我有一个需要加入一个表本身13次Hive，这是行动非常缓慢。我在用Hive对付泰兹。
当我看一看Hive泳道，似乎只有Map任务是执行和这样做的顺序，似乎这是一个原因，它需要这么长的时间。

其中一件事，我怀疑也可能是问题是，我加入使用3列，但我不知道这会如何影响时间。
有没有办法加快这个查询的执行速度？

WITH merged AS (
SELECT
    mp_0.bp AS bp,
    mp_0.name as name,
    mp_0.country as country,

    mp_0.pos AS pos_0,
    mp_0.min_p AS min_p_0,
    mp_0.max_p AS max_p_0,

    mp_1.pos AS pos_1,
    mp_1.min_p AS min_p_1,
    mp_1.max_p AS max_p_1,

    mp_2.pos AS pos_2,
    mp_2.min_p AS min_p_2,
    mp_2.max_p AS max_p_2,

    mp_3.pos AS pos_3,
    mp_3.min_p AS min_p_3,
    mp_3.max_p AS max_p_3,

    mp_4.pos AS pos_4,
    mp_4.min_p AS min_p_4,
    mp_4.max_p AS max_p_4,

    mp_5.pos AS pos_5,
    mp_5.min_p AS min_p_5,
    mp_5.max_p AS max_p_5,

    mp_6.pos AS pos_6,
    mp_6.min_p AS min_p_6,
    mp_6.max_p AS max_p_6,

    mp_7.pos AS pos_7,
    mp_7.min_p AS min_p_7,
    mp_7.max_p AS max_p_7,

    mp_8.pos AS pos_8,
    mp_8.min_p AS min_p_8,
    mp_8.max_p AS max_p_8,

    mp_9.pos AS pos_9,
    mp_9.min_p AS min_p_9,
    mp_9.max_p AS max_p_9,

    mp_10.pos AS pos_10,
    mp_10.min_p AS min_p_10,
    mp_10.max_p AS max_p_10,

    mp_11.pos AS pos_11,
    mp_11.min_p AS min_p_11,
    mp_11.max_p AS max_p_11,

    mp_12.pos AS pos_12,
    mp_12.min_p AS min_p_12,
    mp_12.max_p AS max_p_12,

    mp_13.pos AS pos_13,
    mp_13.min_p AS min_p_13,
    mp_13.max_p AS max_p_13
FROM
    data.customers mp_0
    INNER JOIN  data.customers mp_1
        ON mp_0.name = mp_1.name
            AND mp_0.day = mp_1.day
            AND mp_0.identify = mp_1.identify
            AND mp_0.bp = mp_1.bp
            AND mp_1.position = 1
            AND mp_1.day <= 123456
            AND mp_1.day > 123456 - 8
    INNER JOIN  data.customers mp_2
        ON mp_0.name = mp_2.name
            AND mp_0.day = mp_2.day
            AND mp_0.identify = mp_2.identify
            AND mp_0.bp = mp_2.bp
            AND mp_2.position = 2
            AND mp_2.day <= 123456
            AND mp_2.day > 123456 - 8
    INNER JOIN  data.customers mp_3
        ON mp_0.name = mp_3.name
            AND mp_0.day = mp_3.day
            AND mp_0.identify = mp_3.identify
            AND mp_0.bp = mp_3.bp
            AND mp_3.position = 3
            AND mp_3.day <= 123456
            AND mp_3.day > 123456 - 8
    INNER JOIN  data.customers mp_4
        ON mp_0.name = mp_4.name
            AND mp_0.day = mp_4.day
            AND mp_0.identify = mp_4.identify
            AND mp_0.bp = mp_4.bp
            AND mp_4.position = 4
            AND mp_4.day <= 123456
            AND mp_4.day > 123456 - 8
    INNER JOIN  data.customers mp_5
        ON mp_0.name = mp_5.name
            AND mp_0.day = mp_5.day
            AND mp_0.identify = mp_5.identify
            AND mp_0.bp = mp_5.bp
            AND mp_5.position = 5
            AND mp_5.day <= 123456
            AND mp_5.day > 123456 - 8
    INNER JOIN  data.customers mp_6
        ON mp_0.name = mp_6.name
            AND mp_0.day = mp_6.day
            AND mp_0.identify = mp_6.identify
            AND mp_0.bp = mp_6.bp
            AND mp_6.position = 6
            AND mp_6.day <= 123456
            AND mp_6.day > 123456 - 8
    INNER JOIN  data.customers mp_7
        ON mp_0.name = mp_7.name
            AND mp_0.day = mp_7.day
            AND mp_0.identify = mp_7.identify
            AND mp_0.bp = mp_7.bp
            AND mp_7.position = 7
            AND mp_7.day <= 123456
            AND mp_7.day > 123456 - 8
    INNER JOIN  data.customers mp_8
        ON mp_0.name = mp_8.name
            AND mp_0.day = mp_8.day
            AND mp_0.identify = mp_8.identify
            AND mp_0.bp = mp_8.bp
            AND mp_8.position = 8
            AND mp_8.day <= 123456
            AND mp_8.day > 123456 - 8
    INNER JOIN  data.customers mp_9
        ON mp_0.name = mp_9.name
            AND mp_0.day = mp_9.day
            AND mp_0.identify = mp_9.identify
            AND mp_0.bp = mp_9.bp
            AND mp_9.position = 9
            AND mp_9.day <= 123456
            AND mp_9.day > 123456 - 8
    INNER JOIN  data.customers mp_10
        ON mp_0.name = mp_10.name
            AND mp_0.day = mp_10.day
            AND mp_0.identify = mp_10.identify
            AND mp_0.bp = mp_10.bp
            AND mp_10.position = 10
            AND mp_10.day <= 123456
            AND mp_10.day > 123456 - 8
    INNER JOIN  data.customers mp_11
        ON mp_0.name = mp_11.name
            AND mp_0.day = mp_11.day
            AND mp_0.identify = mp_11.identify
            AND mp_0.bp = mp_11.bp
            AND mp_11.position = 11
            AND mp_11.day <= 123456
            AND mp_11.day > 123456 - 8
    INNER JOIN  data.customers mp_12
        ON mp_0.name = mp_12.name
            AND mp_0.day = mp_12.day
            AND mp_0.identify = mp_12.identify
            AND mp_0.bp = mp_12.bp
            AND mp_12.position = 12
            AND mp_12.day <= 123456
            AND mp_12.day > 123456 - 8
    INNER JOIN  data.customers mp_13
        ON mp_0.name = mp_13.name
            AND mp_0.day = mp_13.day
            AND mp_0.identify = mp_13.identify
            AND mp_0.bp = mp_13.bp
            AND mp_13.position = 13
            AND mp_13.day <= 123456
            AND mp_13.day > 123456 - 8
WHERE
    mp_0.position = 0
    AND mp_0.day <= 123456
    AND mp_0.day > 123456 - 8
)
INSERT OVERWRITE TABLE data.processed PARTITION (day = 123456)
SELECT
    *
FROM
  (SELECT m.*, row_number() OVER (PARTITION BY bp ORDER BY RAND()) as rn FROM merged m) t
WHERE
  t.rn <= 1000

我是按bp取样的，所以每个bp随机抽取1000行。另外，表是按天分区的，所以这个查询需要8天的数据。