腾讯云TDSQL PostgreSQL版 -最佳实践｜优化 SQL 语句数据库

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

【腾讯云TDSQL PostgreSQL版 -最佳实践｜优化 SQL 语句】--------------------------------------------------------------------------------
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

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

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。