In the tornado documentation they show how they can have a very large through-put from 4 frontends. I'd like to run an app in the same way, and would like to have the frontends running as daemon processes managed with an init.d script*.
I'm fairly new to Python so don't really know where to start. Currently I'm starting the Tornado server manually in the terminal, passing in a new port number each time.
I've tried using the python-daemon package in conjunction with the lockfile package but the lockfiles that are created don't have the process ids in them and I can't see how to then kill the processes gracefully later on.
I don't really know where to go from here, and the Tornado docs leave a large chunk out regarding deployment.
* If there's a better way to manage the processes so that they can be monitored and managed as a group then please let me know.
Try Supervisor. It's great for managing multiple daemon processes. You configure your applications in the supervisord.conf file and supervisord itself is launched from an init.d script.
I can vouch for Supervisor too. We have been using tornado in production with 4 instances using supervisor and it is working uber smooth
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I have a Django app that is intended to be run on Virtualbox VMs on LANs. The basic user will be a savvy IT end-user, not a sysadmin.
Part of that app's job is to connect to external databases on the LAN, run some python batches against those databases and save the results in its local db. The user can then explore the systems using Django pages.
Run time for the batches isn't all that long, but runs to minutes, tens of minutes potentially, not seconds. Run frequency is infrequent at best, I think you could spend days without needing a refresh.
This is not celery's normal use case of long tasks which will eventually push the results back into the web UI via ajax and/or polling. It is more similar to a dev's occasional use of the django-admin commands, but this time intended for an end user.
The user should be able to initiate a run of one or several of those batches when they want in order to refresh the calculations of a given external database (the target db is a parameter to the batch).
Until the batches are done for a given db, the app really isn't useable. You can access its pages, but many functions won't be available.
It is very important, from a support point of view that the batches remain easily runnable at all times. Dropping down to the VMs SSH would probably require frequent handholding which wouldn't be good - it is best that you could launch them from the Django webpages.
What I currently have:
Each batch is in its own script.
I can run it on the command line (via if __name__ == "main":).
The batches are also hooked up as celery tasks and work fine that way.
Given the way I have written them, it would be relatively easy for me to allow running them from subprocess calls in Python. I haven't really looked into it, but I suppose I could make them into django-admin commands as well.
The batches already have their own rudimentary status checks. For example, they can look at the calculated data and tell whether they have been run and display that in Django pages without needing to look at celery task status backends.
The batches themselves are relatively robust and I can make them more so. This is about their launch mechanism.
What's not so great.
In Mac dev environment I find the celery/celerycam/rabbitmq stack to be somewhat unstable. It seems as if sometime rabbitmqs daemon balloons up in CPU/RAM use and then needs to be terminated. That mightily confuses the celery processes and I find I have to kill -9 various tasks and relaunch them manually. Sometimes celery still works but celerycam doesn't so no task updates. Some of these issues may be OSX specific or may be due to the DEBUG flag being switched for now, which celery warns about.
So then I need to run the batches on the command line, which is what I was trying to avoid, until the whole celery stack has been reset.
This might be acceptable on a normal website, with an admin watching over it. But I can't have that happen on a remote VM to which only the user has access.
Given that these are somewhat fire-and-forget batches, I am wondering if celery isn't overkill at this point.
Some options I have thought about:
writing a cleanup shell/Python script to restart rabbitmq/celery/celerycam and generally make it more robust. i.e. whatever is required to make celery & all more stable. I've already used psutil to figure out rabbit/celery process are running and display their status in Django.
Running the batches via subprocess instead and avoiding celery. What about django-admin commands here? Does that make a difference? Still needs to be run from the web pages.
an alternative task/process manager to celery with less capability but also less moving parts?
not using subprocess but relying on Python multiprocessing module? To be honest, I have no idea how that compares to launches via subprocess.
environment:
nginx, wsgi, ubuntu on virtualbox, chef to build VMs.
I'm not sure how your celery configuration makes it unstable but sounds like it's still the best fit for your problem. I'm using redis as the queue system and it works better than rabbitmq from my own experience. Maybe you can try it see if it improves things.
Otherwise, just use cron as a driver to run periodic tasks. You can just let it run your script periodically and update the database, your UI component will poll the database with no conflict.
I need to run some tasks in background of web app (checking the code out, etc) without blocking the views.
The twist in typical Queue/Celery scenario is that I have to ensure that the tasks will complete, surviving even web app crash or restart until those tasks complete, whatever their final result.
I was thinking about recording parameters for multiprocessing.Pool in a database and starting all the incomplete tasks at webapp restart. It's doable, but I'm wondering if there's a simpler or more cost-effective aproach?
UPDATE: Why not Celery itself? Well, I used Celery in some projects and it's really a great solution, but for this task it's on the big side: it requires a separate server, communication, etc., while all I need is spawning a few processes/threads, doing some work in them (git clone ..., svn co ...) and checking whether they succeeded or failed. Another issue is that I need the solution to be as small as possible since I have to make it follow elaborate corporate guidelines, procedures, etc., and the human administrative and bureaucratic overhead I'd have to go through to get Celery onboard is something I'd prefer to avoid if I can.
I would suggest you to use Celery.
Celery does not require its own server, you can have a worker running on the same machine. You can also have a "poor man's queue" using an SQL database instead of a "real" queue/messaging server such as RabbitMQ - this setup would look very much like what you're describing, only with a separate process doing the long-running tasks.
The problem with starting long-running tasks from the webserver process is that in the production environment the web "workers" are normally managed by the webserver - multiple workers can be spawned or killed at any time. The viability of your approach would highly depend on the web server you're using and its configuration. Also, with multiple workers each trying to do a task you may have some concurrency issues.
Apart from Celery, another option is to look at UWSGI's spooler subsystem, especially if you're already using UWSGI.
One can easily start Django using management command like this:
management.call_command('runserver', interactive=False)
But it actually blocks execution.
Any workaround apart from subprocess/threading/multiprocessing.
I mean how to do it in more native fashion?
A management command is not "starting django".
You "start django" by deploying on any number of web servers, each of which has methods to run in the background.
https://docs.djangoproject.com/en/dev/howto/deployment/
Dynamically deploying django isn't something I've seen, but I suppose you could write some scripts that generate webserver configuration files.
manage.py runserver should never be used for production environments / uses.
If that was just an example, and you actually want to run other asynchronous management commands, the accepted community answer to to use a task queue like Celery.
http://docs.celeryproject.org/en/latest/django/
You could then fire off 10000 non blocking management commands to be consumed "in the future" at some point by celery workers.
Ubuntu 11.10, Python 2.6. Background: I have an existing Python app that is using Twisted to sit in a loop and wait for RESTful commands to come in. So the app starts up, kicks off threads that do various things, and main sets up callbacks for Twisted, then calls Twisted.reactor.run(), which blocks forever. When a request comes in, the appropriate handler is called, stuff happens, a reply is sent back.
My job is now to remove Twisted because management has decided they don't like it. We're moving to Apache as our web server.
Using the documentation, I have successfully installed and configured Apache2.0 to serve web pages. I also installed mod_wsgi, and was able to configure it and Apache to execute arbitrary Python code when a request comes in. So I'm good on that side.
What I'm missing is how to connect my Python application to the Apache/mod_wsgi bits, since the application needs to be persistent and always running. It was suggested that I open a pipe between my wsgi script and my main application, and serialize the requests that way. But it seems like this is something that should already be out there, I just don't know enough to know what to search for.
Any pushes in the right direction are greatly appreciated.
Further edit for clarity: I'm not making a webserver. The application in question is a host app that is running on a virtual machine. It happens to be controlled by a RESTful interface via HTTP. So all it needs to do is be able to listen for incoming commands and reply to them.
mod_wsgi may not be the proper tool for this job, which is fine, I just don't know what is.
Does the daemon mode of mod_wsgi offer enough persistence in your case? Or if you want to run the main process separately from Apache, how about mod_fastcgi? Maybe running Apache as a reverse proxy could be an option too.
It was suggested that I open a pipe between my wsgi script and my main application, and serialize the requests that way.
That's what multiprocessing queues are for.
http://docs.python.org/library/multiprocessing.html
http://docs.python.org/library/multiprocessing.html#pipes-and-queues
You'll be even happier if you start using Celery.
Celery will allow you to "remove Twisted because management has decided they don't like it."
However. Switch to celery means that things like "So the app starts up, kicks off threads that do various things, and main sets up callbacks for Twisted, then calls Twisted.reactor.run(), which blocks forever" all have to be completely rethought. Instead of some main polling loop, you now have multiple, independent processes that are coordinated by celery.
What you'll find is all the housekeeping in your application -- all the coordination among threads -- the callbacks -- all that -- will go away. You'll be left with a few Python scripts that do the "real work" and Celery to manage the distributed task queue.
What options do I have for the deployment of a CPU bound Python-WSGI application on Windows?
The application benefits greatly from multiple CPUs (image manipulation/encoding) but the GIL prevents it from using them.
My understanding is:
mod_wsgi has no support for WSGIDaemonProcess on Windows and Apache itself only runs with one process
all fork based solutions (flup, spawning, gunicorn) only work on unix
Are there any other deployment options I'm missing?
PS: I asked that on serverfault but someone suggested to ask here.
I have successfully used isapi-wsgi to deploy WSGI web apps on Windows IIS (I assume that since you're deploying on Windows, IIS is an option).
Create an IIS Application Pool to host your application in and configure it as a Web Garden (Properties | Performance | Maximum number of worker processes).
Disclaimer: I have never used this feature myself (I have always used the default App Pool configuration, where Max. number of worker processes is 1). But it is my understanding that this will spin up more processes to handle requests.
It would be a bit of a mess, but you could use the subprocess module to fire off worker processes yourself. I'm pretty sure that Popen.wait() and/or Popen.communicate() ought to release the GIL. You've still got the process creation overhead though, so you might not gain a lot/anything over standard CGI.
Another option is to have separate server/worker processes running the whole time and use some form of IPC, although this isn't going to be an easy option. Have a look at the multiprocessing module, and potentially also Pyro.