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I have configured Nginx with uWSGI and Django on CentOS 6 x64 (3.06GHz i3 540, 4GB), which should easily handle 2500 rq/s but when I run ab test ( ab -n 1000 -c 100 ) performance stops at 92 - 100 rq/s.

Nginx:
    user nginx;
    worker_processes 2;
    events {
        worker_connections 2048;
        use epoll;
    }

uWSGI:

    Emperor
    /usr/sbin/uwsgi --master --no-orphans --pythonpath /var/python --emperor /var/python/*/uwsgi.ini


[uwsgi]
socket = 127.0.0.2:3031
master = true
processes = 5

env = DJANGO_SETTINGS_MODULE=x.settings
env = HTTPS=on
module = django.core.handlers.wsgi:WSGIHandler()

disable-logging = true
catch-exceptions = false

post-buffering = 8192
harakiri = 30
harakiri-verbose = true

vacuum = true
listen = 500
optimize = 2

sysclt changes:
# Increase TCP max buffer size setable using setsockopt()
net.ipv4.tcp_rmem = 4096 87380 8388608
net.ipv4.tcp_wmem = 4096 87380 8388608

net.core.rmem_max = 8388608
net.core.wmem_max = 8388608
net.core.netdev_max_backlog = 5000
net.ipv4.tcp_max_syn_backlog = 5000
net.ipv4.tcp_window_scaling = 1
net.core.somaxconn = 2048

# Avoid a smurf attack
net.ipv4.icmp_echo_ignore_broadcasts = 1

# Optimization for port usefor LBs
# Increase system file descriptor limit
fs.file-max = 65535

I did sysctl -p to enable changes.

Idle server info:

top - 13:34:58 up 102 days, 18:35,  1 user,  load average: 0.00, 0.00, 0.00

Tasks: 118 total,   1 running, 117 sleeping,   0 stopped,   0 zombie

Cpu(s):  0.0%us,  0.0%sy,  0.0%ni,100.0%id,  0.0%wa,  0.0%hi,  0.0%si,  0.0%st

Mem:   3983068k total,  2125088k used,  1857980k free,   262528k buffers

Swap:  2104504k total,        0k used,  2104504k free,   606996k cached



free -m

 total       used       free     shared    buffers     cached

Mem:          3889       2075       1814          0        256        592

-/+ buffers/cache:       1226       2663

Swap:         2055          0       2055




**During the test:**

top - 13:45:21 up 102 days, 18:46,  1 user,  load average: 3.73, 1.51, 0.58

Tasks: 122 total,   8 running, 114 sleeping,   0 stopped,   0 zombie

Cpu(s): 93.5%us,  5.2%sy,  0.0%ni,  0.2%id,  0.0%wa,  0.1%hi,  1.1%si,  0.0%st

Mem:   3983068k total,  2127564k used,  1855504k free,   262580k buffers

Swap:  2104504k total,        0k used,  2104504k free,   608760k cached


free -m

total       used       free     shared    buffers     cached

Mem:          3889       2125       1763          0        256        595

-/+ buffers/cache:       1274       2615

Swap:         2055          0       2055


iotop

30141 be/4 nginx       0.00 B/s    7.78 K/s  0.00 %  0.00 % nginx: wo~er process

Where is the bottleneck ? Or what am I doing wrong ?

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1  
"Where is the bottleneck?" is a good question, so I'll ask it back to you in different words: is the CPU maxed out (see top)? Are the disks struggling (iotop)? Are you hitting swap (free)? Are you filling the network pipe (iftop)? –  Shish Nov 23 '11 at 11:21
    
I have posted some server stats, it seems that is cpu –  dancio Nov 23 '11 at 11:56
    
now its serving cached static content, and @sam Your comment is really helpful thanks –  dancio Nov 23 '11 at 12:46
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1 Answer

up vote 2 down vote accepted

Clearly, whatever task you're using is CPU bound. You may want to consider profiling your Django app to find out where your application is lagging. There are several profiling solutions for Python WSGI applications (although Django is not strictly WSGI compliant, especially with middleware, so YMMV):

  1. linesman (shameless plug, this is my project!)
  2. keas.profile
  3. repoze.profile
  4. dozer (but you'll need to use the 0.2 alpha)

This will allow you to identify bottlenecks in your application--i.e., in which functions is your application spending most of its time?

Another thing to check is, how long does it take for uwsgi/nginx to pick up a request? Are requests being queued up? How long does the average request take from start to finish? Also, more importantly, what's your baseline? Try running the same test with 1 concurrent user to find this out. Then, gradually increase the number of users until you can identify where the number of users reaches a peak.

With this information, you can start to establish a pattern--and that is the key to load testing!

Good luck!

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