我在这里有一个奇怪的查询执行性能案例。查询在WHERE子句中具有日期值，执行速度因日期值而异。
实际上：
对于过去30天范围内的日期，执行需要 3分钟
对于过去30天范围之前的日期，执行需要几秒
下面列出了查询，其中的日期是最近30天的范围：
select
sk2_.code as col_0_0_,
bra4_.code as col_1_0_,
st0_.quantity as col_2_0_,
bat1_.forecast as col_3_0_
from
TBL_st st0_,
TBL_bat bat1_,
TBL_sk sk2_,
TBL_bra bra4_
where
st0_.batc_id=bat1_.id
and bat1_.sku_id=sk2_.id
and bat1_.bran_id=bra4_.id
and not (exists (select
1
from
TBL_st st6_,
TBL_bat bat7_,
TBL_sk sk10_
where
st6_.batc_id=bat7_.id
and bat7_.sku_id=sk10_.id
and bat7_.bran_id=bat1_.bran_id
and sk10_.code=sk2_.code
and st6_.date>st0_.date
and sk10_.acco_id=1
and st6_.date>='2017-04-20'
and st6_.date<='2017-04-30'))
and sk2_.acco_id=1
and st0_.date>='2017-04-20'
and st0_.date<='2017-04-30'
以下是查询的计划，其中包含过去30天范围内的日期：
Nested Loop (cost=289.06..19764.03 rows=1 width=430) (actual time=3482.062..326049.246 rows=249 loops=1)
-> Nested Loop Anti Join (cost=288.91..19763.86 rows=1 width=433) (actual time=3482.023..326048.023 rows=249 loops=1)
Join Filter: ((st6_.date > st0_.date) AND ((sk10_.code)::text = (sk2_.code)::text))
Rows Removed by Join Filter: 210558
-> Nested Loop (cost=286.43..13719.38 rows=1 width=441) (actual time=4.648..2212.042 rows=2474 loops=1)
-> Nested Loop (cost=286.00..6871.33 rows=13335 width=436) (actual time=4.262..657.823 rows=666738 loops=1)
-> Index Scan using uk_TBL_sk0_account_code on TBL_sk sk2_ (cost=0.14..12.53 rows=1 width=426) (actual time=1.036..1.084 rows=50 loops=1)
Index Cond: (acco_id = 1)
-> Bitmap Heap Scan on TBL_bat bat1_ (cost=285.86..6707.27 rows=15153 width=26) (actual time=3.675..11.308 rows=13335 loops=50)
Recheck Cond: (sku_id = sk2_.id)
Heap Blocks: exact=241295
-> Bitmap Index Scan on ix_al_batc_sku_id (cost=0.00..282.07 rows=15153 width=0) (actual time=3.026..3.026 rows=13335 loops=50)
Index Cond: (sku_id = sk2_.id)
-> Index Scan using ix_al_stle_batc_id on TBL_st st0_ (cost=0.42..0.50 rows=1 width=21) (actual time=0.002..0.002 rows=0 loops=666738)
Index Cond: (batc_id = bat1_.id)
Filter: ((date >= '2017-04-20 00:00:00'::timestamp without time zone) AND (date <= '2017-04-30 00:00:00'::timestamp without time zone))
Rows Removed by Filter: 1
-> Nested Loop (cost=2.49..3023.47 rows=1 width=434) (actual time=111.345..130.883 rows=86 loops=2474)
-> Hash Join (cost=2.06..2045.18 rows=1905 width=434) (actual time=0.010..28.028 rows=54853 loops=2474)
Hash Cond: (bat7_.sku_id = sk10_.id)
-> Index Scan using ix_al_batc_bran_id on TBL_bat bat7_ (cost=0.42..1667.31 rows=95248 width=24) (actual time=0.009..11.045 rows=54853 loops=2474)
Index Cond: (bran_id = bat1_.bran_id)
-> Hash (cost=1.63..1.63 rows=1 width=426) (actual time=0.026..0.026 rows=50 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 11kB
-> Seq Scan on TBL_sk sk10_ (cost=0.00..1.63 rows=1 width=426) (actual time=0.007..0.015 rows=50 loops=1)
Filter: (acco_id = 1)
-> Index Scan using ix_al_stle_batc_id on TBL_st st6_ (cost=0.42..0.50 rows=1 width=16) (actual time=0.002..0.002 rows=0 loops=135706217)
Index Cond: (batc_id = bat7_.id)
Filter: ((date >= '2017-04-20 00:00:00'::timestamp without time zone) AND (date <= '2017-04-30 00:00:00'::timestamp without time zone))
Rows Removed by Filter: 1
-> Index Scan using TBL_bra_pk on TBL_bra bra4_ (cost=0.14..0.16 rows=1 width=13) (actual time=0.003..0.003 rows=1 loops=249)
Index Cond: (id = bat1_.bran_id)
Planning time: 8.108 ms
Execution time: 326049.583 ms
以下是与过去30天范围之前的日期相同的查询：
select
sk2_.code as col_0_0_,
bra4_.code as col_1_0_,
st0_.quantity as col_2_0_,
bat1_.forecast as col_3_0_
from
TBL_st st0_,
TBL_bat bat1_,
TBL_sk sk2_,
TBL_bra bra4_
where
st0_.batc_id=bat1_.id
and bat1_.sku_id=sk2_.id
and bat1_.bran_id=bra4_.id
and not (exists (select
1
from
TBL_st st6_,
TBL_bat bat7_,
TBL_sk sk10_
where
st6_.batc_id=bat7_.id
and bat7_.sku_id=sk10_.id
and bat7_.bran_id=bat1_.bran_id
and sk10_.code=sk2_.code
and st6_.date>st0_.date
and sk10_.acco_id=1
and st6_.date>='2017-01-20'
and st6_.date<='2017-01-30'))
and sk2_.acco_id=1
and st0_.date>='2017-01-20'
and st0_.date<='2017-01-30'
以下是查询的计划，其中包含最近30天范围之前的日期：
Hash Join (cost=576.33..27443.95 rows=48 width=430) (actual time=132.732..3894.554 rows=250 loops=1)
Hash Cond: (bat1_.bran_id = bra4_.id)
-> Merge Anti Join (cost=572.85..27439.82 rows=48 width=433) (actual time=132.679..3894.287 rows=250 loops=1)
Merge Cond: ((sk2_.code)::text = (sk10_.code)::text)
Join Filter: ((st6_.date > st0_.date) AND (bat7_.bran_id = bat1_.bran_id))
Rows Removed by Join Filter: 84521
-> Nested Loop (cost=286.43..13719.38 rows=48 width=441) (actual time=26.105..1893.523 rows=2491 loops=1)
-> Nested Loop (cost=286.00..6871.33 rows=13335 width=436) (actual time=1.159..445.683 rows=666738 loops=1)
-> Index Scan using uk_TBL_sk0_account_code on TBL_sk sk2_ (cost=0.14..12.53 rows=1 width=426) (actual time=0.035..0.084 rows=50 loops=1)
Index Cond: (acco_id = 1)
-> Bitmap Heap Scan on TBL_bat bat1_ (cost=285.86..6707.27 rows=15153 width=26) (actual time=1.741..7.148 rows=13335 loops=50)
Recheck Cond: (sku_id = sk2_.id)
Heap Blocks: exact=241295
-> Bitmap Index Scan on ix_al_batc_sku_id (cost=0.00..282.07 rows=15153 width=0) (actual time=1.119..1.119 rows=13335 loops=50)
Index Cond: (sku_id = sk2_.id)
-> Index Scan using ix_al_stle_batc_id on TBL_st st0_ (cost=0.42..0.50 rows=1 width=21) (actual time=0.002..0.002 rows=0 loops=666738)
Index Cond: (batc_id = bat1_.id)
Filter: ((date >= '2017-01-20 00:00:00'::timestamp without time zone) AND (date <= '2017-01-30 00:00:00'::timestamp without time zone))
Rows Removed by Filter: 1
-> Materialize (cost=286.43..13719.50 rows=48 width=434) (actual time=15.584..1986.953 rows=84560 loops=1)
-> Nested Loop (cost=286.43..13719.38 rows=48 width=434) (actual time=15.577..1983.384 rows=2491 loops=1)
-> Nested Loop (cost=286.00..6871.33 rows=13335 width=434) (actual time=0.843..482.864 rows=666738 loops=1)
-> Index Scan using uk_TBL_sk0_account_code on TBL_sk sk10_ (cost=0.14..12.53 rows=1 width=426) (actual time=0.005..0.052 rows=50 loops=1)
Index Cond: (acco_id = 1)
-> Bitmap Heap Scan on TBL_bat bat7_ (cost=285.86..6707.27 rows=15153 width=24) (actual time=2.051..7.902 rows=13335 loops=50)
Recheck Cond: (sku_id = sk10_.id)
Heap Blocks: exact=241295
-> Bitmap Index Scan on ix_al_batc_sku_id (cost=0.00..282.07 rows=15153 width=0) (actual time=1.424..1.424 rows=13335 loops=50)
Index Cond: (sku_id = sk10_.id)
-> Index Scan using ix_al_stle_batc_id on TBL_st st6_ (cost=0.42..0.50 rows=1 width=16) (actual time=0.002..0.002 rows=0 loops=666738)
Index Cond: (batc_id = bat7_.id)
Filter: ((date >= '2017-01-20 00:00:00'::timestamp without time zone) AND (date <= '2017-01-30 00:00:00'::timestamp without time zone))
Rows Removed by Filter: 1
-> Hash (cost=2.10..2.10 rows=110 width=13) (actual time=0.033..0.033 rows=110 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 14kB
-> Seq Scan on TBL_bra bra4_ (cost=0.00..2.10 rows=110 width=13) (actual time=0.004..0.013 rows=110 loops=1)
Planning time: 14.542 ms
Execution time: 3894.793 ms
有没有人知道为什么会这样。
有没有人有类似的经历？
非常感谢你。 亲切的问候，Petar
答案 0 :(得分：0)
我不确定，但我刚才有类似的情况（在ORACLE上，但我想这并不重要）。
在我的情况下，差异源于数据量之间的差异，这意味着：如果您拥有过去30天的1％数据，则使用索引。当你需要“较旧的”数据（其余99％的数据）时，它决定不使用索引并进行全面扫描（以嵌套循环的形式而不是散列连接）。
如果您确定数据分发正常，那么可以尝试收集统计数据（当时为我工作）。最终你可以开始分析这个查询的每一个和平，并从那里看到瓶颈和工作的确切部分。
答案 1 :(得分：0)
BTree索引的日期可能会有一些问题，特别是如果您要从表中删除旧数据（即删除所有超过90天的数据）。这可能导致表变得偏斜，所有行都在树的一个分支下。即使不删除旧日期，如果“新”行比“旧”行多，它仍然可能发生。
但是我没有看到您的查询计划使用st0_.date上的索引，所以我认为这不是问题。如果您可以负担得起st0_的表锁定，则可以通过对任何包含st0_.date的索引运行REINDEX操作来验证该理论。
相反，我认为您需要匹配2017-01-20至2017-01-30范围的行比2017-04-20至2017-04-30范围更多的行。两个查询中的第一个双缩进嵌套循环是相同的，因此我将忽略它。第二个双重意图节不同，在慢速查询中代价更高：
-> Materialize (cost=286.43..13719.50 rows=48 width=434) (actual time=15.584..1986.953 rows=84560 loops=1)
-> Nested Loop (cost=286.43..13719.38 rows=48 width=434) (actual time=15.577..1983.384 rows=2491 loops=1)
-> Nested Loop (cost=286.00..6871.33 rows=13335 width=434) (actual time=0.843..482.864 rows=666738
vs
-> Nested Loop (cost=2.49..3023.47 rows=1 width=434) (actual time=111.345..130.883 rows=86 loops=2474)
-> Hash Join (cost=2.06..2045.18 rows=1905 width=434) (actual time=0.010..28.028 rows=54853 loops=2474)
对材料进行材料化可能是一项昂贵的操作，不一定会随估计成本而扩展。看一下https://www.postgresql.org/docs/10/static/using-explain.html，然后搜索“ Materialize”。另外请注意，慢速版本中的估计行数要高得多。
我不确定100％，但是我相信调整“ work_mem”参数可以在此区域产生一些效果（https://www.postgresql.org/docs/9.4/static/runtime-config-resource.html#GUC-WORK-MEM）。为了验证这一理论，您可以使用以下方法更改每次会话的值：
SET LOCAL work_mem = '8MB';