fastkafka VS spec

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

AsyncAPI: Interested in how to define your WebSocket APIs? One of the most advanced realtime specifications is the AsyncAPI specification, which comes with various generators for code and documentation, as well as renderers for the specifications.

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.

Automatic Docs: Stay ahead with automatic AsyncAPI documentation

FastStream supports in-memory testing, AsyncAPI schema generation and more... If you are interested, please support our project by giving a GH start and joining our discord server.

However, in order for the system to work effectively, there must be a common understanding between the components regarding events and their data structures. This is where AsyncAPI comes in; it helps define a contract that describes how the components communicate and behave effectively.

You can also take a look at https://www.asyncapi.com/ (a spec for asynchronous APIs). It's useful for this use case, that is, building a well structured websocket interface with pub/sub.

Sure! Next step I am working on AsyncAPI scheme generation by your application code. It's also includes a project generation from scheme, scheme web view (lika the Swagger for OpanAPI), etc. It will a much difficult than just another broker implementation...

Onboarding - Enable developers to quickly learn how to consume the exposed APIs. For example, offer OpenAPI or AsyncAPI documentation and provide a portal and sandbox.