我使用 AWS RDS PG 12.5（db.t3.xlarge / 4vCPUs / 16GB RAM / SSD 存储）。
我试图通过调整work_mem参数来优化查询，以避免在磁盘上溢出数据以对数据进行排序。
正如预期的那样，当增加work_memfrom 4MBto时100MB，使用快速排序而不是外部合并磁盘。
但是，总执行时间更长（2293msvs 2541ms）。
- 为什么快速排序没有显着的收益？我认为 RAM 优于磁盘排序。（
540ms外部合并磁盘与527ms快速排序） - 为什么 seqscans、hash 和 merge 操作更慢？（为什么
work_mem会影响这些操作？）
我发现了这个类似的 SO 帖子，但他们的问题是他们的排序只是整个执行时间的一小部分。
任何见解都会受到欢迎。
查询：
select
dd.date
, jf.job_id
, od.id
, od.company_size_category
, od.invoicing_entity
, os.sector_category_id
, os.sector_id
, jf.pageviews
, jf.apply_clicks
, concat(sector_category_id, '_', company_size_category, '_', invoicing_entity) as bench_id
from organization_dimensions od
left join job_facts jf
on od.id = jf.organization_id
left join date_dimensions dd
on jf.date = dd.date
left join organizations_sectors os
on od.id = os.organization_id
where dd.date >= '2021-01-01' and dd.date < '2021-02-01'
order by 1, 2, 3
;
work_mem=4MB 的查询计划（链接到 depesz）：
Gather Merge (cost=182185.20..197262.15 rows=129222 width=76) (actual time=1988.652..2293.219 rows=981409 loops=1)
Workers Planned: 2
Workers Launched: 2
Buffers: shared hit=105939, temp read=10557 written=10595
-> Sort (cost=181185.18..181346.71 rows=64611 width=76) (actual time=1975.907..2076.591 rows=327136 loops=3)
Sort Key: dd.date, jf.job_id, od.id
Sort Method: external merge Disk: 32088kB
Worker 0: Sort Method: external merge Disk: 22672kB
Worker 1: Sort Method: external merge Disk: 22048kB
Buffers: shared hit=105939, temp read=10557 written=10595
-> Hash Join (cost=1001.68..173149.42 rows=64611 width=76) (actual time=14.719..1536.513 rows=327136 loops=3)
Hash Cond: (jf.organization_id = od.id)
Buffers: shared hit=105821
-> Hash Join (cost=177.27..171332.76 rows=36922 width=21) (actual time=0.797..1269.917 rows=148781 loops=3)
Hash Cond: (jf.date = dd.date)
Buffers: shared hit=104722
-> Parallel Seq Scan on job_facts jf (cost=0.00..152657.47 rows=4834347 width=21) (actual time=0.004..432.145 rows=3867527 loops=3)
Buffers: shared hit=104314
-> Hash (cost=176.88..176.88 rows=31 width=4) (actual time=0.554..0.555 rows=31 loops=3)
Buckets: 1024 Batches: 1 Memory Usage: 10kB
Buffers: shared hit=348
-> Seq Scan on date_dimensions dd (cost=0.00..176.88 rows=31 width=4) (actual time=0.011..0.543 rows=31 loops=3)
Filter: ((date >= '2021-01-01'::date) AND (date < '2021-02-01'::date))
Rows Removed by Filter: 4028
Buffers: shared hit=348
-> Hash (cost=705.43..705.43 rows=9518 width=27) (actual time=13.813..13.815 rows=9828 loops=3)
Buckets: 16384 Batches: 1 Memory Usage: 709kB
Buffers: shared hit=1071
-> Hash Right Join (cost=367.38..705.43 rows=9518 width=27) (actual time=5.035..10.702 rows=9828 loops=3)
Hash Cond: (os.organization_id = od.id)
Buffers: shared hit=1071
-> Seq Scan on organizations_sectors os (cost=0.00..207.18 rows=9518 width=12) (actual time=0.015..0.995 rows=9518 loops=3)
Buffers: shared hit=336
-> Hash (cost=299.39..299.39 rows=5439 width=19) (actual time=4.961..4.962 rows=5439 loops=3)
Buckets: 8192 Batches: 1 Memory Usage: 339kB
Buffers: shared hit=735
-> Seq Scan on organization_dimensions od (cost=0.00..299.39 rows=5439 width=19) (actual time=0.011..3.536 rows=5439 loops=3)
Buffers: shared hit=735
Planning Time: 0.220 ms
Execution Time: 2343.474 ms
work_mem=100MB 的查询计划（链接到 depesz）：
Gather Merge (cost=179311.70..194388.65 rows=129222 width=76) (actual time=2205.016..2541.827 rows=981409 loops=1)
Workers Planned: 2
Workers Launched: 2
Buffers: shared hit=105939
-> Sort (cost=178311.68..178473.21 rows=64611 width=76) (actual time=2173.869..2241.519 rows=327136 loops=3)
Sort Key: dd.date, jf.job_id, od.id
Sort Method: quicksort Memory: 66835kB
Worker 0: Sort Method: quicksort Memory: 56623kB
Worker 1: Sort Method: quicksort Memory: 51417kB
Buffers: shared hit=105939
-> Hash Join (cost=1001.68..173149.42 rows=64611 width=76) (actual time=36.991..1714.073 rows=327136 loops=3)
Hash Cond: (jf.organization_id = od.id)
Buffers: shared hit=105821
-> Hash Join (cost=177.27..171332.76 rows=36922 width=21) (actual time=2.232..1412.442 rows=148781 loops=3)
Hash Cond: (jf.date = dd.date)
Buffers: shared hit=104722
-> Parallel Seq Scan on job_facts jf (cost=0.00..152657.47 rows=4834347 width=21) (actual time=0.005..486.592 rows=3867527 loops=3)
Buffers: shared hit=104314
-> Hash (cost=176.88..176.88 rows=31 width=4) (actual time=1.904..1.906 rows=31 loops=3)
Buckets: 1024 Batches: 1 Memory Usage: 10kB
Buffers: shared hit=348
-> Seq Scan on date_dimensions dd (cost=0.00..176.88 rows=31 width=4) (actual time=0.013..1.892 rows=31 loops=3)
Filter: ((date >= '2021-01-01'::date) AND (date < '2021-02-01'::date))
Rows Removed by Filter: 4028
Buffers: shared hit=348
-> Hash (cost=705.43..705.43 rows=9518 width=27) (actual time=34.586..34.589 rows=9828 loops=3)
Buckets: 16384 Batches: 1 Memory Usage: 709kB
Buffers: shared hit=1071
-> Hash Right Join (cost=367.38..705.43 rows=9518 width=27) (actual time=13.367..27.326 rows=9828 loops=3)
Hash Cond: (os.organization_id = od.id)
Buffers: shared hit=1071
-> Seq Scan on organizations_sectors os (cost=0.00..207.18 rows=9518 width=12) (actual time=0.019..1.443 rows=9518 loops=3)
Buffers: shared hit=336
-> Hash (cost=299.39..299.39 rows=5439 width=19) (actual time=13.314..13.315 rows=5439 loops=3)
Buckets: 8192 Batches: 1 Memory Usage: 339kB
Buffers: shared hit=735
-> Seq Scan on organization_dimensions od (cost=0.00..299.39 rows=5439 width=19) (actual time=0.016..6.407 rows=5439 loops=3)
Buffers: shared hit=735
Planning Time: 0.221 ms
Execution Time: 2601.698 ms