本篇内容介绍了“怎么正确使用PostgreSQL中的OR”的有关知识,在实际案例的操作过程中,不少人都会遇到这样的困境,接下来就让小编带领大家学习一下如何处理这些情况吧!希望大家仔细阅读,能够学有所成!
在SQL语句中,对OR使用不当可能会导致较差的查询效率。这并不意味着不能用OR而是在使用OR时需考虑可能存在的性能问题。
测试数据:
DROP TABLE a;
CREATE TABLE a(id integer NOT NULL, a_val text NOT NULL);
INSERT INTO a
SELECT i, md5(i::text)
FROM generate_series(1, 1000000) i;
DROP TABLE b;
CREATE TABLE b(id integer NOT NULL, b_val text NOT NULL);
INSERT INTO b
SELECT i, md5(i::text)
FROM generate_series(1, 1000000) i;
ALTER TABLE a ADD PRIMARY KEY (id);
ALTER TABLE b ADD PRIMARY KEY (id);
ALTER TABLE b ADD FOREIGN KEY (id) REFERENCES a;
VACUUM (ANALYZE) a;
VACUUM (ANALYZE) b;
OR vs IN
条件语句p1 OR p2,如可以考虑使用IN来改写,比如:
[local:/data/pg12]:5432 pg12@testdb=# EXPLAIN verbose
SELECT id FROM a
WHERE id = 42
OR id = 4711;
QUERY PLAN
---------------------------------------------------------------------------
Bitmap Heap Scan on public.a (cost=8.87..16.80 rows=2 width=4)
Output: id
Recheck Cond: ((a.id = 42) OR (a.id = 4711))
-> BitmapOr (cost=8.87..8.87 rows=2 width=0)
-> Bitmap Index Scan on a_pkey (cost=0.00..4.43 rows=1 width=0)
Index Cond: (a.id = 42)
-> Bitmap Index Scan on a_pkey (cost=0.00..4.43 rows=1 width=0)
Index Cond: (a.id = 4711)
(8 rows)
[local:/data/pg12]:5432 pg12@testdb=#
[local:/data/pg12]:5432 pg12@testdb=# EXPLAIN verbose
SELECT id FROM a
WHERE id in (42,4711);
QUERY PLAN
----------------------------------------------------------------------------
Index Only Scan using a_pkey on public.a (cost=0.42..8.88 rows=2 width=4)
Output: id
Index Cond: (a.id = ANY ('{42,4711}'::integer[]))
(3 rows)
[local:/data/pg12]:5432 pg12@testdb=#
使用OR操作符,PG优化器走的是Bitmap Index Scan,使用IN,优化器选择的路径是Index Only Scan,相对于Bitmap Index Scan少了Bitmap的建立,成本自然要低不少。
OR and Join
在Join场景中,如果在参与join的表上都存在查询条件然后在where子句中应用OR关联,那么优化器会选择a和b连接然后使用Filter过滤,由于先进行join而没有进行谓词下推,因此为了得到1行而filter了999999行,代价巨大。
[local:/data/pg12]:5432 pg12@testdb=# EXPLAIN verbose
SELECT id, a.a_val, b.b_val
FROM a JOIN b USING (id)
WHERE a.id = 42
OR b.id = 42;
QUERY PLAN
---------------------------------------------------------------------------------------------
Gather (cost=21965.00..45327.62 rows=2 width=70)
Output: a.id, a.a_val, b.b_val
Workers Planned: 2
-> Parallel Hash Join (cost=20965.00..44327.42 rows=1 width=70)
Output: a.id, a.a_val, b.b_val
Inner Unique: true
Hash Cond: (a.id = b.id)
Join Filter: ((a.id = 42) OR (b.id = 42))
-> Parallel Seq Scan on public.a (cost=0.00..12500.67 rows=416667 width=37)
Output: a.id, a.a_val
-> Parallel Hash (cost=12500.67..12500.67 rows=416667 width=37)
Output: b.b_val, b.id
-> Parallel Seq Scan on public.b (cost=0.00..12500.67 rows=416667 width=37)
Output: b.b_val, b.id
(14 rows)
在这种情况下,可以通过使用UNION来关联两个JOIN提升性能
[local:/data/pg12]:5432 pg12@testdb=# EXPLAIN verbose
pg12@testdb-# SELECT id, a.a_val, b.b_val
pg12@testdb-# FROM a JOIN b USING (id)
pg12@testdb-# WHERE a.id = 42
pg12@testdb-# UNION
pg12@testdb-# SELECT id, a.a_val, b.b_val
pg12@testdb-# FROM a JOIN b USING (id)
pg12@testdb-# WHERE b.id = 42
pg12@testdb-# ;
QUERY PLAN
----------------------------------------------------------------------------------------------------
Unique (cost=33.83..33.85 rows=2 width=68)
Output: a.id, a.a_val, b.b_val
-> Sort (cost=33.83..33.84 rows=2 width=68)
Output: a.id, a.a_val, b.b_val
Sort Key: a.id, a.a_val, b.b_val
-> Append (cost=0.85..33.82 rows=2 width=68)
-> Nested Loop (cost=0.85..16.90 rows=1 width=70)
Output: a.id, a.a_val, b.b_val
-> Index Scan using a_pkey on public.a (cost=0.42..8.44 rows=1 width=37)
Output: a.id, a.a_val
Index Cond: (a.id = 42)
-> Index Scan using b_pkey on public.b (cost=0.42..8.44 rows=1 width=37)
Output: b.id, b.b_val
Index Cond: (b.id = 42)
-> Nested Loop (cost=0.85..16.90 rows=1 width=70)
Output: a_1.id, a_1.a_val, b_1.b_val
-> Index Scan using a_pkey on public.a a_1 (cost=0.42..8.44 rows=1 width=37)
Output: a_1.id, a_1.a_val
Index Cond: (a_1.id = 42)
-> Index Scan using b_pkey on public.b b_1 (cost=0.42..8.44 rows=1 width=37)
Output: b_1.id, b_1.b_val
Index Cond: (b_1.id = 42)
(22 rows)
[local:/data/pg12]:5432 pg12@testdb=#
两个子连接选择了成本最低的NL join,总成本是原来SQL语句成本的0.1%都不到,差了3个数量级。
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