数仓调优实践丨多次关联发散导致数据爆炸案例分析改写

本文分享自华为云社区《GaussDB(DWS)性能调优：求字段全体值中大于本行值的最小值------多次关联发散导致数据爆炸案例分析改写》，作者： Zawami 。

1、【问题描述】

语句中存在同一个表多次自关联，且均为发散关联，数据爆炸导致性能瓶颈。

2、【原始SQL】

explain verbose
WITH TMP AS
(
    SELECT WH_ID
         , (IFNULL(SUBSTR(THE_DATE,1,10),'1900-01-01') || ' ' || STOP_TIME)::TIMESTAMP AS STOP_TIME
         , (IFNULL(SUBSTR(THE_DATE,1,10),'1900-01-01') || ' ' || '23:59:59')::TIMESTAMP AS MAX_ASD
      FROM DMISC.DM_DIM_CBG_WH_HOLIDAY_D
    WHERE IS_OPEN = 'Y'
      AND STOP_TIME IS NOT NULL
)
SELECT T1.WH_ID
     , T1.THE_DATE
     , T1.IS_OPEN
     , MIN(T2.STOP_TIME) AS STOP_TIME
     , MIN(T2.MAX_ASD) AS TODAY_MAX_ASD
     , MIN(T3.MAX_ASD) AS NEXT_MAX_ASD
FROM (SELECT WH_ID
           , THE_DATE
           , IS_OPEN
           , (IFNULL(SUBSTR(THE_DATE,1,10),'1900-01-01') || ' ' || STOP_TIME)::TIMESTAMP AS STOP_TIME
        FROM DMISC.DM_DIM_CBG_WH_HOLIDAY_D
     ) T1
LEFT JOIN TMP T2
ON T1.WH_ID = T2.WH_ID
AND T1.THE_DATE < T2.STOP_TIME

LEFT JOIN TMP T3
ON T1.WH_ID = T3.WH_ID
AND ADDDATE(T1.THE_DATE,1) < T3.STOP_TIME

GROUP BY T1.WH_ID, T1.THE_DATE, T1.IS_OPEN;

从SQL中不难看出，物理表HOLIDAY_D使用WH_ID为关联键，并使用其它字段做不等值关联。

3、【性能分析】

QUERY PLAN                                                                                                                                                                                                                                                     |
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 id |                                    operation                                     |    E-rows     | E-distinct |   E-memory    | E-width |     E-costs                                                                                                    |
----+----------------------------------------------------------------------------------+---------------+------------+---------------+---------+-----------------                                                                                               |
  1 | ->  Row Adapter                                                                  |         51584 |            |               |      67 | 377559930171.36                                                                                                |
  2 |    ->  Vector Streaming (type: GATHER)                                           |         51584 |            |               |      67 | 377559930171.36                                                                                                |
  3 |       ->  Vector Hash Aggregate                                                  |         51584 |            | 16MB          |      67 | 377559929546.36                                                                                                |
  4 |          ->  Vector CTE Append(5, 7)                                             | 5699739636332 |            | 1MB           |      43 | 292063834485.54                                                                                                |
  5 |             ->  Vector Streaming(type: BROADCAST)                                |        757752 |            | 2MB           |      22 | 1474.87                                                                                                        |
  6 |                ->  CStore Scan on dmisc.dm_dim_cbg_wh_holiday_d  [5, CTE tmp(1)] |        757752 |            | 1MB           |      22 | 1474.87                                                                                                        |
  7 |             ->  Vector Hash Left Join (8, 11)                                    | 5699739636332 |            | 107MB(6863MB) |      43 | 292063833010.67                                                                                                |
  8 |                ->  Vector Hash Right Join (9, 10)                                |     542231841 | 50         | 16MB          |      27 | 22365789.31                                                                                                    |
  9 |                   ->  Vector CTE Scan on tmp(1) t3                               |         31573 | 50         | 1MB           |      48 | 15155.04                                                                                                       |
 10 |                   ->  CStore Scan on dmisc.dm_dim_cbg_wh_holiday_d               |         51584 | 50         | 1MB           |      19 | 556.58                                                                                                         |
 11 |                ->  Vector CTE Scan on tmp(1) t2                                  |         31573 | 50         | 1MB           |      48 | 15155.04                                                                                                       |

由于SQL非常慢，难以打出performance计划，我们先看verbose计划。从计划中我们看到，经过两次的关联发散，估计数据量达到了5万亿行；因为hash join根据WH_ID列进行关联，实际不会有这么多。所以调优的思路就是取消一些发散，让中间结果集行数变少。

4、【改写SQL】

分析SQL，可知发散是为了寻找所有 STOP_TIME中大于本行 THE_DATE的最小值。像这种每行都需要用到本行数据和所有数据的逻辑，或许可以使用窗口函数进行编写；但囿于笔者能力，先提供单次自关联的方法。

SQL改写如下：

explain performance
	WITH TMP AS
    (
        SELECT WH_ID
             , (IFNULL(SUBSTR(THE_DATE,1,10),'1900-01-01') || ' ' || STOP_TIME)::TIMESTAMP AS STOP_TIME
             , (IFNULL(SUBSTR(THE_DATE,1,10),'1900-01-01') || ' ' || '23:59:59')::TIMESTAMP AS MAX_ASD
          FROM DMISC.DM_DIM_CBG_WH_HOLIDAY_D
        WHERE IS_OPEN = 'Y'
    	  AND STOP_TIME IS NOT NULL
    )
    SELECT T1.WH_ID
         , T1.THE_DATE
		 , T1.IS_OPEN
         , MIN(CASE WHEN T1.THE_DATE < T2.STOP_TIME THEN STOP_TIME ELSE NULL END) AS STOP_TIME
         , MIN(CASE WHEN T1.THE_DATE < T2.STOP_TIME THEN T2.MAX_ASD ELSE NULL END) AS TODAY_MAX_ASD
         , MIN(CASE WHEN ADDDATE(T1.THE_DATE, 1) < T2.STOP_TIME THEN T2.MAX_ASD ELSE NULL END) AS NEXT_MAX_ASD
    FROM (SELECT DISTINCT WH_ID
               , THE_DATE
               , IS_OPEN
            FROM DMISC.DM_DIM_CBG_WH_HOLIDAY_D
         ) T1
    LEFT JOIN TMP T2
    ON T1.WH_ID = T2.WH_ID
	GROUP BY
		T1.WH_ID
         , T1.THE_DATE
		 , T1.IS_OPEN
    ;

经过改写，取消了一次自关联，SQL的中间结果集变小。在关联后，通过条件聚合来得到需要的值。

 id |                            operation                            |        A-time        |  A-rows  | E-rows | E-distinct |  Peak Memory   | E-memory |  A-width  | E-width | E-costs  
----+-----------------------------------------------------------------+----------------------+----------+--------+------------+----------------+----------+-----------+---------+----------
  1 | ->  Row Adapter                                                 | 7490.354             |    34035 |    200 |            | 70KB           |          |           |      58 | 15149.80 
  2 |    ->  Vector Streaming (type: GATHER)                          | 7488.129             |    34035 |    200 |            | 216KB          |          |           |      58 | 15149.80 
  3 |       ->  Vector Hash Aggregate                                 | [7481.430, 7481.430] |    34035 |    200 |            | [9MB, 9MB]     | 16MB     | [112,112] |      58 | 15137.30 
  4 |          ->  Vector Hash Left Join (5, 7)                       | [909.377, 909.377]   | 31204164 | 109803 |            | [2MB, 2MB]     | 16MB     |           |      34 | 3880.50  
  5 |             ->  Vector Sonic Hash Aggregate                     | [5.876, 5.876]       |    34035 |  34036 | 6807       | [3MB, 3MB]     | 16MB     | [51,51]   |      18 | 1127.67  
  6 |                ->  CStore Scan on dmisc.dm_dim_cbg_wh_holiday_d | [0.199, 0.199]       |    34036 |  34036 |            | [792KB, 792KB] | 1MB      |           |      18 | 532.04   
  7 |             ->  CStore Scan on dmisc.dm_dim_cbg_wh_holiday_d    | [40.794, 40.794]     |    25122 |  21960 | 19         | [1MB, 1MB]     | 1MB      | [59,59]   |      24 | 617.13   

从执行计划中可以看到，中间结果集大小已经在可接受的范围内。但是又看到聚合 3千万数据使用了6s+的时间，这是过慢的，需要看执行计划中的DN信息寻找原因 。

                                                                                               Datanode Information (identified by plan id)                                                                                                
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  1 --Row Adapter
        (actual time=7486.498..7490.354 rows=34035 loops=1)
        (CPU: ex c/r=107, ex row=34035, ex cyc=3668104, inc cyc=22468059912)
  2 --Vector Streaming (type: GATHER)
        (actual time=7486.466..7488.129 rows=34035 loops=1)
        (Buffers: shared hit=1)
        (CPU: ex c/r=660037, ex row=34035, ex cyc=22464391808, inc cyc=22464391808)
  3 --Vector Hash Aggregate
        dn_6083_6084 (actual time=7479.644..7481.430 rows=34035 loops=1) (projection time=4488.807)
        dn_6083_6084 (Buffers: shared hit=40)
        dn_6083_6084 (CPU: ex c/r=631, ex row=31204164, ex cyc=19718763112, inc cyc=22443886288)
  4 --Vector Hash Left Join (5, 7)
        dn_6083_6084 (actual time=48.009..909.377 rows=31204164 loops=1)
        dn_6083_6084 (Buffers: shared hit=36)
        dn_6083_6084 (CPU: ex c/r=43699, ex row=59157, ex cyc=2585141400, inc cyc=2725123176)
  5 --Vector Sonic Hash Aggregate
        dn_6083_6084 (actual time=5.177..5.876 rows=34035 loops=1)
        dn_6083_6084 (Buffers: shared hit=11)
        dn_6083_6084 (CPU: ex c/r=500, ex row=34036, ex cyc=17027544, inc cyc=17619064)
  6 --CStore Scan on dmisc.dm_dim_cbg_wh_holiday_d
        dn_6083_6084 (actual time=0.043..0.199 rows=34036 loops=1) (CU ScanInfo: smallCu: 0, totalCu: 1, avrCuRow: 34036, totalDeadRows: 0)
        dn_6083_6084 (Buffers: shared hit=11)
        dn_6083_6084 (CPU: ex c/r=17, ex row=34036, ex cyc=591520, inc cyc=591520)
  7 --CStore Scan on dmisc.dm_dim_cbg_wh_holiday_d
        dn_6083_6084 (actual time=6.464..40.794 rows=25122 loops=1) (filter time=0.872 projection time=33.671) (RoughCheck CU: CUNone: 0, CUTagNone: 0, CUSome: 1) (CU ScanInfo: smallCu: 0, totalCu: 1, avrCuRow: 34036, totalDeadRows: 0)
        dn_6083_6084 (Buffers: shared hit=25)
        dn_6083_6084 (CPU: ex c/r=3595, ex row=34036, ex cyc=122362712, inc cyc=122362712)

从中可以看出，所有算子都只在一个DN上运行了。这可以视为严重的计算倾斜 ，若对单点性能有更高要求需要继续优化。查看DMISC.DM_DIM_CBG_WH_HOLIDAY_D表的定义，发现它是一个复制表(distribute by replication)，在进行各层运算的时候只用其中一个DN来算。而在本SQL中，使用到这张表的时候，关联键都是WH_ID。

再查看调整分布列为WH_ID的倾斜情况：

select * from pg_catalog.table_skewness('DMISC.DM_DIM_CBG_WH_HOLIDAY_D', 'wh_id');

结果有23行，小于集群DN个数 ，且存在倾斜 。但是本SQL需要使用该表的全量数据，故可以把这张表改为使用WH_ID作为分步键进行重分布。

由表分布方式为复制表导致的计算倾斜无法使用skew hint解决，可以改变物理表分布方式或者创建临时表来解决（复制表通常较小）。由于表在SQL中的使用情况和表的倾斜情况，不适合更改物理表分步键为WH_ID，故本例中试使用创建临时表指定重分布方式的办法解决。

DROP TABLE IF EXISTS holiday_d_tmp;
CREATE TEMP TABLE holiday_d_tmp WITH ( orientation = COLUMN, compression = low ) distribute BY hash ( wh_id ) AS ( SELECT * FROM DMISC.DM_DIM_CBG_WH_HOLIDAY_D );
EXPLAIN performance WITH TMP AS (
	SELECT
		WH_ID,
		( IFNULL ( SUBSTR( THE_DATE, 1, 10 ), '1900-01-01' ) || ' ' || STOP_TIME ) :: TIMESTAMP AS STOP_TIME,
		( IFNULL ( SUBSTR( THE_DATE, 1, 10 ), '1900-01-01' ) || ' ' || '23:59:59' ) :: TIMESTAMP AS MAX_ASD 
	FROM
		holiday_d_tmp 
	WHERE
		IS_OPEN = 'Y' 
		AND STOP_TIME IS NOT NULL 
	) SELECT
	T1.WH_ID,
	T1.THE_DATE,
	T1.IS_OPEN,
	MIN ( CASE WHEN T1.THE_DATE < T2.STOP_TIME THEN STOP_TIME ELSE NULL END ) AS STOP_TIME,
	MIN ( CASE WHEN T1.THE_DATE < T2.STOP_TIME THEN T2.MAX_ASD ELSE NULL END ) AS TODAY_MAX_ASD,
	MIN ( CASE WHEN ADDDATE ( T1.THE_DATE, 1 ) < T2.STOP_TIME THEN T2.MAX_ASD ELSE NULL END ) AS NEXT_MAX_ASD 
FROM
	( SELECT WH_ID, THE_DATE, IS_OPEN FROM holiday_d_tmp ) T1
	LEFT JOIN TMP T2 ON T1.WH_ID = T2.WH_ID 
GROUP BY
	T1.WH_ID,
	T1.THE_DATE,
	T1.IS_OPEN;

下面是对应的执行计划：

 id |                                      operation                                       |      A-time      |  A-rows  |  E-rows  | E-distinct |  Peak Memory   | E-memory | A-width | E-width | E-costs  
----+--------------------------------------------------------------------------------------+------------------+----------+----------+------------+----------------+----------+---------+---------+----------
  1 | ->  Row Adapter                                                                      | 673.495          |    34035 |    34032 |            | 70KB           |          |         |      58 | 68112.60 
  2 |    ->  Vector Streaming (type: GATHER)                                               | 671.103          |    34035 |    34032 |            | 216KB          |          |         |      58 | 68112.60 
  3 |       ->  Vector Hash Aggregate                                                      | [0.079, 672.724] |    34035 |    34032 |            | [1MB, 1MB]     | 16MB     | [0,114] |      58 | 67794.10 
  4 |          ->  Vector Hash Left Join (5, 6)                                            | [0.047, 76.395]  | 31205167 | 27587201 |            | [324KB, 485KB] | 16MB     |         |      34 | 8876.88  
  5 |             ->  CStore Scan on pg_temp_cn_5003_6_22022_139764371019520.holiday_d_tmp | [0.004, 0.098]   |    34036 |    34036 | 1          | [760KB, 792KB] | 1MB      |         |      18 | 1553.65  
  6 |             ->  CStore Scan on pg_temp_cn_5003_6_22022_139764371019520.holiday_d_tmp | [0.008, 3.253]   |    25122 |    22018 | 1          | [880KB, 1MB]   | 1MB      | [0,61]  |      24 | 1557.76  

从计划中我们可以看到，耗时比单个DN运算快了不少，当然这里没有算上创建临时表的时间约0.2s。

5、【调优总结】

在本案例中，因为实际执行SQL时间太长先看了verbose计划而非performance计划，发现中间结果集发散 问题后，进行等价逻辑改写 ，把两个(等值-不等值)关联改为一个等值关联和条件聚合 。之后，我们发现SQL因复制表存在计算倾斜 问题，考虑SQL消费表数据的方式 和表的统计数据 ，采用了使用临时表重新指定分布方式的方法，解决了计算倾斜问题，SQL从单点25min+优化到单点800ms。

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