此查询目前需要4分钟才能运行：
with name1 as (
select col1 as a1, col2 as a2, sum(FEE) as a3
from s1, date
where return_date = datesk and year = 2000
group by col1, col2
)
select c_id
from name1 ala1, ss, cc
where ala1.a3 > (
select avg(a3) * 1.2 from name1 ctr2
where ala1.a2 = ctr2.a2
)
and s_sk = ala1.a2
and s_state = 'TN'
and ala1.a1 = c_sk
order by c_id
limit 100;
我已设置work_mem=’1000MB’和enable-nestloop=off
此查询的EXPLAIN ANALYZE为：http://explain.depesz.com/s/DUa
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------
--------------------
Limit (cost=59141.02..59141.09 rows=28 width=17) (actual time=253707.928..253707.940 rows=100 loops=1)
CTE name1
-> HashAggregate (cost=11091.33..11108.70 rows=1390 width=14) (actual time=105.223..120.358 rows=50441 loops=1)
Group Key: s1.col1, s1.col2
-> Hash Join (cost=2322.69..11080.90 rows=1390 width=14) (actual time=10.390..79.897 rows=55820 loops=1)
Hash Cond: (s1.return_date = date.datesk)
-> Seq Scan on s1 (cost=0.00..7666.14 rows=287514 width=18) (actual time=0.005..33.801 rows=287514 loops=1)
-> Hash (cost=2318.11..2318.11 rows=366 width=4) (actual time=10.375..10.375 rows=366 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 13kB
-> Seq Scan on date (cost=0.00..2318.11 rows=366 width=4) (actual time=5.224..10.329 rows=366 loops=1)
Filter: (year = 2000)
Rows Removed by Filter: 72683
-> Sort (cost=48032.32..48032.39 rows=28 width=17) (actual time=253707.923..253707.930 rows=100 loops=1)
Sort Key: cc.c_id
Sort Method: top-N heapsort Memory: 32kB
-> Hash Join (cost=43552.37..48031.65 rows=28 width=17) (actual time=253634.511..253696.291 rows=18976 loops=1)
Hash Cond: (cc.c_sk = ala1.a1)
-> Seq Scan on cc (cost=0.00..3854.00 rows=100000 width=21) (actual time=0.009..18.527 rows=100000 loops=1)
-> Hash (cost=43552.02..43552.02 rows=28 width=4) (actual time=253634.420..253634.420 rows=18976 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 668kB
-> Hash Join (cost=1.30..43552.02 rows=28 width=4) (actual time=136.819..253624.375 rows=18982 loops=1)
Hash Cond: (ala1.a2 = ss.s_sk)
-> CTE Scan on name1 ala1 (cost=0.00..43548.70 rows=463 width=8) (actual time=136.756..253610.817 rows=18982 loops=1)
Filter: (a3 > (SubPlan 2))
Rows Removed by Filter: 31459
SubPlan 2
-> Aggregate (cost=31.29..31.31 rows=1 width=32) (actual time=5.025..5.025 rows=1 loops=50441)
-> CTE Scan on name1 ctr2 (cost=0.00..31.27 rows=7 width=32) (actual time=0.032..3.860 rows=8241 loops=50441)
Filter: (ala1.a2 = a2)
Rows Removed by Filter: 42200
-> Hash (cost=1.15..1.15 rows=12 width=4) (actual time=0.036..0.036 rows=12 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 1kB
-> Seq Scan on ss (cost=0.00..1.15 rows=12 width=4) (actual time=0.025..0.033 rows=12 loops=1)
Filter: (s_state = 'TN'::bpchar)
Planning time: 0.316 ms
Execution time: 253708.351 ms
(36 rows)
使用enable_nestloop = on; EXPLAIN ANLYZE结果为：http://explain.depesz.com/s/NPo
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------
--------------
Limit (cost=54916.36..54916.43 rows=28 width=17) (actual time=257869.004..257869.015 rows=100 loops=1)
CTE name1
-> HashAggregate (cost=11091.33..11108.70 rows=1390 width=14) (actual time=92.354..104.103 rows=50441 loops=1)
Group Key: s1.col1, s1.col2
-> Hash Join (cost=2322.69..11080.90 rows=1390 width=14) (actual time=9.371..68.156 rows=55820 loops=1)
Hash Cond: (s1.return_date = date.datesk)
-> Seq Scan on s1 (cost=0.00..7666.14 rows=287514 width=18) (actual time=0.011..25.637 rows=287514 loops=1)
-> Hash (cost=2318.11..2318.11 rows=366 width=4) (actual time=9.343..9.343 rows=366 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 13kB
-> Seq Scan on date (cost=0.00..2318.11 rows=366 width=4) (actual time=4.796..9.288 rows=366 loops=1)
Filter: (year = 2000)
Rows Removed by Filter: 72683
-> Sort (cost=43807.66..43807.73 rows=28 width=17) (actual time=257868.994..257868.998 rows=100 loops=1)
Sort Key: cc.c_id
Sort Method: top-N heapsort Memory: 32kB
-> Nested Loop (cost=0.29..43806.98 rows=28 width=17) (actual time=120.358..257845.941 rows=18976 loops=1)
-> Nested Loop (cost=0.00..43633.22 rows=28 width=4) (actual time=120.331..257692.654 rows=18982 loops=1)
Join Filter: (ala1.a2 = ss.s_sk)
Rows Removed by Join Filter: 208802
-> CTE Scan on name1 ala1 (cost=0.00..43548.70 rows=463 width=8) (actual time=120.316..257652.636 rows=18982 loops=1)
Filter: (a3 > (SubPlan 2))
Rows Removed by Filter: 31459
SubPlan 2
-> Aggregate (cost=31.29..31.31 rows=1 width=32) (actual time=5.105..5.105 rows=1 loops=50441)
-> CTE Scan on name1 ctr2 (cost=0.00..31.27 rows=7 width=32) (actual time=0.032..3.952 rows=8241 loops=50441)
Filter: (ala1.a2 = a2)
Rows Removed by Filter: 42200
-> Materialize (cost=0.00..1.21 rows=12 width=4) (actual time=0.000..0.001 rows=12 loops=18982)
-> Seq Scan on ss (cost=0.00..1.15 rows=12 width=4) (actual time=0.007..0.012 rows=12 loops=1)
Filter: (s_state = 'TN'::bpchar)
-> Index Scan using cc_pkey on cc (cost=0.29..6.20 rows=1 width=21) (actual time=0.007..0.007 rows=1 loops=18982)
Index Cond: (c_sk = ala1.a1)
Planning time: 0.453 ms
Execution time: 257869.554 ms
(34 rows)
使用enable_nestloop = off可以快速运行许多其他查询，此查询没有太大区别。原始数据不是很大，所以4分钟太多了。我期待大约4-5秒。
为什么需要这么长时间！ 我在postgres版本9.4和9.5中尝试了这个。它是一样的。也许我可以创建brin索引。但我不确定要创建哪些列。
配置设置：
effective_cache_size | 89GB
shared_buffers | 18GB
work_mem | 1000MB
maintenance_work_mem | 500MB
checkpoint_segments | 32
constraint_exclusion | on
checkpoint_completion_target | 0.5
答案 0 :(得分：3)
就像John Bollinger所评论的那样，您的子查询会针对主查询的每一行进行评估。但是，由于您在一个简单的列上进行平均，您可以轻松地将子查询移到CTE并计算一次平均值，这将极大地加速：
with name1 as (
select col1 as a1, col2 as a2, sum(FEE) as a3
from s1, date
where return_date = datesk and year = 2000
group by col1, col2
), avg_a3_by_a2 as (
select a2, avg(a3) * 1.2 as avg12
from name1
group by a2
)
select c_id
from name1, avg_a3_by_a2, ss, cc
where name1.a3 > avg_a3_by_a2.avg12
and name1.a2 = avg_a3_by_a2.a2
and s_sk = name1.a2
and s_state = 'TN'
and name1.a1 = c_sk
order by c_id
limit 100;
新CTE计算a2的每个不同值的平均值+ 20％。
还请使用JOIN语法而不是逗号分隔的FROM项，因为它使您的代码更具可读性。如果您在查询中开始使用别名，请在所有表和列上一致地使用它们。由于缺乏信息，我无法纠正这两个问题。