Develop Reference

Run Python Pyramid server in production w/o Apache

So we used to run our Pyramid server with Apache in production. But we are moving to Docker containerization for easier prod deployments etc, and we want to adhere to the philosophy of "one process per container"..so instead of running Apache in the container + 4 python procs, we just want 1 python proc.
So my question is - is there a way to run a Pyramid server in production directly? Without using WSGI+Apache?
https://www.digitalocean.com/community/tutorials/how-to-use-the-pyramid-framework-to-build-your-python-web-app-on-ubuntu
My understanding is that pserve is for development only?
Create an application.py file and fill it with the following contents:
from wsgiref.simple_server import make_server
from pyramid.config import Configurator
from pyramid.response import Response
def hello_world(request):
return Response('<h1>Hello world!</h1>')
if __name__ == '__main__':
config = Configurator()
config.add_view(hello_world)
app = config.make_wsgi_app()
server = make_server('0.0.0.0', 8080, app)
server.serve_forever()
Will the above work as a production-grade server?

The latest official recommendation is one concern per container. From the Docker docs (emphasis my own):
Each container should have only one concern. Decoupling applications
into multiple containers makes it easier to scale horizontally and
reuse containers. For instance, a web application stack might consist
of three separate containers, each with its own unique image, to
manage the web application, database, and an in-memory cache in a
decoupled manner.
Limiting each container to one process is a good rule of thumb, but it
is not a hard and fast rule. For example, not only can containers be
spawned with an init process, some programs might spawn additional
processes of their own accord. For instance, Celery can spawn multiple
worker processes, and Apache can create one process per request.
In your case, your web application server is a single concern. Running Apache+WSGI is totally fine. Don't worry about the processes—That's Apache's job.
For a better understanding of the "one concern" rule, this post is a good overview of what problems its trying to solve.

You can use Waitress, which, according to their documentation, is
... meant to be a production-quality pure-Python WSGI server with very
acceptable performance. It has no dependencies except ones which live
in the Python standard library.
Waitress is a part of the Pylons Project just like Pyramid is.

It looks like Bjoern is a solid choice when it comes to running Python directly, where the Python server has WSGI bindings:
https://www.appdynamics.com/blog/engineering/a-performance-analysis-of-python-wsgi-servers-part-2/
https://github.com/jonashaag/bjoern

Make parallel concurrent calls to external apis from django

I have a django app (django+python+apache mod_wsgi) which act as a middleware between two systems, app gets requests from one system and it makes multiple requests to other apis to fetch and prepare the desired response, and then it passes the response to the requesting system.
So it's basically acting as a one-to-many middleware, problem is making calls sequentially is taking too much time, I tried to use threading for IO concurrency. However this is not working (I've read django works on single thread; correct me if I'm wrong), I've not worked with making requests in parallel on a webserver and have no idea how to do it.
Following is the current implementation :
with futures.ThreadPoolExecutor(max_workers=MAX_BATCH_SIZE) as executor:
future_to_url = {}
for pnode in plist_node:
config = {'url':rurl}
futur = executor.submit(self.get_result_from_url, config)
Can someone please suggest the right way to do this ?

Make a non-blocking request with requests when running Flask with Gunicorn and Gevent

My Flask application will receive a request, do some processing, and then make a request to a slow external endpoint that takes 5 seconds to respond. It looks like running Gunicorn with Gevent will allow it to handle many of these slow requests at the same time. How can I modify the example below so that the view is non-blocking?
import requests
#app.route('/do', methods = ['POST'])
def do():
result = requests.get('slow api')
return result.content
gunicorn server:app -k gevent -w 4

If you're deploying your Flask application with gunicorn, it is already non-blocking. If a client is waiting on a response from one of your views, another client can make a request to the same view without a problem. There will be multiple workers to process multiple requests concurrently. No need to change your code for this to work. This also goes for pretty much every Flask deployment option.

First a bit of background, A blocking socket is the default kind of socket, once you start reading your app or thread does not regain control until data is actually read, or you are disconnected. This is how python-requests, operates by default. There is a spin off called grequests which provides non blocking reads.
The major mechanical difference is that send, recv, connect and accept
can return without having done anything. You have (of course) a number
of choices. You can check return code and error codes and generally
drive yourself crazy. If you don’t believe me, try it sometime
Source: https://docs.python.org/2/howto/sockets.html
It also goes on to say:
There’s no question that the fastest sockets code uses non-blocking
sockets and select to multiplex them. You can put together something
that will saturate a LAN connection without putting any strain on the
CPU. The trouble is that an app written this way can’t do much of
anything else - it needs to be ready to shuffle bytes around at all
times.
Assuming that your app is actually supposed to do something more than
that, threading is the optimal solution
But do you want to add a whole lot of complexity to your view by having it spawn it's own threads. Particularly when gunicorn as async workers?
The asynchronous workers available are based on Greenlets (via
Eventlet and Gevent). Greenlets are an implementation of cooperative
multi-threading for Python. In general, an application should be able
to make use of these worker classes with no changes.
and
Some examples of behavior requiring asynchronous workers: Applications
making long blocking calls (Ie, external web services)
So to cut a long story short, don't change anything! Just let it be. If you are making any changes at all, let it be to introduce caching. Consider using Cache-control an extension recommended by python-requests developers.

You can use grequests. It allows other greenlets to run while the request is made. It is compatible with the requests library and returns a requests.Response object. The usage is as follows:
import grequests
#app.route('/do', methods = ['POST'])
def do():
result = grequests.map([grequests.get('slow api')])
return result[0].content
Edit: I've added a test and saw that the time didn't improve with grequests since gunicorn's gevent worker already performs monkey-patching when it is initialized: https://github.com/benoitc/gunicorn/blob/master/gunicorn/workers/ggevent.py#L65

uWSGI, cherrypy and threading

preface: I would like to separate these problems into smaller questions, but apparently, I am missing some pieces of the puzzle and it seems impossible to me.
I developed my cherrypy application using cherrypy's built in WSGI server. I naively assumed that when the time comes, I will be able to use created WSGI Application class and deploy it using any WSGI compliant server.
I used this blog post to create my own (but very similar) cherrypy Plugin and Tool to connect to database using SQLAlchemy during http requests.
I expected that any server will somehow work like cherrypy's built in server:
main process will spawn X threads to satisfy X concurrent requests
my engine Plugin will create SQLalchemy engine with connection pool = X (so any request will have its connection)
on request arrival, my Tool will supply sql alchemy connection from pool
This flow does not match with uWSGI (as long as I understand it).
I assign my application.py in uWSGI configuration. This file looks something like this:
cherrypy.tools.db = DbConnectorTool()
cherrypy.engine.dbengine = DbEnginePlugin(cherrypy.engine, settings.database)
cherrypy.config.update({
'engine.dbengine.on': True
})
from myapp.application import Application
root = Application(settings)
application = cherrypy.Application(root, script_name='', config=settings)
I was using this application.py to mount my application into cherrypy's built in server when I was developing and testing it.
The problems are that uWSGI does not create any threads itself and my SQLAlchemy plugin is not working with it, because no cherrypy.engine is created.
Does uWSGI support threading in the meaning of using threads to serve multiple concurrent requests? Can I start these threads in my application.py? Will uWSGI understand it and use these threads for concurrent requests? And how can this be done? I think cherrypy can be used somehow, or not?
And what about my SQLAlchemy Plugin, how can I start cherrypy.engine when using only WSGI cherrypy.Application?
Any help or information that could help me will be appreciated.
Edit:
My uWSGI configuration:
<uwsgi>
<socket>127.0.0.1:9001</socket>
<master/>
<daemonize>/var/log/uwsgi/app.log</daemonize>
<logdate/>
<threads/>
<pidfile>/home/web/uwsgi.pid</pidfile>
<uid>uwsgi</uid>
<gid>uwsgi</gid>
<workers>2</workers>
<harakiri>90</harakiri>
<harakiri-verbose/>
<home>/home/web/</home>
<pythonpath>/home/web/instance</pythonpath>
<module>core.application</module>
<no-orphans/>
<touch-reload>/home/web/uwsgi-reload-web</touch-reload>
</uwsgi>

uWSGI uses worker processes, not threads. It's worth noting that it means that the globals are not shared between all requests any more. You can use SharedArea for global data.
The processes are forked by default, so make sure you're ok with that or adjust settings (see Things to know).
Get Cherrypy's WSGI application with cherrypy.tree.mount(root, config=settings) call.
If your DB plugin does not have threading / shared data issues, chances are it will work. Like you say, you may need cherrypy.engine.start(), but definitely not cherrypy.engine.block(), since your main thread is now uWSGI worker.

You should post your uWSGI config, otherwise it will be hard to understand what is going on.
By the way to spawn additional threads (per worker) you simply need to add --threads N

Django Asynchronous Processing

I have a bunch of Django requests which executes some mathematical computations ( written in C and executed via a Cython module ) which may take an indeterminate amount ( on the order of 1 second ) of time to execute. Also the requests don't need to access the database and are all independent of each other and Django.
Right now everything is synchronous ( using Gunicorn with sync worker types ) but I'd like to make this asynchronous and nonblocking. In short I'd like to do something:
Receive the AJAX request
Allocate task to an available worker ( without blocking the main Django web application )
Worker executes task in some unknown amount of time
Django returns the result of the computation (a list of strings) as JSON whenever the task completes
I am very new to asynchronous Django, and so my question is what is the best stack for doing this.
Is this sort of process something a task queue is well suited for? Would anyone recommend Tornado + Celery + RabbitMQ, or perhaps something else?
Thanks in advance!

Celery would be perfect for this.
Since what you're doing is relatively simple (read: you don't need complex rules about how tasks should be routed), you could probably get away with using the Redis backend, which means you don't need to setup/configure RabbitMQ (which, in my experience, is more difficult).
I use Redis with the most a dev build of Celery, and here are the relevant bits of my config:
# Use redis as a queue
BROKER_BACKEND = "kombu.transport.pyredis.Transport"
BROKER_HOST = "localhost"
BROKER_PORT = 6379
BROKER_VHOST = "0"
# Store results in redis
CELERY_RESULT_BACKEND = "redis"
REDIS_HOST = "localhost"
REDIS_PORT = 6379
REDIS_DB = "0"
I'm also using django-celery, which makes the integration with Django happy.
Comment if you need any more specific advice.

Since you are planning to make it async (presumably using something like gevent), you could also consider making a threaded/forked backend web service for the computational work.
The async frontend server could handle all the light work, get data from databases that are suitable for async (redis or mysql with a special driver), etc. When a computation has to be done, the frontend server can post all input data to the backend server and retrieve the result when the backend server is done computing it.
Since the frontend server is async, it will not block while waiting for the results. The advantage of this as opposed to using celery, is that you can return the result to the client as soon as it becomes available.
client browser <> async frontend server <> backend server for computations