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本篇內(nèi)容介紹了“怎么正確使用PostgreSQL中的OR”的有關(guān)知識,在實(shí)際案例的操作過程中,不少人都會遇到這樣的困境,接下來就讓小編帶領(lǐng)大家學(xué)習(xí)一下如何處理這些情況吧!希望大家仔細(xì)閱讀,能夠?qū)W有所成!
在SQL語句中,對OR使用不當(dāng)可能會導(dǎo)致較差的查詢效率。這并不意味著不能用OR而是在使用OR時需考慮可能存在的性能問題。
測試數(shù)據(jù):
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
條件語句p1 OR p2,如可以考慮使用IN來改寫,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優(yōu)化器走的是Bitmap Index Scan,使用IN,優(yōu)化器選擇的路徑是Index Only Scan,相對于Bitmap Index Scan少了Bitmap的建立,成本自然要低不少。
OR and Join
在Join場景中,如果在參與join的表上都存在查詢條件然后在where子句中應(yīng)用OR關(guān)聯(lián),那么優(yōu)化器會選擇a和b連接然后使用Filter過濾,由于先進(jìn)行join而沒有進(jìn)行謂詞下推,因此為了得到1行而filter了999999行,代價巨大。
[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)
在這種情況下,可以通過使用UNION來關(guān)聯(lián)兩個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=#
兩個子連接選擇了成本最低的NL join,總成本是原來SQL語句成本的0.1%都不到,差了3個數(shù)量級。
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