PostgreSQL 多查询条件，多个索引的选择算法与问题诊断方法

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背景

当一个SQL中涉及多个条件，并且多个条件有多种索引可选时，数据库优化器是如何选择使用哪个索引的？

例如

有一张表，有2个字段，单列一个索引，双列一个复合索引.

建表。  
postgres=# create table tbl(id int, gid int);  
CREATE TABLE  
  
插入1000万记录，其中ID唯一，GID只有10个值。  
postgres=# insert into tbl select generate_series(1,10000000), random()*9 ;  
INSERT 0 10000000  
  
创建两个索引。  
postgres=# create index idx1 on tbl(id);  
CREATE INDEX  
postgres=# create index idx2 on tbl(gid,id);  
CREATE INDEX  

下面三条SQL，会如何选择使用哪个索引呢？

select * from tbl where id in (1,2,3,4,5,6,7,8,9,10) and gid=123;  
  
select * from tbl where id in (1,2,3,4,5,6,7,8,9,10) and gid=1;  
  
select * from tbl where id in (1,2,3,4,5,6,7,8,9,10);  

问题思考

人为选择

这三条QUERY，实际上有三重含义：

1、gid=123的行根本不存在。

如果让你来选索引，你肯定会选复合索引，马上就能定位到数据不存在扫描最少的BLOCK。

select * from tbl where id in (1,2,3,4,5,6,7,8,9,10) and gid=123;

2、gid=1存在，同时id里面的条件也存在。

如果让你来选索引，应该也是选择复合索引，因为精确定位到了所有的行。

当然如果id in里面很多记录不存在，那么你可能就会选择id单列索引，因为这个索引本身更小，可能扫描更少的BLOCK。

select * from tbl where id in (1,2,3,4,5,6,7,8,9,10) and gid=1;

3、只有id的条件。

此时，肯定选单列索引了。

select * from tbl where id in (1,2,3,4,5,6,7,8,9,10);

实际情况如何呢？

1、数据库执行计划与预期一致

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl where id in (1,2,3,4,5,6,7,8,9,10) and gid=123;  
                                                      QUERY PLAN                                                        
----------------------------------------------------------------------------------------------------------------------  
 Index Only Scan using idx2 on public.tbl  (cost=0.43..2.46 rows=1 width=8) (actual time=0.037..0.037 rows=0 loops=1)  
   Output: id, gid  
   Index Cond: (tbl.gid = 123)  
   Filter: (tbl.id = ANY ('{1,2,3,4,5,6,7,8,9,10}'::integer[]))  
   Heap Fetches: 0  
   Buffers: shared hit=3  
 Planning time: 0.829 ms  
 Execution time: 0.086 ms  
(8 rows)  

2、与预期一致

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl where id in (1,2,3,4,5,6,7,8,9,10) and gid=1;  
                                                      QUERY PLAN                                                         
-----------------------------------------------------------------------------------------------------------------------  
 Index Only Scan using idx2 on public.tbl  (cost=0.43..15.46 rows=1 width=8) (actual time=0.026..0.037 rows=2 loops=1)  
   Output: id, gid  
   Index Cond: ((tbl.gid = 1) AND (tbl.id = ANY ('{1,2,3,4,5,6,7,8,9,10}'::integer[])))  
   Heap Fetches: 2  
   Buffers: shared hit=31  
 Planning time: 0.121 ms  
 Execution time: 0.058 ms  
(7 rows)  

3、与预期一致

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl where id in (1,2,3,4,5,6,7,8,9,10);  
                                                     QUERY PLAN                                                       
--------------------------------------------------------------------------------------------------------------------  
 Index Scan using idx1 on public.tbl  (cost=0.43..15.52 rows=10 width=8) (actual time=0.021..0.035 rows=10 loops=1)  
   Output: id, gid  
   Index Cond: (tbl.id = ANY ('{1,2,3,4,5,6,7,8,9,10}'::integer[]))  
   Buffers: shared hit=31  
 Planning time: 0.104 ms  
 Execution time: 0.055 ms  
(6 rows)  

问题升华

数据库生成执行计划靠的是统计信息，如果统计信息不准确，那么执行计划必然不准确。

例如我们人为关闭TBL的自动统计信息收集，然后写入一批新的数据。

postgres=# alter table tbl set (autovacuum_enabled =off);  
ALTER TABLE  
postgres=# insert into tbl select generate_series(1,10000000), 100;  
INSERT 0 10000000  

这个数据的特点是GID=100，在原有的统计信息中,gid=100的行是不存在的，所以下面的SQL优化器显然做出了错误的决定。

postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl where id in (1,2,3,4,5,6,7,8,9,10) and gid=100;  
                                                        QUERY PLAN                                                          
--------------------------------------------------------------------------------------------------------------------------  
 Index Only Scan using idx2 on public.tbl  (cost=0.44..2.46 rows=1 width=8) (actual time=0.030..2051.851 rows=10 loops=1)  
   Output: id, gid  
   Index Cond: (tbl.gid = 100)  
   Filter: (tbl.id = ANY ('{1,2,3,4,5,6,7,8,9,10}'::integer[]))  
   Rows Removed by Filter: 9999990  
   Heap Fetches: 10000000  
   Buffers: shared hit=71574  
 Planning time: 0.130 ms  
 Execution time: 2051.900 ms  
(9 rows)  

更新统计信息后，执行计划就准确了。

postgres=# analyze tbl;  
ANALYZE  
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from tbl where id in (1,2,3,4,5,6,7,8,9,10) and gid=100;  
                                                       QUERY PLAN                                                          
-------------------------------------------------------------------------------------------------------------------------  
 Index Only Scan using idx2 on public.tbl  (cost=0.44..20.57 rows=10 width=8) (actual time=0.027..0.043 rows=10 loops=1)  
   Output: id, gid  
   Index Cond: ((tbl.gid = 100) AND (tbl.id = ANY ('{1,2,3,4,5,6,7,8,9,10}'::integer[])))  
   Heap Fetches: 10  
   Buffers: shared hit=31  
 Planning time: 0.212 ms  
 Execution time: 0.067 ms  
(7 rows)  

如何自动收集统计信息

开启autovacuum , track_counts即可。

有几个微调参数，决定了什么时候扫描是否需要收集统计信息，以及当前表的变化量。

track_counts = on  
  
#------------------------------------------------------------------------------  
# AUTOVACUUM PARAMETERS  
#------------------------------------------------------------------------------  
  
autovacuum = on                 # Enable autovacuum subprocess?  'on'  
                                        # requires track_counts to also be on.  
#log_autovacuum_min_duration = -1       # -1 disables, 0 logs all actions and  
                                        # their durations, > 0 logs only  
                                        # actions running at least this number  
                                        # of milliseconds.  
#autovacuum_max_workers = 3             # max number of autovacuum subprocesses  
                                        # (change requires restart)  
autovacuum_naptime = 3s         # time between autovacuum runs  
#autovacuum_vacuum_threshold = 50       # min number of row updates before  
                                        # vacuum  
#autovacuum_analyze_threshold = 50      # min number of row updates before  
                                        # analyze  
#autovacuum_vacuum_scale_factor = 0.2   # fraction of table size before vacuum  
#autovacuum_analyze_scale_factor = 0.1  # fraction of table size before analyze  
#autovacuum_freeze_max_age = 200000000  # maximum XID age before forced vacuum  
                                        # (change requires restart)  
#autovacuum_multixact_freeze_max_age = 400000000        # maximum multixact age  
                                        # before forced vacuum  
                                        # (change requires restart)  
autovacuum_vacuum_cost_delay = 0ms      # default vacuum cost delay for  
                                        # autovacuum, in milliseconds;  
                                        # -1 means use vacuum_cost_delay  
#autovacuum_vacuum_cost_limit = -1      # default vacuum cost limit for  
                                        # autovacuum, -1 means use  
                                        # vacuum_cost_limit  