Recently I came to know about Django channels.
Can somebody tell me the difference between channels and celery, as well as where to use celery and channels.
Channels in Django are meant for asynchronous handling of requests.
The standard model Django uses is Request-Response but that has significant limitations. We cannot do anything outside the restrictions of that model.
Channels came about to allow Web Socket support and building complex applications around Web Sockets, so that we can send multiple messages, manage sessions, etc.
Celery is a completely different thing, it is an asynchronous task queue/job queue based on distributed message passing. It is primarily for queuing tasks and scheduling them to run at specific intervals.
Simply put Channels are used when you need asynchronous data communication like a chat application, and Celery is for scheduling tasks and events like a server scraping the web for a certain type of news at fixed intervals.
Channels in Django is for WebSocket, long-poll HTTP.
Celery is for background task, queue.
Django Channels:
beyond HTTP - to handle WebSockets, chat protocols, IoT protocols, and
more.
Passes messages between client and server (Full duplex connection)
Handle HTTP and Web-sockets requests
Asynchronous
Example :
Real time chat application
Update social feeds
Multiplayer game
Sending Notifications
Celery :
It’s a task queue with focus on real-time processing, while also supporting task scheduling.
Perform long running background tasks
Perform periodic tasks
Asynchronous
Example :
Processing Videos/images
Sending bulk emails
Further reading
Example of Celery and Django Channels
Asynchronous vs Synchronous
Channels is a project that takes Django and extends its abilities beyond HTTP - to handle Web-sockets, chat protocols, IoT protocols, and more. It’s built on a Python specification called ASGI.
Channels changes Django to weave asynchronous code underneath and through Django’s synchronous core, allowing Django projects to handle not only HTTP, but protocols that require long-running connections too - WebSockets, MQTT, chatbots, amateur radio, and more.
It does this while preserving Django’s synchronous and easy-to-use nature, allowing you to choose how you write your code - synchronous in a style like Django views, fully asynchronous, or a mixture of both. On top of this, it provides integrations with Django’s auth system, session system, and more, making it easier than ever to extend your HTTP-only project to other protocols.
It also bundles this event-driven architecture with channel layers, a system that allows you to easily communicate between processes, and separate your project into different processes.
Celery is an asynchronous task queue based on distributed message passing. It provides functionalities to run real-time operations and schedule some tasks to be executed later. These tasks can be executed asynchronous or synchronous, this means you may prefer to run them at the background or chain them to make one task to be fulfilled after the successful execution of an another task.
Other answers, greatly explained the diff, but in facts Channels & Celery can both do asynchronous pooled tasks in common.
Channels and Celery both use a backend for messages and worker daemon(s). So the same kind of thing could be implemented with both.
But keep in mind that Celery is primary made for and can handle most issue of task pooling (retries, result backend, etc), where Channels is absolutely not made for.
Django channels gives to Django the ability to handle more than just plain HTTP requests, including Websockets and HTTP2. Think of this as 2 way duplex communication that happens asynchronously
No browser refreshing. Multiple clients can send and receive data via websocket and django channels orchestrates this intercommunication example a group chat with simultaneously clients accessing at the same time. You can achieve background processing of long running code simliar to that of a celery to a certain extent, but the application of channels is different to that of celery.
Celery is an asynchronous task queue/job queue based on distributed message passing. As well as scheduling. In layman's terms, I want to fire and run a task in the background or I want to have a periodical task that fires and runs in the back on a set interval. You can also fire task in a synchronous way as well fire and wait until complete and continue.
So the key difference is in the use case they serve and objectives of the frameworks
Related
We are working on an Internet and Intranet platform, that serves client-requests over website applications.
There are heavy-weight computations on database entries and files. We want to update the state of those computations via push-notification to the client and make changes to files without the risk of race-conditions. The architecture is supposed to run on both, low- scaled one-server environments and high-scaled cluster environments.
So far, we are running a Django Webserver with Postgresql, the Python-Library Channels and RabbitMQ as Messagebroker.
Once a HTTP-Request from a client arrives in Django, we trigger the task via task.delay() and immediatly return the task_id to the client. The client then opens a websocket to another Django-route and hands over the task_ids he is interested in. Django then polls the state of the task via AsyncResult(task_id).state. Once the state changes, we read the results via AsyncResult(task_id).get and push the task_results to the client.
Here a similar sequence diagramm, from another project I found online.
Source(18.09.21)
Something that is not seen on the diagram, the channels_worker have to fetch the file they are working on from Django. A part of the result is not for the client, but to update the file. Django locks and updates the file localy as soon, as the client asks for and Django receives the task_results from celery (the changes only add attributes and will not be in conflict with each other).
My thoughts about this architecture are:
monitoring of the celery-events is bad so far.
It is only triggered by the client, which has to know about the tasks to begin with.
Django is not suited for monitoring
and polling is not efficient in general.
The file management seems fishy.
I would prefer a proper monitoring, where events are pushed to Django and the client. The client have to be able to consume the events at any time later.
I have some thoughts about solutions, but I would like to hear your opinion first. Later I can bring them in the discussion too.
Greetings
Python
Edit 1
From other sources I got helpful information regarding a good strategy.
Instead of Django "monitoring" the celery tasks, we can use a dedicated Websocket-Service, like FastAPI thand monitors task events and propagates them to the clients via websocket.
The client doesn't have to know about it's running tasks per se. Instead we can have ownership of tasks and the client only has to authenticate himself. The whole Security Blog will be implemented anyways and its supported by Celery.
For the file management, we should use a dedicated object storage like minio. This service can become subscriber to task-events related to files.
We all like Python, but we don't have to re-invent the wheel whenever we want a better monitoring or more control on the behavior of our systems.
That being said, I would recommend re-architecture the solution to decrease the complexity of your django application by exploring what native cloud solutions are offering in terms of micro-service architecture (API-Gateway), AWS SQS and SNS, computation, and storage options for your files.
Such an approach will carry out a lot of the monitoring, configurations, file management activities, and most importantly your monolithic application could scale without code changes or additional configurations.
I am creating an application that basically has multiple connections to a third party Chat Streaming API(Socket based).
The way it works is - Every user has an account on my app and another account on the third party app. He gives me an access token for the third party chat app and I connect to the third party API to stream his chats. This happens for hundreds of users.
I need to create a socket connection pool for every user and run parallel threads. I am using a python library(for that API) and am able to achieve real time feeds for single users. How do I implement an asynchronous socket connection pool in Python or NodeJS? I have a Linux micro instance on EC2 and I need to run this application for 1000 users.
I am exploring Redis+Tornado to implement this. Are there any better alternatives?
This will be messy and also a couple of things to consider.
If you are going to use multiple threads remember that you can only run so many per CPU as the OS permits, rather go multiprocessing.
If you are going async with long polling processes it will prevent other clients from processing requests.
Solution
When your application absolutely needs to be real-time I would suggest websockets for server-client interaction.
Then from your clients request start a single process that listens\polls on your streaming API using multiprocessing in python. So you will essentially create a separate process for each client.
And now, to make your WebSocketHandler and Background API Streamer interact with each other you can use the Observer Pattern (https://en.wikipedia.org/wiki/Observer_pattern) to notify the WebSocket that you have received data from the API.
Make sure that you assign a unique ID to every client and make sure that you only post the data to the intended client when using websockets.
EDIT:
Web:
Also on your question regarding Tornado. It is a good lightweight framework for running a couple of users maybe 1000. But anything more than that I would suggest looking at Django as it will allow you to be more productive in producing code and also there are lots of tools out there that the community have developed over time.
Database:
Red.is is a good choice if you need a very fast no-sql db, also have a look at mongodb. If you require a multi-region DB I would suggest going with Cassandra or CouchDB due to the partitioned nodes. The image below might help you better decide which DB to use.
I have a mature, production django-tastypie server with an angular client service where I need to update the client with real-time data. I was thinking about websockets for the client.
my question is which strategy is best for the server side:
use some django plugin that handles asynchronism
raise a new tornado server for the sake of handling the async part (and then make it learn my django users/authentication model)
embed tornado inside django (like this)
what is your recommendation? or maybe something else I didn't think of?
I would suggest using this:
https://github.com/jrief/django-websocket-redis
I have used it in production and it works very well; the main benefit is that it is non-blocking and integrates with Django's auth system (you can message specific users, groups or all users via the websocket).
#Randi's suggestion to use Celery if you need to run async tasks is a very good one: Celery is excellent. Combine that with django-websocket-redis and you can perform long-running async tasks and give your users real-time updates as the task runs.
An alternative, but probably less appropriate solution would be to poll the server for changes/notifications from your angular client every 2-5 seconds. However, this is the "old school" approach and will potentially add A LOT of load to your server. This approach would save the overhead of managing extra services and would probably be better choice if you have a small number of users.
Why do we need RabbitMQ when we have a more powerful network framework in Python called Twisted. I am trying to understand the reason why someone would want to use RabbitMQ.
Could you please provide a scenario or an example using RabbitMQ?
Also, where can I find a tutorial on how to use RabbitMQ?
Let me tell you a few reasons that makes using MOM (Message Oriented Middleware) probably the best choice.
Decoupling:
It can decouple/separate the core components of the application. There is no need to bring all the benefits of the decoupled architecture here. I just want to point it out that this is one of the main requirement of writing a quality and maintainable software.
Flexibility:
It is actually very easy to connect two totally different applications written on different languages together by using AMQP protocol. These application will talk to each other by the help of a "translator" which is MOM.
Scalability:
By using MOM we can scale the system horizontally. One message producer can transmit to unlimited number of message consumers a task, a command or a message for processing and for scaling this system all we need to do is just create new message consumers. Lets say we are getting 1000 pictures per second and we must resize them. Solving this problem with traditional methods could be a headache. With MOM we can transmit images to the message consumers which can do their job asynchronously and make sure data integrity is intact.
They are other benefits of using MOM as well but these 3 are the most significant in my opinion.
Twisted is not a queue implementation. Apart from that RabbitMQ offers enterprise-level queuing features and implements the AMQP protocol which is often needed in an enterprise world.
Twisted is a networking library that implements a number of network protocols as well as allowing you to create your own. One of the protocols that you can use with Twisted is AMQP https://launchpad.net/txamqp
RabbitMQ is an AMQP broker, i.e. a services that runs outside of your application, probably on a separate cluster of servers. AMQP is merely the protocol that is used to communicate with a message queueing broker like RabbitMQ. You get a lot of things from RabbitMQ. You can send messages persistently with guaranteed delivery so they will arrive even if your app crashes, and even if the RabbitMQ broker ends up being restarted. You get load balancing between message consumers if you have multiple consumers on the same queue. You get interoperability with apps in other languages as long as you use a reasonably open serialization format for your message bodies. AMQP allows you to break up a monolithic app into many loosely coupled parts that can run on different servers. This is a big win for long term maintenance of an application.
RabbitMQ is a bit more than mere messaging... It's a common platform that has ability to inter-connect applications. Using RabbitMQ a Java application can speak to a Linux server and/or a .NET app, to a Ruby & rails + almost anything that finds its place in the corporate web development. And most importantly it implements the "fire and forget" model proposed by AMQP. Its just a perfect replacement for JMS or ESB, especially if you are dealing with cross platform architecture, with a guaranty of reliability. There is even a special feature called RPC (Remote procedure call) that adds to the ease of development in the distributed arch.
Apart from all these, in the world financial services like Stock-exchange or share-market where a lot of reliable and efficient routing is required (suppose you don't know the actual number of people subscribed to your services, but want to ensure that who ever does so, receives your pings whether they are connected in this moment, or will connect later), RabbitMQ rules because it's based on ERLANG & the Open-telecom platform that assures high performance while using minimum resources. For the most convenient introduction to RabbitMQ, see rabbitmq.com/getstarted.html for your native development language.
RabbitMQ is an implementation of AMQP, which defines an interoperable protocol for message oriented middleware. As such, it defines semantics for message creation, publication, routing and consumption that can be implemented on any platform.
Conceptually, it could be considered as a specialization of a networking engine like Twisted, but based on an industry accepted standard.
Here is a blog from Ross Mason that discusses the interest of interoperable publish-subscribe with AMQP: http://blogs.mulesoft.org/inter-operable-publishsubscribe-with-amqp/
I use RabbitMQ as message broker for Celery.
Also, I have worked with Twisted. It is different.
See here for more on AMQP: http://en.wikipedia.org/wiki/Advanced_Message_Queuing_Protocol
RabbitMQ works on message queueing technologies like AMQP which helps keep things clean and latency-free.
And the best scenario to make use of RabbitMQ is for background processing of data which can take more time to be processed and cannot be served over HTTP. For example, if you want to download a report from your web app. And that report generation takes like 15-20 mins to be processed and get downloaded. Then in that case you should be pushing the download request to the RabbitMQ queue and then you should be expecting that report to be delivered to you via email or notification.
To know about exactly how RabbitMQ works or how it solves such use cases you should check out this YouTube video - https://youtu.be/vvxvlF6CJkg and https://youtu.be/0dXwJa7veI8
I have a Pylons web application served by Apache (mod_wsgi, prefork). Because of Apache, there are multiple separate processes running my application code concurrently. Some of the non-critical tasks that the application does I want to defer for processing in background to improve "live" response times. So I'm thinking of task queue, many Apache processes adding tasks to this queue, a single separate Python process processing them one-by-one and removing from queue.
The queue should preferably be persisted to disk so queued unprocessed tasks are not lost because of power outage, server restart etc. The question is what would be a reasonable way to implement such queue?
As for the things I've tried: I started with simple SQLite database and single table in it for storing queue items. In load testing, when increasing level of concurrency, I started getting "database locked" errors, as expected. The quick'n'dirty fix was to replace SQLite with MySQL--it handles concurrency issues well but feels like an overkill for the simple thing I need to do. Queue-related DB operations also show up prominently in my profiling reports.
A message broker like Apache's ActiveMQ is an ideal solution here.
The pipeline could be following:
Application process that is responsible for handling HTTP requests generates replies quickly and sends low-priority, heavy tasks to AMQ queue.
One or more another processes are subscribed to consume AMQ queue and do what is intended to do with these heavy tasks.
The requirement of queue persistence is fulfilled out of the box since ActiveMQ stores messages that are not yet consumed in persistent storage. Furthermore it scales quite well since you're free to deploy multiple HTTP-apps, multiple consumer apps and AMQ itself on different machines each.
We use something like this in our project written in Python utilizing STOMP as underlying communication protocol.
A web server (any web server) is multi-producer, single-consumer process.
A simple solution is to build a wsgiref or Werkzeug backend server to handle your backend requests.
Since this "backend" server is build using WSGI technology, it's very, very similar to the front-end web server. Except. It doesn't produce HTML responses (JSON is usually simpler). Other than that, it's very straightforward.
You design RESTful transactions for this backend. You use all of the various WSGI features for URI parsing, authorization, authentication, etc. You -- generally -- don't need session management, since RESTful servers don't usually offer sessions.
If you get into serious scalability issues, you simply wrap your backend server in lighttpd or some other web engine to create a multi-threaded backend.