What is AIOHTTP and what are its top alternatives?
Top Alternatives to AIOHTTP
- GraphQL
GraphQL is a data query language and runtime designed and used at Facebook to request and deliver data to mobile and web apps since 2012. ...
- Tornado
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. ...
- Sanic
Sanic is a Flask-like Python 3.5+ web server that's written to go fast. It's based on the work done by the amazing folks at magicstack. On top of being Flask-like, Sanic supports async request handlers. ...
- Flask
Flask is intended for getting started very quickly and was developed with best intentions in mind. ...
- Node.js
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. ...
- asyncio
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. ...
- Twisted
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. ...
- Django
Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. ...
AIOHTTP alternatives & related posts
- Schemas defined by the requests made by the user74
- Will replace RESTful interfaces62
- The future of API's60
- The future of databases48
- Self-documenting12
- Get many resources in a single request11
- Query Language5
- Ask for what you need, get exactly that5
- Fetch different resources in one request3
- Evolve your API without versions3
- Type system3
- Easy setup2
- GraphiQL2
- Ease of client creation2
- Good for apps that query at build time. (SSR/Gatsby)1
- Backed by Facebook1
- Easy to learn1
- "Open" document1
- Better versioning1
- Standard1
- 1. Describe your data1
- Fast prototyping1
- Hard to migrate from GraphQL to another technology4
- More code to type.4
- Takes longer to build compared to schemaless.2
- All the pros sound like NFT pitches1
- Works just like any other API at runtime1
related GraphQL posts
I just finished the very first version of my new hobby project: #MovieGeeks. It is a minimalist online movie catalog for you to save the movies you want to see and for rating the movies you already saw. This is just the beginning as I am planning to add more features on the lines of sharing and discovery
For the #BackEnd I decided to use Node.js , GraphQL and MongoDB:
Node.js has a huge community so it will always be a safe choice in terms of libraries and finding solutions to problems you may have
GraphQL because I needed to improve my skills with it and because I was never comfortable with the usual REST approach. I believe GraphQL is a better option as it feels more natural to write apis, it improves the development velocity, by definition it fixes the over-fetching and under-fetching problem that is so common on REST apis, and on top of that, the community is getting bigger and bigger.
MongoDB was my choice for the database as I already have a lot of experience working on it and because, despite of some bad reputation it has acquired in the last months, I still believe it is a powerful database for at least a very long list of use cases such as the one I needed for my website
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.
Tornado
- Open source37
- So fast31
- Great for microservices architecture27
- Websockets20
- Simple17
- Asynchronous14
- Python11
- Lightweight7
- 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.
- Asyncio5
- Fast2
- Easy to use server2
- Websockets1
related Sanic posts
Flask
- Lightweight319
- Python275
- Minimal217
- Open source149
- Documentation100
- Easy to use69
- Well designed55
- Easy to setup and get it going55
- Easy to develop and maintain applications51
- Easy to get started48
- Beautiful code22
- Rapid development19
- Powerful16
- Expressive15
- Flexibilty14
- Customizable14
- Simple to use14
- Love it13
- Awesome13
- Get started quickly13
- Speed12
- Perfect for small to large projects with superb docs.11
- Easy to integrate11
- For it flexibility10
- Productive9
- Flexibilty and easy to use9
- Not JS8
- Flask8
- User friendly7
- Secured6
- Unopinionated5
- Orm3
- Secure2
- Not JS10
- Context7
- 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.
Hey, so I developed a basic application with Python. But to use it, you need a python interpreter. I want to add a GUI to make it more appealing. What should I choose to develop a GUI? I have very basic skills in front end development (CSS, JavaScript). I am fluent in python. I'm looking for a tool that is easy to use and doesn't require too much code knowledge. I have recently tried out Flask, but it is kinda complicated. Should I stick with it, move to Django, or is there another nice framework to use?
Node.js
- Npm1.4K
- Javascript1.3K
- Great libraries1.1K
- High-performance1K
- Open source802
- Great for apis485
- Asynchronous475
- Great community420
- Great for realtime apps390
- Great for command line utilities296
- Websockets82
- Node Modules82
- Uber Simple69
- Great modularity59
- Allows us to reuse code in the frontend58
- Easy to start42
- Great for Data Streaming35
- Realtime32
- Awesome28
- Non blocking IO25
- Can be used as a proxy18
- High performance, open source, scalable17
- Non-blocking and modular16
- Easy and Fun15
- Easy and powerful14
- Same lang as AngularJS13
- Future of BackEnd13
- Fullstack12
- Fast11
- Cross platform10
- Scalability10
- Simple9
- Mean Stack8
- Great for webapps7
- Easy concurrency7
- Typescript6
- React6
- Fast, simple code and async6
- Friendly6
- Great speed5
- Easy to use and fast and goes well with JSONdb's5
- Scalable5
- Its amazingly fast and scalable5
- Control everything5
- Fast development5
- Isomorphic coolness4
- Easy to use4
- It's fast4
- Great community3
- Scales, fast, simple, great community, npm, express3
- TypeScript Support3
- Sooper easy for the Backend connectivity3
- Not Python3
- One language, end-to-end3
- Easy3
- Easy to learn3
- Less boilerplate code3
- Performant and fast prototyping3
- Blazing fast3
- Event Driven2
- Lovely2
- Npm i ape-updating2
- Creat for apis1
- Node0
- Bound to a single CPU46
- New framework every day44
- Lots of terrible examples on the internet38
- Asynchronous programming is the worst31
- Callback23
- Javascript18
- Dependency based on GitHub11
- Dependency hell11
- Low computational power10
- Very very Slow7
- Can block whole server easily7
- Callback functions may not fire on expected sequence6
- Unneeded over complication3
- Unstable3
- Breaking updates3
- No standard approach2
- Bad transitive dependency management1
- Can't read server session1
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:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
- 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?
- Easy-to-understand concurrency5
- Twisted prevails3
- It works1
- Solid, flexible, powerful1
related Twisted posts
- Rapid development660
- Open source480
- Great community415
- Easy to learn371
- Mvc270
- Beautiful code225
- Elegant217
- Free200
- Great packages197
- Great libraries186
- Restful74
- Comes with auth and crud admin panel73
- Powerful72
- Great documentation69
- Great for web64
- Python52
- Great orm39
- Great for api37
- All included28
- Fast25
- Web Apps23
- Clean21
- Used by top startups20
- Easy setup19
- Sexy17
- Convention over configuration14
- ORM14
- Allows for very rapid development with great libraries13
- The Django community12
- Great MVC and templating engine10
- King of backend world10
- Full stack8
- Batteries included7
- Its elegant and practical7
- Fast prototyping6
- Very quick to get something up and running6
- Cross-Platform6
- Have not found anything that it can't do6
- Mvt6
- Zero code burden to change databases5
- Easy to develop end to end AI Models5
- Easy Structure , useful inbuilt library5
- Map4
- Easy to change database manager4
- Easy4
- Great peformance4
- Many libraries4
- Python community4
- Modular4
- Easy to use4
- Just the right level of abstraction3
- Scaffold3
- Full-Text Search3
- Scalable1
- Node js1
- Rails0
- Fastapi0
- Underpowered templating26
- Autoreload restarts whole server22
- Underpowered ORM22
- URL dispatcher ignores HTTP method15
- Internal subcomponents coupling10
- Not nodejs8
- Configuration hell8
- Admin7
- Not as clean and nice documentation like Laravel5
- Python3
- Not typed3
- Bloated admin panel included3
- Overwhelming folder structure2
- InEffective Multithreading2
- Not type safe1
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.
Hey, so I developed a basic application with Python. But to use it, you need a python interpreter. I want to add a GUI to make it more appealing. What should I choose to develop a GUI? I have very basic skills in front end development (CSS, JavaScript). I am fluent in python. I'm looking for a tool that is easy to use and doesn't require too much code knowledge. I have recently tried out Flask, but it is kinda complicated. Should I stick with it, move to Django, or is there another nice framework to use?