标签：style   io   ar   os   sp   strong   数据   on   bs   

1. 测试数据

表结构 一：

CREATE TABLE fact_data

(

  day_id integer,

  popdb_id integer,

  region_id integer,

  area_id integer,

  city_id integer,

  product_id integer,

  f1 integer,

  f2 integer,

  f3 integer,

  f4 integer,

  f5 integer,

  f6 integer,

  f7 integer,

  f8 integer,

  user_id integer

)

表结构二

CREATE TABLE fact_data_hstore

(

  day_id integer,

  popdb_id integer,

  region_id integer,

  area_id integer,

  city_id integer,

  product_id integer,

  user_id integer,

  data hstore

)

2．测试报告under Windows

2 CPUs， 每个CPU 4 核，3GHZ

磁盘 EVA 4400 36 disks

Postgresql 9.3

2.1测试结果（一个字段 四百万条记录）

SQLServer：4060579 records

 select region_id, avg(f1*1.0) from fact_data  group by region_id

结果集合：返回9条记录，2个字段，耗时 500毫秒

Postgresql：  4060579 records

 select region_id, avg(f1) from fact_data  group by region_id

结果集合：返回9条记录，2个字段，耗时 1310毫秒

Postgresql：  4060579 records不加索引

 select region_id, avg((data->‘f1‘)::integer ) from fact_data_hstore

group by region_id

结果集合：返回9条记录，2个字段，耗时 3051毫秒

Postgresql：  4060579 records 增加索引

 select region_id, avg((data->‘f1‘)::integer ) from fact_data_hstore

group by region_id

结果集合：返回9条记录，2个字段，耗时 3000毫秒

索引对这个SQL语句性能没有作用。

2.2测试结果（八个字段 四百万条记录）

SQLServer：4060579 records

select region_id,avg(f1) as af1

from fact_data

group by region_id

结果集合：返回9条记录，2个字段，耗时 1316毫秒

Postgresql：  4060579 records

 select region_id,avg(f1) as af1, avg(f2) as af2, avg(f3) as af3, avg(f4) as af4, avg(f5) as af5, avg(f6) as af6, avg(f7) as af7, avg(f8) as af8

from fact_data

group by region_id

结果集合：返回9条记录，9个字段，耗时 2850毫秒

Postgresql：  4060579 records不加索引

select region_id, avg((data->‘f1‘)::integer) as af1, avg((data->‘f2‘)::integer) as af2, avg((data->‘f3‘)::integer) as af3, avg((data->‘f4‘)::integer) as af4,

avg((data->‘f5‘)::integer) as af5, avg((data->‘f6‘)::integer) as af6, avg((data->‘f7‘)::integer) as af7, avg((data->‘f8‘)::integer) as af8

from fact_data_hstore

group by region_id

结果集合：返回9条记录，9个字段，耗时 12690毫秒

2.3测试结果（八个字段 一百万条记录）

Postgresql：  1000000 records

 select region_id,avg(f1) as af1, avg(f2) as af2, avg(f3) as af3, avg(f4) as af4, avg(f5) as af5, avg(f6) as af6, avg(f7) as af7, avg(f8) as af8

from fact_data_1m

group by region_id

结果集合：返回9条记录，9个字段，耗时 720毫秒

Postgresql：  1000000 records不加索引

select region_id, avg((data->‘f1‘)::integer) as af1, avg((data->‘f2‘)::integer) as af2, avg((data->‘f3‘)::integer) as af3, avg((data->‘f4‘)::integer) as af4,

avg((data->‘f5‘)::integer) as af5, avg((data->‘f6‘)::integer) as af6, avg((data->‘f7‘)::integer) as af7, avg((data->‘f8‘)::integer) as af8

from fact_data_hstore_1m

group by region_id

结果集合：返回9条记录，9个字段，耗时 3109毫秒

3．测试报告under Linux virtual machine

虚拟机 2个虚核，普通硬盘

3.1测试结果（一个字段 四百万条记录）

Postgresql：  4060579 records

 select region_id, avg(f1) from fact_data  group by region_id

结果集合：返回9条记录，2个字段，耗时 860毫秒

Postgresql：  4060579 records不加索引

 select region_id, avg((data->‘f1‘)::integer ) from fact_data_hstore

group by region_id

结果集合：返回9条记录，2个字段，耗时 1630毫秒

3.2测试结果（八个字段 四百万条记录）

Postgresql：  4060579 records

 select region_id,avg(f1) as af1, avg(f2) as af2, avg(f3) as af3, avg(f4) as af4, avg(f5) as af5, avg(f6) as af6, avg(f7) as af7, avg(f8) as af8

from fact_data

group by region_id

结果集合：返回9条记录，9个字段，耗时 1708毫秒

Postgresql：  4060579 records不加索引

select region_id, avg((data->‘f1‘)::integer) as af1, avg((data->‘f2‘)::integer) as af2, avg((data->‘f3‘)::integer) as af3, avg((data->‘f4‘)::integer) as af4,

avg((data->‘f5‘)::integer) as af5, avg((data->‘f6‘)::integer) as af6, avg((data->‘f7‘)::integer) as af7, avg((data->‘f8‘)::integer) as af8

from fact_data_hstore

group by region_id

结果集合：返回9条记录，9个字段，耗时 6771毫秒

3.3测试结果（八个字段 一百万条记录）

Postgresql：  1000000 records

 select region_id,avg(f1) as af1, avg(f2) as af2, avg(f3) as af3, avg(f4) as af4, avg(f5) as af5, avg(f6) as af6, avg(f7) as af7, avg(f8) as af8

from fact_data_1m

group by region_id

结果集合：返回9条记录，9个字段，耗时 470毫秒

Postgresql：  1000000 records不加索引

select region_id, avg((data->‘f1‘)::integer) as af1, avg((data->‘f2‘)::integer) as af2, avg((data->‘f3‘)::integer) as af3, avg((data->‘f4‘)::integer) as af4,

avg((data->‘f5‘)::integer) as af5, avg((data->‘f6‘)::integer) as af6, avg((data->‘f7‘)::integer) as af7, avg((data->‘f8‘)::integer) as af8

from fact_data_hstore_1m

group by region_id

结果集合：返回9条记录，9个字段，耗时 1700毫秒

4．LU表分拆测试

添加LU_POPDB

CREATE TABLE lu_pop

(

  popdb_id integer,

  region_id integer,

  area_id integer,

  city_id integer

)

WITH (

  OIDS=FALSE

);

ALTER TABLE lu_pop

  OWNER TO postgres;

  insert into lu_pop

  select distinct popdb_id,region_id,area_id,city_id from  fact_data

Postgresql：

select p.region_id, avg(f1) as af1, avg(f2) as af2, avg(f3) as af3, avg(f4) as af4, avg(f5) as af5, avg(f6) as af6, avg(f7) as af7, avg(f8) as af8 from

 fact_data  f

 inner join lu_pop p on f.popdb_id = p.popdb_id

 group by p.region_id

结果集合：

四百万 返回9条记录，9个字段，耗时 3201毫秒

一百万 返回9条记录，9个字段，耗时 820毫秒

Postgresql： 

select p.region_id, avg((data->‘f1‘)::integer) as af1, avg((data->‘f2‘)::integer) as af2, avg((data->‘f3‘)::integer) as af3, avg((data->‘f4‘)::integer) as af4,

avg((data->‘f5‘)::integer) as af5, avg((data->‘f6‘)::integer) as af6, avg((data->‘f7‘)::integer) as af7, avg((data->‘f8‘)::integer) as af8

from fact_data_hstore f

 inner join lu_pop p on f.popdb_id = p.popdb_id

group by p.region_id

结果集合：

四百万返回9条记录，9个字段，耗时 9101毫秒

一百万返回9条记录，9个字段，耗时 2310毫秒

添加LU_POPDB_hstore

CREATE TABLE lu_pop_hstore

(

  popdb_id integer,

  hierarchies hstore

)

WITH (

  OIDS=FALSE

);

ALTER TABLE lu_pop_hstore

  OWNER TO postgres;

  insert into lu_pop_hstore

  select popdb_id,hstore(‘region_id=>‘||region_id||‘,‘||‘area_id=>‘||area_id||‘,‘||‘city_id=>‘||city_id) from  lu_pop;

select p.hierarchies->‘region_id‘, avg(f1) as af1, avg(f2) as af2, avg(f3) as af3, avg(f4) as af4, avg(f5) as af5, avg(f6) as af6, avg(f7) as af7, avg(f8) as af8 from

 fact_data  f

 inner join lu_pop_hstore p on f.popdb_id = p.popdb_id

 group by p.hierarchies->‘region_id‘

结果集合：

四百万数据 返回9条记录，9个字段，耗时 3760毫秒

一百万数据 返回9条记录，9个字段，耗时 990毫秒

 select p.hierarchies->‘region_id‘, avg((data->‘f1‘)::integer) as af1, avg((data->‘f2‘)::integer) as af2, avg((data->‘f3‘)::integer) as af3, avg((data->‘f4‘)::integer) as af4,

avg((data->‘f5‘)::integer) as af5, avg((data->‘f6‘)::integer) as af6, avg((data->‘f7‘)::integer) as af7, avg((data->‘f8‘)::integer) as af8

from fact_data_hstore f

inner join lu_pop_hstore p on f.popdb_id = p.popdb_id

 group by p.hierarchies->‘region_id‘

结果集合：

四百万数据 返回9条记录，9个字段，耗时 9852毫秒

一百万数据 返回9条记录，9个字段，耗时 2500毫秒

5．测试结论

分列存放Fact数据 VS Hstore存放事实数据

分列存放的fact数据比Hstore存放的事实数据性能高150%到300%倍左右。因而用Hstore存放事实数据性能较差。

独立LU表VS把LU全部放入事实表

拆分LU表与把LU全部放入事实表相比，全部放入事实表比拆分到LU表中性能高30%到80%倍左右，把LU拆分出来的性能较差。

独立LU表VS 独立LU Hstore表

按照分列存放LU的数据比用Hstore格式存放LU数据，性能高8% 到20% 左右。因而用HStore存放LU数据带来的性能降低还是可以接受的。

数据量四百万 VS 一百万

响应时间和数据量几乎成正比，因而一定要进行表分区，让数据量控制在可以接受的范围之内。

Windows VS Linux 虚拟机

即使Windows的硬件比较强大，但是性能还是比Linux虚拟机差 50% 到80%。

Postgresql 报表性能测试

标签：style   io   ar   os   sp   strong   数据   on   bs   

原文地址：http://www.cnblogs.com/shadowzhang/p/Postgresql_performance.html