我有一个案例，我需要定期清理孤儿的桌子，所以我正在寻找一个高性能的解决方案。我尝试过使用＆＃39; IN＆＃39;条款，但它不是很快。列具有两个表中的所有必需索引。（id - 主键，component_id - index，component_type - index）
DELETE FROM component_apportionment
WHERE id in (
SELECT a.id
FROM component_apportionment a
LEFT JOIN component_live c
ON (c.component_id = a.component_id
AND
c.component_type = a.component_type)
WHERE c.id is null);
基本上情况是删除记录 ＆＃39; component_apportionment＆＃39; 表格中不存在 ＆ ＃39; component_live＆＃39; 表。
上面查询的查询计划也很糟糕：
Delete on component_apportionment_copy1 (cost=3860927.55..3860929.09 rows=1 width=18) (actual time=183479.848..183479.848 rows=0 loops=1)
-> Nested Loop (cost=3860927.55..3860929.09 rows=1 width=18) (actual time=183479.811..183479.813 rows=1 loops=1)
-> HashAggregate (cost=3860927.12..3860927.13 rows=1 width=20) (actual time=183479.793..183479.793 rows=1 loops=1)
Group Key: a.id
-> Merge Right Join (cost=3753552.72..3860927.12 rows=1 width=20) (actual time=172941.125..183479.787 rows=1 loops=1)
Merge Cond: ((c.component_id = a.component_id) AND ((c.component_type)::text = (a.component_type)::text))
Filter: (c.id IS NULL)
Rows Removed by Filter: 5968195
-> Sort (cost=3390767.32..3413658.29 rows=9156391 width=21) (actual time=169852.438..172642.897 rows=8043013 loops=1)
Sort Key: c.component_id, c.component_type
Sort Method: external merge Disk: 310232kB
-> Seq Scan on component_live c (cost=0.00..2117393.91 rows=9156391 width=21) (actual time=0.004..155656.568 rows=9333382 loops=1)
-> Materialize (cost=362785.40..375049.75 rows=2452871 width=21) (actual time=3088.653..5343.013 rows=5968195 loops=1)
-> Sort (cost=362785.40..368917.58 rows=2452871 width=21) (actual time=3088.648..3989.163 rows=2452871 loops=1)
Sort Key: a.component_id, a.component_type
Sort Method: external merge Disk: 81504kB
-> Seq Scan on component_apportionment_copy1 a (cost=0.00..44969.71 rows=2452871 width=21) (actual time=0.920..882.040 rows=2452871 loops=1)
-> Index Scan using component_apportionment_copy1_pkey on component_apportionment_copy1 (cost=0.43..1.95 rows=1 width=14) (actual time=0.012..0.012 rows=1 loops=1)
Index Cond: (id = a.id)
Planning time: 5.573 ms
Execution time: 183554.675 ms
非常感谢任何帮助。 感谢
在最坏的情况下，表格各有约80万条记录。两个表都有使用过的列的索引。
查询：
EXPLAIN (analyze, verbose, buffers) DELETE FROM component_apportionment_copy1
WHERE not exists (select 1
from component_live c
where c.component_id = component_apportionment_copy1.component_id);
Delete on vector.component_apportionment_copy1 (cost=2276557.80..2446287.39 rows=2104532 width=12) (actual time=203643.560..203643.560 rows=0 loops=1)
Buffers: shared hit=20875 read=2025400, temp read=46067 written=45813
-> Hash Anti Join (cost=2276557.80..2446287.39 rows=2104532 width=12) (actual time=202212.975..203643.486 rows=1 loops=1)
Output: component_apportionment_copy1.ctid, c.ctid
Hash Cond: (component_apportionment_copy1.component_id = c.component_id)
Buffers: shared hit=20874 read=2025400, temp read=46067 written=45813
-> Seq Scan on vector.component_apportionment_copy1 (cost=0.00..44969.71 rows=2452871 width=10) (actual time=0.003..659.668 rows=2452871 loops=1)
Output: component_apportionment_copy1.ctid, component_apportionment_copy1.component_id
Buffers: shared hit=20441
-> Hash (cost=2117393.91..2117393.91 rows=9156391 width=10) (actual time=198536.786..198536.786 rows=9333382 loops=1)
Output: c.ctid, c.component_id
Buckets: 16384 Batches: 128 Memory Usage: 3195kB
Buffers: shared hit=430 read=2025400, temp written=36115
-> Seq Scan on vector.component_live c (cost=0.00..2117393.91 rows=9156391 width=10) (actual time=0.039..194415.641 rows=9333382 loops=1)
Output: c.ctid, c.component_id
Buffers: shared hit=430 read=2025400
Planning time: 6.639 ms
Execution time: 203643.594 ms
它对两个表和更多数据进行seq扫描 - 它会越慢。
答案 0 :(得分：8)
你有太多的联接：
set enable_seqscan = false; -- forcing to use indexes
DELETE FROM component_apportionment
WHERE not exists (select 1
from component_live c
where c.component_id = component_apportionment.component_id);
会做同样的事情并且应该更快，特别是如果你在component_id列上有索引。
答案 1 :(得分：1)
exists方式：
delete
from component_apportionment ca
where not exists
(select 1
from component_live cl
where cl.component_id = ca.component_id
);
或in方式：
delete
from component_apportionment
where component_id not in
(select component_id
from component_live
);
此外，在两个表上创建指向component_id列的索引。
<强>更新
我制作了一个测试脚本：
-- table creating and populating (1,000,000 records each)
drop table if exists component_apportionment;
drop table if exists component_live;
create table component_live (component_id numeric primary key);
create table component_apportionment (id serial primary key, component_id numeric);
create index component_apportionment_idx on component_apportionment (component_id);
insert into component_live select g from generate_series(1,1000000) g;
insert into component_apportionment (component_id) select trunc(random()*1000000) from generate_series(1,1000000) g;
analyze verbose component_live;
analyze verbose component_apportionment;
EXPLAIN (analyze, verbose, buffers)
select component_id
from component_apportionment ca
where not exists
(select 1
from component_live cl
where cl.component_id = ca.component_id
);
Merge Anti Join (cost=0.85..61185.85 rows=1 width=6) (actual time=0.013..1060.014 rows=2 loops=1)
Output: ca.component_id
Merge Cond: (ca.component_id = cl.component_id)
Buffers: shared hit=1010548
-> Index Only Scan using component_apportionment_idx on admin.component_apportionment ca (cost=0.42..24015.42 rows=1000000 width=6) (actual time=0.006..460.318 rows=1000000 loops=1)
Output: ca.component_id
Heap Fetches: 1000000
Buffers: shared hit=1003388
-> Index Only Scan using component_live_pkey on admin.component_live cl (cost=0.42..22170.42 rows=1000000 width=6) (actual time=0.005..172.502 rows=999998 loops=1)
Output: cl.component_id
Heap Fetches: 999998
Buffers: shared hit=7160
Total runtime: 1060.035 ms