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腾讯云TDSQL PostgreSQL版 -最佳实践 |优化 SQL 语句

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11月 28, 2021

查看是否为分布键查询
postgres=# explain select * from tbase_1 where f1=1;
QUERY PLAN

Remote Fast Query Execution (cost=0.00…0.00 rows=0 width=0)
Node/s: dn001, dn002
-> Gather (cost=1000.00…7827.20 rows=1 width=14)
Workers Planned: 2
-> Parallel Seq Scan on tbase_1 (cost=0.00…6827.10 rows=1 width=14)
Filter: (f1 = 1)
(6 rows)
postgres=# explain select * from tbase_1 where f2=1;
QUERY PLAN

Remote Fast Query Execution (cost=0.00…0.00 rows=0 width=0)
Node/s: dn001
-> Gather (cost=1000.00…7827.20 rows=1 width=14)
Workers Planned: 2
-> Parallel Seq Scan on tbase_1 (cost=0.00…6827.10 rows=1 width=14)
Filter: (f2 = 1)
(6 rows)
如上,第一个查询为非分布键查询,需要发往所有节点,这样最慢的节点决定了整个业务的速度,需要保持所有节点的响应性能一致,如第二个查询所示,业务设计查询时尽可能带上分布键。

查看是否使用索引
postgres=# create index tbase_2_f2_idx on tbase_2(f2);
CREATE INDEX
postgres=# explain select * from tbase_2 where f2=1;
QUERY PLAN

Remote Fast Query Execution (cost=0.00…0.00 rows=0 width=0)
Node/s: dn001, dn002
-> Index Scan using tbase_2_f2_idx on tbase_2 (cost=0.42…4.44 rows=1 width=14)
Index Cond: (f2 = 1)
(4 rows)
postgres=# explain select * from tbase_2 where f3=‘1’;
QUERY PLAN

Remote Fast Query Execution (cost=0.00…0.00 rows=0 width=0)
Node/s: dn001, dn002
-> Gather (cost=1000.00…7827.20 rows=1 width=14)
Workers Planned: 2
-> Parallel Seq Scan on tbase_2 (cost=0.00…6827.10 rows=1 width=14)
Filter: (f3 = ‘1’::text)
(6 rows)
postgres=#
第一个查询使用了索引,第二个没有使用索引,通常情况下,使用索引可以加速查询速度,但索引也会增加更新的开销。

查看是否为分布 key join
postgres=# explain select tbase_1.* from tbase_1,tbase_2 where tbase_1.f1=tbase_2.f1 ;
QUERY PLAN

Remote Subquery Scan on all (dn001,dn002) (cost=29.80…186.32 rows=3872 width=40)
-> Hash Join (cost=29.80…186.32 rows=3872 width=40)
Hash Cond: (tbase_1.f1 = tbase_2.f1)
-> Remote Subquery Scan on all (dn001,dn002) (cost=100.00…158.40 rows=880 width=40)
Distribute results by S: f1
-> Seq Scan on tbase_1 (cost=0.00…18.80 rows=880 width=40)
-> Hash (cost=18.80…18.80 rows=880 width=4)
-> Seq Scan on tbase_2 (cost=0.00…18.80 rows=880 width=4)
(8 rows)
postgres=# explain select tbase_1.* from tbase_1,tbase_2 where tbase_1.f2=tbase_2.f1 ;
QUERY PLAN

Remote Fast Query Execution (cost=0.00…0.00 rows=0 width=0)
Node/s: dn001, dn002
-> Hash Join (cost=18904.69…46257.08 rows=500564 width=14)
Hash Cond: (tbase_1.f2 = tbase_2.f1)
-> Seq Scan on tbase_1 (cost=0.00…9225.64 rows=500564 width=14)
-> Hash (cost=9225.64…9225.64 rows=500564 width=4)
-> Seq Scan on tbase_2 (cost=0.00…9225.64 rows=500564 width=4)
(7 rows)
第一个查询需要数据重分布,而第二个不需要,分布键 join 查询性能会更高。

查看 join 发生的节点
postgres=# explain select tbase_1.* from tbase_1,tbase_2 where tbase_1.f1=tbase_2.f1 ;
QUERY PLAN

Hash Join (cost=29.80…186.32 rows=3872 width=40)
Hash Cond: (tbase_1.f1 = tbase_2.f1)
-> Remote Subquery Scan on all (dn001,dn002) (cost=100.00…158.40 rows=880 width=40)
-> Seq Scan on tbase_1 (cost=0.00…18.80 rows=880 width=40)
-> Hash (cost=126.72…126.72 rows=880 width=4)
-> Remote Subquery Scan on all (dn001,dn002) (cost=100.00…126.72 rows=880 width=4)
-> Seq Scan on tbase_2 (cost=0.00…18.80 rows=880 width=4)
(7 rows)
postgres=# set prefer_olap to on;
SET
postgres=# explain select tbase_1.* from tbase_1,tbase_2 where tbase_1.f1=tbase_2.f1 ;
QUERY PLAN

Remote Subquery Scan on all (dn001,dn002) (cost=29.80…186.32 rows=3872 width=40)
-> Hash Join (cost=29.80…186.32 rows=3872 width=40)
Hash Cond: (tbase_1.f1 = tbase_2.f1)
-> Remote Subquery Scan on all (dn001,dn002) (cost=100.00…158.40 rows=880 width=40)
Distribute results by S: f1
-> Seq Scan on tbase_1 (cost=0.00…18.80 rows=880 width=40)
-> Hash (cost=18.80…18.80 rows=880 width=4)
-> Seq Scan on tbase_2 (cost=0.00…18.80 rows=880 width=4)
(8 rows)
第一个 join 在 cn 节点执行,第二个在 dn 上重分布后再 join,业务设计上,一般 OLTP 类业务在 cn 上进行少数据量 join ,性能会更好。

查看并行的 worker 数
postgres=# explain select count(1) from tbase_1;
QUERY PLAN

Finalize Aggregate (cost=118.81…118.83 rows=1 width=8)
-> Remote Subquery Scan on all (dn001,dn002) (cost=118.80…118.81 rows=1 width=0)
-> Partial Aggregate (cost=18.80…18.81 rows=1 width=8)
-> Seq Scan on tbase_1 (cost=0.00…18.80 rows=880 width=0)
(4 rows)
postgres=# analyze tbase_1;
ANALYZE
postgres=# explain select count(1) from tbase_1;
QUERY PLAN

Parallel Finalize Aggregate (cost=14728.45…14728.46 rows=1 width=8)
-> Parallel Remote Subquery Scan on all (dn001,dn002) (cost=14728.33…14728.45 rows=1 width=0)
-> Gather (cost=14628.33…14628.44 rows=1 width=8)
Workers Planned: 2
-> Partial Aggregate (cost=13628.33…13628.34 rows=1 width=8)
-> Parallel Seq Scan on tbase_1 (cost=0.00…12586.67 rows=416667 width=0)
(6 rows)
上面第一个查询没走并行,第二个查询 analyze 后走并行才是正确的,建议大数据量更新再执行 analyze。

查看各节点的执行计划是否一致
./tbase_run_sql_dn_master.sh “explain select * from tbase_2 where f2=1”
dn006 — psql -h 172.16.0.13 -p 11227 -d postgres -U tbase -c “explain select * from tbase_2 where f2=1”
QUERY PLAN

Bitmap Heap Scan on tbase_2 (cost=2.18…7.70 rows=4 width=40)
Recheck Cond: (f2 = 1)
-> Bitmap Index Scan on tbase_2_f2_idx (cost=0.00…2.18 rows=4 width=0)
Index Cond: (f2 = 1)
(4 rows)
dn002 — psql -h 172.16.0.42 -p 11012 -d postgres -U tbase -c “explain select * from tbase_2 where f2=1”
QUERY PLAN

Index Scan using tbase_2_f2_idx on tbase_2 (cost=0.42…4.44 rows=1 width=14)
Index Cond: (f2 = 1)
(2 rows)
两个 dn 的执行计划不一致,最大可能是数据倾斜或者是执行计划被禁用。
如有可能,DBA 可以配置在系统空闲时执行全库 analyze 和 vacuum。

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