FastStream releases a new minor version 0.4.0 today
This release adds support for Confluent's Python Client for Apache Kafka™. Confluent's Python Client for Apache Kafka does not natively support async functions, and its integration with modern async-based services is a bit trickier. That was the reason why our initial support for Kafka broker used aiokafka. However, that choice was less fortunate as it is not as well maintained as the Confluent version. After receiving numerous requests, we finally decided to bite the bullet and create an async wrapper around Confluent's Python Client and add full support for it in FastStream.
faststream-gen(https://github.com/airtai/faststream-gen/) uses GPT models to automatically generate microservices using the FastStream(https://github.com/airtai/faststream) framework for Apache Kafka, RabbitMQ and NATS. Simply describe your microservice in plain English, and it will generate a production-level FastStream application ready to deploy in a few minutes and under $1 cost, together with unit and integration tests, documentation and Docker images.
faststream-gen(https://github.com/airtai/faststream-gen/) uses GPT models to automatically generate microservices using the FastStream(https://github.com/airtai/faststream) framework for Apache Kafka, RabbitMQ and NATS. Simply describe your microservice in plain English, and it will generate a production-level FastStream application ready to deploy in a few minutes and under $1 cost, together with unit and integration tests, documentation and Docker images.
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)
A few months back, we released FastKafka, a free and open-source framework 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. The latest version adds support for newly released Pydantic v2.0, making it significantly faster.
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.
If you are working with streaming data, please test it and give us feedback for making it better. Visit the GitHub repository to get started, and don’t hesitate to reach out to the FastKafka team. We appreciate all comments, questions, feature requests, critiques even! :)
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This release adds support for Confluent's Python Client for Apache Kafka™. Confluent's Python Client for Apache Kafka does not natively support async functions, and its integration with modern async-based services is a bit trickier. That was the reason why our initial support for Kafka broker used aiokafka. However, that choice was less fortunate as it is not as well maintained as the Confluent version. After receiving numerous requests, we finally decided to bite the bullet and create an async wrapper around Confluent's Python Client and add full support for it in FastStream.
You can find the release here - https://github.com/airtai/faststream/releases/tag/0.4.0
Please take a look at it, play with it, and if you are satisfied, then go ahead use it in your projects: https://faststream.airt.ai/0.4/confluent/