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2017-11-09 ApacheCN 开源组织,第二期邀请成员活动,一起走的更远 : http://www.apachecn.org/member/209.html


MachineLearning 优酷地址 : http://i.youku.com/apachecn

转至元数据结尾
转至元数据起始

警告

此功能是实验性的,可能会在将来的版本中完全更改或删除。Elastic将采取最大的努力来解决此问题,但实验功能不受SLA官方功能的支持。

导数管道聚合,其计算父直方图(或日期 - 图形)聚合中指定度量的导数。指定的度量必须是数字,并且必须设置直方图min_doc_count为0(默认为直方图聚合)。

语法

derivative(导数) 聚合结构如下:

 

"derivative": {
  "buckets_path": "the_sum"
}

derivative(导数) 参数如下:

参数名称描述是否必填默认值
buckets_path想要计算导数值的桶路径,点击 the section called “buckets_path Syntaxedit查看更多细节必填 
gap_policy当数据缺口出现时采用的策略,点击the section called “Dealing with gaps in the dataedit”查看更多细节可选skip
format用于规范聚合输出值的格式可选null

 

 

一级导数

以下代码段计算每月总销售额的导数:

POST /sales/_search
{
    "size": 0,
    "aggs" : {
        "sales_per_month" : {
            "date_histogram" : {
                "field" : "date",
                "interval" : "month"
            },
            "aggs": {
                "sales": {
                    "sum": {
                        "field": "price"
                    }
                },
                "sales_deriv": {
                    "derivative": {
                        "buckets_path": "sales" #1
                    }
                }
            }
        }
    }
}

1buckets_path指示这个derivative聚合是想要得到sales_per_month日期直方图聚合中sales聚合值的导数。

响应可能如下所示:

{
   "took": 11,
   "timed_out": false,
   "_shards": ...,
   "hits": ...,
   "aggregations": {
      "sales_per_month": {
         "buckets": [
            {
               "key_as_string": "2015/01/01 00:00:00",
               "key": 1420070400000,
               "doc_count": 3,
               "sales": {
                  "value": 550.0
               } #1
            },
            {
               "key_as_string": "2015/02/01 00:00:00",
               "key": 1422748800000,
               "doc_count": 2,
               "sales": {
                  "value": 60.0
               },
               "sales_deriv": {
                  "value": -490.0 #2
               }
            },
            {
               "key_as_string": "2015/03/01 00:00:00",
               "key": 1425168000000,
               "doc_count": 2, #3
               "sales": {
                  "value": 375.0
               },
               "sales_deriv": {
                  "value": 315.0
               }
            }
         ]
      }
   }
}

1由于我们至少需要2个数据点来计算导数,因此第一个桶没有值
2导数的单位默认和sales聚合以及父直方图相同。所以在这种情况下,如果价格字段的单位是美元,导数的单位就是美元/月
3doc_count表示桶中的文档数

 

 

二级导数

可以把导数管道聚合链接到另一个管道聚合的结果,计算二级导数。如以下示例所示,它将计算总月销售额的第一和第二阶导数:

POST /sales/_search
{
    "size": 0,
    "aggs" : {
        "sales_per_month" : {
            "date_histogram" : {
                "field" : "date",
                "interval" : "month"
            },
            "aggs": {
                "sales": {
                    "sum": {
                        "field": "price"
                    }
                },
                "sales_deriv": {
                    "derivative": {
                        "buckets_path": "sales"
                    }
                },
                "sales_2nd_deriv": {
                    "derivative": {
                        "buckets_path": "sales_deriv" #1
                    }
                }
            }
        }
    }
}

1二阶导数的buckets_path指向一阶导数的名称

响应可能如下所示:

{
   "took": 50,
   "timed_out": false,
   "_shards": ...,
   "hits": ...,
   "aggregations": {
      "sales_per_month": {
         "buckets": [
            {
               "key_as_string": "2015/01/01 00:00:00",
               "key": 1420070400000,
               "doc_count": 3,
               "sales": {
                  "value": 550.0
               } #1
            },
            {
               "key_as_string": "2015/02/01 00:00:00",
               "key": 1422748800000,
               "doc_count": 2,
               "sales": {
                  "value": 60.0
               },
               "sales_deriv": {
                  "value": -490.0
               } #2
            },
            {
               "key_as_string": "2015/03/01 00:00:00",
               "key": 1425168000000,
               "doc_count": 2,
               "sales": {
                  "value": 375.0
               },
               "sales_deriv": {
                  "value": 315.0
               },
               "sales_2nd_deriv": {
                  "value": 805.0
               }
            }
         ]
      }
   }
}

1由于我们至少需要2个数据点,所以前两个桶没有二级导数
2一级导数计算二级导数

 

 

Units(单位)

导数聚合允许指定导数值的单位。这将在响应normalized_value中返回一个额外的字段,汇报在X轴单位下的导数。在下面的例子中,我们计算出每月销售额的导数,但要求销售的导数按天计算:

POST /sales/_search
{
    "size": 0,
    "aggs" : {
        "sales_per_month" : {
            "date_histogram" : {
                "field" : "date",
                "interval" : "month"
            },
            "aggs": {
                "sales": {
                    "sum": {
                        "field": "price"
                    }
                },
                "sales_deriv": {
                    "derivative": {
                        "buckets_path": "sales",
                        "unit": "day" #1
                    }
                }
            }
        }
    }
}
1unit指定用于导数计算的X轴的单位

响应可能如下所示:

{
   "took": 50,
   "timed_out": false,
   "_shards": ...,
   "hits": ...,
   "aggregations": {
      "sales_per_month": {
         "buckets": [
            {
               "key_as_string": "2015/01/01 00:00:00",
               "key": 1420070400000,
               "doc_count": 3,
               "sales": {
                  "value": 550.0
               } #1
            },
            {
               "key_as_string": "2015/02/01 00:00:00",
               "key": 1422748800000,
               "doc_count": 2,
               "sales": {
                  "value": 60.0
               },
               "sales_deriv": {
                  "value": -490.0, #2
                  "normalized_value": -15.806451612903226 #3
               }
            },
            {
               "key_as_string": "2015/03/01 00:00:00",
               "key": 1425168000000,
               "doc_count": 2,
               "sales": {
                  "value": 375.0
               },
               "sales_deriv": {
                  "value": 315.0,
                  "normalized_value": 11.25
               }
            }
         ]
      }
   }
}

1,2value值是原单位:按月
3normalized_value值是请求的单位:按天
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