WHY prepared Statement running slower in some situation

在某些情况下，使用PREPARED STATEMENT或函数，可能会比直接执行SQL更慢。为什么呢？
这个要从执行计划说起，
下面来看一个测试表
digoal=> /d tbl_user
            Table "digoal.tbl_user"
  Column   |         Type          | Modifiers
-----------+-----------------------+-----------
 id        | integer               | not null
 firstname | character varying(32) |
 lastname  | character varying(32) |
 corp      | character varying(32) |
 age       | integer               |
Indexes:
    "tbl_user_pkey" PRIMARY KEY, btree (id)
    "idx_user_age" btree (age)
digoal=> insert into tbl_user select generate_series(1,100000),'zhou','digoal','sky-mobi',27 ;
INSERT 0 100000
digoal=> insert into tbl_user select generate_series(100001,100100),'zhou','digoal','sky-mobi',generate_series(1,100) ;
INSERT 0 100
digoal=> analyze tbl_user;
ANALYZE
digoal=> select age,count(*) from tbl_user group by age order by count(*);
 age | count 
-----+--------
  68 |      1
   8 |      1
  11 |      1
  80 |      1
  16 |      1
  39 |      1
  54 |      1
   3 |      1
  47 |      1
  61 |      1
  96 |      1
  67 |      1
  87 |      1
  14 |      1
  46 |      1
  99 |      1
  48 |      1
  17 |      1
  28 |      1
  83 |      1
  36 |      1
  94 |      1
  15 |      1
  84 |      1
  88 |      1
  66 |      1
  77 |      1
  38 |      1
   4 |      1
  30 |      1
  89 |      1
  60 |      1
  50 |      1
  74 |      1
  33 |      1
  73 |      1
  95 |      1
   6 |      1
  40 |      1
  56 |      1
  53 |      1
  62 |      1
  71 |      1
  19 |      1
  29 |      1
  93 |      1
   2 |      1
  21 |      1
  57 |      1
  51 |      1
  72 |      1
  92 |      1
  97 |      1
  23 |      1
  41 |      1
  31 |      1
  35 |      1
  65 |      1
  75 |      1
  52 |      1
  76 |      1
  20 |      1
  69 |      1
   5 |      1
  44 |      1
   7 |      1
  37 |      1
  85 |      1
  34 |      1
  82 |      1
  81 |      1
  25 |      1
  32 |      1
  12 |      1
  58 |      1
   1 |      1
  10 |      1
  79 |      1
  26 |      1
  42 |      1
  90 |      1
  18 |      1
  59 |      1
  78 |      1
  98 |      1
 100 |      1
  86 |      1
  13 |      1
  49 |      1
  22 |      1
  63 |      1
   9 |      1
  24 |      1
  91 |      1
  64 |      1
  70 |      1
  45 |      1
  43 |      1
  55 |      1
  27 | 100001
(100 rows)

# 如果按照AGE为条件查询，可能全表扫描（如age=27），或走索引(如age=1)。
digoal=> explain analyze select * from tbl_user where age=1;
                                                       QUERY PLAN                                                      
------------------------------------------------------------------------------------------------------------------------
 Index Scan using idx_user_age on tbl_user  (cost=0.00..4.32 rows=3 width=29) (actual time=0.018..0.019 rows=1 loops=1)
   Index Cond: (age = 1)
 Total runtime: 0.042 ms
(3 rows)

Time: 0.516 ms
digoal=> explain analyze select * from tbl_user where age=27;
                                                   QUERY PLAN                                                   
-----------------------------------------------------------------------------------------------------------------
 Seq Scan on tbl_user  (cost=0.00..1989.25 rows=100000 width=29) (actual time=0.010..21.112 rows=100001 loops=1)
   Filter: (age = 27)
 Total runtime: 27.784 ms
(3 rows)

Time: 28.114 ms

# 使用prepared statement看看情况如何:
digoal=> prepare p_user (int) as select * from tbl_user where age=$1;
PREPARE
Time: 12.408 ms
digoal=> explain analyze execute p_user(1);
                                                         QUERY PLAN                                                         
-----------------------------------------------------------------------------------------------------------------------------
 Index Scan using idx_user_age on tbl_user  (cost=0.00..105.55 rows=3229 width=29) (actual time=0.012..0.012 rows=1 loops=1)
   Index Cond: (age = $1)
 Total runtime: 0.038 ms
(3 rows)

Time: 0.191 ms
digoal=> explain analyze execute p_user(27);
                                                            QUERY PLAN                                                            
-----------------------------------------------------------------------------------------------------------------------------------
 Index Scan using idx_user_age on tbl_user  (cost=0.00..105.55 rows=3229 width=29) (actual time=0.016..23.100 rows=100001 loops=1)
   Index Cond: (age = $1)
 Total runtime: 30.069 ms
(3 rows)

Time: 30.403 ms
10W左右数据量的情况下用索引比全表扫描相差3ms左右
插入1000W左右数据再看看情况,
digoal=> insert into tbl_user select generate_series(100101,9999999),'zhou','digoal','sky-mobi',27 ;
INSERT 0 9899899
digoal=> explain analyze execute p_user(1);
                                                            QUERY PLAN                                                            
-----------------------------------------------------------------------------------------------------------------------------------
 Index Scan using idx_user_age on tbl_user  (cost=0.00..159018.74 rows=5000094 width=29) (actual time=0.015..0.015 rows=1 loops=1)
   Index Cond: (age = $1)
 Total runtime: 0.036 ms
(3 rows)

Time: 0.535 ms
digoal=> explain analyze execute p_user(27);
                                                                 QUERY PLAN                                                        
       
------------------------------------------------------------------------------------------------------------------------------------
--------
 Index Scan using idx_user_age on tbl_user  (cost=0.00..159018.74 rows=5000094 width=29) (actual time=0.014..2352.636 rows=9999900 l
oops=1)
   Index Cond: (age = $1)
 Total runtime: 3042.357 ms
(3 rows)

Time: 3042.689 ms

digoal=> explain analyze select * from tbl_user where age=27;
                                                      QUERY PLAN                                                      
-----------------------------------------------------------------------------------------------------------------------
 Seq Scan on tbl_user  (cost=0.00..198533.34 rows=9999854 width=29) (actual time=0.011..2062.048 rows=9999900 loops=1)
   Filter: (age = 27)
 Total runtime: 2737.911 ms
(3 rows)

Time: 2738.430 ms
digoal=> explain analyze select * from tbl_user where age=1;
                                                        QUERY PLAN                                                        
---------------------------------------------------------------------------------------------------------------------------
 Index Scan using idx_user_age on tbl_user  (cost=0.00..15.03 rows=333 width=29) (actual time=0.012..0.013 rows=1 loops=1)
   Index Cond: (age = 1)
 Total runtime: 0.031 ms
(3 rows)

Time: 0.403 ms

相差约300mS

并不是说这样就不建议使用prepared statement了，prepared statement的使用对于降低CPU开销和服务端代码重用来说是非常有效的。