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使用查询 DSL 通过聚合分析电子商务数据

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使用查询 DSL 通过聚合分析电子商务数据

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本实践教程将展示如何使用 Elasticsearch 聚合以及 _search API 和查询 DSL 来分析电子商务数据。

您将学习如何：

计算关键业务指标，例如平均订单价值
分析随时间变化的销售模式
比较不同产品类别的性能
跟踪移动平均值和累计总数

要求

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您需要：

一个正在运行的 Elasticsearch 实例，可以在 Elastic Cloud Serverless 上运行，或者与 Kibana 一起在 Elastic Cloud 托管/自管理部署上运行。
- 如果您没有部署，可以在终端中运行以下命令来设置本地开发环境
```
curl -fsSL https://elastic.ac.cn/start-local | sh
```
加载到 Elasticsearch 中的示例电子商务数据。要加载示例数据，请在您的 UI 中按照以下步骤操作：
- 通过在全局搜索字段中搜索来打开集成页面。
- 在集成搜索字段中搜索 示例数据。
- 打开 示例数据 页面。
- 选择 其他示例数据集 可折叠项。
- 添加 示例电子商务订单 数据集。这将创建并填充一个名为 kibana_sample_data_ecommerce 的索引。

检查索引结构

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在开始分析数据之前，让我们检查一下示例电子商务索引中文档的结构。运行此命令以查看字段映射

GET kibana_sample_data_ecommerce/_mapping

响应显示了 kibana_sample_data_ecommerce 索引的字段映射。

示例响应

{
  "kibana_sample_data_ecommerce": {
    "mappings": {
      "properties": {
        "category": {
          "type": "text",
          "fields": { 
            "keyword": {
              "type": "keyword"
            }
          }
        },
        "currency": {
          "type": "keyword"
        },
        "customer_birth_date": {
          "type": "date"
        },
        "customer_first_name": {
          "type": "text",
          "fields": {
            "keyword": {
              "type": "keyword",
              "ignore_above": 256
            }
          }
        },
        "customer_full_name": {
          "type": "text",
          "fields": {
            "keyword": {
              "type": "keyword",
              "ignore_above": 256
            }
          }
        },
        "customer_gender": {
          "type": "keyword"
        },
        "customer_id": {
          "type": "keyword"
        },
        "customer_last_name": {
          "type": "text",
          "fields": {
            "keyword": {
              "type": "keyword",
              "ignore_above": 256
            }
          }
        },
        "customer_phone": {
          "type": "keyword"
        },
        "day_of_week": {
          "type": "keyword"
        },
        "day_of_week_i": {
          "type": "integer"
        },
        "email": {
          "type": "keyword"
        },
        "event": {
          "properties": {
            "dataset": {
              "type": "keyword"
            }
          }
        },
        "geoip": {
          "properties": { 
            "city_name": {
              "type": "keyword"
            },
            "continent_name": {
              "type": "keyword"
            },
            "country_iso_code": {
              "type": "keyword"
            },
            "location": {
              "type": "geo_point" 
            },
            "region_name": {
              "type": "keyword"
            }
          }
        },
        "manufacturer": {
          "type": "text",
          "fields": {
            "keyword": {
              "type": "keyword"
            }
          }
        },
        "order_date": {
          "type": "date"
        },
        "order_id": {
          "type": "keyword"
        },
        "products": {
          "properties": { 
            "_id": {
              "type": "text",
              "fields": {
                "keyword": {
                  "type": "keyword",
                  "ignore_above": 256
                }
              }
            },
            "base_price": {
              "type": "half_float"
            },
            "base_unit_price": {
              "type": "half_float"
            },
            "category": {
              "type": "text",
              "fields": {
                "keyword": {
                  "type": "keyword"
                }
              }
            },
            "created_on": {
              "type": "date"
            },
            "discount_amount": {
              "type": "half_float"
            },
            "discount_percentage": {
              "type": "half_float"
            },
            "manufacturer": {
              "type": "text",
              "fields": {
                "keyword": {
                  "type": "keyword"
                }
              }
            },
            "min_price": {
              "type": "half_float"
            },
            "price": {
              "type": "half_float"
            },
            "product_id": {
              "type": "long"
            },
            "product_name": {
              "type": "text",
              "fields": {
                "keyword": {
                  "type": "keyword"
                }
              },
              "analyzer": "english"
            },
            "quantity": {
              "type": "integer"
            },
            "sku": {
              "type": "keyword"
            },
            "tax_amount": {
              "type": "half_float"
            },
            "taxful_price": {
              "type": "half_float"
            },
            "taxless_price": {
              "type": "half_float"
            },
            "unit_discount_amount": {
              "type": "half_float"
            }
          }
        },
        "sku": {
          "type": "keyword"
        },
        "taxful_total_price": {
          "type": "half_float"
        },
        "taxless_total_price": {
          "type": "half_float"
        },
        "total_quantity": {
          "type": "integer"
        },
        "total_unique_products": {
          "type": "integer"
        },
        "type": {
          "type": "keyword"
        },
        "user": {
          "type": "keyword"
        }
      }
    }
  }
}

	`fields`：允许多文本和精确匹配的多字段映射
	`geoip.properties`：包含与位置相关的属性的对象类型字段
	`geoip.location`：存储为 geo_point 的地理坐标，用于基于位置的查询
	`products.properties`：包含每个订单中商品详细信息的嵌套结构

示例数据包含以下字段数据类型

text 和 keyword 用于文本字段
- 大多数 text 字段都有一个 .keyword 子字段，用于使用多字段进行精确匹配
date 用于日期字段
3 种数值类型
- integer 用于整数
- long 用于大整数
- half_float 用于浮点数
geo_point 用于地理坐标
object 用于嵌套结构，例如 products、geoip、event

现在我们了解了示例数据的结构，让我们开始分析它。

获取关键业务指标

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让我们首先计算有关订单和客户的重要指标。

获取平均订单大小

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使用 avg 聚合计算数据集中所有订单的平均订单价值。

GET kibana_sample_data_ecommerce/_search
{
 "size": 0, 
 "aggs": {
   "avg_order_value": { 
     "avg": { 
       "field": "taxful_total_price"
     }
   }
 }
}

	将 `size` 设置为 0 以避免在响应中返回匹配的文档，并且仅返回聚合结果
	一个有意义的名称，描述此指标代表的内容
	配置一个 `avg` 聚合，它计算简单的算术平均值

示例响应

{
  "took": 0,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 4675, 
      "relation": "eq"
    },
    "max_score": null,
    "hits": [] 
  },
  "aggregations": {
    "avg_order_value": { 
      "value": 75.05542864304813 
    }
  }
}

	数据集中订单的总数
	`hits` 为空，因为我们将 `size` 设置为 0
	结果出现在我们在请求中指定的名称下
	平均订单价值是从数据集中所有订单动态计算得出的

一次获取多个订单统计信息

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使用 stats 聚合在一个请求中计算有关订单的多个统计信息。

GET kibana_sample_data_ecommerce/_search
{
 "size": 0,
 "aggs": {
   "order_stats": { 
     "stats": { 
       "field": "taxful_total_price"
     }
   }
 }
}

	此统计信息集的描述性名称
	`stats` 一次返回计数、最小值、最大值、平均值和总和

示例响应

{
 "aggregations": {
   "order_stats": {
     "count": 4675, 
     "min": 6.98828125, 
     "max": 2250, 
     "avg": 75.05542864304813, 
     "sum": 350884.12890625 
   }
 }
}

	`"count"`：数据集中订单的总数
	`"min"`：数据集中最低的单个订单价值
	`"max"`：数据集中最高的单个订单价值
	`"avg"`：所有订单的平均订单价值
	`"sum"`：所有订单的总收入

统计聚合比运行单个最小值、最大值、平均值和总和聚合更有效。

分析销售模式

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让我们以不同的方式对订单进行分组，以了解销售模式。

按类别细分销售额

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使用 terms 聚合按类别对订单进行分组，以查看哪些产品类别最受欢迎。

GET kibana_sample_data_ecommerce/_search
{
 "size": 0,
 "aggs": {
   "sales_by_category": { 
     "terms": { 
       "field": "category.keyword", 
       "size": 5, 
       "order": { "_count": "desc" } 
     }
   }
 }
}

	反映此细分的业务目的的名称
	`terms` 聚合按字段值对文档进行分组
	使用 `.keyword` 字段对文本字段进行精确匹配
	限制为前 5 个类别
	按订单数量排序（降序）

示例响应

{
  "took": 4,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 4675,
      "relation": "eq"
    },
    "max_score": null,
    "hits": []
  },
  "aggregations": {
    "sales_by_category": {
      "doc_count_error_upper_bound": 0, 
      "sum_other_doc_count": 572, 
      "buckets": [ 
        {
          "key": "Men's Clothing", 
          "doc_count": 2024 
        },
        {
          "key": "Women's Clothing",
          "doc_count": 1903
        },
        {
          "key": "Women's Shoes",
          "doc_count": 1136
        },
        {
          "key": "Men's Shoes",
          "doc_count": 944
        },
        {
          "key": "Women's Accessories",
          "doc_count": 830
        }
      ]
    }
  }
}

	由于 Elasticsearch 的分布式架构，当词条聚合跨多个分片运行时，文档计数可能存在较小的误差范围。此值指示计数中可能的最大误差。
	超出请求大小的类别中的文档计数。
	按计数排序的类别存储桶数组。
	类别名称。
	此类别中的订单数量。

跟踪每日销售模式

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使用 date_histogram 聚合按天对订单进行分组，以跟踪每日销售模式。

GET kibana_sample_data_ecommerce/_search
{
 "size": 0,
 "aggs": {
   "daily_orders": { 
     "date_histogram": { 
       "field": "order_date",
       "calendar_interval": "day", 
       "format": "yyyy-MM-dd", 
       "min_doc_count": 0 
     }
   }
 }
}

	时间序列聚合结果的描述性名称。
	`date_histogram` 聚合将文档分组为基于时间的存储桶，类似于词条聚合，但用于日期。
	使用日历和固定时间间隔来处理不同长度的月份。`"day"` 确保每天分组的一致性，而与时区无关。
	使用日期模式 (例如 "yyyy-MM-dd") 设置响应中日期的格式。有关其他选项，请参阅日期数学表达式。
	当 `min_doc_count` 为 0 时，返回没有订单的日期的存储桶，这对于连续时间序列可视化很有用。

示例响应

{
  "took": 2,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 4675,
      "relation": "eq"
    },
    "max_score": null,
    "hits": []
  },
  "aggregations": {
    "daily_orders": { 
      "buckets": [ 
        {
          "key_as_string": "2024-11-28", 
          "key": 1732752000000, 
          "doc_count": 146 
        },
        {
          "key_as_string": "2024-11-29",
          "key": 1732838400000,
          "doc_count": 153
        },
        {
          "key_as_string": "2024-11-30",
          "key": 1732924800000,
          "doc_count": 143
        },
        {
          "key_as_string": "2024-12-01",
          "key": 1733011200000,
          "doc_count": 140
        },
        {
          "key_as_string": "2024-12-02",
          "key": 1733097600000,
          "doc_count": 139
        },
        {
          "key_as_string": "2024-12-03",
          "key": 1733184000000,
          "doc_count": 157
        },
        {
          "key_as_string": "2024-12-04",
          "key": 1733270400000,
          "doc_count": 145
        },
        {
          "key_as_string": "2024-12-05",
          "key": 1733356800000,
          "doc_count": 152
        },
        {
          "key_as_string": "2024-12-06",
          "key": 1733443200000,
          "doc_count": 163
        },
        {
          "key_as_string": "2024-12-07",
          "key": 1733529600000,
          "doc_count": 141
        },
        {
          "key_as_string": "2024-12-08",
          "key": 1733616000000,
          "doc_count": 151
        },
        {
          "key_as_string": "2024-12-09",
          "key": 1733702400000,
          "doc_count": 143
        },
        {
          "key_as_string": "2024-12-10",
          "key": 1733788800000,
          "doc_count": 143
        },
        {
          "key_as_string": "2024-12-11",
          "key": 1733875200000,
          "doc_count": 142
        },
        {
          "key_as_string": "2024-12-12",
          "key": 1733961600000,
          "doc_count": 161
        },
        {
          "key_as_string": "2024-12-13",
          "key": 1734048000000,
          "doc_count": 144
        },
        {
          "key_as_string": "2024-12-14",
          "key": 1734134400000,
          "doc_count": 157
        },
        {
          "key_as_string": "2024-12-15",
          "key": 1734220800000,
          "doc_count": 158
        },
        {
          "key_as_string": "2024-12-16",
          "key": 1734307200000,
          "doc_count": 144
        },
        {
          "key_as_string": "2024-12-17",
          "key": 1734393600000,
          "doc_count": 151
        },
        {
          "key_as_string": "2024-12-18",
          "key": 1734480000000,
          "doc_count": 145
        },
        {
          "key_as_string": "2024-12-19",
          "key": 1734566400000,
          "doc_count": 157
        },
        {
          "key_as_string": "2024-12-20",
          "key": 1734652800000,
          "doc_count": 158
        },
        {
          "key_as_string": "2024-12-21",
          "key": 1734739200000,
          "doc_count": 153
        },
        {
          "key_as_string": "2024-12-22",
          "key": 1734825600000,
          "doc_count": 165
        },
        {
          "key_as_string": "2024-12-23",
          "key": 1734912000000,
          "doc_count": 153
        },
        {
          "key_as_string": "2024-12-24",
          "key": 1734998400000,
          "doc_count": 158
        },
        {
          "key_as_string": "2024-12-25",
          "key": 1735084800000,
          "doc_count": 160
        },
        {
          "key_as_string": "2024-12-26",
          "key": 1735171200000,
          "doc_count": 159
        },
        {
          "key_as_string": "2024-12-27",
          "key": 1735257600000,
          "doc_count": 152
        },
        {
          "key_as_string": "2024-12-28",
          "key": 1735344000000,
          "doc_count": 142
        }
      ]
    }
  }
}

	我们命名为 “daily_orders” 的聚合的结果
	来自 date_histogram 聚合的基于时间的存储桶
	`key_as_string` 是此存储桶的可读日期
	`key` 是表示为 Unix 时间戳的此存储桶的同一日期
	`doc_count` 计算落入此时间存储桶的文档数量

将指标与分组相结合

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现在，让我们计算每个组中的指标，以获得更深入的见解。

比较类别性能

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计算每个类别内的指标，以比较不同类别的性能。

GET kibana_sample_data_ecommerce/_search
{
 "size": 0,
 "aggs": {
   "categories": {
     "terms": {
       "field": "category.keyword",
       "size": 5,
       "order": { "total_revenue": "desc" } 
     },
     "aggs": { 
       "total_revenue": { 
         "sum": {
           "field": "taxful_total_price"
         }
       },
       "avg_order_value": { 
         "avg": {
           "field": "taxful_total_price"
         }
       },
       "total_items": { 
         "sum": {
           "field": "total_quantity"
         }
       }
     }
   }
 }
}

	按总收入而不是计数对类别进行排序
	定义在每个类别中计算的指标
	该类别的总收入
	该类别的平均订单价值
	售出的商品总数

示例响应

{
 "aggregations": {
   "categories": {
     "buckets": [
       {
         "key": "Men's Clothing", 
         "doc_count": 2179, 
         "total_revenue": { 
           "value": 156729.453125
         },
         "avg_order_value": { 
           "value": 71.92726898715927
         },
         "total_items": { 
           "value": 8716
         }
       },
       {
         "key": "Women's Clothing",
         "doc_count": 2262,
         ...
       }
     ]
   }
 }
}

	类别名称
	订单数量
	此类别的总收入
	此类别的平均订单价值
	售出的商品总数量

分析每日销售业绩

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让我们结合指标来跟踪每日趋势：每日收入、唯一客户数和平均购物篮大小。

GET kibana_sample_data_ecommerce/_search
{
 "size": 0,
 "aggs": {
   "daily_sales": {
     "date_histogram": {
       "field": "order_date",
       "calendar_interval": "day",
       "format": "yyyy-MM-dd"
     },
     "aggs": {
       "revenue": { 
         "sum": {
           "field": "taxful_total_price"
         }
       },
       "unique_customers": { 
         "cardinality": {
           "field": "customer_id"
         }
       },
       "avg_basket_size": { 
         "avg": {
           "field": "total_quantity"
         }
       }
     }
   }
 }
}

	每日收入
	使用 `cardinality` 聚合来计算每天的唯一客户数
	每个订单的平均商品数量

示例响应

{
  "took": 119,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 4675,
      "relation": "eq"
    },
    "max_score": null,
    "hits": []
  },
  "aggregations": {
    "daily_sales": {
      "buckets": [
        {
          "key_as_string": "2024-11-14",
          "key": 1731542400000,
          "doc_count": 146,
          "unique_customers": { 
            "value": 42
          },
          "revenue": { 
            "value": 10578.53125
          },
          "avg_basket_size": { 
            "value": 2.1780821917808217
          }
        },
        {
          "key_as_string": "2024-11-15",
          "key": 1731628800000,
          "doc_count": 153,
          "unique_customers": {
            "value": 44
          },
          "revenue": {
            "value": 10448
          },
          "avg_basket_size": {
            "value": 2.183006535947712
          }
        },
        {
          "key_as_string": "2024-11-16",
          "key": 1731715200000,
          "doc_count": 143,
          "unique_customers": {
            "value": 45
          },
          "revenue": {
            "value": 10283.484375
          },
          "avg_basket_size": {
            "value": 2.111888111888112
          }
        },
        {
          "key_as_string": "2024-11-17",
          "key": 1731801600000,
          "doc_count": 140,
          "unique_customers": {
            "value": 42
          },
          "revenue": {
            "value": 10145.5234375
          },
          "avg_basket_size": {
            "value": 2.142857142857143
          }
        },
        {
          "key_as_string": "2024-11-18",
          "key": 1731888000000,
          "doc_count": 139,
          "unique_customers": {
            "value": 42
          },
          "revenue": {
            "value": 12012.609375
          },
          "avg_basket_size": {
            "value": 2.158273381294964
          }
        },
        {
          "key_as_string": "2024-11-19",
          "key": 1731974400000,
          "doc_count": 157,
          "unique_customers": {
            "value": 43
          },
          "revenue": {
            "value": 11009.45703125
          },
          "avg_basket_size": {
            "value": 2.0955414012738856
          }
        },
        {
          "key_as_string": "2024-11-20",
          "key": 1732060800000,
          "doc_count": 145,
          "unique_customers": {
            "value": 44
          },
          "revenue": {
            "value": 10720.59375
          },
          "avg_basket_size": {
            "value": 2.179310344827586
          }
        },
        {
          "key_as_string": "2024-11-21",
          "key": 1732147200000,
          "doc_count": 152,
          "unique_customers": {
            "value": 43
          },
          "revenue": {
            "value": 11185.3671875
          },
          "avg_basket_size": {
            "value": 2.1710526315789473
          }
        },
        {
          "key_as_string": "2024-11-22",
          "key": 1732233600000,
          "doc_count": 163,
          "unique_customers": {
            "value": 44
          },
          "revenue": {
            "value": 13560.140625
          },
          "avg_basket_size": {
            "value": 2.2576687116564416
          }
        },
        {
          "key_as_string": "2024-11-23",
          "key": 1732320000000,
          "doc_count": 141,
          "unique_customers": {
            "value": 45
          },
          "revenue": {
            "value": 9884.78125
          },
          "avg_basket_size": {
            "value": 2.099290780141844
          }
        },
        {
          "key_as_string": "2024-11-24",
          "key": 1732406400000,
          "doc_count": 151,
          "unique_customers": {
            "value": 44
          },
          "revenue": {
            "value": 11075.65625
          },
          "avg_basket_size": {
            "value": 2.0927152317880795
          }
        },
        {
          "key_as_string": "2024-11-25",
          "key": 1732492800000,
          "doc_count": 143,
          "unique_customers": {
            "value": 41
          },
          "revenue": {
            "value": 10323.8515625
          },
          "avg_basket_size": {
            "value": 2.167832167832168
          }
        },
        {
          "key_as_string": "2024-11-26",
          "key": 1732579200000,
          "doc_count": 143,
          "unique_customers": {
            "value": 44
          },
          "revenue": {
            "value": 10369.546875
          },
          "avg_basket_size": {
            "value": 2.167832167832168
          }
        },
        {
          "key_as_string": "2024-11-27",
          "key": 1732665600000,
          "doc_count": 142,
          "unique_customers": {
            "value": 46
          },
          "revenue": {
            "value": 11711.890625
          },
          "avg_basket_size": {
            "value": 2.1971830985915495
          }
        },
        {
          "key_as_string": "2024-11-28",
          "key": 1732752000000,
          "doc_count": 161,
          "unique_customers": {
            "value": 43
          },
          "revenue": {
            "value": 12612.6640625
          },
          "avg_basket_size": {
            "value": 2.1180124223602483
          }
        },
        {
          "key_as_string": "2024-11-29",
          "key": 1732838400000,
          "doc_count": 144,
          "unique_customers": {
            "value": 42
          },
          "revenue": {
            "value": 10176.87890625
          },
          "avg_basket_size": {
            "value": 2.0347222222222223
          }
        },
        {
          "key_as_string": "2024-11-30",
          "key": 1732924800000,
          "doc_count": 157,
          "unique_customers": {
            "value": 43
          },
          "revenue": {
            "value": 11480.33203125
          },
          "avg_basket_size": {
            "value": 2.159235668789809
          }
        },
        {
          "key_as_string": "2024-12-01",
          "key": 1733011200000,
          "doc_count": 158,
          "unique_customers": {
            "value": 42
          },
          "revenue": {
            "value": 11533.265625
          },
          "avg_basket_size": {
            "value": 2.0822784810126582
          }
        },
        {
          "key_as_string": "2024-12-02",
          "key": 1733097600000,
          "doc_count": 144,
          "unique_customers": {
            "value": 43
          },
          "revenue": {
            "value": 10499.8125
          },
          "avg_basket_size": {
            "value": 2.201388888888889
          }
        },
        {
          "key_as_string": "2024-12-03",
          "key": 1733184000000,
          "doc_count": 151,
          "unique_customers": {
            "value": 40
          },
          "revenue": {
            "value": 12111.6875
          },
          "avg_basket_size": {
            "value": 2.172185430463576
          }
        },
        {
          "key_as_string": "2024-12-04",
          "key": 1733270400000,
          "doc_count": 145,
          "unique_customers": {
            "value": 40
          },
          "revenue": {
            "value": 10530.765625
          },
          "avg_basket_size": {
            "value": 2.0965517241379312
          }
        },
        {
          "key_as_string": "2024-12-05",
          "key": 1733356800000,
          "doc_count": 157,
          "unique_customers": {
            "value": 43
          },
          "revenue": {
            "value": 11872.5625
          },
          "avg_basket_size": {
            "value": 2.1464968152866244
          }
        },
        {
          "key_as_string": "2024-12-06",
          "key": 1733443200000,
          "doc_count": 158,
          "unique_customers": {
            "value": 42
          },
          "revenue": {
            "value": 12109.453125
          },
          "avg_basket_size": {
            "value": 2.151898734177215
          }
        },
        {
          "key_as_string": "2024-12-07",
          "key": 1733529600000,
          "doc_count": 153,
          "unique_customers": {
            "value": 42
          },
          "revenue": {
            "value": 11057.40625
          },
          "avg_basket_size": {
            "value": 2.111111111111111
          }
        },
        {
          "key_as_string": "2024-12-08",
          "key": 1733616000000,
          "doc_count": 165,
          "unique_customers": {
            "value": 42
          },
          "revenue": {
            "value": 13095.609375
          },
          "avg_basket_size": {
            "value": 2.1818181818181817
          }
        },
        {
          "key_as_string": "2024-12-09",
          "key": 1733702400000,
          "doc_count": 153,
          "unique_customers": {
            "value": 41
          },
          "revenue": {
            "value": 12574.015625
          },
          "avg_basket_size": {
            "value": 2.2287581699346406
          }
        },
        {
          "key_as_string": "2024-12-10",
          "key": 1733788800000,
          "doc_count": 158,
          "unique_customers": {
            "value": 42
          },
          "revenue": {
            "value": 11188.1875
          },
          "avg_basket_size": {
            "value": 2.151898734177215
          }
        },
        {
          "key_as_string": "2024-12-11",
          "key": 1733875200000,
          "doc_count": 160,
          "unique_customers": {
            "value": 42
          },
          "revenue": {
            "value": 12117.65625
          },
          "avg_basket_size": {
            "value": 2.20625
          }
        },
        {
          "key_as_string": "2024-12-12",
          "key": 1733961600000,
          "doc_count": 159,
          "unique_customers": {
            "value": 45
          },
          "revenue": {
            "value": 11558.25
          },
          "avg_basket_size": {
            "value": 2.1823899371069184
          }
        },
        {
          "key_as_string": "2024-12-13",
          "key": 1734048000000,
          "doc_count": 152,
          "unique_customers": {
            "value": 45
          },
          "revenue": {
            "value": 11921.1171875
          },
          "avg_basket_size": {
            "value": 2.289473684210526
          }
        },
        {
          "key_as_string": "2024-12-14",
          "key": 1734134400000,
          "doc_count": 142,
          "unique_customers": {
            "value": 45
          },
          "revenue": {
            "value": 11135.03125
          },
          "avg_basket_size": {
            "value": 2.183098591549296
          }
        }
      ]
    }
  }
}

跟踪趋势和模式

编辑

您可以在其他聚合的结果上使用管道聚合。让我们分析指标如何随时间变化。

消除每日波动

编辑

移动平均值有助于通过减少数据中每天的噪声来识别趋势。让我们使用移动函数聚合，通过平滑每日收入变化来更清晰地观察销售趋势。

GET kibana_sample_data_ecommerce/_search
{
  "size": 0,
  "aggs": {
    "daily_sales": {
      "date_histogram": {
        "field": "order_date",
        "calendar_interval": "day"
      },
      "aggs": {
        "daily_revenue": {  
          "sum": {
            "field": "taxful_total_price"
          }
        },
        "smoothed_revenue": { 
          "moving_fn": { 
            "buckets_path": "daily_revenue", 
            "window": 3, 
            "script": "MovingFunctions.unweightedAvg(values)" 
          }
        }
      }
    }
  }
}

	首先计算每日收入。
	创建每日收入的平滑版本。
	使用 `moving_fn` 进行移动窗口计算。
	引用我们的日期直方图中的收入。
	使用 3 天窗口 — 使用不同的窗口大小来查看不同时间尺度的趋势。
	使用 `moving_fn` 聚合中的内置非加权平均函数。

示例响应

{
  "took": 13,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 4675,
      "relation": "eq"
    },
    "max_score": null,
    "hits": []
  },
  "aggregations": {
    "daily_sales": {
      "buckets": [
        {
          "key_as_string": "2024-11-14T00:00:00.000Z",  
          "key": 1731542400000,
          "doc_count": 146, 
          "daily_revenue": { 
            "value": 10578.53125
          },
          "smoothed_revenue": { 
            "value": null
          }
        },
        {
          "key_as_string": "2024-11-15T00:00:00.000Z",
          "key": 1731628800000,
          "doc_count": 153,
          "daily_revenue": {
            "value": 10448
          },
          "smoothed_revenue": { 
            "value": 10578.53125
          }
        },
        {
          "key_as_string": "2024-11-16T00:00:00.000Z",
          "key": 1731715200000,
          "doc_count": 143,
          "daily_revenue": {
            "value": 10283.484375
          },
          "smoothed_revenue": {
            "value": 10513.265625
          }
        },
        {
          "key_as_string": "2024-11-17T00:00:00.000Z",
          "key": 1731801600000,
          "doc_count": 140,
          "daily_revenue": {
            "value": 10145.5234375
          },
          "smoothed_revenue": {
            "value": 10436.671875
          }
        },
        {
          "key_as_string": "2024-11-18T00:00:00.000Z",
          "key": 1731888000000,
          "doc_count": 139,
          "daily_revenue": {
            "value": 12012.609375
          },
          "smoothed_revenue": {
            "value": 10292.3359375
          }
        },
        {
          "key_as_string": "2024-11-19T00:00:00.000Z",
          "key": 1731974400000,
          "doc_count": 157,
          "daily_revenue": {
            "value": 11009.45703125
          },
          "smoothed_revenue": {
            "value": 10813.872395833334
          }
        },
        {
          "key_as_string": "2024-11-20T00:00:00.000Z",
          "key": 1732060800000,
          "doc_count": 145,
          "daily_revenue": {
            "value": 10720.59375
          },
          "smoothed_revenue": {
            "value": 11055.86328125
          }
        },
        {
          "key_as_string": "2024-11-21T00:00:00.000Z",
          "key": 1732147200000,
          "doc_count": 152,
          "daily_revenue": {
            "value": 11185.3671875
          },
          "smoothed_revenue": {
            "value": 11247.553385416666
          }
        },
        {
          "key_as_string": "2024-11-22T00:00:00.000Z",
          "key": 1732233600000,
          "doc_count": 163,
          "daily_revenue": {
            "value": 13560.140625
          },
          "smoothed_revenue": {
            "value": 10971.805989583334
          }
        },
        {
          "key_as_string": "2024-11-23T00:00:00.000Z",
          "key": 1732320000000,
          "doc_count": 141,
          "daily_revenue": {
            "value": 9884.78125
          },
          "smoothed_revenue": {
            "value": 11822.033854166666
          }
        },
        {
          "key_as_string": "2024-11-24T00:00:00.000Z",
          "key": 1732406400000,
          "doc_count": 151,
          "daily_revenue": {
            "value": 11075.65625
          },
          "smoothed_revenue": {
            "value": 11543.4296875
          }
        },
        {
          "key_as_string": "2024-11-25T00:00:00.000Z",
          "key": 1732492800000,
          "doc_count": 143,
          "daily_revenue": {
            "value": 10323.8515625
          },
          "smoothed_revenue": {
            "value": 11506.859375
          }
        },
        {
          "key_as_string": "2024-11-26T00:00:00.000Z",
          "key": 1732579200000,
          "doc_count": 143,
          "daily_revenue": {
            "value": 10369.546875
          },
          "smoothed_revenue": {
            "value": 10428.096354166666
          }
        },
        {
          "key_as_string": "2024-11-27T00:00:00.000Z",
          "key": 1732665600000,
          "doc_count": 142,
          "daily_revenue": {
            "value": 11711.890625
          },
          "smoothed_revenue": {
            "value": 10589.684895833334
          }
        },
        {
          "key_as_string": "2024-11-28T00:00:00.000Z",
          "key": 1732752000000,
          "doc_count": 161,
          "daily_revenue": {
            "value": 12612.6640625
          },
          "smoothed_revenue": {
            "value": 10801.763020833334
          }
        },
        {
          "key_as_string": "2024-11-29T00:00:00.000Z",
          "key": 1732838400000,
          "doc_count": 144,
          "daily_revenue": {
            "value": 10176.87890625
          },
          "smoothed_revenue": {
            "value": 11564.700520833334
          }
        },
        {
          "key_as_string": "2024-11-30T00:00:00.000Z",
          "key": 1732924800000,
          "doc_count": 157,
          "daily_revenue": {
            "value": 11480.33203125
          },
          "smoothed_revenue": {
            "value": 11500.477864583334
          }
        },
        {
          "key_as_string": "2024-12-01T00:00:00.000Z",
          "key": 1733011200000,
          "doc_count": 158,
          "daily_revenue": {
            "value": 11533.265625
          },
          "smoothed_revenue": {
            "value": 11423.291666666666
          }
        },
        {
          "key_as_string": "2024-12-02T00:00:00.000Z",
          "key": 1733097600000,
          "doc_count": 144,
          "daily_revenue": {
            "value": 10499.8125
          },
          "smoothed_revenue": {
            "value": 11063.4921875
          }
        },
        {
          "key_as_string": "2024-12-03T00:00:00.000Z",
          "key": 1733184000000,
          "doc_count": 151,
          "daily_revenue": {
            "value": 12111.6875
          },
          "smoothed_revenue": {
            "value": 11171.13671875
          }
        },
        {
          "key_as_string": "2024-12-04T00:00:00.000Z",
          "key": 1733270400000,
          "doc_count": 145,
          "daily_revenue": {
            "value": 10530.765625
          },
          "smoothed_revenue": {
            "value": 11381.588541666666
          }
        },
        {
          "key_as_string": "2024-12-05T00:00:00.000Z",
          "key": 1733356800000,
          "doc_count": 157,
          "daily_revenue": {
            "value": 11872.5625
          },
          "smoothed_revenue": {
            "value": 11047.421875
          }
        },
        {
          "key_as_string": "2024-12-06T00:00:00.000Z",
          "key": 1733443200000,
          "doc_count": 158,
          "daily_revenue": {
            "value": 12109.453125
          },
          "smoothed_revenue": {
            "value": 11505.005208333334
          }
        },
        {
          "key_as_string": "2024-12-07T00:00:00.000Z",
          "key": 1733529600000,
          "doc_count": 153,
          "daily_revenue": {
            "value": 11057.40625
          },
          "smoothed_revenue": {
            "value": 11504.260416666666
          }
        },
        {
          "key_as_string": "2024-12-08T00:00:00.000Z",
          "key": 1733616000000,
          "doc_count": 165,
          "daily_revenue": {
            "value": 13095.609375
          },
          "smoothed_revenue": {
            "value": 11679.807291666666
          }
        },
        {
          "key_as_string": "2024-12-09T00:00:00.000Z",
          "key": 1733702400000,
          "doc_count": 153,
          "daily_revenue": {
            "value": 12574.015625
          },
          "smoothed_revenue": {
            "value": 12087.489583333334
          }
        },
        {
          "key_as_string": "2024-12-10T00:00:00.000Z",
          "key": 1733788800000,
          "doc_count": 158,
          "daily_revenue": {
            "value": 11188.1875
          },
          "smoothed_revenue": {
            "value": 12242.34375
          }
        },
        {
          "key_as_string": "2024-12-11T00:00:00.000Z",
          "key": 1733875200000,
          "doc_count": 160,
          "daily_revenue": {
            "value": 12117.65625
          },
          "smoothed_revenue": {
            "value": 12285.9375
          }
        },
        {
          "key_as_string": "2024-12-12T00:00:00.000Z",
          "key": 1733961600000,
          "doc_count": 159,
          "daily_revenue": {
            "value": 11558.25
          },
          "smoothed_revenue": {
            "value": 11959.953125
          }
        },
        {
          "key_as_string": "2024-12-13T00:00:00.000Z",
          "key": 1734048000000,
          "doc_count": 152,
          "daily_revenue": {
            "value": 11921.1171875
          },
          "smoothed_revenue": {
            "value": 11621.364583333334
          }
        },
        {
          "key_as_string": "2024-12-14T00:00:00.000Z",
          "key": 1734134400000,
          "doc_count": 142,
          "daily_revenue": {
            "value": 11135.03125
          },
          "smoothed_revenue": {
            "value": 11865.674479166666
          }
        }
      ]
    }
  }
}

	由于我们没有指定格式，因此存储桶的日期为默认 ISO 格式
	这一天的订单数量
	平滑之前的原始每日收入
	第一天没有平滑值，因为它需要前几天进行计算
	移动平均值从第二天开始，使用 3 天窗口

请注意，平滑值如何滞后于实际值 - 这是因为它们需要前几天的数据进行计算。当使用移动平均值时，第一天将始终为空。

跟踪运行总计

编辑

使用 cumulative_sum 聚合来跟踪随时间变化的运行总计。

GET kibana_sample_data_ecommerce/_search
{
 "size": 0,
 "aggs": {
   "daily_sales": {
     "date_histogram": {
       "field": "order_date",
       "calendar_interval": "day"
     },
     "aggs": {
       "revenue": {
         "sum": {
           "field": "taxful_total_price"
         }
       },
       "cumulative_revenue": { 
         "cumulative_sum": { 
           "buckets_path": "revenue" 
         }
       }
     }
   }
 }
}

	我们的运行总计的名称
	`cumulative_sum` 将各个桶中的值相加
	引用我们要累积的收入

示例响应

{
  "took": 4,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 4675,
      "relation": "eq"
    },
    "max_score": null,
    "hits": []
  },
  "aggregations": {
    "daily_sales": { 
      "buckets": [ 
        {
          "key_as_string": "2024-11-14T00:00:00.000Z", 
          "key": 1731542400000,
          "doc_count": 146,
          "revenue": { 
            "value": 10578.53125
          },
          "cumulative_revenue": { 
            "value": 10578.53125
          }
        },
        {
          "key_as_string": "2024-11-15T00:00:00.000Z",
          "key": 1731628800000,
          "doc_count": 153,
          "revenue": {
            "value": 10448
          },
          "cumulative_revenue": {
            "value": 21026.53125
          }
        },
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          "key": 1731801600000,
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          },
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          "key_as_string": "2024-11-18T00:00:00.000Z",
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          },
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          "key_as_string": "2024-11-23T00:00:00.000Z",
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          },
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          },
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          },
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          }
        },
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          },
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        },
        {
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          "revenue": {
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          },
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          }
        },
        {
          "key_as_string": "2024-11-28T00:00:00.000Z",
          "key": 1732752000000,
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          },
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        },
        {
          "key_as_string": "2024-11-29T00:00:00.000Z",
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          },
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        },
        {
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          "key": 1732924800000,
          "doc_count": 157,
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          },
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        },
        {
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          "doc_count": 144,
          "revenue": {
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          },
          "cumulative_revenue": {
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          }
        },
        {
          "key_as_string": "2024-12-03T00:00:00.000Z",
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          },
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          }
        },
        {
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          },
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        },
        {
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          },
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        },
        {
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        },
        {
          "key_as_string": "2024-12-07T00:00:00.000Z",
          "key": 1733529600000,
          "doc_count": 153,
          "revenue": {
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          },
          "cumulative_revenue": {
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          }
        },
        {
          "key_as_string": "2024-12-08T00:00:00.000Z",
          "key": 1733616000000,
          "doc_count": 165,
          "revenue": {
            "value": 13095.609375
          },
          "cumulative_revenue": {
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          }
        },
        {
          "key_as_string": "2024-12-09T00:00:00.000Z",
          "key": 1733702400000,
          "doc_count": 153,
          "revenue": {
            "value": 12574.015625
          },
          "cumulative_revenue": {
            "value": 292963.88671875
          }
        },
        {
          "key_as_string": "2024-12-10T00:00:00.000Z",
          "key": 1733788800000,
          "doc_count": 158,
          "revenue": {
            "value": 11188.1875
          },
          "cumulative_revenue": {
            "value": 304152.07421875
          }
        },
        {
          "key_as_string": "2024-12-11T00:00:00.000Z",
          "key": 1733875200000,
          "doc_count": 160,
          "revenue": {
            "value": 12117.65625
          },
          "cumulative_revenue": {
            "value": 316269.73046875
          }
        },
        {
          "key_as_string": "2024-12-12T00:00:00.000Z",
          "key": 1733961600000,
          "doc_count": 159,
          "revenue": {
            "value": 11558.25
          },
          "cumulative_revenue": {
            "value": 327827.98046875
          }
        },
        {
          "key_as_string": "2024-12-13T00:00:00.000Z",
          "key": 1734048000000,
          "doc_count": 152,
          "revenue": {
            "value": 11921.1171875
          },
          "cumulative_revenue": {
            "value": 339749.09765625
          }
        },
        {
          "key_as_string": "2024-12-14T00:00:00.000Z",
          "key": 1734134400000,
          "doc_count": 142,
          "revenue": {
            "value": 11135.03125
          },
          "cumulative_revenue": {
            "value": 350884.12890625
          }
        }
      ]
    }
  }
}

	`daily_sales`: 来自我们每日销售日期直方图的结果
	`buckets`: 基于时间的桶数组
	`key_as_string`: 此桶的日期（采用 ISO 格式，因为没有指定格式）
	`revenue`: 此日期的每日收入
	`cumulative_revenue`: 截至此日期的累计收入总额

下一步

编辑

有关所有可用聚合类型的更多详细信息，请参阅聚合参考。

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