The bottle run() function, when called without any parameters, starts a local development server on port 8080. You can access and test your application via http://localhost:8080/ if you are on the same host.
To get your application available to the outside world, specify the IP of the interface the server should listen to (e.g. run(host='192.168.0.1')) or let the server listen to all interfaces at once (e.g. run(host='0.0.0.0')). The listening port can be changed in a similar way, but you need root or admin rights to choose a port below 1024. Port 80 is the standard for HTTP servers:
run(host='0.0.0.0', port=80) # Listen to HTTP requests on all interfaces
The built-in default server is based on wsgiref WSGIServer. This non-threading HTTP server is perfectly fine for development and early production, but may become a performance bottleneck when server load increases. There are three ways to eliminate this bottleneck:
Multi-threaded servers are the ‘classic’ way to do it. They are very robust, reasonably fast and easy to manage. As a drawback, they can only handle a limited number of connections at the same time and utilize only one CPU core due to the “Global Interpreter Lock” (GIL) of the Python runtime. This does not hurt most applications, they are waiting for network IO most of the time anyway, but may slow down CPU intensive tasks (e.g. image processing).
Asynchronous servers are very fast, can handle a virtually unlimited number of concurrent connections and are easy to manage. To take full advantage of their potential, you need to design your application accordingly and understand the concepts of the specific server.
Multi-processing (forking) servers are not limited by the GIL and utilize more than one CPU core, but make communication between server instances more expensive. You need a database or external message query to share state between processes, or design your application so that it does not need any shared state. The setup is also a bit more complicated, but there are good tutorials available.
The easiest way to increase performance is to install a multi-threaded server library like paste or cherrypy and tell Bottle to use that instead of the single-threaded default server:
bottle.run(server='paste')
Bottle ships with a lot of ready-to-use adapters for the most common WSGI servers and automates the setup process. Here is an incomplete list:
| Name | Homepage | Description |
|---|---|---|
| cgi | Run as CGI script | |
| flup | flup | Run as FastCGI process |
| gae | gae | Helper for Google App Engine deployments |
| wsgiref | wsgiref | Single-threaded default server |
| cherrypy | cherrypy | Multi-threaded and very stable |
| paste | paste | Multi-threaded, stable, tried and tested |
| rocket | rocket | Multi-threaded |
| waitress | waitress | Multi-threaded, poweres Pyramid |
| gunicorn | gunicorn | Pre-forked, partly written in C |
| eventlet | eventlet | Asynchronous framework with WSGI support. |
| gevent | gevent | Asynchronous (greenlets) |
| diesel | diesel | Asynchronous (greenlets) |
| fapws3 | fapws3 | Asynchronous (network side only), written in C |
| tornado | tornado | Asynchronous, powers some parts of Facebook |
| twisted | twisted | Asynchronous, well tested but... twisted |
| meinheld | meinheld | Asynchronous, partly written in C |
| bjoern | bjoern | Asynchronous, very fast and written in C |
| auto | Automatically selects an available server adapter |
The full list is available through server_names.
If there is no adapter for your favorite server or if you need more control over the server setup, you may want to start the server manually. Refer to the server documentation on how to run WSGI applications. Here is an example for paste:
application = bottle.default_app()
from paste import httpserver
httpserver.serve(application, host='0.0.0.0', port=80)
Instead of running your own HTTP server from within Bottle, you can attach Bottle applications to an Apache server using mod_wsgi.
All you need is an app.wsgi file that provides an application object. This object is used by mod_wsgi to start your application and should be a WSGI-compatible Python callable.
File /var/www/yourapp/app.wsgi:
# Change working directory so relative paths (and template lookup) work again
os.chdir(os.path.dirname(__file__))
import bottle
# ... build or import your bottle application here ...
# Do NOT use bottle.run() with mod_wsgi
application = bottle.default_app()
The Apache configuration may look like this:
<VirtualHost *>
ServerName example.com
WSGIDaemonProcess yourapp user=www-data group=www-data processes=1 threads=5
WSGIScriptAlias / /var/www/yourapp/app.wsgi
<Directory /var/www/yourapp>
WSGIProcessGroup yourapp
WSGIApplicationGroup %{GLOBAL}
Order deny,allow
Allow from all
</Directory>
</VirtualHost>
New in version 0.9.
New App Engine applications using the Python 2.7 runtime environment support any WSGI application and should be configured to use the Bottle application object directly. For example suppose your application’s main module is myapp.py:
import bottle
@bottle.route('/')
def home():
return '<html><head></head><body>Hello world!</body></html>'
app = bottle.default_app()
Then you can configure App Engine’s app.yaml to use the app object like so:
application: myapp
version: 1
runtime: python27
api_version: 1
handlers:
- url: /.*
script: myapp.app
Bottle also provides a gae server adapter for legacy App Engine applications using the Python 2.5 runtime environment. It works similar to the cgi adapter in that it does not start a new HTTP server, but prepares and optimizes your application for Google App Engine and makes sure it conforms to their API:
bottle.run(server='gae') # No need for a host or port setting.
It is always a good idea to let GAE serve static files directly. Here is example for a working app.yaml (using the legacy Python 2.5 runtime environment):
application: myapp
version: 1
runtime: python
api_version: 1
handlers:
- url: /static
static_dir: static
- url: /.*
script: myapp.py
A single Python process can utilize only one CPU at a time, even if there are more CPU cores available. The trick is to balance the load between multiple independent Python processes to utilize all of your CPU cores.
Instead of a single Bottle application server, you start one instance for each CPU core available using different local port (localhost:8080, 8081, 8082, ...). You can choose any server adapter you want, even asynchronous ones. Then a high performance load balancer acts as a reverse proxy and forwards each new requests to a random port, spreading the load between all available back-ends. This way you can use all of your CPU cores and even spread out the load between different physical servers.
One of the fastest load balancers available is Pound but most common web servers have a proxy-module that can do the work just fine.
Pound example:
ListenHTTP
Address 0.0.0.0
Port 80
Service
BackEnd
Address 127.0.0.1
Port 8080
End
BackEnd
Address 127.0.0.1
Port 8081
End
End
End
Apache example:
<Proxy balancer://mycluster>
BalancerMember http://127.0.0.1:8080
BalancerMember http://127.0.0.1:8081
</Proxy>
ProxyPass / balancer://mycluster
Lighttpd example:
server.modules += ( "mod_proxy" )
proxy.server = (
"" => (
"wsgi1" => ( "host" => "127.0.0.1", "port" => 8080 ),
"wsgi2" => ( "host" => "127.0.0.1", "port" => 8081 )
)
)
A CGI server starts a new process for each request. This adds a lot of overhead but is sometimes the only option, especially on cheap hosting packages. The cgi server adapter does not actually start a CGI server, but transforms your bottle application into a valid CGI application:
bottle.run(server='cgi')