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Flask vs Julia: What are the differences?
What is Flask? a microframework for Python based on Werkzeug, Jinja 2 and good intentions. Flask is intended for getting started very quickly and was developed with best intentions in mind.
What is Julia? A high-level, high-performance dynamic programming language for technical computing. Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
Flask can be classified as a tool in the "Microframeworks (Backend)" category, while Julia is grouped under "Languages".
"Lightweight" is the primary reason why developers consider Flask over the competitors, whereas "Lisp-like Macros" was stated as the key factor in picking Julia.
Flask and Julia are both open source tools. It seems that Flask with 45.2K GitHub stars and 12.7K forks on GitHub has more adoption than Julia with 22.7K GitHub stars and 3.43K GitHub forks.
According to the StackShare community, Flask has a broader approval, being mentioned in 511 company stacks & 531 developers stacks; compared to Julia, which is listed in 5 company stacks and 5 developer stacks.
My journey to developing REST APIs started with Flask Restful, and I've found it to be enough for the needs of my project back then. Now that I've started investing more time on personal projects, I've yet to decide if I should move to use Django for writing REST APIs. I often see job posts looking for Python+Django developers, but it's usually for full-stack developers. I'm primarily interested in Data Engineering, so most of my web projects are back end.
Should I continue with what I know (Flask) or move on to Django?
If you want to be a Web developer with knowledge in another frontend and NoSql technology, maybe continue with Flask. However, if you want to create very fast solutions to grow up with a new business and merge these with data analysis and other tools, Django is the answer. Basically read more about the service architecture where you feel more comfortable, Microservice or Monolithic, but please will not married with any because they solve issues to different contexts.
Which is the best Python framework for microservices?
We are using Nameko for building microservices in Python. The things we really like are dependency injection and the ease with which one can expose endpoints via RPC over RabbitMQ. We are planning to try a tool that helps us write polyglot microservices and nameko is not super compatible with it. Also, we are a bit worried about the not so good community support from nameko and looking for a python alternate to write microservices.
Bottle is much less bloated and fast. Its built-in templating system is one of the fastest as it compiles the templates in bytecode. Also Bottle has no depenencies, preventing dependency bloat.
I have just started learning Python 3 weeks ago. I want to create a REST API using python. The API will be used to save form data in an Oracle database. The front end is using AngularJS 8 with Angular Material. In python, there are so many frameworks to develop REST APIs.
I am looking for some suggestions which REST framework to choose?
Here are some features I am looking for:
Easy integration and unit testing, like in Angular. We just want to run a command.
Code packaging, like in java maven project we can build and package. I am looking for something which I can push in as an artifact and deploy whole code as a package.
Support for swagger/ OpenAPI
Support for JSON Web Token
Support for test case coverage report
Framework can have features included or can be available by extension. Also, you can suggest a framework other than the ones I have mentioned.
For starters flask provides a beautiful and easy way to create REST APIs. Also its supported by excellent beginner docs as well as a very active community. Another good thing with Flask is its widely available list of plugins which allow you to build as you go. Its also good in performance and can scale to a quite decent level. However, if you are sure your project is going to be fairly big, it would be better to start with Django as it provides a lot of features out of the box and is extremely stable in performance. Both these frameworks have support for Swagger, JWT, Coverage Report although you have to install plugins for them. Deploying both of these are fairly simple and there is huge documentation available. Django has one of the best documentations I have come across. I hope I was able to answer your queries.
After writing a project in Julia we decided to stick with Kotlin. Julia is a nice language and has superb REPL support, but poor tooling and the lack of reproducibility of the program runs makes it too expensive to work with. Kotlin on the other hand now has nice Jupyter support, which mostly covers REPL requirements.
Pros of Flask
- For it flexibility10
- Flexibilty and easy to use9
- User friendly7
- Secured6
- Unopinionated5
- Secure2
- Customizable2
- Simple to use1
- Powerful1
- Rapid development1
- Flask1
- Easy to get started1
- Easy to develop and maintain applications1
- Easy to setup and get it going1
- Easy to use1
- Documentation1
- Beautiful code1
- Orm1
- Not JS1
- Perfect for small to large projects with superb docs.1
- Easy to integrate1
- Speed1
- Get started quickly1
- Python1
- Minimal1
- Lightweight1
- Flexibilty0
- Well designed0
- Productive0
- Awesome0
- Open source0
- Expressive0
- Love it0
Pros of Julia
- Fast Performance and Easy Experimentation25
- Designed for parallelism and distributed computation22
- Free and Open Source19
- Dynamic Type System17
- Calling C functions directly17
- Multiple Dispatch16
- Lisp-like Macros16
- Powerful Shell-like Capabilities10
- Jupyter notebook integration10
- REPL8
- String handling4
- Emojis as variable names4
- Interoperability3
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Cons of Flask
- Not JS10
- Context7
- Not fast5
- Don't has many module as in spring1
Cons of Julia
- Immature library management system5
- Slow program start4
- JIT compiler is very slow3
- Poor backwards compatibility3
- Bad tooling2
- No static compilation2
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