davorrunje

FastStream allows for declarative way of defining consumers and producer based on Pydantic classes representing message types. Currently, it supports Apache Kafka, RabiitMQ and NATS, while other protocols are planned in the near future. It automatically generates documentation from the code, generates Docker image with the service and has support for in-memory execution of unit and integration tests without the actual broker.

Please check it out at: https://faststream.airt.ai/

Thanx :)

We are working a lot on improving productivity of a developer. The current code generator is just a start and we have a ton of new stuff in development. And yes, for someone who had spend years writing RoR apps such as myself, the inspiration for much of this is quite clear :)

Actually, serving ML inferences was the initial motivation for building the library. We couldn’t find anything similar to FastAPI for Apache Kafka and then we decided to build one.

It is an interesting feature request, I create an issue that you can follow the progress:

https://github.com/airtai/faststream/issues/758

FastStream simplifies the process of writing producers and consumers for message queues, handling all the parsing, networking and documentation generation automatically.

Here's a look at the core features that make FastStream a go-to framework for modern, data-centric microservices:

- Multiple Brokers: FastStream provides a unified API to work across multiple message brokers (Kafka, RabbitMQ support)

- Pydantic Validation: Leverage Pydantic's validation capabilities to serialize and validates incoming messages

- Automatic Docs: Stay ahead with automatic AsyncAPI documentation

- Intuitive: Full-typed editor support makes your development experience smooth, catching errors before they reach runtime

- Powerful Dependency Injection System: Manage your service dependencies efficiently with FastStream's built-in DI system

- Testable: Supports in-memory tests, making your CI/CD pipeline faster and more reliable

- Extendable: Use extensions for lifespans, custom serialization and middlewares

- Integrations: FastStream is fully compatible with any HTTP framework you want (FastAPI especially)

- Built for Automatic Code Generation: FastStream is optimized for automatic code generation using advanced models like GPT and Llama. Please check out the tutorial available here: https://faststream-gen.airt.ai/Tutorial/Cryptocurrency_Tutor...

FastKafka, a modern Python library for building asynchronous web services that interact with Kafka topics, has just released a new version that includes an in-memory mockup of Kafka brokers. This new feature allows developers to test their code without the need to run actual Kafka brokers, making the testing process faster and more efficient. Built on top of Pydantic, AIOKafka, and AsyncAPI, FastKafka continues to simplify the process of writing producers and consumers for Kafka topics, making it even easier for developers to work with Kafka in their Python projects.

Thanks for the support :)

Right now, we just log the error but we can implement a callback easily. I’ll create an issue and it should be implemented in the next release in two weeks.

FastKafka is a new open-source software available on GitHub under the Apache 2 license that simplifies the process of writing producers and consumers for Kafka topics. Inspired by FastAPI, FastKafka uses the same paradigms for routing, validation, and documentation, making it easy to learn and integrate into your existing projects.

Built on top of Pydantic, AIOKafka, and AsyncAPI, FastKafka handles all the parsing, networking, task scheduling, and documentation generation automatically, making it an ideal choice for developers looking to streamline their workflow and accelerate their projects.

With FastKafka, you can quickly prototype and develop high-performance Kafka-based services with minimal code. And, because it's open-source, you can use it in your projects for free and contribute to its development if you'd like.

We're excited to announce the release of FastKafka and invite you to test the tool and provide feedback for making it better. Visit the GitHub repository to get started, and don't hesitate to reach out to the FastKafka team with any questions or suggestions.