fastkafka VS pythagora

Our journey with FastStream started when we needed to integrate our machine learning models into a customer's Apache Kafka environment. To streamline this process, we created FastKafka using AIOKafka, AsyncAPI, and asyncio. It was our first step in making message queue management easier.

After a short discussion, we concluded we were just too spoiled to use low-level libraries that were nothing more than just tiny wrappers around C++ libs and that we could just build our own. So, we shamelessly made one by reusing beloved paradigms from FastAPI and we shamelessly named it FastKafka. The point was to set the expectations right - you get pretty much what you would expect: function decorators for consumers and producers with type hints specifying Pydantic classes for JSON encoding/decoding, automatic message routing to Kafka brokers and documentation generation.

FastStream simplifies the process of writing producers and consumers for message queues, handling all the parsing, networking and documentation generation automatically. It is a new package based on the ideas and experiences gained from FastKafka and Propan. By joining our forces, we picked up the best from both packages and created a unified way to write services capable of processing streamed data regardless of the underlying protocol. We'll continue to maintain both packages, but new development will be in this project.

FastStream (https://github.com/airtai/faststream) is a new Python framework, born from Propan and FastKafka teams' collaboration (both are deprecated now). It extremely simplifies event-driven system development, handling all the parsing, networking, and documentation generation automatically. Now FastStream supports RabbitMQ and Kafka, but supported brokers are constantly growing (wait for NATS and Redis a bit). FastStream itself is a really great tool to build event-driven services. Also, it has a native FastAPI integration. Just create a StreamRouter (very close to APIRouter) and register event handlers the same with the regular HTTP-endpoints way:

Inspired by FastAPI, FastKafka uses the same paradigms for routing, validation, and documentation, making it easy to learn and integrate into your existing streaming data projects. Please check out the latest version adds supporting the newly released Pydantic v2.0, making it significantly faster. https://github.com/airtai/fastkafka

Product which you can try - https://github.com/Pythagora-io/pythagora, check also video - Open-Source AI Agent Can Build FULL STACK Apps (FREE “Devin” Alternative) (youtube.com)

P.S. If you found this post helpful, it would mean a lot to me if you starred the Pythagora Github repo and if you try Pythagora out, please let us know how it went on [email protected].

Thanks, yes, it can, we actually started off with integration tests. Take a look at the integration tests README. They work by recording server activity (db queries, 3rd party API requests, etc.) during the processing of an API request.

Recently, I open sourced Pythagora - a dev tool that tracks the server activity and creates integration tests from it. However, many people said that they wanted to analyze better what is inside the tests and to use tests as code documentation. So, I used GPT-4 to export Pythagora tests (which are basically JSON files that contain all captured data) to Jest code which can be reviewed and used for documentation.

I'm working on Pythagora, an open source tool that writes automated integration tests by itself (well, with a bit of help from GPT-4) without you, the dev, having to write a single line of code. Basically, you can get from 0 to 80% code code coverage within 30 minutes (video).

Hey! I'm working on Pythagora (https://github.com/Pythagora-io/pythagora) - it's an open source tool that creates automated integration tests by analyzing server activity without you having to write a single line of code.

Pythagora is an NPM package designed to create automated tests for your Node.js applications by analyzing server activity. It records all requests to your app's endpoints, along with responses and server actions, such as Mongo and Redis queries. Pythagora during testing simulates the server conditions from the time when the request was captured, allowing for consistent and accurate testing across different environments.

Finally, if you read this far and would like to support us, please consider starring the Pythagora Github repository here – it would mean the world to us.