今天要说的这个优化是从前面一篇讲解《performance tuning case :use cursor or trigger replace group by and order by》
http://blog.163.com/digoal@126/blog/static/16387704020128142829610/
的延展.
CASE
例如一个表中有一个字段是性别, 这个表不管有多少条记录, 性别这个字段一般来说也就2个值
select count(distinct sex) from table; 得到的结果当然是2. 但是如果数据量很大的情况下, 这种运算就非常耗时, 下面来测试一下 :
PostgreSQL
测试表
digoal=> create table sex (sex char(1), otherinfo text);
CREATE TABLE 测试数据
digoal=> insert into sex select 'm', generate_series(1,10000000)||'this is test';
INSERT 0 10000000
digoal=> insert into sex select 'w', generate_series(1,10000000)||'this is test';
INSERT 0 10000000 测试SQL1
digoal=> \timing on
digoal=> select count(distinct sex) from sex;
count
-------
2
(1 row)
Time: 47254.221 ms 测试SQL2
digoal=> select sex from sex t group by sex;
sex
-----
w
m
(2 rows)
Time: 6534.433 ms 执行计划
digoal=> explain select count(distinct sex) from sex;
QUERY PLAN
---------------------------------------------------------------------
Aggregate (cost=377385.25..377385.26 rows=1 width=2)
-> Seq Scan on sex (cost=0.00..327386.00 rows=19999700 width=2)
digoal=> explain select sex from sex t group by sex;
QUERY PLAN
-----------------------------------------------------------------------
HashAggregate (cost=377385.25..377385.27 rows=2 width=2)
-> Seq Scan on sex t (cost=0.00..327386.00 rows=19999700 width=2) 创建索引
digoal=> create index idx_sex_1 on sex(sex);
CREATE INDEX
digoal=> set enable_seqscan=off;
SET 使用索引后的执行计划, PostgreSQL可以使用Index Only Scan.
digoal=> explain select count(distinct sex) from sex;
QUERY PLAN
--------------------------------------------------------------------------------------------
Aggregate (cost=532235.01..532235.02 rows=1 width=2)
-> Index Only Scan using idx_sex_1 on sex (cost=0.00..482234.97 rows=20000016 width=2)
digoal=> explain select sex from sex t group by sex;
QUERY PLAN
----------------------------------------------------------------------------------------------
Group (cost=0.00..532235.01 rows=2 width=2)
-> Index Only Scan using idx_sex_1 on sex t (cost=0.00..482234.97 rows=20000016 width=2) 创建索引后SQL耗时
digoal=> select count(distinct sex) from sex;
count
-------
2
(1 row)
Time: 49589.947 ms
digoal=> select sex from sex t group by sex;
sex
-----
m
w
(2 rows)
Time: 6608.053 ms O
测试表
SQL> create table sex(sex char(1), otherinfo varchar2(64));
Table created. 测试数据
SQL> insert into sex select 'm', rownum||'this is test' from dual connect by level <=10000001;
10000001 rows created.
SQL> commit;
Commit complete.
SQL> insert into sex select 'w', rownum||'this is test' from dual connect by level <=10000001;
10000001 rows created.
SQL> commit;
Commit complete. 测试SQL1:
SQL> set autotrace on
SQL> set timing on
SQL> select count(distinct sex) from sex;
COUNT(DISTINCTSEX)
------------------
2
Elapsed: 00:00:03.62
Execution Plan
----------------------------------------------------------
Plan hash value: 2096505595
---------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 3 | 13106 (3)| 00:02:38 |
| 1 | SORT GROUP BY | | 1 | 3 | | |
| 2 | TABLE ACCESS FULL| SEX | 24M| 69M| 13106 (3)| 00:02:38 |
---------------------------------------------------------------------------
Note
-----
- dynamic sampling used for this statement
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
74074 consistent gets
0 physical reads
0 redo size
525 bytes sent via SQL*Net to client
487 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
1 rows processed 测试SQL2
SQL> select sex from sex t group by sex;
S
-
w
m
Elapsed: 00:00:03.23
Execution Plan
----------------------------------------------------------
Plan hash value: 2807610159
---------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 24M| 69M| 14908 (14)| 00:02:59 |
| 1 | HASH GROUP BY | | 24M| 69M| 14908 (14)| 00:02:59 |
| 2 | TABLE ACCESS FULL| SEX | 24M| 69M| 13106 (3)| 00:02:38 |
---------------------------------------------------------------------------
Note
-----
- dynamic sampling used for this statement
Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
74074 consistent gets
0 physical reads
0 redo size
563 bytes sent via SQL*Net to client
487 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
2 rows processed 创建索引
SQL> create index idx_sex_1 on sex(sex);
Index created.
Elapsed: 00:00:33.40 创建索引后的测试, 执行时间没有明显变化.
SQL> select count(distinct sex) from sex;
COUNT(DISTINCTSEX)
------------------
2
Elapsed: 00:00:04.32
Execution Plan
----------------------------------------------------------
Plan hash value: 1805173869
-----------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 3 | 6465 (3)| 00:01:18 |
| 1 | SORT GROUP BY | | 1 | 3 | | |
| 2 | INDEX FAST FULL SCAN| IDX_SEX_1 | 24M| 69M| 6465 (3)| 00:01:18 |
-----------------------------------------------------------------------------------
Note
-----
- dynamic sampling used for this statement
Statistics
----------------------------------------------------------
5 recursive calls
0 db block gets
36421 consistent gets
36300 physical reads
0 redo size
525 bytes sent via SQL*Net to client
487 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
1 rows processed
SQL> select sex from sex t group by sex;
S
-
w
m
Elapsed: 00:00:03.21
Execution Plan
----------------------------------------------------------
Plan hash value: 2807610159
---------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 24M| 69M| 14908 (14)| 00:02:59 |
| 1 | HASH GROUP BY | | 24M| 69M| 14908 (14)| 00:02:59 |
| 2 | TABLE ACCESS FULL| SEX | 24M| 69M| 13106 (3)| 00:02:38 |
---------------------------------------------------------------------------
Note
-----
- dynamic sampling used for this statement
Statistics
----------------------------------------------------------
5 recursive calls
0 db block gets
74170 consistent gets
0 physical reads
0 redo size
563 bytes sent via SQL*Net to client
487 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
2 rows processed 对比以上测试, O的性能要明显优于PostgreSQL.
将count(distinct sex)修改如下后PostgreSQL的执行速度有明显改善, 但是性能还是低于O一截, 约一半.
digoal=> select count(*) from (select sex from sex t group by sex) t;
count
-------
2
(1 row)
Time: 6231.965 ms 开始优化咯
那么如何优化呢?
在PostgreSQL中的递归SQL在这里就派上大用场了, 结合btree索引扫描. 性能可以提升几万倍.
来看如下优化过程 :
创建测试表 :
create table user_download_log (user_id int not null, listid int not null, apkid int not null, get_time timestamp(0) not null, otherinfo text); 插入测试数据
insert into user_download_log select generate_series(0,10000000),generate_series(0,10000000),generate_series(0,10000000),generate_series(clock_timestamp(),clock_timestamp()+interval '10000000 min',interval '1 min'), 'this is test'; 创建索引 :
create index i1 on user_download_log (user_id);
create index i2 on user_download_log (otherinfo); 查看数据分布 :
用来说明递归SQL适合哪种场景的优化.
select count(distinct user_id), count(distinct otherinfo) from user_download_log;
count | count
----------+-------
10000001 | 1 查看未优化时以下SQL的执行计划以及耗时.
digoal=> explain analyze select count(distinct otherinfo) from user_download_log;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------
----
Aggregate (cost=208334.36..208334.37 rows=1 width=13) (actual time=6295.493..6295.494 rows=1 loops=1)
-> Seq Scan on user_download_log (cost=0.00..183334.29 rows=10000029 width=13) (actual time=0.014..1612.333 rows=10000001 loops
=1)
Total runtime: 6295.550 ms 优化后的SQL :
digoal=> with recursive skip as (
digoal(> (
digoal(> select min(t.otherinfo) as otherinfo from user_download_log t where t.otherinfo is not null
digoal(> )
digoal(> union all
digoal(> (
digoal(> select (select min(t.otherinfo) from user_download_log t where t.otherinfo > s.otherinfo and t.otherinfo is not null)
digoal(> from skip s where s.otherinfo is not null
digoal(> ) -- 这里的where s.otherinfo is not null 一定要加,否则就死循环了.
digoal(> )
digoal-> select count(distinct otherinfo) from skip;
count
-------
1
(1 row) 优化后的SQL执行计划以及耗时, 性能提升了36390倍, 相比O也提升了上万倍.
digoal=> explain analyze with recursive skip as (
(
select min(t.otherinfo) as otherinfo from user_download_log t where t.otherinfo is not null
)
union all
(
select (select min(t.otherinfo) from user_download_log t where t.otherinfo > s.otherinfo and t.otherinfo is not null)
from skip s where s.otherinfo is not null
) -- 这里的where s.otherinfo is not null 一定要加,否则就死循环了.
)
select count(distinct otherinfo) from skip;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------
-----------------------------------------
Aggregate (cost=10.55..10.56 rows=1 width=32) (actual time=0.094..0.094 rows=1 loops=1)
CTE skip
-> Recursive Union (cost=0.03..8.28 rows=101 width=32) (actual time=0.044..0.073 rows=2 loops=1)
-> Result (cost=0.03..0.04 rows=1 width=0) (actual time=0.042..0.042 rows=1 loops=1)
InitPlan 1 (returns $1)
-> Limit (cost=0.00..0.03 rows=1 width=13) (actual time=0.038..0.039 rows=1 loops=1)
-> Index Only Scan using i2 on user_download_log t (cost=0.00..296844.61 rows=10000029 width=13) (actual
time=0.037..0.037 rows=1 loops=1)
Index Cond: (otherinfo IS NOT NULL)
Heap Fetches: 1
-> WorkTable Scan on skip s (cost=0.00..0.62 rows=10 width=32) (actual time=0.013..0.013 rows=0 loops=2)
Filter: (otherinfo IS NOT NULL)
Rows Removed by Filter: 0
SubPlan 3
-> Result (cost=0.03..0.04 rows=1 width=0) (actual time=0.018..0.018 rows=1 loops=1)
InitPlan 2 (returns $3)
-> Limit (cost=0.00..0.03 rows=1 width=13) (actual time=0.017..0.017 rows=0 loops=1)
-> Index Only Scan using i2 on user_download_log t (cost=0.00..107284.96 rows=3333343 width=13) (
actual time=0.015..0.015 rows=0 loops=1)
Index Cond: ((otherinfo > s.otherinfo) AND (otherinfo IS NOT NULL))
Heap Fetches: 0
-> CTE Scan on skip (cost=0.00..2.02 rows=101 width=32) (actual time=0.047..0.077 rows=2 loops=1)
Total runtime: 0.173 ms
(21 rows) 换一个字段, 数据分布广泛的字段上使用以上优化方法, 看是否妥当, 以下是原始SQL的执行计划以及耗时 :
digoal=> explain analyze select count(distinct user_id) from user_download_log;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------
---
Aggregate (cost=208334.36..208334.37 rows=1 width=4) (actual time=4008.858..4008.858 rows=1 loops=1)
-> Seq Scan on user_download_log (cost=0.00..183334.29 rows=10000029 width=4) (actual time=0.014..1606.607 rows=10000001 loops=
1)
Total runtime: 4008.916 ms 换一个字段, 数据分布广泛的字段上使用以上优化方法, 看是否妥当, 以下是采用递归SQL后的执行计划以及耗时 :
显然性能是下降的, 所以使用递归SQL不适合数据分布广泛的字段的group by或者count(distinct)操作.
digoal=> explain analyze with recursive skip as (
(
select min(t.user_id) as user_id from user_download_log t where t.user_id is not null
)
union all
(
select (select min(t.user_id) from user_download_log t where t.user_id > s.user_id and t.user_id is not null)
from skip s where s.user_id is not null
) -- 这里的where s.user_id is not null 一定要加,否则就死循环了.
)
select count(distinct user_id) from skip;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------
-----------------------------------------------
Aggregate (cost=10.44..10.45 rows=1 width=4) (actual time=186741.338..186741.339 rows=1 loops=1)
CTE skip
-> Recursive Union (cost=0.03..8.17 rows=101 width=4) (actual time=0.047..178296.238 rows=10000002 loops=1)
-> Result (cost=0.03..0.04 rows=1 width=0) (actual time=0.046..0.046 rows=1 loops=1)
InitPlan 1 (returns $1)
-> Limit (cost=0.00..0.03 rows=1 width=4) (actual time=0.042..0.042 rows=1 loops=1)
-> Index Only Scan using i1 on user_download_log t (cost=0.00..285759.50 rows=10000029 width=4) (actual t
ime=0.040..0.040 rows=1 loops=1)
Index Cond: (user_id IS NOT NULL)
Heap Fetches: 1
-> WorkTable Scan on skip s (cost=0.00..0.61 rows=10 width=4) (actual time=0.017..0.017 rows=1 loops=10000002)
Filter: (user_id IS NOT NULL)
Rows Removed by Filter: 0
SubPlan 3
-> Result (cost=0.03..0.04 rows=1 width=0) (actual time=0.016..0.016 rows=1 loops=10000001)
InitPlan 2 (returns $3)
-> Limit (cost=0.00..0.03 rows=1 width=4) (actual time=0.015..0.015 rows=1 loops=10000001)
-> Index Only Scan using i1 on user_download_log t (cost=0.00..103588.85 rows=3333343 width=4) (a
ctual time=0.014..0.014 rows=1 loops=10000001)
Index Cond: ((user_id > s.user_id) AND (user_id IS NOT NULL))
Heap Fetches: 10000000
-> CTE Scan on skip (cost=0.00..2.02 rows=101 width=4) (actual time=0.050..183449.391 rows=10000002 loops=1)
Total runtime: 186909.323 ms
(21 rows)
Time: 186910.482 ms 以下是同样的数据结构以及测试数据在O下的测试.
SQL> create table test (id int, otherinfo varchar2(32)) nologging;
Table created.
SQL> insert into test select rownum,'this is test' from dual connect by level <=10000001;
10000001 rows created.
SQL> commit;
SQL> create index i1 on test(id);
SQL> create index i2 on test(otherinfo);
SQL> explain plan for select count(distinct id) from test;
Explained.
SQL> select * from table(dbms_xplan.display());
PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------------------------------------------------------------------
Plan hash value: 1403727100
------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 13 | 4178 (3)| 00:00:51 |
| 1 | SORT GROUP BY | | 1 | 13 | | |
| 2 | INDEX FAST FULL SCAN| I1 | 9834K| 121M| 4178 (3)| 00:00:51 |
------------------------------------------------------------------------------
Note
-----
- dynamic sampling used for this statement
13 rows selected.
SQL> explain plan for select count(distinct otherinfo) from test;
Explained.
SQL> select * from table(dbms_xplan.display());
PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------------------------------------------------------------------
Plan hash value: 2603667166
---------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 18 | 5837 (3)| 00:01:11 |
| 1 | SORT GROUP BY | | 1 | 18 | | |
| 2 | TABLE ACCESS FULL| TEST | 9834K| 168M| 5837 (3)| 00:01:11 |
---------------------------------------------------------------------------
Note
-----
- dynamic sampling used for this statement
13 rows selected.
SQL> set timing on
SQL> select count(distinct otherinfo) from test;
COUNT(DISTINCTOTHERINFO)
------------------------
1
Elapsed: 00:00:02.49
SQL> select count(distinct id) from test;
COUNT(DISTINCTID)
-----------------
10000001
Elapsed: 00:00:07.13 从执行耗时可以看出PostgreSQL在数据分布稀疏的字段上使用递归SQL优化后的性能相比O有41213倍的性能提升.
补充
递归查询中不允许使用聚合函数 :
with recursive skip as (
(
select min(t.otherinfo) as otherinfo from user_download_log t where t.otherinfo is not null
)
union all
(
select min(t.otherinfo) from user_download_log t, skip s
where t.otherinfo > s.otherinfo
and t.otherinfo is not null
and s.otherinfo is not null
) -- 这里的where s.otherinfo is not null 一定要加,否则就死循环了.
)
select * from skip;
ERROR: aggregate functions not allowed in a recursive query's recursive term
LINE 7: select min(t.otherinfo) from user_download_log t, skip s...
^
Time: 0.581 ms 修改如下即可 :
with recursive skip as (
(
select min(t.otherinfo) as otherinfo from user_download_log t where t.otherinfo is not null
)
union all
(
select (select min(t.otherinfo) from user_download_log t where t.otherinfo > s.otherinfo and t.otherinfo is not null)
from skip s where s.otherinfo is not null
) -- 这里的where s.otherinfo is not null 一定要加,否则就死循环了.
)
select * from skip; 细心的朋友发现O测试中未对表进行分析, 以下是分析后的结果, 执行计划无变化 :
SQL> analyze table sex estimate statistics for all columns sample 10 percent;
Table analyzed.
SQL> analyze index idx_sex_1 estimate statistics sample 10 percent;
Index analyzed.
SQL> select sex from sex t group by sex;
S
-
w
m
Elapsed: 00:00:03.17
Execution Plan
----------------------------------------------------------
Plan hash value: 2807610159
---------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
---------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 2 | 2 | 14519 (12)| 00:02:55 |
| 1 | HASH GROUP BY | | 2 | 2 | 14519 (12)| 00:02:55 |
| 2 | TABLE ACCESS FULL| SEX | 20M| 19M| 13062 (2)| 00:02:37 |
---------------------------------------------------------------------------
Statistics
----------------------------------------------------------
1 recursive calls
0 db block gets
74074 consistent gets
0 physical reads
0 redo size
563 bytes sent via SQL*Net to client
487 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
2 rows processed
SQL> select count(distinct sex) from sex;
COUNT(DISTINCTSEX)
------------------
2
Elapsed: 00:00:03.85
Execution Plan
----------------------------------------------------------
Plan hash value: 1805173869
-----------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
-----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 1 | 6454 (3)| 00:01:18 |
| 1 | SORT GROUP BY | | 1 | 1 | | |
| 2 | INDEX FAST FULL SCAN| IDX_SEX_1 | 20M| 19M| 6454 (3)| 00:01:18 |
-----------------------------------------------------------------------------------
Statistics
----------------------------------------------------------
1 recursive calls
0 db block gets
36325 consistent gets
0 physical reads
0 redo size
525 bytes sent via SQL*Net to client
487 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
1 rows processed O在这类应用场景中还有一个选择，使用位图索引。
摘录一段O位图索引的介绍
位图索引 Bitmap index
场合：列的基数很少，可枚举，重复值很多，数据不会被经常更新
原理：一个键值对应很多行（rowid）， 格式：键值 start_rowid end_rowid 位图
优点：OLAP 例如报表类数据库 重复率高的数据 特定类型的查询例如count、or、and等逻辑操作因为只需要进行位运算即可得到我们需要的结果
缺点：不适合重复率低的字段，还有经常DML操作（insert，update，delete），因为位图索引的锁代价极高，修改一个位图索引段影响整个位图段，例如修改
一个键值，会影响同键值的多行，所以对于OLTP 系统位图索引基本上是不适用的
因bitmap在OLTP使用场景较少，PostgreSQL 没有实现这个类型的索引。
http://www.postgresql.org/message-id/flat/27879.1098227105@sss.pgh.pa.us#27879.1098227105@sss.pgh.pa.us
https://en.wikipedia.org/wiki/Bitmap_index
http://grokbase.com/t/postgresql/pgsql-hackers/051xeh5b0a/implementing-bitmap-indexes
想了解更多PG索引的情况，请参考
http://leopard.in.ua/2015/04/13/postgresql-indexes