556942.0
                }
            },
            {
                "key": "2023-10",
                "doc_count": 2346896,
                "sale_sum": {
                    "value": 4097690.0
                }
            },
            {
                "key": "2023-11",
                "doc_count": 2442224,
                "sale_sum": {
                    "value": 4120708.0
                }
            },
            {
                "key": "2023-12",
                "doc_count": 2359840,
                "sale_sum": {
                    "value": 4167825.0
                }
            },
            {
                "key": "2024-01",
                "doc_count": 3619202,
                "sale_sum": {
                    "value": 6506941.0
                }
            },
            {
                "key": "2024-02",
                "doc_count": 2673919,
                "sale_sum": {
                    "value": 5066871.0
                }
            },
            {
                "key": "2024-03",
                "doc_count": 3078439,
                "sale_sum": {
                    "value": 4810836.0
                }
            }
        ]
    }
}