NTILE 和 CUME_DIST

Snowflake 中的 Window Functions

Jake Roach

Field Data engineer

创建行分桶

如何按训练为健身会员分级，以便投放合适课程？

             member_id  |  gym_location  |  calories_burned  |  marketing_group
            ----------- | -------------- | ----------------- | -----------------
               m_192    |    Miami       |         45        |         1
               m_74     |    Miami       |         59        |         1
               m_233    |    Portland    |         60        |         1

               m_14     |    Cleveland   |         72        |         2
               m_346    |    Portland    |         77        |         2
               m_289    |    Cleveland   |         81        |         2

               m_565    |    Miami       |        1085       |         50
                                        ...

NTILE

SELECT
    <fields>,
    <2>,
    <1>,


    NTILE(<n>) OVER(
        PARTITION BY <2>
        ORDER BY <1>
    )


...;

NTILE 用于创建 N 个等大小的"桶"

<n>：桶的数量

<1>：用于分桶的字段

<2>：用于通过 PARTITION BY 均匀分配记录的字段

对健身数据分桶

SELECT
    member_id,
    gym_location,
    calories_burned,

    -- 创建 50 个等大小的数据桶

    NTILE(50) OVER(
        ORDER BY calories_burned   -- 决定每个桶中的记录
    ) AS marketing_group


FROM FITNESS.workouts
ORDER BY marketing_group, calories_burned;  -- 对最终结果集排序

对健身数据分桶

         member_id  |  gym_location  |  calories_burned  |  marketing_group
        ----------- | -------------- | ----------------- | -----------------
           m_192    |    Miami       |         45        |         1
           m_74     |    Miami       |         59        |         1
           m_233    |    Portland    |         60        |         1

           m_14     |    Cleveland   |         72        |         2
           m_346    |    Portland    |         77        |         2
           m_289    |    Cleveland   |         81        |         2

           m_565    |    Miami       |        1085       |         50
                                        ...

均匀分桶的健身数据

SELECT
    member_id,
    gym_location,
    calories_burned,

    NTILE(50) OVER(

        -- 按每个 gym_location 将记录平均分配到桶中
        PARTITION BY gym_location

        ORDER BY calories_burned
    ) AS marketing_group

FROM FITNESS.workouts
ORDER BY marketing_group, calories_burned;

均匀分桶的健身数据

         member_id  |  gym_location  |  calories_burned  |  marketing_group
        ----------- | -------------- | ----------------- | -----------------
           m_192    |    Miami       |         45        |         1
           m_233    |    Portland    |         60        |         1
           m_14     |    Cleveland   |         72        |         1

           m_74     |    Miami       |         59        |         2
           m_346    |    Portland    |         77        |         2
           m_289    |    Cleveland   |         81        |         2

                                    ...

理解分布

各会员训练的燃烧卡路里如何分布？

某次训练在该分布中处于何处？

有多少比例的会员燃烧的卡路里小于或等于某会员？

  member_id  |  cals_burned  |   cd   
 ----------- | ------------- | ------
    m_192    |       45      |  .016
    m_74     |       59      |  .033
    m_233    |       60      |  .049
    m_14     |       72      |  .066
    m_346    |       77      |  .082
    m_289    |       81      |  .098

                    ....

    m_565    |      1085     |  1.000

CUME_DIST

SELECT
    member_id,
    gym_location,
    calories_burned,

    CUME_DIST() OVER(
        PARTITION BY gym_location,   -- 为每个地点创建分布
        ORDER BY calories_burned
    ) AS cd

FROM FITNESS.workouts

ORDER BY gym_location, cd;  -- 对最终结果集排序

CUME_DIST

SELECT
    <fields>,
    <1>,
    <2>,


    CUME_DIST() OVER(
        PARTITION BY <1>
        ORDER BY <2>
    )


...;

将每条记录与该列/字段的分布比较，得到"累积分布"

<1>：用于划分窗口的字段

<2>：用于生成分布的字段

该记录以下（含本条）的比例是多少？

CUME_DIST

               member_id  |  gym_location  | calories_burned  |   cd   
              ----------- | -------------- | ---------------- | -------
                 m_192    |    Miami       |        45        |  .033
                 m_74     |    Miami       |        59        |  .066
                 m_288    |    Miami       |        83        |  .098
                 m_541    |    Miami       |        85        |  .131

                                          ...

                 m_233    |    Portland    |        60        |  .071
                 m_346    |    Portland    |        77        |  .142

                                          ...

Vamos praticar!

Snowflake 中的 Window Functions