我有这样的关系
R ( EDGE INTEGER, DIHEDRAL INTEGER, FACE INTEGER , VALENCY INTEGER)
我测试了两次，64行表R和128行表R.但是更简单的一个比第二个花费更多的时间。解释如下（它在explain.depesz.com上显示错误）。谁能帮我检查一下原因？谢谢。
计划64行：
HashAggregate (cost=260.16..260.17 rows=1 width=12) (actual rows=64 loops=1)
-> Nested Loop (cost=89.44..260.15 rows=1 width=12) (actual rows=256 loops=1)
Join Filter: ((f1.face < f2.face) AND (e3.edge <> f1.edge) AND (e4.edge <> e3.edge) AND (f1.edge = f2.edge) AND (f1.face =
e3.face))
Rows Removed by Join Filter: 142606080
-> Nested Loop (cost=41.91..167.59 rows=1 width=16) (actual rows=557056 loops=1)
-> Nested Loop (cost=41.91..125.71 rows=1 width=8) (actual rows=256 loops=1)
Join Filter: ((e5.edge <> f2.edge) AND (e5.edge <> e2.edge) AND (e2.face = e5.face))
Rows Removed by Join Filter: 1113856
-> Hash Join (cost=41.91..83.73 rows=1 width=16) (actual rows=512 loops=1)
Hash Cond: (f2.face = e2.face)
Join Filter: (e2.edge <> f2.edge)
Rows Removed by Join Filter: 256
-> Seq Scan on r f2 (cost=0.00..41.76 rows=12 width=8) (actual rows=384 loops=1)
Filter: (valency = 3)
Rows Removed by Filter: 1920
-> Hash (cost=41.76..41.76 rows=12 width=8) (actual rows=2176 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 85kB
-> Seq Scan on r e2 (cost=0.00..41.76 rows=12 width=8) (actual rows=2176 loops=1)
Filter: (dihedral = 2)
Rows Removed by Filter: 128
-> Seq Scan on r e5 (cost=0.00..41.76 rows=12 width=8) (actual rows=2176 loops=512)
Filter: (dihedral = 2)
Rows Removed by Filter: 128
-> Seq Scan on r e3 (cost=0.00..41.76 rows=12 width=8) (actual rows=2176 loops=256)
Filter: (dihedral = 2)
Rows Removed by Filter: 128
-> Hash Join (cost=47.53..92.32 rows=11 width=16) (actual rows=256 loops=557056)
Hash Cond: (e4.face = f1.face)
Join Filter: (e4.edge <> f1.edge)
Rows Removed by Join Filter: 128
-> Seq Scan on r e4 (cost=0.00..36.01 rows=2301 width=8) (actual rows=2304 loops=557056)
-> Hash (cost=47.52..47.52 rows=1 width=8) (actual rows=128 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 5kB
-> Seq Scan on r f1 (cost=0.00..47.52 rows=1 width=8) (actual rows=128 loops=1)
Filter: ((valency = 3) AND (dihedral = 1))
Rows Removed by Filter: 2176
Total runtime: 159268.541 ms
(37 rows)
计划128行
HashAggregate (cost=501.28..501.29 rows=1 width=12) (actual rows=128 loops=1)
-> Nested Loop (cost=171.98..501.27 rows=2 width=12) (actual rows=512 loops=1)
Join Filter: ((e3.edge <> f1.edge) AND (e4.edge <> e3.edge) AND (f1.face = e3.face))
Rows Removed by Join Filter: 2227712
-> Seq Scan on r e3 (cost=0.00..80.31 rows=22 width=8) (actual rows=4352 loops=1)
Filter: (dihedral = 2)
Rows Removed by Filter: 256
-> Materialize (cost=171.98..420.08 rows=2 width=20) (actual rows=512 loops=4352)
-> Nested Loop (cost=171.98..420.07 rows=2 width=20) (actual rows=512 loops=1)
Join Filter: ((f1.face < f2.face) AND (f1.edge = f2.edge))
Rows Removed by Join Filter: 261632
-> Nested Loop (cost=80.59..242.23 rows=1 width=8) (actual rows=512 loops=1)
Join Filter: ((e5.edge <> f2.edge) AND (e5.edge <> e2.edge) AND (e2.face = e5.face))
Rows Removed by Join Filter: 4455936
-> Seq Scan on r e5 (cost=0.00..80.31 rows=22 width=8) (actual rows=4352 loops=1)
Filter: (dihedral = 2)
Rows Removed by Filter: 256
-> Materialize (cost=80.59..161.05 rows=2 width=16) (actual rows=1024 loops=4352)
-> Hash Join (cost=80.59..161.04 rows=2 width=16) (actual rows=1024 loops=1)
Hash Cond: (f2.face = e2.face)
Join Filter: (e2.edge <> f2.edge)
Rows Removed by Join Filter: 512
-> Seq Scan on r f2 (cost=0.00..80.31 rows=22 width=8) (actual rows=768 loops=1)
Filter: (valency = 3)
Rows Removed by Filter: 3840
-> Hash (cost=80.31..80.31 rows=22 width=8) (actual rows=4352 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 170kB
-> Seq Scan on r e2 (cost=0.00..80.31 rows=22 width=8) (actual rows=4352 loops=1)
Filter: (dihedral = 2)
Rows Removed by Filter: 256
-> Hash Join (cost=91.39..177.51 rows=22 width=16) (actual rows=512 loops=512)
Hash Cond: (e4.face = f1.face)
Join Filter: (e4.edge <> f1.edge)
Rows Removed by Join Filter: 256
-> Seq Scan on r e4 (cost=0.00..69.25 rows=4425 width=8) (actual rows=4608 loops=512)
-> Hash (cost=91.38..91.38 rows=1 width=8) (actual rows=256 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 10kB
-> Seq Scan on r f1 (cost=0.00..91.38 rows=1 width=8) (actual rows=256 loops=1)
Filter: ((valency = 3) AND (dihedral = 1))
Rows Removed by Filter: 4352
Total runtime: 1262.761 ms
(41 rows)
答案 0 :(得分：2)
查询计划程序使用行计数/索引大小/等的统计信息。估计如何从查询中获得最佳性能。紧跟在查询之后的行的大量插入可能无法显示最佳性能，因为这些统计信息可能已过期。
为确保规划人员做出明智的选择，您需要在运行EXPLAIN查询之前致电ANALYZE。
在您的特定情况下，计划员可能在第一种情况下（64行）做出了错误的选择，而在第二种情况下（128行）则是一个不错的选择。