FlatBuffers发布时,顺便也公布了它的性能数据,具体数据请见Benchmark

它的测试用例由以下数据构成"a set of about 10 objects containing an array, 4 strings, and a large variety of int/float scalar values of all sizes, meant to be representative of game data, e.g. a scene format."

我感觉这样测试如同儿戏,便自己设计了一个测试用例,主要关注CPU计算时间和内存空间占用两个指标,参考对象是protobuf。

测试用例为:序列化一个通讯录personal_info_list(table),通讯录可以认为是有每个人的信息(personal_info)的集合。每个人信息personal_info(table)有:个人id(uint)、名字(string)、年龄(byte)、性别(enum, byte)和电话号码(ulong)。本来我想用struct表示personal_info(table),但是struct不允许有数组或string成员,无奈我用table描述它了。相应的idl文件如下:

//////////////////////////////////////////////////////
//// FILE : tellist.fbs
//// DESC : basic message for msg-center
//// AUTHOR : v 0.1 written by Alex Stocks on June 22, 2014
//// LICENCE :
//// MOD :
//////////////////////////////////////////////////////// namespace as.tellist; enum GENDER_TYPE : byte
{
MALE = 0,
FEMALE = 1,
OTHER = 2
} table personal_info
{
id : uint;
name : string;
age : byte;
gender : GENDER_TYPE;
phone_num : ulong;
} table personal_info_list
{
info : [personal_info];
} root_type personal_info_list;

因为要以protobuf做性能参考,列出protobuf的idl文件如下:

//////////////////////////////////////////////////////
//// FILE : tellist.proto
//// DESC : basic message for msg-center
//// AUTHOR : v 0.1 written by Alex Stocks on June 22, 2014
//// LICENCE :
//// MOD :
//////////////////////////////////////////////////////// package as.tellist; enum gender_type
{
MALE = 0;
FEMALE = 1;
OTHER = 2;
} message personal_info
{
optional uint32 id = 1;
optional string name = 2;
optional uint32 age = 3;
optional gender_type gender = 4;
optional uint64 phone_num = 5;
} message personal_info_list
{
repeated personal_info info = 1;
}

若用C的struct描述对应的头文件(其对应的程序称之为“二进制”),如下:

/**
 * FILE : tellist.h
 * DESC : to test tellist
 * AUTHOR : v1.0 written by Alex Stocks
 * DATE : on June 28, 2014
 * LICENCE : GPL 2.0
 * MOD :
 **/ #ifndef __TELLIST_H__
#define __TELLIST_H__ enum
{
GENDER_TYPE_MALE = 0,
GENDER_TYPE_FEMALE = 1,
GENDER_TYPE_OTHER = 2,
}; inline const char **EnumNamesGENDER_TYPE()
{
static const char *names[] = { "MALE", "FEMALE", "OTHER"};
return names;
} inline const char *EnumNameGENDER_TYPE(int e)
{
return EnumNamesGENDER_TYPE()[e];
} typedef struct personal_info_tag
{
unsigned id;
unsigned char age;
char gender;
unsigned long long phone_num;
char name[32];
} personal_info; typedef struct personal_info_list_tag
{
int size;
personal_info info[0];
} personal_info_list; #endif // the end of the header file tellist.h

测试时,在内存中构造37个personal_info对象,并序列化之,重复这个过程100万次,然后再进行反序列化,再重复100万次。

测试结果如下(补充:tellist_pb是protobuf测试程序,tellist_fb是FlatBuffers测试程序,tellist_fb是二进制测试程序,):

测试环境:12Core Intel(R) Xeon(R) CPU E5-2620 0 @ 2.00GHz
free
             total       used       free     shared    buffers     cached
Mem:      66081944   62222028    3859916          0     196448   43690828
-/+ buffers/cache:   18334752   47747192
Swap:       975864     855380     120484 protobuf三次测试结果:
bin/tellist_pb 
encode: loop = 1000000, time diff = 14210ms
decode: loop = 1000000, time diff = 11185ms
buf size:841 bin/tellist_pb 
encode: loop = 1000000, time diff = 14100ms
decode: loop = 1000000, time diff = 11234ms
buf size:841 bin/tellist_pb 
encode: loop = 1000000, time diff = 14145ms
decode: loop = 1000000, time diff = 11237ms
buf size:841
序列化后占用内存空间841Byte,encode平均运算时间42455ms / 3 = 14151.7ms,decode平均计算时间33656ms / 3 = 11218.7ms flatbuffers三次测试结果:
 bin/tellist_fb 
encode: loop = 1000000, time diff = 11666ms
decode: loop = 1000000, time diff = 1141ms
buf size:1712 bin/tellist_fb 
encode: loop = 1000000, time diff = 11539ms
decode: loop = 1000000, time diff = 1200ms
buf size:1712 bin/tellist_fb 
encode: loop = 1000000, time diff = 11737ms
decode: loop = 1000000, time diff = 1141ms
buf size:1712
序列化后占用内存空间1712Byte,encode平均运算时间34942ms / 3 = 11647.3ms,decode平均计算时间3482ms / 3 = 1160.7ms 二进制三次测试结果:
bin/tellist 
encode: loop = 1000000, time diff = 4967ms
decode: loop = 1000000, time diff = 688ms
buf size:304  bin/tellist 
encode: loop = 1000000, time diff = 4971ms
decode: loop = 1000000, time diff = 687ms
buf size:304 bin/tellist 
encode: loop = 1000000, time diff = 4966ms
decode: loop = 1000000, time diff = 686ms
buf size:304
序列化后占用内存空间304Byte,encode平均运算时间14904ms / 3 = 4968ms,decode平均计算时间2061ms / 3 = 687ms 测试环境:1 Core Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz
free
             total       used       free     shared    buffers     cached
Mem:        753932     356036     397896          0      50484     224848
-/+ buffers/cache:      80704     673228
Swap:      1324028        344    1323684
protobuf三次测试结果:
./bin/tellist_pb 
encode: loop = 1000000, time diff = 12451ms
decode: loop = 1000000, time diff = 9662ms
buf size:841 ./bin/tellist_pb 
encode: loop = 1000000, time diff = 12545ms
decode: loop = 1000000, time diff = 9840ms
buf size:841 ./bin/tellist_pb 
encode: loop = 1000000, time diff = 12554ms
decode: loop = 1000000, time diff = 10460ms
buf size:841
序列化后占用内存空间841Byte,encode平均运算时间37550ms / 3 = 12516.7ms,decode平均计算时间29962ms / 3 = 9987.3ms flatbuffers三次测试结果:
bin/tellist_fb 
encode: loop = 1000000, time diff = 9640ms
decode: loop = 1000000, time diff = 1164ms
buf size:1712 bin/tellist_fb 
encode: loop = 1000000, time diff = 9595ms
decode: loop = 1000000, time diff = 1170ms
buf size:1712 bin/tellist_fb 
encode: loop = 1000000, time diff = 9570ms
decode: loop = 1000000, time diff = 1172ms
buf size:1712
序列化后占用内存空间1712Byte,encode平均运算时间28805ms / 3 = 9345ms,decode平均计算时间3506ms / 3 = 1168.7ms 二进制三次测试结果:
bin/tellist 
encode: loop = 1000000, time diff = 4194ms
decode: loop = 1000000, time diff = 538ms
buf size:304 bin/tellist 
encode: loop = 1000000, time diff = 4387ms
decode: loop = 1000000, time diff = 544ms
buf size:304 bin/tellist 
encode: loop = 1000000, time diff = 4181ms
decode: loop = 1000000, time diff = 533ms
buf size:304
序列化后占用内存空间304Byte,encode平均运算时间12762ms / 3 = 4254ms,decode平均计算时间1615ms / 3 = 538.3ms

上面的二进制程序的结果无论在内存空间占用还是cpu计算时间这两个指标上都是最快的。但本文只讨论FlatBuffers和protobuf,所以不让它的结果参与比较。

从以上数据看出,在内存空间占用这个指标上,FlatBuffers占用的内存空间比protobuf多了两倍。序列化时二者的cpu计算时间FB比PB快了3000ms左右,反序列化时二者的cpu计算时间FB比PB快了9000ms左右。FB在计算时间上占优势,而PB则在内存空间上占优(相比FB,这也正是它计算时间比较慢的原因)。

上面的测试环境是在公司的linux server端和我自己的mac pro分别进行的。请手机端开发者自己也在手机端进行下测试, 应该能得到类似的结果。Google宣称FB适合游戏开发是有道理的,如果在乎计算时间我想它也适用于后台开发。

另外,FB大量使用了C++11的语法,其从idl生成的代码接口不如protubuf友好。不过相比使用protobuf时的一堆头文件和占18M之多的lib库,FlatBuffers仅仅一个"flatbuffers/flatbuffers.h"就足够了。

测试程序已经上传到百度网盘,点击这个链接即可下载。欢迎各位的批评意见。

FlatBuffers与protobuf性能比较的更多相关文章

  1. GJM : FlatBuffers 与 protobuf 性能比较 [转载 ]

    原帖地址:http://blog.csdn.net/menggucaoyuan/article/details/34409433 原作者:企鹅  menggucaoyuan 未经原作者同意不允许转载 ...

  2. FlatBuffers与protobuf性能比較

    FlatBuffers发布时.顺便也发布了它的性能数据,详细数据请见Benchmark. 它的測试用例由下面数据构成"a set of about 10 objects containing ...

  3. Google序列化库FlatBuffers 1.1发布,及与protobuf的比较

    个人总结: FlatBuffer相对于Protobuffer来讲,优势如下: 1. 由于省去了编解码的过程,所以从速度上快于Protobuffer,个人测试结果100w次编解码,编码上FlatBuff ...

  4. FlatBuffers使用简介

    @[tools|flatbuffers|opensource] 概述### Google在今年6月份发布了跨平台序列化工具FlatBuffers,提供了C++/Java/Go/C#接口支持,这是一个注 ...

  5. 从微信SDK看ProtoBuffer文件的生成

    前言 Protocol Buffers (下面简称PB)是一种轻便高效的结构化数据存储格式,可以用于结构化数据串行化,很适合做数据存储或 RPC 数据交换格式.它可用于通讯协议.数据存储等领域的语言无 ...

  6. 在Android中使用FlatBuffers(中篇)

    本文来自网易云社区. FlatBuffers.Protobuf及JSON对比测试 FlatBuffers相对于Protobuf的表现又如何呢?这里我们用数据说话,对比一下FlatBuffers格式.J ...

  7. 金蝶随手记团队分享:还在用JSON? Protobuf让数据传输更省更快(实战篇)

    本文作者:丁同舟,来自金蝶随手记技术团队. 1.前言 本文接上篇<金蝶随手记团队分享:还在用JSON? Protobuf让数据传输更省更快(原理篇)>,以iOS端的Objective-C代 ...

  8. (62)通信协议之一protobuf

     Protobuf协议特点分析 KingKa.吴永聪 1.protobuf是什么? protobuf(Google Protocol Buffers)是Google提供的一个具有高效的协议数据交换格式 ...

  9. C++序列化、反序列化

    几个常见的库 http://stackoverflow.com/questions/3637581/fastest-c-serialization Boost: Fast, assorted C++ ...

随机推荐

  1. YTU 2616: A代码完善--简易二元运算

    2616: A代码完善--简易二元运算 时间限制: 1 Sec  内存限制: 128 MB 提交: 280  解决: 187 题目描述 注:本题只需要提交填写部分的代码,请按照C++方式提交. 编写二 ...

  2. 关于c#字典key不存在的测试

    之前一直隐约记得没有创建key会报异常,测试了下. 测试结果: 写入值,如果不存在key,会自动创建. 取值,如果不存在key,会报异常. 一般用c#提供了尝试取值方法,不过有out参数,考虑写扩展 ...

  3. Fragment学习(一)

    Fragment界面添加 了解过fragment的生命周期等简单知识,于是去看官方文档来了解更多相关内容,要添加fragment到我们的UI界面中,给出了两种常用的方法,第一个是在activity的布 ...

  4. 编译Apache Hadoop2.2.0源代码

    Hadoop2的学习资料很少,只有官网的少数文档.如果想更深入的研究hadoop2,除了仅看官网的文档外,还要学习如何看源码,通过不断的调试跟踪源码,学习hadoop的运行机制. 1.安装CentOS ...

  5. 漫游Kafka实战篇之客户端API

    Kafka Producer APIs 旧版的Procuder API有两种:kafka.producer.SyncProducer和kafka.producer.async.AsyncProduce ...

  6. codevs 4927 线段树练习5

    赶在期末考试之前把这道傻逼题调了出来. #include<iostream> #include<cstdio> #include<cstring> #include ...

  7. Cmockery macro demo hacking

    /********************************************************************* * Cmockery macro demo hacking ...

  8. HDU 5319 Painter (模拟)

    题意: 一个画家画出一张,有3种颜色的笔,R.G.B.R看成'\',B看成'/',G看成这两种的重叠(即叉形).给的是一个矩阵,矩阵中只有4种符号,除了3种颜色还有'.',代表没有涂色.问最小耗费多少 ...

  9. poj 2661 Factstone Benchmark (Stirling数)

    //题意是对于给定的x,求满足n! <= 2^(2^x)的最大的n//两边同取以二为底的对数,可得: lg2(n!) <= 2^x 1.   log2(n!) = log2(1) + lo ...

  10. ORACLE学习笔记 索引和约束

    /*** 约束 ***/ * 如果某个约束只作用于单独的字段,即可以在字段级定义约束,也可以在表级定义约 束,但如果某个约束作用于多个字段,  必须在表级定义约束* 在定义约束时可以通过CONSTRA ...