Redshift相関サブクエリパターンがサポートされていない

Question

これは、特に効率的でエレガントなコードではないことがわかりました。私はその後、私が取り組んできたモデルをテストするための試みで、内側には、次のクエリを実行しようとしてる

CREATE TEMP TABLE INSIDE (CONNECT_DATE DATE, DAILY_CONNECTIONS INT);` 

：私はそうのように作られINSIDEと呼ばれる一時テーブルから照会しています。

SELECT * 
, q5.DAN_PREDICTION - q5.LINEAR_PREDICTION AS PREDICTION_COMPARISON 
, q5.DAN_PREDICTION - q5.ACTUAL_MONTH_END_AMOUNT AS DAN_VARIANCE 
, q5.LINEAR_PREDICTION - q5.ACTUAL_MONTH_END_AMOUNT AS LINEAR_VARIANCE 
FROM (SELECT * 
    , q4.mtd + q4.last_yr_remainder + q4.run_rate * q4.days_remaining AS DAN_PREDICTION 
    , q4.mtd + q4.curr_yr_7_day * days_remaining AS LINEAR_PREDICTION 
    FROM(
SELECT 
     * 
     , q3.curr_yr_7_day - q3.last_yr_7_day AS RUN_RATE 
     FROM(
     SELECT 
      CONNECT_DATE 
     , DAILY_CONNECTIONS 
     , (cur_yr_1_prev + cur_yr_2_prev + cur_yr_3_prev + cur_yr_4_prev + cur_yr_5_prev + cur_yr_6_prev + cur_yr_7_prev)/7 AS CURR_YR_7_DAY 
     , (last_yr_1_prev + last_yr_2_prev + last_yr_3_prev + last_yr_4_prev + last_yr_5_prev + last_yr_6_prev + last_yr_7_prev)/7 AS LAST_YR_7_DAY 
     , (SELECT ISNULL(SUM(ins.DAILY_CONNECTIONS), 0) 
      FROM INSIDE ins 
      WHERE DATEPART(MONTH, ins.CONNECT_DATE) = DATEPART(MONTH, q2.CONNECT_DATE) 
      AND DATEPART(YEAR, ins.CONNECT_DATE) = DATEPART(YEAR, q2.CONNECT_DATE) 
      AND ins.CONNECT_DATE <= q2.CONNECT_DATE) AS MTD 
     , (SELECT ISNULL(SUM(ins.DAILY_CONNECTIONS), 0) 
      FROM INSIDE ins 
      WHERE DATEPART(MONTH, ins.CONNECT_DATE) = DATEPART(MONTH, q2.CONNECT_DATE) 
      AND DATEPART(YEAR, ins.CONNECT_DATE) = DATEPART(YEAR, q2.CONNECT_DATE)-1 
      AND ins.CONNECT_DATE > DATEADD(YEAR, -1, q2.CONNECT_DATE)) AS LAST_YR_REMAINDER 
     , (SELECT TOP 1 DATEPART(DAY, last_day(CONNECT_DATE)) 
      FROM INSIDE 
      WHERE CONNECT_DATE = q2.CONNECT_DATE)-DATEPART(DAY, q2.CONNECT_DATE) DAYS_REMAINING 
     , (SELECT ISNULL(SUM(ins.DAILY_CONNECTIONS), 0) 
      FROM INSIDE ins 
      WHERE DATEPART(MONTH, ins.CONNECT_DATE) = DATEPART(MONTH, q2.CONNECT_DATE) 
      AND DATEPART(YEAR, ins.CONNECT_DATE) = DATEPART(YEAR, q2.CONNECT_DATE)) AS ACTUAL_MONTH_END_AMOUNT 
     FROM 
      (SELECT 
      q1.CONNECT_DATE CONNECT_DATE 
      , q1.DAILY_CONNECTIONS DAILY_CONNECTIONS 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(DAY,-1,q1.connect_date)), 0) CUR_YR_1_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(DAY,-2,q1.connect_date)), 0) CUR_YR_2_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(DAY,-3,q1.connect_date)), 0) CUR_YR_3_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(DAY,-4,q1.connect_date)), 0) CUR_YR_4_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(DAY,-5,q1.connect_date)), 0) CUR_YR_5_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(DAY,-6,q1.connect_date)), 0) CUR_YR_6_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(DAY,-7,q1.connect_date)), 0) CUR_YR_7_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(YEAR, -1,(DATEADD(DAY,-1,q1.connect_date)))), 0) LAST_YR_1_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(YEAR, -1,(DATEADD(DAY,-2,q1.connect_date)))), 0) LAST_YR_2_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(YEAR, -1,(DATEADD(DAY,-3,q1.connect_date)))), 0) LAST_YR_3_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(YEAR, -1,(DATEADD(DAY,-4,q1.connect_date)))), 0) LAST_YR_4_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(YEAR, -1,(DATEADD(DAY,-5,q1.connect_date)))), 0) LAST_YR_5_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(YEAR, -1,(DATEADD(DAY,-6,q1.connect_date)))), 0) LAST_YR_6_PREV 
      , ISNULL((SELECT DAILY_CONNECTIONS FROM INSIDE WHERE CONNECT_DATE = DATEADD(YEAR, -1,(DATEADD(DAY,-7,q1.connect_date)))), 0) LAST_YR_7_PREV 
      FROM INSIDE q1 ORDER BY q1.CONNECT_DATE 
     ) q2 ORDER BY q2.connect_date 
    ) q3 
    ) q4 
) q5 

内部q1クエリを実行しても問題ありません。 q2でサブクエリを実行すると問題が発生します。私は赤方偏移でサポートされていないサブクエリの種類のドキュメントを見直してきたし、これらを支配するかを理解していない

Amazon Invalid operation: This type of correlated subquery pattern is not supported due to internal error;

：一度にそれらのいずれつ以上（MTD、LAST_YR_REMAINDERなど）を実行すると、次のエラーが生成さ壊れている。どんな助けでも大歓迎です。

Answer 1

インラインサブクエリが多すぎます。 Redshiftを効率的に実行できるように、共通テーブル式（CTE）を使用してロジックを分解してみてください。

ほとんどのインラインサブクエリは、デカルト積での集計として書き直すことができます。

WITH cte1 AS (
    SELECT i1.CONNECT_DATE  CONNECT_DATE 
      ,i1.DAILY_CONNECTIONS DAILY_CONNECTIONS 
      -- Sub-selects converted to an aggregate over a sparse matrix 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(DAY, -1, i1.connect_date)     THEN DAILY_CONNECTIONS ELSE NULL END) CUR_YR_1_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(DAY, -2, i1.connect_date)     THEN DAILY_CONNECTIONS ELSE NULL END) CUR_YR_2_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(DAY, -3, i1.connect_date)     THEN DAILY_CONNECTIONS ELSE NULL END) CUR_YR_3_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(DAY, -4, i1.connect_date)     THEN DAILY_CONNECTIONS ELSE NULL END) CUR_YR_4_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(DAY, -5, i1.connect_date)     THEN DAILY_CONNECTIONS ELSE NULL END) CUR_YR_5_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(DAY, -6, i1.connect_date)     THEN DAILY_CONNECTIONS ELSE NULL END) CUR_YR_6_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(DAY, -7, i1.connect_date)     THEN DAILY_CONNECTIONS ELSE NULL END) CUR_YR_7_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(YEAR, -1, (DATEADD(DAY,-1, i1.connect_date))) THEN DAILY_CONNECTIONS ELSE NULL END) LAST_YR_1_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(YEAR, -1, (DATEADD(DAY,-2, i1.connect_date))) THEN DAILY_CONNECTIONS ELSE NULL END) LAST_YR_2_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(YEAR, -1, (DATEADD(DAY,-3, i1.connect_date))) THEN DAILY_CONNECTIONS ELSE NULL END) LAST_YR_3_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(YEAR, -1, (DATEADD(DAY,-4, i1.connect_date))) THEN DAILY_CONNECTIONS ELSE NULL END) LAST_YR_4_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(YEAR, -1, (DATEADD(DAY,-5, i1.connect_date))) THEN DAILY_CONNECTIONS ELSE NULL END) LAST_YR_5_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(YEAR, -1, (DATEADD(DAY,-6, i1.connect_date))) THEN DAILY_CONNECTIONS ELSE NULL END) LAST_YR_6_PREV 
      ,MAX(CASE WHEN i2.CONNECT_DATE = DATEADD(YEAR, -1, (DATEADD(DAY,-7, i1.connect_date))) THEN DAILY_CONNECTIONS ELSE NULL END) LAST_YR_7_PREV 
      ,SUM(CASE WHEN DATEPART(MONTH, i2.CONNECT_DATE) = DATEPART(MONTH, i1.CONNECT_DATE) 
        AND DATEPART(YEAR, i2.CONNECT_DATE) = DATEPART(YEAR, i1.CONNECT_DATE) 
        AND i2.CONNECT_DATE <= i1.CONNECT_DATE 
        THEN i2.DAILY_CONNECTIONS 
       ELSE NULL END) AS MTD 
      ,SUM(CASE WHEN DATEPART(MONTH, i2.CONNECT_DATE) = DATEPART(MONTH, i1.CONNECT_DATE) 
        AND DATEPART(YEAR, i2.CONNECT_DATE) = DATEPART(YEAR, i1.CONNECT_DATE)-1 
        AND i2.CONNECT_DATE > DATEADD(YEAR, -1, i1.CONNECT_DATE) 
        THEN i2.DAILY_CONNECTIONS 
       ELSE NULL END) AS LAST_YR_REMAINDER 
      ,MAX(CASE WHEN i2.CONNECT_DATE = i1.CONNECT_DATE-DATEPART(DAY, i1.CONNECT_DATE) 
        THEN DATEPART(DAY, last_day(CONNECT_DATE)) 
       ELSE NULL END) AS DAYS_REMAINING 
      ,SUM(CASE WHEN DATEPART(MONTH, i2.CONNECT_DATE) = DATEPART(MONTH, i1.CONNECT_DATE) 
        AND DATEPART(YEAR, i2.CONNECT_DATE) = DATEPART(YEAR, i1.CONNECT_DATE) 
        THEN i2.DAILY_CONNECTIONS 
       ELSE NULL END) AS ACTUAL_MONTH_END_AMOUNT 
    FROM  INSIDE i1 
    -- Create an intentional cartesian product 
    CROSS JOIN INSIDE i2 
    /* Consider limiting the cartesian to a specific overlap range. E.g. 
    WHERE i2.CONNECT_DATE <= DATEADD(YEAR, -1, (DATEADD(DAY,-7, i1.connect_date))) 
    */ 
    -- Use group by to collapse the cartesian back to the original size 
    GROUP BY 1, 2 
    ORDER BY 1 
), cte2 AS (
    SELECT CONNECT_DATE 
     , DAILY_CONNECTIONS 
     , (cur_yr_1_prev + cur_yr_2_prev + cur_yr_3_prev + cur_yr_4_prev + cur_yr_5_prev + cur_yr_6_prev + cur_yr_7_prev)/7 AS CURR_YR_7_DAY 
     , (last_yr_1_prev + last_yr_2_prev + last_yr_3_prev + last_yr_4_prev + last_yr_5_prev + last_yr_6_prev + last_yr_7_prev)/7 AS LAST_YR_7_DAY 
     , MTD, LAST_YR_REMAINDER, DAYS_REMAINING, ACTUAL_MONTH_END_AMOUNT 
    FROM cte1 
    ORDER BY connect_date 
), cte3 AS (
    SELECT *, curr_yr_7_day - last_yr_7_day AS RUN_RATE 
    FROM cte2 
), cte4 AS (
    SELECT * 
      , mtd + last_yr_remainder + run_rate * days_remaining AS DAN_PREDICTION 
      , mtd + curr_yr_7_day * days_remaining AS LINEAR_PREDICTION 
    FROM cte3 
) 
SELECT * 
    , DAN_PREDICTION - LINEAR_PREDICTION AS PREDICTION_COMPARISON 
    , DAN_PREDICTION - ACTUAL_MONTH_END_AMOUNT AS DAN_VARIANCE 
    , LINEAR_PREDICTION - ACTUAL_MONTH_END_AMOUNT AS LINEAR_VARIANCE 
FROM cte4