背景
有一个这样的场景，一张小表A，里面存储了一些ID，大约几百个。
（比如说巡逻车辆ID，环卫车辆的ID，公交车，微公交的ID）。
另外有一张日志表B，每条记录中的ID是来自前面那张小表的，但不是每个ID都出现在这张日志表中，比如说一天可能只有几十个ID会出现在这个日志表的当天的数据中。
（比如车辆的行车轨迹数据，每秒上报轨迹，数据量就非常庞大）。
那么我怎么快速的找出今天没有出现的ID呢。
（哪些巡逻车辆没有出现在这个片区，是不是偷懒了？哪些环卫车辆没有出行，哪些公交或微公交没有出行）？
select id from A where id not in (select id from B where time between ? and ?);
这个QUERY会很慢，有什么优化方法呢。
当然，你还可以让车辆签到的方式来解决这个问题，但是总有未签到的，或者没有这种设计的时候，那么怎么解决呢？
优化方法
其实方法也很精妙，和我之前做的两个CASE很相似。
《时序数据合并场景加速分析和实现 - 复合索引，窗口分组查询加速，变态递归加速》
《distinct xx和count(distinct xx)的变态递归优化方法 - 索引收敛(skip scan)扫描》
在B表中，其实ID的值是很稀疏的，只是由于是流水，所以总量大。
优化的手段就是对B的取值区间，做递归的收敛查询，然后再做NOT IN就很快了。
例子
建表
create table a(id int primary key, info text);
create table b(id int primary key, aid int, crt_time timestamp);
create index b_aid on b(aid);
插入测试数据
-- a表插入1000条
insert into a select generate_series(1,1000), md5(random()::text);
-- b表插入500万条，只包含aid的500个id。
insert into b select generate_series(1,5000000), generate_series(1,500), clock_timestamp();
优化前的性能
\timing
explain (analyze,verbose,timing,costs,buffers) select * from a where id not in (select aid from b);
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------
Seq Scan on public.a (cost=0.00..67030021.50 rows=500 width=37) (actual time=2932.080..618776.881 rows=500 loops=1)
Output: a.id, a.info
Filter: (NOT (SubPlan 1))
Rows Removed by Filter: 500
Buffers: shared hit=27037, temp read=4264454 written=8545
SubPlan 1
-> Materialize (cost=0.00..121560.00 rows=5000000 width=4) (actual time=0.002..298.049 rows=2500125 loops=1000)
Output: b.aid
Buffers: shared hit=27028, temp read=4264454 written=8545
-> Seq Scan on public.b (cost=0.00..77028.00 rows=5000000 width=4) (actual time=0.009..888.427 rows=5000000 loops=1)
Output: b.aid
Buffers: shared hit=27028
Planning time: 0.969 ms
Execution time: 618794.299 ms
(14 rows)
另外你有一种选择是使用outer join, b表同样需要全扫一遍，有很大的改进，不过还可以更好，继续往后看。
postgres=# explain (analyze,verbose,timing,costs,buffers) select a.id from a left join b on (a.id=b.aid) where b.* is null;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------
Hash Right Join (cost=31.50..145809.50 rows=25000 width=4) (actual time=2376.777..2376.862 rows=500 loops=1)
Output: a.id
Hash Cond: (b.aid = a.id)
Filter: (b.* IS NULL)
Rows Removed by Filter: 5000000
Buffers: shared hit=27037
-> Seq Scan on public.b (cost=0.00..77028.00 rows=5000000 width=44) (actual time=0.012..1087.997 rows=5000000 loops=1)
Output: b.aid, b.*
Buffers: shared hit=27028
-> Hash (cost=19.00..19.00 rows=1000 width=4) (actual time=0.355..0.355 rows=1000 loops=1)
Output: a.id
Buckets: 1024 Batches: 1 Memory Usage: 44kB
Buffers: shared hit=9
-> Seq Scan on public.a (cost=0.00..19.00 rows=1000 width=4) (actual time=0.010..0.183 rows=1000 loops=1)
Output: a.id
Buffers: shared hit=9
Planning time: 0.302 ms
Execution time: 2376.934 ms
(18 rows)
递归收敛优化后的性能
explain (analyze,verbose,timing,costs,buffers)
select * from a where id not in
(
with recursive skip as (
(
select min(aid) aid from b where aid is not null
)
union all
(
select (select min(aid) aid from b where b.aid > s.aid and b.aid is not null)
from skip s where s.aid is not null
) -- 这里的where s.aid is not null 一定要加,否则就死循环了.
)
select aid from skip where aid is not null
);
QUERY PLAN
------------------------------------------------------------------------------------------------------
Seq Scan on public.a (cost=54.98..76.48 rows=500 width=37) (actual time=10.837..10.957 rows=500 loops=1)
Output: a.id, a.info
Filter: (NOT (hashed SubPlan 5))
Rows Removed by Filter: 500
Buffers: shared hit=2012
SubPlan 5
-> CTE Scan on skip (cost=52.71..54.73 rows=100 width=4) (actual time=0.042..10.386 rows=500 loops=1)
Output: skip.aid
Filter: (skip.aid IS NOT NULL)
Rows Removed by Filter: 1
Buffers: shared hit=2003
CTE skip
-> Recursive Union (cost=0.46..52.71 rows=101 width=4) (actual time=0.037..10.104 rows=501 loops=1)
Buffers: shared hit=2003
-> Result (cost=0.46..0.47 rows=1 width=4) (actual time=0.036..0.036 rows=1 loops=1)
Output: $1
Buffers: shared hit=4
InitPlan 3 (returns $1)
-> Limit (cost=0.43..0.46 rows=1 width=4) (actual time=0.031..0.032 rows=1 loops=1)
Output: b_1.aid
Buffers: shared hit=4
-> Index Only Scan using b_aid on public.b b_1 (cost=0.43..131903.43 rows=5000000 width=4) (actual time=0.030..0.030 rows=1 loops=1)
Output: b_1.aid
Index Cond: (b_1.aid IS NOT NULL)
Heap Fetches: 1
Buffers: shared hit=4
-> WorkTable Scan on skip s (cost=0.00..5.02 rows=10 width=4) (actual time=0.019..0.019 rows=1 loops=501)
Output: (SubPlan 2)
Filter: (s.aid IS NOT NULL)
Rows Removed by Filter: 0
Buffers: shared hit=1999
SubPlan 2
-> Result (cost=0.47..0.48 rows=1 width=4) (actual time=0.018..0.018 rows=1 loops=500)
Output: $3
Buffers: shared hit=1999
InitPlan 1 (returns $3)
-> Limit (cost=0.43..0.47 rows=1 width=4) (actual time=0.017..0.017 rows=1 loops=500)
Output: b.aid
Buffers: shared hit=1999
-> Index Only Scan using b_aid on public.b (cost=0.43..66153.48 rows=1666667 width=4) (actual time=0.017..0.017 rows=1 loops=500)
Output: b.aid
Index Cond: ((b.aid > s.aid) AND (b.aid IS NOT NULL))
Heap Fetches: 499
Buffers: shared hit=1999
Planning time: 0.323 ms
Execution time: 11.082 ms
(46 rows)
采用收敛查询优化后，耗时从最初的 618794毫秒 降低到了 11毫秒 ，感觉一下子节约了好多青春。