Introduction to Faust: A Python Stream Processing Library for Kafka

In distributed systems and real‑time data processing, stream processing is crucial because data arrives quickly and must be handled promptly.

Frameworks such as Storm, Spark Streaming, Flink and Kafka Streams exist; Faust brings Kafka Streams concepts to Python, offering a concise, high‑performance library that works with NumPy, PyTorch, Pandas, and other Python data tools.

Overview

Faust is an open‑source Python stream‑processing library from Robinhood, currently at version 1.10.4. It implements Kafka Streams‑style APIs, supports asynchronous processing, and runs in a distributed, highly available fashion.

Installation $ pip install -U faust Optional dependencies such as rocksdb or Redis can be added for storage and caching.

Simple Example

import faust
app = faust.App('hello-world',
                broker='kafka://localhost:9092',
                value_serializer='raw')
greetings_topic = app.topic('greetings')

@app.agent(greetings_topic)
async def greet(greetings):
    async for greeting in greetings:
        print(greeting)

The application is started with: $ faust -A hello_world worker -l info Messages can be sent to the topic using:

$ faust -A hello_world send @greet "Hello Faust"

Faust also supports typed data models:

class Greeting(faust.Record):
    from_name: str
    to_name: str

app = faust.App('hello-app', broker='kafka://localhost')
topic = app.topic('hello-topic', value_type=Greeting)

@app.agent(topic)
async def hello(greetings):
    async for greeting in greetings:
        print(f'Hello from {greeting.from_name} to {greeting.to_name}')

@app.timer(interval=1.0)
async def example_sender(app):
    await hello.send(value=Greeting(from_name='Faust', to_name='you'))

if __name__ == '__main__':
    app.main()

Running the worker reads data from Kafka in real time and processes it according to the defined agents.

Conclusion

Faust brings Kafka Streams to Python, providing a simple decorator‑based API, type‑hinted data models, and full async support, making it a powerful tool for building high‑performance real‑time data pipelines.