GreatSQL优化技巧：手动实现谓词下推

                                                                                                                                                <h1>GreatSQL优化技巧：手动实现谓词下推</h1> 

导语

最近总是听到用 AI 来优化 SQL 的言论，今天心血来潮试了一下，把表结构、统计信息、SQL语句、执行计划都告诉AI，AI给出了一大堆的建议，它会从索引，语句改写，参数调整各个方面给出优化策略，看似面面俱到，但是如果不懂优化理论，随便使用其给出的优化建议，可能的结果就是，一顿操作猛如虎，一看战绩零杠五。所以本人还是老老实实的总结优化技巧吧，这些案例技巧或许某天会成为 AI 的营养餐。

SQL 案例

SQL 案例语句：(实际业务场景太复杂，截取片段来说明本文主题)

SELECT ta.*, tb.*
  FROM (SELECT *
          FROM (SELECT a.contactid,
                       a.subs_number,
                       a.log_time,
                       ROW_NUMBER() OVER(PARTITION BY a.contactid, a.subs_number ORDER BY a.log_time DESC) rn,
                       a.log_id
                  FROM a
                 WHERE a.contactid IS NOT NULL
                   AND a.log_time >= '2025-05-30 00:00:00'
                   AND a.log_time <= '2025-06-02') cc
         WHERE rn = 1) ta
  LEFT JOIN (SELECT b.*,
                    ROW_NUMBER() OVER(PARTITION BY b.basesn ORDER BY b.create_time DESC) rn
               FROM b
              WHERE b.create_time IS NOT NULL) tb
    ON ta.contactid = tb.basesn
   AND tb.rn = 1

下面支撑该案例 SQL 的测试表结构，符合案例 SQL 特点的测试数据。

   CREATE TABLE a(log_id bigint,CONTACTID INT,subs_number INT,log_time datetime,PRIMARY KEY (log_id),KEY idx_logtime(log_time));
   CREATE TABLE b(id bigint PRIMARY KEY,basesn INT,create_time datetime,KEY idx_basesn(basesn));

   delimiter //

   CREATE OR REPLACE PROCEDURE P1() IS
   BEGIN
     FOR I IN 1 .. 10000 LOOP
        INSERT INTO a(log_id,contactid,subs_number,log_time) VALUES(i,TRUNC(rand()*8000),TRUNC(rand()*9000),SYSDATE-rand()*90);
     END LOOP;

     FOR I IN 1 .. 1000000 LOOP
        INSERT INTO b(id,basesn,create_time) VALUES(i,TRUNC(rand()*800000),SYSDATE-rand()*90);
     END LOOP;
   END;
   //
   delimiter ;

两表的统计信息如下：

greatsql> SHOW index FROM a;
+-------+------------+-------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+
| Table | Non_unique | Key_name    | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment | Visible | Expression |
+-------+------------+-------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+
| a     |          0 | PRIMARY     |            1 | log_id      | A         |       10000 |     NULL |   NULL |      | BTREE      |         |               | YES     | NULL       |
| a     |          1 | idx_logtime |            1 | log_time    | A         |        9990 |     NULL |   NULL | YES  | BTREE      |         |               | YES     | NULL       |
+-------+------------+-------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+
2 rows in set (0.00 sec)

greatsql> SHOW index FROM b;
+-------+------------+------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+
| Table | Non_unique | Key_name   | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment | Visible | Expression |
+-------+------------+------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+
| b     |          0 | PRIMARY    |            1 | id          | A         |      916864 |     NULL |   NULL |      | BTREE      |         |               | YES     | NULL       |
| b     |          1 | idx_basesn |            1 | basesn      | A         |      515268 |     NULL |   NULL | YES  | BTREE      |         |               | YES     | NULL       |
+-------+------------+------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+
2 rows in set (0.00 sec)

语句分析

SQL有两个派生表ta，tb，这两表做left join 左外连接。派生表ta，作为左外连接的左表，内层表a有log_time过滤条件，该列有单列索引，查询两三天的数据数据量命中几百行，查询一个月左右的数据量命中几千到1万左右。派生表tb，作为左外连接的右表，内层表b全表百万级别的数据量，条件create_time is not null过滤性不好。两个派生表都使用了窗口函数ROW_NUMBER()

执行计划分析

语句实际执行计划如下：

EXPLAIN: -> Nested loop left join  (cost=22497.56 rows=0) (actual time=6181.328..6182.085 rows=331 loops=1)
    -> Filter: (cc.rn = 1)  (cost=1.21..35.91 rows=30) (actual time=2.712..2.794 rows=331 loops=1)
        -> Table scan on cc  (cost=2.50..2.50 rows=0) (actual time=2.704..2.752 rows=331 loops=1)
            -> Materialize  (cost=0.00..0.00 rows=0) (actual time=2.698..2.698 rows=331 loops=1)
                -> Window aggregate: row_number() OVER (PARTITION BY a.CONTACTID,a.subs_number ORDER BY a.log_time desc )   (actual time=2.189..2.323 rows=331 loops=1)
                    -> Sort: a.CONTACTID, a.subs_number, a.log_time DESC  (cost=149.21 rows=331) (actual time=2.168..2.190 rows=331 loops=1)
                        -> Filter: (a.CONTACTID is not null)  (cost=149.21 rows=331) (actual time=0.156..1.847 rows=331 loops=1)
                            -> Index range scan on a using idx_logtime over ('2025-05-30 00:00:00' <= log_time <= '2025-06-02 00:00:00'), with index condition: ((a.log_time >= TIMESTAMP'2025-05-30 00:00:00') and (a.log_time <= TIMESTAMP'2025-06-02 00:00:00'))  (cost=149.21 rows=331) (actual time=0.147..1.806 rows=331 loops=1)
    -> Index lookup on tb using <auto_key0> (basesn=cc.contactid, rn=1)  (cost=0.25..766.47 rows=3025) (actual time=18.668..18.668 rows=1 loops=331)
        -> Materialize  (cost=0.00..0.00 rows=0) (actual time=6178.570..6178.570 rows=1000000 loops=1)
            -> Window aggregate: row_number() OVER (PARTITION BY b.basesn ORDER BY b.create_time desc )   (actual time=2153.616..3469.381 rows=1000000 loops=1)
                -> Sort: b.basesn, b.create_time DESC  (cost=100382.85 rows=998296) (actual time=2153.598..2733.042 rows=1000000 loops=1)
                    -> Filter: (b.create_time is not null)  (cost=100382.85 rows=998296) (actual time=0.075..900.074 rows=1000000 loops=1)
                        -> Table scan on b  (cost=100382.85 rows=998296) (actual time=0.074..316.051 rows=1000000 loops=1)

1 row in set (6.22 sec)

两表ta，tb使用Nested loop方式进行连接，ta表作为外层驱动表，结果集rows为331。 tb表作为内层循环表，循环扫描331次，这些都消耗不多。

此SQL耗时多的步骤在对tb的内层表b进行排序(Sort)，做窗口函数聚合计算(Window aggregate)，再做物化处理(Materialize)这三个步骤了，对一百万的数据做这些处理耗时约6s，虽然只执行一次，但对SQL性能的影响是很大的。现在问题聚焦于能不能减少做这些处理的数据量。

从b表的统计信息看，关联字段basesn的选择性不错，本SQL最终结果集也只有331行，关联字段对b表的过滤条件是很好的，当前优化器的行为表现是，因为有窗口函数聚合运算，主查询的关联谓词条件无法推入到tb派生表的内部。了解了这一点，想办法改写语句，让关联字段起到过滤作用。

优化方案

这里我想到的解决方案是：对外层查询表的列CONTACTID去重处理，关联到tb内层查询中，对满足关联条件的数据做Sort，Window aggregate，Materialize这些处理。

为什么增加这一层关联与原语句等价呢，就当作思考题吧，可在评论区评论噢！

语句改写参考如下：

SELECT ta.*, tb.*
  FROM (SELECT *
          FROM (SELECT a.contactid,
                       a.subs_number,
                       a.log_time,
                       row_number() OVER(PARTITION BY a.contactid, a.subs_number ORDER BY a.log_time DESC) rn,
                       a.log_id
                  FROM a
                 WHERE a.contactid IS NOT NULL
                   AND a.log_time >= '2025-05-30 00:00:00'
                   AND a.log_time <= '2025-06-02') cc
         WHERE rn = 1) ta
  LEFT JOIN (SELECT b.*,
                    row_number() OVER(PARTITION BY b.basesn ORDER BY b.create_time DESC) rn
               FROM b
               join (SELECT distinct CONTACTID
                      FROM a
                     WHERE CONTACTID IS NOT NULL
                       AND LOG_TIME >= '2025-05-30 00:00:00'
                       AND LOG_TIME <= '2025-06-02') a1
                 ON a1.CONTACTID = b.basesn
              WHERE b.create_time IS NOT NULL) tb
    ON ta.contactid = tb.basesn
   AND tb.rn = 1

改写后的语句执行计划如下：

EXPLAIN: -> Nested loop left join  (cost=111.18 rows=0) (actual time=14.846..15.281 rows=331 loops=1)
    -> Filter: (cc.rn = 1)  (cost=1.21..35.91 rows=30) (actual time=2.668..2.747 rows=331 loops=1)
        -> Table scan on cc  (cost=2.50..2.50 rows=0) (actual time=2.636..2.683 rows=331 loops=1)
            -> Materialize  (cost=0.00..0.00 rows=0) (actual time=2.630..2.630 rows=331 loops=1)
                -> Window aggregate: row_number() OVER (PARTITION BY a.CONTACTID,a.subs_number ORDER BY a.log_time desc )   (actual time=2.214..2.356 rows=331 loops=1)
                    -> Sort: a.CONTACTID, a.subs_number, a.log_time DESC  (cost=149.21 rows=331) (actual time=2.173..2.198 rows=331 loops=1)
                        -> Filter: (a.CONTACTID is not null)  (cost=149.21 rows=331) (actual time=0.089..1.784 rows=331 loops=1)
                            -> Index range scan on a using idx_logtime over ('2025-05-30 00:00:00' <= log_time <= '2025-06-02 00:00:00'), with index condition: ((a.log_time >= TIMESTAMP'2025-05-30 00:00:00') and (a.log_time <= TIMESTAMP'2025-06-02 00:00:00'))  (cost=149.21 rows=331) (actual time=0.071..1.730 rows=331 loops=1)
    -> Index lookup on tb using <auto_key0> (basesn=cc.contactid, rn=1)  (cost=0.25..2.57 rows=10) (actual time=0.037..0.038 rows=1 loops=331)
        -> Materialize  (cost=0.00..0.00 rows=0) (actual time=12.159..12.159 rows=382 loops=1)
            -> Window aggregate: row_number() OVER (PARTITION BY b.basesn ORDER BY b.create_time desc )   (actual time=11.614..11.781 rows=382 loops=1)
                -> Sort: b.basesn, b.create_time DESC  (actual time=11.608..11.636 rows=382 loops=1)
                    -> Stream results  (cost=237.31 rows=518) (actual time=1.673..11.394 rows=382 loops=1)
                        -> Nested loop inner join  (cost=237.31 rows=518) (actual time=1.670..11.247 rows=382 loops=1)
                            -> Filter: (a1.CONTACTID is not null)  (cost=214.40..35.91 rows=297) (actual time=1.430..1.545 rows=321 loops=1)
                                -> Table scan on a1  (cost=215.02..221.21 rows=298) (actual time=1.429..1.502 rows=321 loops=1)
                                    -> Materialize  (cost=215.00..215.00 rows=298) (actual time=1.428..1.428 rows=321 loops=1)
                                        -> Table scan on <temporary>  (cost=179.02..185.21 rows=298) (actual time=1.303..1.349 rows=321 loops=1)
                                            -> Temporary table with deduplication  (cost=179.00..179.00 rows=298) (actual time=1.302..1.302 rows=321 loops=1)
                                                -> Filter: (a.CONTACTID is not null)  (cost=149.21 rows=298) (actual time=0.110..1.143 rows=331 loops=1)
                                                    -> Index range scan on a using idx_logtime over ('2025-05-30 00:00:00' <= log_time <= '2025-06-02 00:00:00'), with index condition: ((a.log_time >= TIMESTAMP'2025-05-30 00:00:00') and (a.log_time <= TIMESTAMP'2025-06-02 00:00:00'))  (cost=149.21 rows=331) (actual time=0.108..1.108 rows=331 loops=1)
                            -> Filter: (b.create_time is not null)  (cost=0.48 rows=2) (actual time=0.028..0.030 rows=1 loops=321)
                                -> Index lookup on b using idx_basesn (basesn=a1.CONTACTID)  (cost=0.48 rows=2) (actual time=0.027..0.029 rows=1 loops=321)

1 row in set (0.03 sec)

可以看出改写后的SQL耗时0.03s，比原来的6.2s，性能提升了约200倍。表面上SQL是比原来复杂了一点，但整体执行效率却得到了很大的提升。

总结

SQL优化的核心思想是减少I/O开销，无论什么优化技巧都是围绕这个主题，根据SQL具体情况演变出的形形色色的方法而已。万变不离其宗，本案例也是如此。

通过手动改写SQL，实现谓词下推，减少了内层表需要处理的数据量，从而提升了SQL性能。

当然，我们期待GreatSQL的优化器能在未来实现这一算法，自动实现谓词下推，不用改动SQL，即可高效执行SQL。

无论哪种数据库的优化器，都会或多或少存在一定缺陷，我们优化DBA需要做的就是，理解其缺陷，再利用现有资源，帮助其找到好的执行计划，来提升SQL性能。

Enjoy GreatSQL :)

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