Using multiple AIOKafkaProducer instances !

[YozEllon]

Hi everyone 👋

1- When I was checking the codes related to produces decorator, I noticed that for each topic, an instance is created separately from AIOKafkaProducer and every time it is done using the object related to the produce topic, can I know the reason? Why don't we use one instance for all topics?

2- When we want to use the producers defined directly in the app, a connection is opened and closed every time, isn't it better to have one open connection in the whole program?

main.py

from fastapi import FastAPI
from kafka import to_output_data, kafka_app

app = FastAPI()

@app.get("/")
async def root()
    async with kafka_app:
        await to_output_data(1.0)

[sternakt]

👋 Hello @YozEllon !

Regarding FastKafka Deprecated:
Firstly, I'd like to inform you that FastKafka has been deprecated, and we recommend transitioning to FastStream. FastStream not only supports everything FastKafka does but also introduces additional features with minimal API changes. Make sure to check out the FastStream documentation for a seamless transition.

Multiple Producers and FastStream:
You are absolutely correct in observing that multiple instances of AIOKafkaProducer are created (one for each topic). In hindsight, it would have been more efficient to use a single instance for all topics. We have recognized this improvement, and in FastStream, we have indeed adopted a more streamlined approach. With FastStream, a single producer instance is used to produce messages to multiple topics.

Persistent Connection in the Whole Program:
Regarding opening and closing connections every time producers are defined directly in the app, you're right; it would be more efficient to maintain a persistent connection throughout the program. In FastKafka and FastStream, we recommend using FastAPI lifespan events to manage the lifecycle of your FastKafka instance. For example, you can modify your FastAPI app instantiation as follows:

from fastapi import FastAPI
from kafka import to_output_data, kafka_app

app = FastAPI(lifespan=kafka_app.fastapi_lifespan("localhost"))

@fastapi_app.get("/")
async def root():
    await to_output_data(1.0)

By incorporating FastAPI lifespan events, you can efficiently manage the lifecycle of your FastKafka instance, maintaining a persistent connection throughout the program.

For more detailed information, you can refer to the FastStream guide on using FastAPI to run FastKafka applications.

Or, if transitioning to FastStream, take a look at This guide on how to integrate with FastAPI

Feel free to reach out if you have any further questions or need additional clarification! 🚀