/**
*作者：张荣华
*日期：2008-11-15
**/

之前网上有很多关于django的测试，他们的测试结果都表明django在fastcgi模式下，使用线程模型要比进程模型快，而且更稳定，具体文章见：
http://irobot.blog.hexun.com/20332312_d.html
http://taoyh163.blog.163.com/blog/static/19580356200802433559850/
但是ahuaxuan根据操作系统的原理判断结果不应该是这样，理论上来讲，进程应该更快。为了证明自己的观点，于是做了以下测试。

那么在讲解我的测试方法之前，按照惯例，现来讲以下dango中fastcgi模式的一些知识点。
dango的fastcgi模式有如下几个重要参数：

 protocol=PROTOCOL    fcgi, scgi, ajp, ... (default fcgi)
  host=HOSTNAME        hostname to listen on..
  port=PORTNUM         port to listen on.
  socket=FILE          UNIX socket to listen on.
  method=IMPL          prefork or threaded (default prefork)
  maxrequests=NUMBER   number of requests a child handles before it is 
                       killed and a new child is forked (0 = no limit).
  maxspare=NUMBER      max number of spare processes / threads
  minspare=NUMBER      min number of spare processes / threads.
  maxchildren=NUMBER   hard limit number of processes / threads
  daemonize=BOOL       whether to detach from terminal.
  pidfile=FILE         write the spawned process-id to this file.
  workdir=DIRECTORY    change to this directory when daemonizing.
  outlog=FILE          write stdout to this file.
  errlog=FILE          write stderr to this file.
  umask=UMASK          umask to use when daemonizing (default 022).

相信做java的同学一看就比较明白了，很多参数和tomcat中是一样的，主要有一个host，port，socket需要讲解一下，host和port我们知道应该是成对出现的，那么socket是什么呢，其实他们都是socket，只不过，host+port模式是tcp sock，而socket是unix sock，他们都是套接字，一个是操作系统本地的，一个是网络套接字而已。

我的测试工具是apachbench，简称ab，在apache的bin目录中有这个工具。我的web服务器是lighttpd1.4。
我一共划分了4个场景，第一个场景是操作数据库的请求，第二个场景是请求缓存的场景，而且使用线程模型，第3和第4个场景都是fastcgi的进程模型。

场景一
涉及到查数据库的url，每次请求一条简单的sql语句。
python manage.py runfcgi method=threaded host=127.0.0.1 port=3033 daemonize=false

请求数    并发数   总时间

5000    50     22.86s
5000    25     23.37s
5000    10     23.37s
5000    100    21.58s

场景二
不涉及到数据的url，执行一段判断后返回(可以认为数据都放在缓存中)。
python manage.py runfcgi method=threaded host=127.0.0.1 port=3033
请求数    并发数   总时间
5000    50     7.734s

Concurrency Level:      50

Time taken for tests:   7.883 seconds

Complete requests:      5000

Failed requests:        0

Write errors:           0

Total transferred:      5505084 bytes

HTML transferred:       4685937 bytes

Requests per second:    634.28 [#/sec] (mean)

Time per request:       78.830 [ms] (mean)

Time per request:       1.577 [ms] (mean, across all concurrent requests)

Transfer rate:          681.98 [Kbytes/sec] received

5000    25     7.545s

Concurrency Level:      25

Time taken for tests:   7.859 seconds

Complete requests:      5000

Failed requests:        0

Write errors:           0

Total transferred:      5504770 bytes

HTML transferred:       4685000 bytes

Requests per second:    636.20 [#/sec] (mean)

Time per request:       39.296 [ms] (mean)

Time per request:       1.572 [ms] (mean, across all concurrent requests)

Transfer rate:          684.01 [Kbytes/sec] received

5000    10     7.481s

Concurrency Level:      10

Time taken for tests:   7.920 seconds

Complete requests:      5000

Failed requests:        0

Write errors:           0

Total transferred:      5503153 bytes

HTML transferred:       4685000 bytes

Requests per second:    631.28 [#/sec] (mean)

Time per request:       15.841 [ms] (mean)

Time per request:       1.584 [ms] (mean, across all concurrent requests)

Transfer rate:          678.52 [Kbytes/sec] received

5000    100    7.776s

Concurrency Level:      100

Time taken for tests:   7.776 seconds

Complete requests:      5000

Failed requests:        0

Write errors:           0

Total transferred:      5504370 bytes

HTML transferred:       4685937 bytes

Requests per second:    643.04 [#/sec] (mean)

Time per request:       155.511 [ms] (mean)

Time per request:       1.555 [ms] (mean, across all concurrent requests)

Transfer rate:          691.32 [Kbytes/sec] received

场景一和场景 二对比可以发现，带有数据操作的请求明显需要更多的时间，之间从缓存中拿数据，每秒中fastcgi可以处理1000个请求。

场景三
不涉及到数据的url，执行一段判断后返回(可以认为数据都放在缓存中)。使用进程模型。
python manage.py runfcgi method=prefork host=127.0.0.1 port=3033
请求数    并发数   总时间
5000    50     22 s

Concurrency Level:      50

Time taken for tests:   22.676 seconds

Complete requests:      5000

Failed requests:        15

   (Connect: 0, Receive: 0, Length: 15, Exceptions: 0)

Write errors:           0

Non-2xx responses:      15

Total transferred:      5519788 bytes

HTML transferred:       4676480 bytes

Requests per second:    220.50 [#/sec] (mean)

Time per request:       226.762 [ms] (mean)

Time per request:       4.535 [ms] (mean, across all concurrent requests)

Transfer rate:          237.71 [Kbytes/sec] received

5000    25     25 s

Concurrency Level:      25

Time taken for tests:   25.330 seconds

Complete requests:      5000

Failed requests:        15

   (Connect: 0, Receive: 0, Length: 15, Exceptions: 0)

Write errors:           0

Non-2xx responses:      15

Total transferred:      5481652 bytes

HTML transferred:       4676480 bytes

Requests per second:    197.40 [#/sec] (mean)

Time per request:       126.649 [ms] (mean)

Time per request:       5.066 [ms] (mean, across all concurrent requests)

Transfer rate:          211.34 [Kbytes/sec] received

5000    10     15 s

Concurrency Level:      10

Time taken for tests:   15.463 seconds

Complete requests:      5000

Failed requests:        9

   (Connect: 0, Receive: 0, Length: 9, Exceptions: 0)

Write errors:           0

Non-2xx responses:      9

Total transferred:      5536528 bytes

HTML transferred:       4679888 bytes

Requests per second:    323.35 [#/sec] (mean)

Time per request:       30.926 [ms] (mean)

Time per request:       3.093 [ms] (mean, across all concurrent requests)

Transfer rate:          349.66 [Kbytes/sec] received

5000    100    21 s

Concurrency Level:      100

Time taken for tests:   21.225 seconds

Complete requests:      5000

Failed requests:        15

   (Connect: 0, Receive: 0, Length: 15, Exceptions: 0)

Write errors:           0

Non-2xx responses:      15

Total transferred:      5541355 bytes

HTML transferred:       4676480 bytes

Requests per second:    235.57 [#/sec] (mean)

Time per request:       424.498 [ms] (mean)

Time per request:       4.245 [ms] (mean, across all concurrent requests)

Transfer rate:          254.96 [Kbytes/sec] received

通过场景二和三的对比，我们可以看出线程模型在默认情况下比进程模型更加快。不过根据操作系统的特性，ahuaxuan认为事有蹊跷。理论上来讲，在速度方面，进程模型不应该比线程模型慢，虽然网上有的文章确实有提到线程模型比进程模型快，不过ahuaxuan觉得他们的测试是有问题的。在研究了django的fastcgi参数之后，再根据做java的经验我发现问题可能出现在进程的创建上。于是调整参数，继续测试。


场景四
不涉及到数据的url，执行一段判断后返回(可以认为数据都放在缓存中)。将最大进程数和最小进程数调整到50。
python manage.py runfcgi method=prefork host=127.0.0.1 port=3033 daemonize=false minspare=50 maxspare=50
请求数    并发数   总时间
5000    100     8.16s
第一次：

Concurrency Level:      100

Time taken for tests:   9.682 seconds

Complete requests:      5000

Failed requests:        0

Write errors:           0

Total transferred:      5557585 bytes

HTML transferred:       4685000 bytes

Requests per second:    516.42 [#/sec] (mean)

Time per request:       193.642 [ms] (mean)

Time per request:       1.936 [ms] (mean, across all concurrent requests)

Transfer rate:          560.55 [Kbytes/sec] received

第二次

Concurrency Level:      100

Time taken for tests:   5.134 seconds

Complete requests:      5000

Failed requests:        0

Write errors:           0

Total transferred:      5560000 bytes

HTML transferred:       4685000 bytes

Requests per second:    973.84 [#/sec] (mean)

Time per request:       102.686 [ms] (mean)

Time per request:       1.027 [ms] (mean, across all concurrent requests)

Transfer rate:          1057.53 [Kbytes/sec] received

分析，一模一样的两次请求，为什么差两倍的速度呢，根据ahuaxuan的分析，问题应该出在进程的创建上，第二次测试，由于进程已经存在，所以速度非常的快，比线程模型快了2倍不到一点。
5000    25     8.90s

Concurrency Level:      25

Time taken for tests:   5.347 seconds

Complete requests:      5000

Failed requests:        0

Write errors:           0

Total transferred:      5559748 bytes

HTML transferred:       4685000 bytes

Requests per second:    935.07 [#/sec] (mean)

Time per request:       26.736 [ms] (mean)

Time per request:       1.069 [ms] (mean, across all concurrent requests)

Transfer rate:          1015.38 [Kbytes/sec] received

5000    10     8.78s

Concurrency Level:      10

Time taken for tests:   5.723 seconds

Complete requests:      5000

Failed requests:        0

Write errors:           0

Total transferred:      5562916 bytes

HTML transferred:       4687811 bytes

Requests per second:    873.64 [#/sec] (mean)

Time per request:       11.446 [ms] (mean)

Time per request:       1.145 [ms] (mean, across all concurrent requests)

Transfer rate:          949.22 [Kbytes/sec] received

5000    50    7.90s

Concurrency Level:      50

Time taken for tests:   5.239 seconds

Complete requests:      5000

Failed requests:        0

Write errors:           0

Total transferred:      5560923 bytes

HTML transferred:       4685937 bytes

Requests per second:    954.43 [#/sec] (mean)

Time per request:       52.387 [ms] (mean)

Time per request:       1.048 [ms] (mean, across all concurrent requests)

Transfer rate:          1036.63 [Kbytes/sec] received

对比场景三和场景四发现，在进程模式下在没有指定maxspare和minspare值的情况下，由于每次并发大的时候都动态的去创建进程，效率明显下降，5000个请求居然需要20s之多。而一旦设置了maxspare和minspare之后，只有第一次请求的时候，需要创建进程，之后经常已经存在，不需要创建，也不需要动态的消亡(maxspare和minspare值太小会导致fastcgi父进程频繁的创建和销毁子进程，非常的消耗cpu)，整个应用程序的处理能力大大提高。

再对比场景二和场景四，可以发现不管是进程模式还是线程模式，每秒都能处理超过1000次的请求。而且在并发较大的情况下，进程模式效率更高。由此可见在网站访问量巨大的情况下，使用进程模型才是比较好的选择，而不是网上所说的使用线程模型。

后来为了作对比，ahuaxuan在线程模型上也加了maxspare=50,minspare=50，不过性能和没有加几乎一样，可见，这两个参数对进程模型的影响比较大。而且也可以进一步说明操作系统创建进程消耗确实大。

从这个对比结果，我们还可以得知：
1线程创建在ubuntu中的代价比进程小的多。(根据观察，在创建进程的时候，cpu上升到100%,而线程模型的cpu只有80%的样子)
2在进程已经存在的情况下，处理请求的能力，进程要比线程能力强。而且要强出1/3左右的样子

最后，贴出我的机器配置
cpu:t8100
内存:2g
硬盘：5400转的希捷

希望本文能够给对django性能有怀疑，以及对fastcgi下认为线程模型更快的同学有所帮助。