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震精 - PostgreSQL decimal64 decimal128 高效率数值 类型扩展

简介:

标签

PostgreSQL , decimal64 , decimal128 , float4 , float8 , numeric


背景

PostgreSQL内置的数值类型包括

整型、浮点、整型序列、"无限"精度数值

Name Storage Size Description Range
smallint 2 bytes small-range integer -32768 to +32767
integer 4 bytes typical choice for integer -2147483648 to +2147483647
bigint 8 bytes large-range integer -9223372036854775808 to +9223372036854775807
decimal variable user-specified precision, exact up to 131072 digits before the decimal point; up to 16383 digits after the decimal point
numeric variable user-specified precision, exact up to 131072 digits before the decimal point; up to 16383 digits after the decimal point
real 4 bytes variable-precision, inexact 6 decimal digits precision
double precision 8 bytes variable-precision, inexact 15 decimal digits precision
smallserial 2 bytes small autoincrementing integer 1 to 32767
serial 4 bytes autoincrementing integer 1 to 2147483647
bigserial 8 bytes large autoincrementing integer 1 to 9223372036854775807

其中除了 "无限"精度数值类型。他类型都是定长存储,使用时不需要调用palloc,效率较高。

如果你要使用超过双精能表示的有效范围的数值,目前只能选择decimal\numeric类型,而这个类型前面说了,由于是变长设计,需要调用palloc,效率一般。

那么在数据分析领域,或者需要处理非常多的数据记录时,numeric类型的开销是较大的。

PostgreSQL社区有一些扩展,可以解决这个问题,

1. 比如2nd的fixeddecimal插件,使用INT8来表示NUMERIC,精度可调。

《PostgreSQL fixeddecimal - 用CPU "硬解码" 提升1倍 数值运算能力 助力金融大数据量计算》

2. 比如社区的pgdecimal插件,支持decimal32和decimal64两种类型。

https://pgxn.org/dist/pgdecimal/1.0.0/

3. 比如vitesse的pgdecimal插件,也就是本文将提到的插件,支持decimal64与decimal128类型,精度基本上足够使用。

推荐使用vitesse提供的pgdecimal插件,因为它效率够高,精度够大。

pgdecimal插件介绍

有两个常见的decimal库,decNumber以及Intel提供的Intel ADX库。

pgdecimal插件选择了decNumber库,因为GCC也在用它(法律风险更小?)

https://github.com/gcc-mirror/gcc/tree/master/libdecnumber

decimal库的性能对比

http://speleotrove.com/decimal/dpintro.html

decNumber与Inter ADX性能接近,但是Inter ADX提供了decimal64/128, int32/64, float/double类型的相互转换,这个很给力。(也许将来vitesse会支持intel adx库吧)

pgdecimal 依赖的decNumber,因此我们必须先安装decNumber

decNumber安装

1. 下载 decNumber package

http://speleotrove.com/decimal/

wget http://speleotrove.com/decimal/decNumber-icu-368.zip  
unzip decNumber-icu-368.zip  

或者从本站链接下载

2. 安装decNumber到postgresql软件目录中(假设postgresql安装在/home/digoal/pgsql9.6)

首先要在postgresql软件的include目录中,创建一个空目录,

mkdir -p /home/digoal/pgsql9.6/include/decnumber  

在decNumber src目录中创建Makefile,install -D 修改为对应要安装的目录。

cd decNumber  
  
vi Makefile  
  
OBJS = decSingle.o decDouble.o decQuad.o decNumber.o decContext.o  
  
CFLAGS = -Wall -g -O2 -fPIC  
  
libdecnumber.a: $(OBJS)  
        ar -rcs libdecnumber.a $(OBJS)  
  
clean:  
        rm -f libdecnumber.a $(OBJS)  
  
install:  
        install -D *.h /home/digoal/pgsql9.6/include/decnumber  
        install -D libdecnumber.a /home/digoal/pgsql9.6/lib  

3. 编译安装decNumber

cd decNumber  
  
make   
make install  

4. decNumber的C库reference如下, pgdecimal插件中用到的decnumber库,需要了解细节的话请参考:

The decNumber C library

pgdecimal安装

git clone https://github.com/vitesse-ftian/pgdecimal  

或者从本站链接下载

cd pgdecimal  

有一个小BUG,.control的版本号没有与sql文件的版本号对齐

mv decimal--2.0.sql decimal--1.0.sql  

另外,需要修改一下Makefile,指定版本,以及decnumber的include和lib目录

vi Makefile  
  
PG_CPPFLAGS = -I/home/digoal/pgsql9.6/include/decnumber  
SHLIB_LINK = -L/home/digoal/pgsql9.6/lib -ldecnumber  
  
DATA = decimal--1.0.sql  

安装

export PATH=/home/digoal/pgsql9.6/bin:$PATH  
USE_PGXS=1 make clean  
USE_PGXS=1 make   
USE_PGXS=1 make install  
  
  
/bin/mkdir -p '/home/digoal/pgsql9.6/lib'  
/bin/mkdir -p '/home/digoal/pgsql9.6/share/extension'  
/bin/mkdir -p '/home/digoal/pgsql9.6/share/extension'  
/usr/bin/install -c -m 755  decimal.so '/home/digoal/pgsql9.6/lib/decimal.so'  
/usr/bin/install -c -m 644 .//decimal.control '/home/digoal/pgsql9.6/share/extension/'  
/usr/bin/install -c -m 644 .//decimal--1.0.sql  '/home/digoal/pgsql9.6/share/extension/'  

使用

psql  
  
postgres=# create extension decimal;  
CREATE EXTENSION  

pgdecimal性能对比

使用int8, float8, decimal64, decimal128, numeric(15,3) 几种类型,分别比较这几种类型的性能。

create table tt(ii bigint, d double precision, d64 decimal64, d128 decimal128, n numeric(15, 3));  
  
postgres=# \timing  
Timing is on.  
  
生成测试数据  
  
postgres=# insert into tt select i, i + 0.123, i + 0.123::decimal64, i + 0.123::decimal128, i + 0.123 from generate_series(1, 1000000) i;  
INSERT 0 1000000  
Time: 2125.723 ms  
  
postgres=# select * from tt limit 2;  
 ii |   d   |  d64  | d128  |   n     
----+-------+-------+-------+-------  
  1 | 1.123 | 1.123 | 1.123 | 1.123  
  2 | 2.123 | 2.123 | 2.123 | 2.123  
(2 rows)  

普通查询性能对比

postgres=# select count(*) from tt where (d + d*d + d*d*d + d*d*d*d) > 10000000;  
 count    
--------  
 999945  
(1 row)  
  
Time: 411.418 ms  
postgres=# select count(*) from tt where (n + n*n + n*n*n + n*n*n*n) > 10000000;  
 count    
--------  
 999945  
(1 row)  
  
Time: 1949.367 ms  
postgres=# select count(*) from tt where (d64 + d64*d64 + d64*d64*d64 + d64*d64*d64*d64) > 10000000;  
 count    
--------  
 999945  
(1 row)  
  
Time: 1165.304 ms  
postgres=# select count(*) from tt where (d128 + d128*d128 + d128*d128*d128 + d128*d128*d128*d128) > 10000000;  
 count    
--------  
 999945  
(1 row)  
  
Time: 1517.179 ms  

排序性能对比

postgres=# select * from tt order by d limit 2 offset 999000;  
   ii   |     d      |    d64     |    d128    |     n        
--------+------------+------------+------------+------------  
 999001 | 999001.123 | 999001.123 | 999001.123 | 999001.123  
 999002 | 999002.123 | 999002.123 | 999002.123 | 999002.123  
(2 rows)  
  
Time: 804.645 ms  
postgres=# select * from tt order by n limit 2 offset 999000;  
   ii   |     d      |    d64     |    d128    |     n        
--------+------------+------------+------------+------------  
 999001 | 999001.123 | 999001.123 | 999001.123 | 999001.123  
 999002 | 999002.123 | 999002.123 | 999002.123 | 999002.123  
(2 rows)  
  
Time: 2828.066 ms  
postgres=# select * from tt order by d64 limit 2 offset 999000;  
   ii   |     d      |    d64     |    d128    |     n        
--------+------------+------------+------------+------------  
 999001 | 999001.123 | 999001.123 | 999001.123 | 999001.123  
 999002 | 999002.123 | 999002.123 | 999002.123 | 999002.123  
(2 rows)  
  
Time: 1826.044 ms  
postgres=# select * from tt order by d128 limit 2 offset 999000;  
   ii   |     d      |    d64     |    d128    |     n        
--------+------------+------------+------------+------------  
 999001 | 999001.123 | 999001.123 | 999001.123 | 999001.123  
 999002 | 999002.123 | 999002.123 | 999002.123 | 999002.123  
(2 rows)  
  
Time: 2118.647 ms  

哈希JOIN性能对比

postgres=# explain select count(*) from tt t1 join tt t2 on t1.d64 * t1.d64 + t1.d64 = t2.d64 + t2.d64 * t2.d64;  
                                    QUERY PLAN                                      
----------------------------------------------------------------------------------  
 Aggregate  (cost=6875071228.00..6875071228.01 rows=1 width=8)  
   ->  Hash Join  (cost=36707.00..5625071228.00 rows=500000000000 width=0)  
         Hash Cond: (((t1.d64 * t1.d64) + t1.d64) = (t2.d64 + (t2.d64 * t2.d64)))  
         ->  Seq Scan on tt t1  (cost=0.00..20300.00 rows=1000000 width=8)  
         ->  Hash  (cost=20300.00..20300.00 rows=1000000 width=8)  
               ->  Seq Scan on tt t2  (cost=0.00..20300.00 rows=1000000 width=8)  
(6 rows)  
  
Time: 0.508 ms  
postgres=# select count(*) from tt t1 join tt t2 on t1.d64 * t1.d64 + t1.d64 = t2.d64 + t2.d64 * t2.d64;  
  count    
---------  
 1000000  
(1 row)  
  
Time: 1681.451 ms  
postgres=# select count(*) from tt t1 join tt t2 on t1.n * t1.n + t1.n = t2.n + t2.n * t2.n;  
  count    
---------  
 1000000  
(1 row)  
  
Time: 2395.894 ms  

嵌套循环性能对比

postgres=# explain select count(*) from tt t1, tt t2 where t1.ii < 10000 and t2.ii < 10000 and t1.d * t1.d + t1.d > t2.d + t2.d * t2.d;  
                                  QUERY PLAN                                     
-------------------------------------------------------------------------------  
 Aggregate  (cost=2699703.15..2699703.16 rows=1 width=8)  
   ->  Nested Loop  (cost=0.00..2614087.74 rows=34246165 width=0)  
         Join Filter: (((t1.d * t1.d) + t1.d) > (t2.d + (t2.d * t2.d)))  
         ->  Seq Scan on tt t1  (cost=0.00..22800.00 rows=10136 width=8)  
               Filter: (ii < 10000)  
         ->  Materialize  (cost=0.00..22850.68 rows=10136 width=8)  
               ->  Seq Scan on tt t2  (cost=0.00..22800.00 rows=10136 width=8)  
                     Filter: (ii < 10000)  
(8 rows)  
  
Time: 0.561 ms  
postgres=# select count(*) from tt t1, tt t2 where t1.ii < 10000 and t2.ii < 10000 and t1.d * t1.d + t1.d > t2.d + t2.d * t2.d;  
  count     
----------  
 49985001  
(1 row)  
  
Time: 19706.890 ms  
postgres=# select count(*) from tt t1, tt t2 where t1.ii < 10000 and t2.ii < 10000 and t1.n * t1.n + t1.n > t2.n + t2.n * t2.n;  
  count     
----------  
 49985001  
(1 row)  
  
Time: 70787.289 ms  
postgres=# select count(*) from tt t1, tt t2 where t1.ii < 10000 and t2.ii < 10000 and t1.d64 * t1.d64 + t1.d64 > t2.d64 + t2.d64 * t2.d64;  
  count     
----------  
 49985001  
(1 row)  
  
Time: 49861.689 ms  
postgres=# select count(*) from tt t1, tt t2 where t1.ii < 10000 and t2.ii < 10000 and t1.d128 * t1.d128 + t1.d128 > t2.d128 + t2.d128 * t2.d128;  
  count     
----------  
 49985001  
(1 row)  
  
Time: 65779.153 ms  

小结

PostgreSQL内置的numeric类型属于"无限"精度数值类型,其他类型都是定长存储,使用时不需要调用palloc,效率较高。

如果你要使用超过双精能表示的有效范围的数值,目前只能选择decimal\numeric类型,而这个类型前面说了,由于是变长设计,需要调用palloc,效率一般。

那么在数据分析领域,或者需要处理非常多的数据记录时,numeric类型的开销是较大的。

从前面的测试数据,可以观察到性能最好的是float8,其次是decimal64, decimal64不需要使用palloc,性能比numeric好1.5倍左右,而decimal128也比numeric性能好不少。

期待将来PostgreSQL内置decimal64, decimal128。

参考

《PostgreSQL fixeddecimal - 用CPU "硬解码" 提升1倍 数值运算能力 助力金融大数据量计算》

https://www.postgresql.org/message-id/flat/CAFWGqnsuyOKdOwsNLVtDU1LLjS%3D66xmxxxS8Chnng_zSB5_uCg%40mail.gmail.com#CAFWGqnsuyOKdOwsNLVtDU1LLjS=66xmxxxS8Chnng_zSB5_uCg@mail.gmail.com

https://github.com/vitesse-ftian/pgdecimal

https://pgxn.org/dist/pgdecimal/1.0.0/

https://github.com/2ndQuadrant/fixeddecimal

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