Server Deployment¶

There are several options for aiohttp server deployment:

Standalone server
Running a pool of backend servers behind of nginx, HAProxy or other reverse proxy server
Using gunicorn behind of reverse proxy

Every method has own benefits and disadvantages.

Standalone¶

Just call aiohttp.web.run_app() function passing aiohttp.web.Application instance.

The method is very simple and could be the best solution in some trivial cases. But it does not utilize all CPU cores.

For running multiple aiohttp server instances use reverse proxies.

Nginx+supervisord¶

Running aiohttp servers behind nginx makes several advantages.

At first, nginx is the perfect frontend server. It may prevent many attacks based on malformed http protocol etc.

Second, running several aiohttp instances behind nginx allows to utilize all CPU cores.

Third, nginx serves static files much faster than built-in aiohttp static file support.

But this way requires more complex configuration.

Nginx configuration¶

Here is short extraction about writing Nginx configuration file. It does not cover all available Nginx options.

For full reference read Nginx tutorial and official Nginx documentation.

First configure HTTP server itself:

http {
  server {
    listen 80;
    client_max_body_size 4G;

    server_name example.com;

    location / {
      proxy_set_header Host $http_host;
      proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
      proxy_redirect off;
      proxy_buffering off;
      proxy_pass http://aiohttp;
    }

    location /static {
      # path for static files
      root /path/to/app/static;
    }

  }
}

This config listens on port 80 for server named example.com and redirects everything to aiohttp backend group.

Also it serves static files from /path/to/app/static path as example.com/static.

Next we need to configure aiohttp upstream group:

http {
  upstream aiohttp {
    # fail_timeout=0 means we always retry an upstream even if it failed
    # to return a good HTTP response

    # Unix domain servers
    server unix:/tmp/example_1.sock fail_timeout=0;
    server unix:/tmp/example_2.sock fail_timeout=0;
    server unix:/tmp/example_3.sock fail_timeout=0;
    server unix:/tmp/example_4.sock fail_timeout=0;

    # Unix domain sockets are used in this example due to their high performance,
    # but TCP/IP sockets could be used instead:
    # server 127.0.0.1:8081 fail_timeout=0;
    # server 127.0.0.1:8082 fail_timeout=0;
    # server 127.0.0.1:8083 fail_timeout=0;
    # server 127.0.0.1:8084 fail_timeout=0;
  }
}

All HTTP requests for http://example.com except ones for http://example.com/static will be redirected to example1.sock, example2.sock, example3.sock or example4.sock backend servers. By default, Nginx uses round-robin algorithm for backend selection.

Note

Nginx is not the only existing reverse proxy server but the most popular one. Alternatives like HAProxy may be used as well.

Supervisord¶

After configuring Nginx we need to start our aiohttp backends. Better to use some tool for starting them automatically after system reboot or backend crash.

There are very many ways to do it: Supervisord, Upstart, Systemd, Gaffer, Circus, Runit etc.

Here we’ll use Supervisord for example:

[program:aiohttp]
numprocs = 4
numprocs_start = 1
process_name = example_%(process_num)s

; Unix socket paths are specified by command line.
command=/path/to/aiohttp_example.py --path=/tmp/example_%(process_num)s.sock

; We can just as easily pass TCP port numbers:
; command=/path/to/aiohttp_example.py --port=808%(process_num)s

user=nobody
autostart=true
autorestart=true

aiohttp server¶

The last step is preparing aiohttp server for working with supervisord.

Assuming we have properly configured aiohttp.web.Application and port is specified by command line, the task is trivial:

# aiohttp_example.py
import argparse
from aiohttp import web

parser = argparse.ArgumentParser(description="aiohttp server example")
parser.add_argument('--path')
parser.add_argument('--port')


if __name__ == '__main__':
    app = web.Application()
    # configure app

    args = parser.parse_args()
    web.run_app(app, path=args.path, port=args.port)

For real use cases we perhaps need to configure other things like logging etc., but it’s out of scope of the topic.

Nginx+Gunicorn¶

aiohttp can be deployed using Gunicorn, which is based on a pre-fork worker model. Gunicorn launches your app as worker processes for handling incoming requests.

In opposite to deployment with bare Nginx the solution does not need to manually run several aiohttp processes and use tool like supervisord for monitoring it. But nothing is for free: running aiohttp application under gunicorn is slightly slower.

Prepare environment¶

You firstly need to setup your deployment environment. This example is based on Ubuntu 16.04.

Create a directory for your application:

>> mkdir myapp
>> cd myapp

Create Python virtual environment:

>> python3 -m venv venv
>> source venv/bin/activate

Now that the virtual environment is ready, we’ll proceed to install aiohttp and gunicorn:

>> pip install gunicorn
>> pip install aiohttp

Application¶

Lets write a simple application, which we will save to file. We’ll name this file my_app_module.py:

from aiohttp import web

async def index(request):
    return web.Response(text="Welcome home!")


my_web_app = web.Application()
my_web_app.router.add_get('/', index)

Application factory¶

As an option an entry point could be a coroutine that accepts no parameters and returns an application instance:

from aiohttp import web

async def index(request):
    return web.Response(text="Welcome home!")


async def my_web_app():
    app = web.Application()
    app.router.add_get('/', index)
    return app

Start Gunicorn¶

When Running Gunicorn, you provide the name of the module, i.e. my_app_module, and the name of the app or application factory, i.e. my_web_app, along with other Gunicorn Settings provided as command line flags or in your config file.

In this case, we will use:

the ‘–bind’ flag to set the server’s socket address;
the ‘–worker-class’ flag to tell Gunicorn that we want to use a custom worker subclass instead of one of the Gunicorn default worker types;
you may also want to use the ‘–workers’ flag to tell Gunicorn how many worker processes to use for handling requests. (See the documentation for recommendations on How Many Workers?)
you may also want to use the ‘–accesslog’ flag to enable the access log to be populated. (See logging for more information.)

The custom worker subclass is defined in aiohttp.GunicornWebWorker:

>> gunicorn my_app_module:my_web_app --bind localhost:8080 --worker-class aiohttp.GunicornWebWorker
[2017-03-11 18:27:21 +0000] [1249] [INFO] Starting gunicorn 19.7.1
[2017-03-11 18:27:21 +0000] [1249] [INFO] Listening at: http://127.0.0.1:8080 (1249)
[2017-03-11 18:27:21 +0000] [1249] [INFO] Using worker: aiohttp.worker.GunicornWebWorker
[2015-03-11 18:27:21 +0000] [1253] [INFO] Booting worker with pid: 1253

Gunicorn is now running and ready to serve requests to your app’s worker processes.

Note

If you want to use an alternative asyncio event loop uvloop, you can use the aiohttp.GunicornUVLoopWebWorker worker class.

More information¶

The Gunicorn documentation recommends deploying Gunicorn behind an Nginx proxy server. See the official documentation for more information about suggested nginx configuration.

Logging configuration¶

aiohttp and gunicorn use different format for specifying access log.

By default aiohttp uses own defaults:

'%a %t "%r" %s %b "%{Referer}i" "%{User-Agent}i"'

For more information please read Format Specification for Access Log.