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« 变化点聚合 累积和聚合 »
Elastic 文档 ›Elasticsearch 指南 [8.17] ›聚合 ›管道聚合

累积基数聚合

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累积基数聚合

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一个父管道聚合，用于计算父直方图（或 date_histogram）聚合中的累积基数。指定的度量必须是基数聚合，并且封闭的直方图必须将 min_doc_count 设置为 0（histogram 聚合的默认值）。

cumulative_cardinality 聚合对于查找“总新项目”很有用，例如每天访问您网站的新访客数量。常规基数聚合会告诉您每天有多少唯一访客，但不会区分“新”访客或“回头”访客。累积基数聚合可用于确定每天有多少唯一访客是“新”访客。

语法

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一个独立的 cumulative_cardinality 聚合如下所示

{
  "cumulative_cardinality": {
    "buckets_path": "my_cardinality_agg"
  }
}

表 59. cumulative_cardinality 参数

参数名称 描述 必需 默认值

buckets_path

我们要查找累积基数的基数聚合的路径（有关更多详细信息，请参阅 buckets_path 语法）

必需

format

输出值的 DecimalFormat 模式。如果指定，格式化的值将返回到聚合的 value_as_string 属性中

可选

null

以下代码片段计算每日总 users 的累积基数

resp = client.search(
    index="user_hits",
    size=0,
    aggs={
        "users_per_day": {
            "date_histogram": {
                "field": "timestamp",
                "calendar_interval": "day"
            },
            "aggs": {
                "distinct_users": {
                    "cardinality": {
                        "field": "user_id"
                    }
                },
                "total_new_users": {
                    "cumulative_cardinality": {
                        "buckets_path": "distinct_users"
                    }
                }
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'user_hits',
  body: {
    size: 0,
    aggregations: {
      users_per_day: {
        date_histogram: {
          field: 'timestamp',
          calendar_interval: 'day'
        },
        aggregations: {
          distinct_users: {
            cardinality: {
              field: 'user_id'
            }
          },
          total_new_users: {
            cumulative_cardinality: {
              buckets_path: 'distinct_users'
            }
          }
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "user_hits",
  size: 0,
  aggs: {
    users_per_day: {
      date_histogram: {
        field: "timestamp",
        calendar_interval: "day",
      },
      aggs: {
        distinct_users: {
          cardinality: {
            field: "user_id",
          },
        },
        total_new_users: {
          cumulative_cardinality: {
            buckets_path: "distinct_users",
          },
        },
      },
    },
  },
});
console.log(response);
GET /user_hits/_search
{
  "size": 0,
  "aggs": {
    "users_per_day": {
      "date_histogram": {
        "field": "timestamp",
        "calendar_interval": "day"
      },
      "aggs": {
        "distinct_users": {
          "cardinality": {
            "field": "user_id"
          }
        },
        "total_new_users": {
          "cumulative_cardinality": {
            "buckets_path": "distinct_users" 
          }
        }
      }
    }
  }
}
Copy as curlTry in Elastic 

buckets_path 指示此聚合使用 distinct_users 聚合的输出来计算累积基数

以下可能是响应

{
   "took": 11,
   "timed_out": false,
   "_shards": ...,
   "hits": ...,
   "aggregations": {
      "users_per_day": {
         "buckets": [
            {
               "key_as_string": "2019-01-01T00:00:00.000Z",
               "key": 1546300800000,
               "doc_count": 2,
               "distinct_users": {
                  "value": 2
               },
               "total_new_users": {
                  "value": 2
               }
            },
            {
               "key_as_string": "2019-01-02T00:00:00.000Z",
               "key": 1546387200000,
               "doc_count": 2,
               "distinct_users": {
                  "value": 2
               },
               "total_new_users": {
                  "value": 3
               }
            },
            {
               "key_as_string": "2019-01-03T00:00:00.000Z",
               "key": 1546473600000,
               "doc_count": 3,
               "distinct_users": {
                  "value": 3
               },
               "total_new_users": {
                  "value": 4
               }
            }
         ]
      }
   }
}

请注意，第二天 2019-01-02 有两个不同的用户，但累积管道聚合生成的 total_new_users 指标仅递增到三个。这意味着当天两个用户中只有一个是新的，另一个用户在前一天已经出现过。这种情况在第三天再次发生，三个用户中只有一个是全新的。

增量累积基数

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cumulative_cardinality 聚合将显示自查询时间段开始以来的总唯一计数。但是，有时查看“增量”计数会很有用。这意味着，每天添加了多少新用户，而不是总累积计数。

这可以通过向我们的查询添加 derivative 聚合来实现

resp = client.search(
    index="user_hits",
    size=0,
    aggs={
        "users_per_day": {
            "date_histogram": {
                "field": "timestamp",
                "calendar_interval": "day"
            },
            "aggs": {
                "distinct_users": {
                    "cardinality": {
                        "field": "user_id"
                    }
                },
                "total_new_users": {
                    "cumulative_cardinality": {
                        "buckets_path": "distinct_users"
                    }
                },
                "incremental_new_users": {
                    "derivative": {
                        "buckets_path": "total_new_users"
                    }
                }
            }
        }
    },
)
print(resp)
response = client.search(
  index: 'user_hits',
  body: {
    size: 0,
    aggregations: {
      users_per_day: {
        date_histogram: {
          field: 'timestamp',
          calendar_interval: 'day'
        },
        aggregations: {
          distinct_users: {
            cardinality: {
              field: 'user_id'
            }
          },
          total_new_users: {
            cumulative_cardinality: {
              buckets_path: 'distinct_users'
            }
          },
          incremental_new_users: {
            derivative: {
              buckets_path: 'total_new_users'
            }
          }
        }
      }
    }
  }
)
puts response
const response = await client.search({
  index: "user_hits",
  size: 0,
  aggs: {
    users_per_day: {
      date_histogram: {
        field: "timestamp",
        calendar_interval: "day",
      },
      aggs: {
        distinct_users: {
          cardinality: {
            field: "user_id",
          },
        },
        total_new_users: {
          cumulative_cardinality: {
            buckets_path: "distinct_users",
          },
        },
        incremental_new_users: {
          derivative: {
            buckets_path: "total_new_users",
          },
        },
      },
    },
  },
});
console.log(response);
GET /user_hits/_search
{
  "size": 0,
  "aggs": {
    "users_per_day": {
      "date_histogram": {
        "field": "timestamp",
        "calendar_interval": "day"
      },
      "aggs": {
        "distinct_users": {
          "cardinality": {
            "field": "user_id"
          }
        },
        "total_new_users": {
          "cumulative_cardinality": {
            "buckets_path": "distinct_users"
          }
        },
        "incremental_new_users": {
          "derivative": {
            "buckets_path": "total_new_users"
          }
        }
      }
    }
  }
}
Copy as curlTry in Elastic 

以下可能是响应

{
   "took": 11,
   "timed_out": false,
   "_shards": ...,
   "hits": ...,
   "aggregations": {
      "users_per_day": {
         "buckets": [
            {
               "key_as_string": "2019-01-01T00:00:00.000Z",
               "key": 1546300800000,
               "doc_count": 2,
               "distinct_users": {
                  "value": 2
               },
               "total_new_users": {
                  "value": 2
               }
            },
            {
               "key_as_string": "2019-01-02T00:00:00.000Z",
               "key": 1546387200000,
               "doc_count": 2,
               "distinct_users": {
                  "value": 2
               },
               "total_new_users": {
                  "value": 3
               },
               "incremental_new_users": {
                  "value": 1.0
               }
            },
            {
               "key_as_string": "2019-01-03T00:00:00.000Z",
               "key": 1546473600000,
               "doc_count": 3,
               "distinct_users": {
                  "value": 3
               },
               "total_new_users": {
                  "value": 4
               },
               "incremental_new_users": {
                  "value": 1.0
               }
            }
         ]
      }
   }
}
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