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 INDEXpostgres=# 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;SETpostgres=# 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;ANALYZEpostgres=# 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。