What is asyncio and what are its top alternatives?
Top Alternatives to asyncio
Flask is intended for getting started very quickly and was developed with best intentions in mind. ...
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. ...
It is a coroutine -based Python networking library that uses greenlet to provide a high-level synchronous API on top of the libev or libuv event loop. ...
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. ...
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. ...
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. ...
It is an Async http client/server framework. It supports both client and server Web-Sockets out-of-the-box and avoids Callback. It provides Web-server with middlewares and pluggable routing. ...
Express is a minimal and flexible node.js web application framework, providing a robust set of features for building single and multi-page, and hybrid web applications. ...
asyncio alternatives & related posts
- Open source146
- Easy to use66
- Easy to setup and get it going54
- Well designed53
- Easy to develop and maintain applications48
- Easy to get started45
- Beautiful code18
- Rapid development17
- Get started quickly11
- Love it11
- Simple to use11
- Easy to integrate10
- Perfect for small to large projects with superb docs.9
- For it flexibility9
- Flexibilty and easy to use8
- User friendly6
- Not JS6
- Not JS10
- Not fast5
- Don't has many module as in spring1
related Flask posts
One of our top priorities at Pinterest is fostering a safe and trustworthy experience for all Pinners. As Pinterest’s user base and ads business grow, the review volume has been increasing exponentially, and more content types require moderation support. To solve greater engineering and operational challenges at scale, we needed a highly-reliable and performant system to detect, report, evaluate, and act on abusive content and users and so we created Pinqueue.
Pinqueue-3.0 serves as a generic platform for content moderation and human labeling. Under the hood, Pinqueue3.0 is a Flask + React app powered by Pinterest’s very own Gestalt UI framework. On the backend, Pinqueue3.0 heavily relies on PinLater, a Pinterest-built reliable asynchronous job execution system, to handle the requests for enqueueing and action-taking. Using PinLater has significantly strengthened Pinqueue3.0’s overall infra with its capability of processing a massive load of events with configurable retry policies.
Hundreds of millions of people around the world use Pinterest to discover and do what they love, and our job is to protect them from abusive and harmful content. We’re committed to providing an inspirational yet safe experience to all Pinners. Solving trust & safety problems is a joint effort requiring expertise across multiple domains. Pinqueue3.0 not only plays a critical role in responsively taking down unsafe content, it also has become an enabler for future ML/automation initiatives by providing high-quality human labels. Going forward, we will continue to improve the review experience, measure review quality and collaborate with our machine learning teams to solve content moderation beyond manual reviews at an even larger scale.
- Task queue97
- Python integration62
- Django integration37
- Scheduled Task29
- Various backend broker6
- Easy to use6
- 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.
- Not native1
related gevent posts
- Great libraries1.1K
- Open source801
- Great for apis485
- Great community420
- Great for realtime apps390
- Great for command line utilities295
- Node Modules81
- Uber Simple68
- Great modularity59
- Allows us to reuse code in the frontend57
- Easy to start42
- Great for Data Streaming35
- Non blocking IO25
- Can be used as a proxy18
- High performance, open source, scalable17
- Non-blocking and modular16
- Easy and Fun15
- Easy and powerful14
- Future of BackEnd13
- Same lang as AngularJS13
- Cross platform10
- Mean Stack8
- Great for webapps7
- Easy concurrency7
- Fast, simple code and async6
- Control everything5
- Its amazingly fast and scalable5
- Easy to use and fast and goes well with JSONdb's5
- Great speed5
- Fast development5
- Isomorphic coolness4
- It's fast4
- Easy to use4
- Easy to learn3
- Great community3
- Not Python3
- Sooper easy for the Backend connectivity3
- TypeScript Support3
- Scales, fast, simple, great community, npm, express3
- One language, end-to-end3
- Less boilerplate code3
- Performant and fast prototyping3
- Blazing fast3
- Npm i ape-updating2
- Event Driven2
- Bound to a single CPU46
- New framework every day42
- Lots of terrible examples on the internet37
- Asynchronous programming is the worst30
- Dependency hell11
- Dependency based on GitHub11
- Low computational power10
- Very very Slow7
- Can block whole server easily7
- Callback functions may not fire on expected sequence6
- Unneeded over complication3
- Breaking updates3
- Bad transitive dependency management1
- Can't read server session1
- No standard approach1
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:
- 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.
- Easy-to-understand concurrency5
- Twisted prevails3
- It works1
- Solid, flexible, powerful1
related Twisted posts
related AIOHTTP 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?
- High performance191
- Robust routing149
- Open source70
- Great community57
- Hybrid web applications35
- Well documented13
- Sinatra inspired9
- Rapid development7
- Isomorphic js.. superfast and easy6
- Light weight4
- Socket connection4
- Resource available for learning4
- Event loop3
- Data stream2
- Not python26
- No multithreading14
- Not fast5
- Easily Insecure for Novices2
related ExpressJS posts
Our whole Node.js backend stack consists of the following tools:
- Lerna as a tool for multi package and multi repository management
- npm as package manager
- NestJS as Node.js framework
- TypeScript as programming language
- ExpressJS as web server
- Swagger UI for visualizing and interacting with the API’s resources
- Postman as a tool for API development
- TypeORM as object relational mapping layer
- JSON Web Token for access token management
The main reason we have chosen Node.js over PHP is related to the following artifacts:
- Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.
Overview: To put it simply, we plan to use the MERN stack to build our web application. MongoDB will be used as our primary database. We will use ExpressJS alongside Node.js to set up our API endpoints. Additionally, we plan to use React to build our SPA on the client side and use Redis on the server side as our primary caching solution. Initially, while working on the project, we plan to deploy our server and client both on Heroku . However, Heroku is very limited and we will need the benefits of an Infrastructure as a Service so we will use Amazon EC2 to later deploy our final version of the application.
Serverside: nodemon will allow us to automatically restart a running instance of our node app when files changes take place. We decided to use MongoDB because it is a non relational database which uses the Document Object Model. This allows a lot of flexibility as compared to a RDMS like SQL which requires a very structural model of data that does not change too much. Another strength of MongoDB is its ease in scalability. We will use Mongoose along side MongoDB to model our application data. Additionally, we will host our MongoDB cluster remotely on MongoDB Atlas. Bcrypt will be used to encrypt user passwords that will be stored in the DB. This is to avoid the risks of storing plain text passwords. Moreover, we will use Cloudinary to store images uploaded by the user. We will also use the Twilio SendGrid API to enable automated emails sent by our application. To protect private API endpoints, we will use JSON Web Token and Passport. Also, PayPal will be used as a payment gateway to accept payments from users.
Client Side: As mentioned earlier, we will use React to build our SPA. React uses a virtual DOM which is very efficient in rendering a page. Also React will allow us to reuse components. Furthermore, it is very popular and there is a large community that uses React so it can be helpful if we run into issues. We also plan to make a cross platform mobile application later and using React will allow us to reuse a lot of our code with React Native. Redux will be used to manage state. Redux works great with React and will help us manage a global state in the app and avoid the complications of each component having its own state. Additionally, we will use Bootstrap components and custom CSS to style our app.
Other: Git will be used for version control. During the later stages of our project, we will use Google Analytics to collect useful data regarding user interactions. Moreover, Slack will be our primary communication tool. Also, we will use Visual Studio Code as our primary code editor because it is very light weight and has a wide variety of extensions that will boost productivity. Postman will be used to interact with and debug our API endpoints.