腾讯云TDSQL PostgreSQL版 -最佳实践 ｜优化 SQL 语句

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。