(类 PG 信创)亿级 table 简单查询的一个优化问题

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这是一个创建于 111 天前的主题，其中的信息可能已经有所发展或是发生改变。

部分信息借由 GPT 进行脱敏处理

现有一个简单表结构：

Table "schema.t_table_m"
     Column       |            Type             |                Modifiers                 | Storage  | Stats target | Description 
-----------------+-----------------------------+------------------------------------------+----------+--------------+-------------
 partition_id    | smallint                    | not null                                 | plain    |              | 分区 ID
 transaction_time| timestamp without time zone | not null default pg_systimestamp()       | plain    |              | 交易时间
 transaction_count | integer                   |                                          | plain    |              | 交易数量
 last_update_time | timestamp without time zone | not null default pg_systimestamp()      | plain    |              | 更新时间
 source_type     | character varying(10)       | not null default NULL::character varying | extended |              | 事件源
 transaction_code | character varying(30)      | not null default NULL::character varying | extended |              | 交易码
 key_value       | character varying(100)      | not null default NULL::character varying | extended |              | RV 变量键
 value_data      | character varying(500)      | not null default NULL::character varying | extended |              | RV 变量值
Indexes:
    "t_table_m_pkey" PRIMARY KEY, ubtree (transaction_time, value_data, key_value, transaction_code, source_type, partition_id) LOCAL WITH (storage_type=USTORE, active_pages=1314323) TABLESPACE pg_default
Partition By LIST(partition_id)
Number of partitions: 31 (View pg_partition to check each partition range.)
Has OIDs: no
Options: orientation=row, compression=no, storage_type=USTORE, segment=off

表由 Mysql 迁移而来，这个分区表中存储的记录条数为 126000000+。在开发侧，他们反馈下面的一个简单查询执行性能非常差，每条 SQL 要跑 15s 左右：

SELECT
  value_data AS dataValue,
  SUM(transaction_count) AS txnCount
FROM
  schema.t_table_m
WHERE
  1 = 1
  AND source_type = '1'
  AND key_value IN (
    'key1',
    'key2',
    'key3',
    'key4',
    'key5',
    'key6',
    'key7',
    'key8',
    'key9',
    'key10'
  )
  AND partition_id IN (1, 31)
  AND transaction_time >= '2024-07-31 15:58:11'
  AND transaction_time <= '2024-08-01 15:58:11'
  AND value_data NOT IN ('null','space','#','*')
  AND transaction_code NOT IN ('code1', 'code2')
GROUP BY
  value_data;

执行计划如下：

postgres=> explain performance
SELECT
.......;

 id |                                           operation                                            |  A-time   | A-rows | E-rows | E-distinct | Peak Memory | A-width | E-width |    E-costs     
----+------------------------------------------------------------------------------------------------+-----------+--------+--------+------------+-------------+---------+---------+----------------
  1 | ->  HashAggregate                                                                              | 12715.021 |      0 |      1 |            | 22KB        |         |       8 | 28.485..28.495
  2 |    ->  Partition Iterator                                                                      | 12715.011 |      0 |      1 |            | 69KB        |         |       8 | 0.010..28.480
  3 |       ->  Partitioned Index Scan using t_table_m_pkey on schema.t_table_m                      | 12714.903 |      0 |      1 |            | 29KB        |         |       8 | 0.010..28.480
(3 rows)

            Predicate Information (identified by plan id)  
------------------------------------------------------------------------------------------
   2 --Partition Iterator
         Iterations: 2
   3 --Partitioned Index Scan using t_table_m_pkey on schema.t_table_m
         Index Cond: ((schema.t_table_m.transaction_time >= '2024-07-31 15:58:11'::timestamp without time zone) AND (schema.t_table_m.transaction_time <= '2024-08-01 15:58:11'::timestamp without time zone) AND ((schema.t_table_m.key_value)::text = ANY ('{key1,key2,key3,key4,key5,key6,key7,key8,key9,key10}'::text[])) AND ((schema.t_table_m.source_type)::text = '1'::text) AND (schema.t_table_m.partition_id = ANY ('{1,31}'::integer[])))
         Filter: (((schema.t_table_m.transaction_code)::text <> 'code1'::text) AND ((schema.t_table_m.transaction_code)::text <> 'code2'::text) AND ((schema.t_table_m.value_data)::text <> ALL ('{"null",space,#,*}'::text[])))
         Selected Partitions:  1,31
(6 rows)

                         Memory Information (identified by plan id)                         
------------------------------------------------------------------------------------------
   1 --HashAggregate
         Peak Memory: 22KB, Estimate Memory: 32MB
   2 --Partition Iterator
         Peak Memory: 69KB, Estimate Memory: 32MB
   3 --Partitioned Index Scan using t_table_m_pkey on schema.t_table_m
         Peak Memory: 29KB, Estimate Memory: 32MB
(6 rows)

                       Targetlist Information (identified by plan id)                       
--------------------------------------------------------------------------------------------
   1 --HashAggregate
         Output: value_data, sum(transaction_count)
         Group By Key: schema.t_table_m.value_data
   2 --Partition Iterator
         Output: value_data, transaction_count
   3 --Partitioned Index Scan using t_table_m_pkey on schema.t_table_m
         Output: value_data, transaction_count
(7 rows)

                        Datanode Information (identified by plan id)                        
--------------------------------------------------------------------------------------------
   1 --HashAggregate
         (actual time=12715.021..12715.021 rows=0 loops=1)
         (Buffers: shared hit=760703)
         (CPU: ex c/r=0, ex row=0, ex cyc=437, inc cyc=847446039235951)
   2 --Partition Iterator
         (actual time=12715.011..12715.011 rows=0 loops=1)
         (CPU: ex c/r=0, ex row=0, ex cyc=-847446039224001, inc cyc=847446039235514)
   3 --Partitioned Index Scan using t_table_m_pkey on schema.t_table_m
         (actual time=12714.903..12714.903 rows=0 loops=2)
         (Buffers: shared hit=760703)
         (CPU: ex c/r=0, ex row=0, ex cyc=1694892078459515, inc cyc=1694892078459515)
(11 rows)

       ====== Query Summary =====        
------------------------------------------
 Datanode executor start time: 0.177 ms
 Datanode executor run time: 12715.042 ms
 Datanode executor end time: 0.021 ms
 Planner runtime: 1.124 ms
 Query Id: 10810046480632471817
 Total runtime: 12715.260 ms
(6 rows)

其中所有涉及列都已经命中索引扫描，测试时尝试更改某些 where 条件使得查询进行全表扫描的话，执行时间会暴增到 2 分钟左右。

经由我们多轮调试后，发现仅仅是单纯地将 where 条件中partition_id IN (1, 31)更改为(partition_id=31 OR partition_id=1)，就能使得查询平均耗时减少到 6s 。其它优化更改的效果低于系统性能波动，无法观测。

原本猜测是因为 IN 语句改写后会触发分区剪枝类的优化，但是经对比发现改写前后的执行计划完全一致：

postgres=> explain performance
SELECT
  value_data AS dataValue,
  SUM(transaction_count) AS txnCount
FROM
  schema.t_table_m
WHERE
  1 = 1
  AND source_type = '1'
  AND key_value = any (array[
    'key1',
    'key2',
    'key3',
    'key4',
    'key5',
    'key6',
    'key7',
    'key8',
    'key9',
    'key10'
  ])
  AND (partition_id = 1 OR partition_id = 31)
  AND transaction_time >= '2024-07-31 15:58:11'
  AND transaction_time <= '2024-08-01 15:58:11'
  AND value_data not in ('null','space','#','*')
  AND transaction_code <> 'code1' 
  AND transaction_code <> 'code2'
GROUP BY
  value_data;


 id |                                           operation                                            |  A-time   | A-rows | E-rows | E-distinct | Peak Memory | A-width | E-width |    E-costs     
----+------------------------------------------------------------------------------------------------+-----------+--------+--------+------------+-------------+---------+---------+----------------
  1 | ->  HashAggregate                                                                              | 6421.454 |      0 |      1 |            | 22KB        |         |       8 | 14.790..14.800
  2 |    ->  Partition Iterator                                                                      | 6421.441 |      0 |      1 |            | 69KB        |         |       8 | 0.010..14.785
  3 |       ->  Partitioned Index Scan using t_table_m_pkey on schema.t_table_m                      | 6421.276 |      0 |      1 |            | 31KB        |         |       8 | 0.010..14.785
(3 rows)

            Predicate Information (identified by plan id)
-----------------------------------------------------------------------------------------
   2 --Partition Iterator
         Iterations: 2
   3 --Partitioned Index Scan using t_table_m_pkey on schema.t_table_m
         Index Cond: ((schema.t_table_m.transaction_time >= '2024-07-31 15:58:11'::timestamp without time zone) AND (schema.t_table_m.transaction_time <= '2024-08-01 15:58:11'::timestamp without time zone) AND ((schema.t_table_m.key_value)::text = ANY ('{key1,key2,key3,key4,key5,key6,key7,key8,key9,key10}'::text[])) AND ((schema.t_table_m.source_type)::text = '1'::text))
         Filter: (((schema.t_table_m.transaction_code)::text <> 'code1'::text) AND ((schema.t_table_m.transaction_code)::text <> 'code2'::text) AND ((schema.t_table_m.partition_id = 31) OR (schema.t_table_m.partition_id = 1)) AND ((schema.t_table_m.value_data)::text <> ALL ('{"null",space,#,*}'::text[])))
         Selected Partitions:  1,31
(6 rows)

            
            Memory Information (identified by plan id)                         
------------------------------------------------------------------------------------------
   1 --HashAggregate
         Peak Memory: 22KB, Estimate Memory: 32MB
   2 --Partition Iterator
         Peak Memory: 69KB, Estimate Memory: 32MB
   3 --Partitioned Index Scan using t_table_m_pkey on schema.t_table_m
         Peak Memory: 31KB, Estimate Memory: 32MB
(6 rows)

                       Targetlist Information (identified by plan id)                       
--------------------------------------------------------------------------------------------
   1 --HashAggregate
         Output: value_data, sum(transaction_count)
         Group By Key: schema.t_table_m.value_data
   2 --Partition Iterator
         Output: value_data, transaction_count
   3 --Partitioned Index Scan using t_table_m_pkey on schema.t_table_m
         Output: value_data, transaction_count
(7 rows)

                        Datanode Information (identified by plan id)                        
--------------------------------------------------------------------------------------------
   1 --HashAggregate
         (actual time=6421.454..6421.454 rows=0 loops=1)
         (Buffers: shared hit=380353)
         (CPU: ex c/r=0, ex row=0, ex cyc=488, inc cyc=847433445353297)
   2 --Partition Iterator
         (actual time=6421.441..6421.441 rows=0 loops=1)
         (CPU: ex c/r=0, ex row=0, ex cyc=-847433445336080, inc cyc=847433445352809)
   3 --Partitioned Index Scan using t_table_m_pkey on schema.t_table_m
         (actual time=6421.276..6421.276 rows=0 loops=2)
         (Buffers: shared hit=380353)
         (CPU: ex c/r=0, ex row=0, ex cyc=1694866890688889, inc cyc=1694866890688889)
(11 rows)

       ====== Query Summary =====        
-----------------------------------------
 Datanode executor start time: 0.198 ms
 Datanode executor run time: 6421.479 ms
 Datanode executor end time: 0.022 ms
 Planner runtime: 1.417 ms
 Query Id: 10810046480632413864
 Total runtime: 6421.729 ms
(6 rows)

两者唯一的区别是，在 Partitioned Index Scan 时，使用 OR 的语句 Buffers: shared hit 是使用 IN 的一半。

其中造成这样的差距的可能原因是什么？

4 条回复 • 2024-08-14 12:04:29 +08:00

967182

111 天前

GPT 这样说：从描述和执行计划中可以看出，使用 IN 与使用 OR 的主要区别在于访问缓冲区（ Buffers ）的行为不同。使用 OR 时，缓冲区的共享命中率只有使用 IN 的一半。这意味着在使用 OR 时，查询过程中的缓存利用效率更高，从而导致查询执行时间较短。

以下是可能导致这种差异的原因：

缓存使用效率：IN 操作符通常会转换成多个条件的 OR 表达式，这可能会增加某些情况下的缓存失效或增加磁盘 IO 。

执行计划差异：尽管在高层次上执行计划看似相同，但在某些底层细节上，优化器可能会对 IN 和 OR 进行不同的处理，导致资源使用的不同。

执行路径：OR 和 IN 在某些数据库系统中的处理路径可能不同，导致数据扫描和缓存的差异。

总的来说，具体的原因可能是数据库在处理 IN 操作符时，在分区剪枝和索引扫描的优化方面没有完全优化，或者在使用 OR 时，查询路径更为直接，减少了缓存未命中的情况，从而使查询执行得更快。

mark2025

111 天前

考虑在 transaction_time 上面创建 BRIN 索引（不是 btree ）

ShuA1

111 天前

建议将 transaction_time 保存为时间戳，bigint 的对比速度会快很多。你这里是日期，会存在隐式转换为时间戳的问题。
另外我看 source_type 字段也是 text 类型，也可以更换为 int4 。
建议你用 explain analyze 进行分析，找出慢的地方，performance 更多是对资源消耗的分析。

MoYi123

111 天前

优化器没写好呗, 能报 bug 的话就报一下, 这种应该很好修.