connexion
dash
connexion | dash | |
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23 | 56 | |
4,420 | 20,544 | |
0.3% | 0.9% | |
8.2 | 9.6 | |
7 days ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
connexion
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Write OpenAPI with TypeSpec
I like the idea, especially the TS-like syntax around enums and union types. I've always preferred the SDL for GraphQL vs writing OpenAPI for similar reasons.
I echo the sentiment others have brought up, which is the trade-offs of a code-driven schema vs schema-driven code.
At work we use Pydantic and FastAPI to generate the OpenAPI contract, but there's some cruft and care needed around exposing those underlying Pydantic models through the API documentation. It's been easy to create schemas that have compatibility problems when run through other code generators. I know there are projects such as connexction[1] which attempt to inverse this, but I don't have much experience with it. In the GraphQL space it seems that code-first approaches are becoming more favored, though there's a different level of complexity needed to create a "typesafe" GraphQL server (eg. model mismatches between root query resolvers and field resolvers).
[1] https://github.com/spec-first/connexion
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Connexion 3 released!
Connexion is a popular Python web framework (~ 5 million downloads per month) that makes spec-first and api-first development easy. You describe your API in an OpenAPI (or swagger) specification with as much detail as you want and Connexion will guarantee that it works as you specified.
- Connexion 3.0 Released
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Show HN: REST Alternative to GraphQL and tRPC
> While REST APIs don't generally provide the same level of control to clients as GraphQL, many times this could be seen as a benefit especially in scenarios where strict control over data access and operations is crucial.
Rest is more secure, cacheable, and more performant on the server side as field resolution doesn't need to happen like it does with GraphQL. It is not more performant on the client side, and this is a trade-off, but I favor rest applications over GraphQL ones as a DevOps engineer. They are much easier to administer infrastructure-wise, I can cache the requests, etc.
Data at our company suggests that several small queries actually do better performance-wise than one large one. We switched to GraphQL a year and a half ago or so, but this piece of data seems to suggest that we might have been better off just sticking with REST. My suggestion to that effect was not met with optimism either on the client or server side. Apparently there are server-side benefits as well, allowing for more modular development or something like that.
I have used OpenAPI using connexion[1]. It was hard to understand at first, but I really liked that the single source of truth was one schema. It also made it really easy to develop against the API because it came with a UI that showed the documentation for all the rest end points and even had test buttons.
1: https://connexion.readthedocs.io/en/latest/
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Ask HN: Why is there no specification for Command Line Interfaces?
What's the use case? I was thinking about this exact issue because my product ships several CLI tools, but I wasn't convinced it would be worth the effort.
An OpenAPI specification describes an HTTP interface, and I see it as useful because it makes it easier to write code in language-of-choice to generate HTTP requests (by generating client libraries from the OpenAPI spec).
For a CLI, the interface is the command-line. Usually people type these commands, or they end up in bash scripts, or sometimes they get called from programming language of choice by shelling out to the CLI. So I could see a use case for a CLI spec, which would make it easier to generate client libraries (which would shell out to the CLI)... but it seems a little niche.
Or maybe, as input to a documentation tool (like Swagger docs). I would imagine if you're using a CLI library like Python's Click, most of that data is already there. Click Parameters documentation: https://click.palletsprojects.com/en/8.1.x/parameters/
Or maybe, you could start from the spec and then generate code which enforces it. So any changes pass through the spec, which would make it easy to write code (server and client-side) / documentation / changelogs. Some projects like this: Guardrail (Scala) https://github.com/guardrail-dev/guardrail , and Connexion (Python) https://github.com/spec-first/connexion .
But without this ecosystem of tooling, documenting your CLI in a specification didn't really seem worth the effort. Of course, that's a bootstrapping problem.
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Flask is Great!
Connexion is a framework on top of Flask that automagically handles HTTP requests defined using OpenAPI/Swagger.
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What is the best practice for mapping JSON requests to objects and back to JSON?
I recommend you create a OpenAPI Specification and implement a python module that you expose via connexion or on the cli via click(for easy testing).
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Flask-Powered APIs: Fast, Reliable, and Used by the World's Top Companies
I'm here because Swagger-CodeGen created flask-Connexion boilerplate for python.
- Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 1 – Real Python
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Does anybody know any good resources I could use to study ISP architecture?
Personally we just prov them using librouteros and flask-connexion/openapi.
dash
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dash VS solara - a user suggested alternative
2 projects | 13 Oct 2023
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[Python] NiceGUI: Lassen Sie jeden Browser das Frontend für Ihren Python-Code sein
Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally, we wanted to offer a gentle learning curve while still providing the ability to go all the way down to HTML, CSS, and JavaScript if needed.
- Visualizing parquet in s3 bucket for data analysis?
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Little guidance of a python newbie
You could use something like Streamlit or Dash. In any case you will be accessing your app through the browser.
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Launch HN: Pynecone (YC W23) – Web Apps in Pure Python
Useful list. Dash & bokeh as two more in the space
https://github.com/plotly/dash
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Python projects with best practices on Github?
I also heard of Dash which serves the same purpose I guess, but I think it has more to offer.
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4 Streamlit Alternatives for Building Python Data Apps
Plotly is a plotting library, and Dash is their open-source framework for building data apps with Python, R or Julia. (Dash also has an Enterprise version, but we'll focus on the open-source library here.)
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NiceGUI: Let any browser be the frontend for your Python code
Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally, we wanted to offer a gentle learning curve while still providing the ability to go all the way down to HTML, CSS, and JavaScript if needed.
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Sharing interactive Plotly graphs
looks like you can get it manually (albeit with a loss of interactivity) https://github.com/plotly/dash/issues/145
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Containerizing Shiny for Python and Shinylive Applications
Shiny is a framework that makes it easy to build interactive web applications. Shiny was introduced 10 years ago as an R package. In his 10th anniversary keynote speech, Joe Cheng announced Shiny for Python at the 2022 RStudio Conference. Python programmers can now try out Shiny to create interactive data-driven web applications. Shiny comes as an alternative to other frameworks, like Dash, or Streamlit.
What are some alternatives?
flask-restful - Simple framework for creating REST APIs
streamlit - Streamlit — A faster way to build and share data apps.
Flask RestPlus - Fully featured framework for fast, easy and documented API development with Flask
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
flasgger - Easy OpenAPI specs and Swagger UI for your Flask API
panel - Panel: The powerful data exploration & web app framework for Python
django-rest-framework - Web APIs for Django. 🎸
uvicorn - An ASGI web server, for Python. 🦄
eve - REST API framework designed for human beings
Flask - The Python micro framework for building web applications.
falcon - The no-magic web data plane API and microservices framework for Python developers, with a focus on reliability, correctness, and performance at scale.
nicegui - Create web-based user interfaces with Python. The nice way.