GaussDB数据库-案例：案例：改建分区表

CY 学数据库 2023-12-18

368

现象描述

如下简单SQL语句查询，性能瓶颈点在normal_date的Scan上。

                                                                  QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------
 Seq Scan on normal_date  (cost=0.00..259.00 rows=30 width=12) (actual time=0.100..3.466 rows=30 loops=1)
   Filter: (("time" >= '2022-09-01 00:00:00'::timestamp without time zone) AND ("time" <= '2022-10-01 00:00:00'::timestamp without time zone))
   Rows Removed by Filter: 9970
 Total runtime: 3.587 ms
(4 rows)
复制

优化分析

从业务层确认表数据(在time字段上)有明显的日期特征，符合分区表的特征。重新规划normal_date表的表定义：字段time为分区键、月为间隔单位定义分区表normal_date_part。修改后结果如下，性能提升近10倍。

                                                                     QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------
 Partition Iterator  (cost=0.00..480.00 rows=30 width=12) (actual time=0.038..0.085 rows=30 loops=1)
   Iterations: 2
   ->  Partitioned Seq Scan on normal_date_part  (cost=0.00..480.00 rows=30 width=12) (actual time=0.049..0.063 rows=30 loops=2)
         Filter: (("time" >= '2022-09-01 00:00:00'::timestamp without time zone) AND ("time" <= '2022-10-01 00:00:00'::timestamp without time zone))
         Rows Removed by Filter: 31
         Selected Partitions:  3..4
 Total runtime: 0.360 ms
(7 rows)复制

gaussdb

「喜欢这篇文章，您的关注和赞赏是给作者最好的鼓励」

关注作者

GaussDB数据库-案例：案例：改建分区表

现象描述

优化分析

评论

相关阅读