What is Django Channels and what are its top alternatives?
Top Alternatives to Django Channels
Twisted is an event-driven networking engine written in Python and licensed under the open source MIT license. Twisted runs on Python 2 and an ever growing subset also works with Python 3. Twisted also supports many common network protocols, including SMTP, POP3, IMAP, SSHv2, and DNS. ...
By using non-blocking network I/O, Tornado can scale to tens of thousands of open connections, making it ideal for long polling, WebSockets, and other applications that require a long-lived connection to each user. ...
Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. ...
Pushpin is a reverse proxy server that makes it easy to build realtime web services. The project is unique among realtime push solutions in that it is designed to address the needs of API creators. ...
This module provides infrastructure for writing single-threaded concurrent code using coroutines, multiplexing I/O access over sockets and other resources, running network clients and servers, and other related primitives. ...
An architectural style for developing web services. A distributed system framework that uses Web protocols and technologies. ...
Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices. ...
Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. ...
Django Channels alternatives & related posts
- Easy-to-understand concurrency5
- Twisted prevails3
- It works1
- Solid, flexible, powerful1
related Twisted posts
- Open source37
- So fast31
- Great for microservices architecture27
- Handles well persistent connexions3
- Event loop is complicated2
related Tornado posts
Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.
- Task queue94
- Python integration61
- Django integration37
- Scheduled Task29
- Easy to use6
- Various backend broker6
- Great community5
- Sometimes loses tasks4
- Depends on broker1
related Celery posts
As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.
Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.
Hi! I am creating a scraping system in Django, which involves long running tasks between 1 minute & 1 Day. As I am new to Message Brokers and Task Queues, I need advice on which architecture to use for my system. ( Amazon SQS, RabbitMQ, or Celery). The system should be autoscalable using Kubernetes(K8) based on the number of pending tasks in the queue.
- Open source3
- Worst community support1
related Pushpin posts
- Cooperative Multitasking4
- I/O Wait4
- Network Call3
- I/O bound computation2
related asyncio posts
Investigating Tortoise ORM and GINO ORM...
I need to introduce some async ORM with the current stack: Tornado with asyncio loop, AIOHTTP, with PostgreSQL and MSSQL. This project revolves heavily around realtime and due to the realtime requirements, blocking during database access is not acceptable.
Considering that these ORMs are both young projects, I wondered if anybody had some experience with similar stack and these async ORMs?
related REST posts
- Great libraries1.1K
- Open source795
- Great for apis484
- Great community420
- Great for realtime apps390
- Great for command line utilities295
- Node Modules81
- Uber Simple67
- Great modularity57
- Allows us to reuse code in the frontend56
- Easy to start40
- Great for Data Streaming35
- Non blocking IO24
- Can be used as a proxy17
- High performance, open source, scalable16
- Non-blocking and modular15
- Easy and Fun14
- Same lang as AngularJS12
- Easy and powerful12
- Future of BackEnd11
- Cross platform9
- Mean Stack7
- Great for webapps6
- Easy concurrency6
- Fast, simple code and async5
- Easy to use and fast and goes well with JSONdb's4
- Great speed4
- Fast development4
- Its amazingly fast and scalable4
- Control everything4
- Easy to use3
- It's fast3
- Isomorphic coolness3
- Not Python2
- Easy to learn2
- TypeScript Support2
- Scales, fast, simple, great community, npm, express2
- One language, end-to-end2
- Sooper easy for the Backend connectivity2
- Great community2
- Less boilerplate code2
- Blazing fast2
- Performant and fast prototyping2
- Event Driven1
- Bound to a single CPU46
- New framework every day42
- Lots of terrible examples on the internet37
- Asynchronous programming is the worst29
- Dependency based on GitHub11
- Dependency hell10
- Low computational power10
- Can block whole server easily7
- Very very Slow6
- Callback functions may not fire on expected sequence6
- Breaking updates3
- Unneeded over complication3
- No standard approach1
- Can't read server session1
- Bad transitive dependency management1
related Node.js posts
When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?
So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.
React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.
Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
- Rapid development637
- Open source470
- Great community404
- Easy to learn354
- Beautiful code216
- Great packages193
- Great libraries180
- Comes with auth and crud admin panel67
- Great documentation62
- Great for web60
- Great orm38
- Great for api36
- All included27
- Web Apps22
- Used by top startups19
- Easy setup15
- Convention over configuration13
- Allows for very rapid development with great libraries9
- The Django community9
- Great MVC and templating engine7
- Its elegant and practical7
- Full stack6
- Fast prototyping6
- King of backend world6
- Have not found anything that it can't do6
- Very quick to get something up and running5
- Batteries included5
- Easy Structure , useful inbuilt library5
- Easy to develop end to end AI Models5
- Python community4
- Many libraries4
- Great peformance4
- Easy to use4
- Full-Text Search3
- Zero code burden to change databases3
- Just the right level of abstraction3
- Easy to change database manager2
- Node js1
- Underpowered templating25
- Underpowered ORM19
- Autoreload restarts whole server19
- URL dispatcher ignores HTTP method15
- Internal subcomponents coupling10
- Not nodejs7
- Configuration hell7
- Not as clean and nice documentation like Laravel5
- Bloated admin panel included3
- Not typed3
- Overwhelming folder structure2
- InEffective Multithreading1
related Django posts
Simple controls over complex technologies, as we put it, wouldn't be possible without neat UIs for our user areas including start page, dashboard, settings, and docs.
Initially, there was Django. Back in 2011, considering our Python-centric approach, that was the best choice. Later, we realized we needed to iterate on our website more quickly. And this led us to detaching Django from our front end. That was when we decided to build an SPA.
For building user interfaces, we're currently using React as it provided the fastest rendering back when we were building our toolkit. It’s worth mentioning Uploadcare is not a front-end-focused SPA: we aren’t running at high levels of complexity. If it were, we’d go with Ember.js.
However, there's a chance we will shift to the faster Preact, with its motto of using as little code as possible, and because it makes more use of browser APIs. One of our future tasks for our front end is to configure our Webpack bundler to split up the code for different site sections. For styles, we use PostCSS along with its plugins such as cssnano which minifies all the code.
All that allows us to provide a great user experience and quickly implement changes where they are needed with as little code as possible.