I am using Redis for caching and queuing of a flask application. However, I observe that my cache object also has worker job related entries which are breaking my cache data fetching code.
Is there any way I can use redis for serving both purpose together ?
Related
I have written an API with Django which purpose is to operate as a bridge between a website back-end and external services we use, so that the website doesn't have to handle many requests to external APIs (CRM, calendar events, email providers etc.).
The API mainly polls other services, parses the results and forwards them to the website backend.
I initially went for a Celery-based task queue, as it seemed to me like the right tool to offload that processing to another instance, but I'm starting to think it doesn't really fit the purpose.
As the website expects synchronous responses, my code contains a lot of :
results = my_task.delay().get()
or
results = chain(fetch_results.s(), parse_results.s()).delay().get()
Which doesn't feel like the proper way to use Celery tasks.
It is efficient when pulling dozens of requests and processing the results in parallel - a periodic refresh task for example - but adds a lot of overhead for simple requests (fetch - parse - forward), which represent most of the traffic.
Should I go full synchronous for those "simple requests" and keep Celery tasks for specific scenarios ? Is there an alternative design (maybe involving asyncio) that would better suit the purpose of my API ?
Using Django, Celery (w/ Amazon SQS) on an EBS EC2 instance.
You could consider using Gevent with your Django webserver to allow it to operate efficiently for the "simple requests" you've mentioned without being blocked. If you proceed with this approach, be sure to pool database connections with PgBouncer or Pgpool-II or a Python library since each greenlet will make its own connection.
Once you've implemented that, it's possible to also use Gevent instead of Celery to handle asynchronous processing by joining on multiple Greenlets that each make an external API request, rather than incur the overhead of passing messages to an external celery worker.
Your implementation is similar to what we've done at Kloudless, which provides a single API to access multiple other APIs, including CRM, calendar, storage, etc.
In the Django docs for setting up mod_wsgi, the tutorial notes:
Django doesn’t serve files itself; it leaves that job to whichever Web
server you choose.
We recommend using a separate Web server – i.e., one that’s not also
running Django – for serving media. Here are some good choices:
Nginx
A stripped-down version of Apache
I understand this might be due to wasted resources when Apache spawns new processes to serve each static file, which Nginx avoids. However, Apache's (newish?) Event MPM seems to act similar to an Nginx instance handing off requests to an Apache worker mpm. Therefore I'd like to ask: instead of setting up Nginx to be a reverse proxy for Apache, would using an Apache Event MPM be sufficient for serving static files in Apache?
Apache doesn't spawn a new process for each static file. Apache keeps persistent processes to handle concurrent and subsequent requests just like nginx. The difference is that nginx uses a full async model, whereas Apache relies on processes and/or threading for concurrency, although event MPM uses an async model for initial request acceptance and keep alive connections now. For the majority of people, Apache alone is still a more than acceptable solution. So don't get ahead of yourself if you are just starting out and think you need a Google/Facebook scale solution from the outset.
More important than separate web server is that if using Apache/mod_wsgi, serve the static files under a different host name. That way you avoid heavy weight cookie information being sent for all static file requests. You can do this using virtual hosts in Apache. Also ensure you are using daemon mode of mod_wsgi for running the Django application as that is a better architecture and provides lots more options for setting timeouts so you can have your application recover from various situations which might otherwise cause the server to lock up when overloaded.
For a system which provides a better out of the box configuration and experience than using Apache/mod_wsgi directly and configuring it yourself, look at using mod_wsgi-express.
https://pypi.python.org/pypi/mod_wsgi
http://blog.dscpl.com.au/2015/04/introducing-modwsgi-express.html
http://blog.dscpl.com.au/2015/04/using-modwsgi-express-with-django.html
http://blog.dscpl.com.au/2015/04/integrating-modwsgi-express-as-django.html
The advice about separating the webservers has two advantages. One clearly outlined by Graham. The other is "predictable resource consumption".
The number of resources per HTML page differ. Leaving one webserver to serve the application and the other to serve static resources, has the advantage that you know exactly how many concurrent visitors you can serve: the MaxClients setting of Apache.
If this slows down the loading of images, those webservers need very few modules and no measurable amount of CPU power so a one core machine with SSD disks is all you need and scaling is cheap.
As Graham indicates it starts with a STATIC_URL that has a different hostname. Run it at the same server at the start. When scaling up, tie that hostname to a reverse proxy that serves from several image server backend machines.
I'm trying to distribute Django and Celery.
I've created a small project with Django and Celery. Django will request a Celery Worker to work on some data on the database. Then the data is passed back to Django.
My idea is that:
Django stack installed on one server
Message queue (RabbitMQ) on one server
Celery worker on one server
Hence 3 Servers in Total
However, the problem is celery has to use some code from Django, for example models, because it accesses the model. Hence, it would also require settings.py file to know what are the servers.
Does this mean that for #3, I would need to install Django and Celery on the server, but disable Django and only run celery? For example celery -A PROJECT_NAME worker -l INFO, but without an Apache Server for Django?
If you want your celery workers to operate on a different server, you need to make sure that all the resources required by the worker are accessible from that server.
For example, if you have a simple task, you can copy only the code required for that task to the server. If your worker needs any other resources like some other code, files, db you need to make sure it has access.
Really, if you want to have two servers working on the same tasks, you will have to use a simple web interface (such as Flask) to communicate between the servers (and extend the functionality of your queue). Then, you will have to ensure they are both using the same data source.
Consider hosting your database remotely, or have the remote server access the database remotely. Either way, any workers running on a server will need access to the database and all source code necessary to complete the task. Then, you must simply have the two servers share a messaging queue.
Source: how to configure and run celery worker on remote system
Some context:
I have a django server hosted on Heroku, with Waitress to serve requests.
A single dyno has several threads that handle requests simultaneously.
The server among other things handles a torrent of events reported by numerous clients.
The events are written to a redis cache, to be later flushed to the DB.
My Goal:
I would like to optimize this by having the redis cache running on the same dyno that handles requests.
Each dyno will have it's own local cache server (which is shared by all worker threads).
Events will be pushed to the local cache (thus improving the response time). A periodic job (on each dyno) will then collect the events from the cache and flush them to the DB.
My problems:
How do I add a redis cache to my dyno (not as an add-on). I understand it is possible but wasn't able to do it. ref: Is redis on Heroku possible without an addon?
A different cache server that I can add to my dyno would also be a good option.
Thanks,
No point in repeating the detailed answer given at Is redis on Heroku possible without an addon?
It's possible, but there's no reason why a sane developer would go through this just to optimize something that is complete negligible and totally unscalable.
If you must have a local cache, try using local memory caching as your intermediate cache.
After reading a lot of blogposts, I decided to switch from crontab to Celery for my middle-scale Django project. I have a few things I didn't understand:
1- I'm planning to start a micro EC2 instance which will be dedicated to RabbitMQ, would this be sufficient for a small-to-medium heavy tasking? (Such as dispatching periodical e-mails to Amazon SES).
2- Computing of tasks, does compution of tasks occur on the Django server or the rabbitMQ server (assuming the rabbitMQ is on a seperate server)?
3- When I need to grow my system and have 2 or more application servers behind a load balancer, do these two celery machines need to connect to the same rabbitMQ vhost? Assuming application servers are the carbon copy and tasks are same and everything is sync on the database level.
I don't know the answer to this question, but you can definitely configure it to be suitable (e.g. use -c1 for a single process worker to avoid using much memory, or eventlet/gevent pools), see also the --autoscale option. The choice of broker transport also matters here, the ones that are not polling are more CPU effective (rabbitmq/redis/beanstalk).
Computing happens on the workers, the broker is only responsible for accepting, routing and delivering messages (and persisting messages to disk when necessary).
To add additional workers these should indeed connect to the same virtual host. You would
only use separate virtual hosts if you would want applications to have separate message buses.