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PostgreSQL携手阿里云

distinct xx和count(distinct xx)的变态递归优化方法

原作者:digoal/德哥  创作时间:2016-12-02 17:18:30+08  
doudou586 发布于2016-12-02 17:18:30           评论: 0   浏览: 2951   顶: 464  踩: 433 

distinct xx和count(distinct xx)的变态递归优化方法 - 索引收敛(skip scan)扫描

作者: digoal

日期: 2016-11-28

标签: PostgreSQL , 递归去重 , 递归优化 , count(distinct ), 稀疏列 , 统计


背景

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, 需要排序,去重。那么有什么优化手段呢?

场景还原

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  

Oracle

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, Oracle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

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 time=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)
                                                                                     (actual 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Oracle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Oracle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  

Oracle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

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