FastAPI StreamingResponse: When Generators Don't Stream
FastAPI's StreamingResponse is a powerful tool for efficiently handling large datasets or responses that need to be generated on the fly. However, using it with generator functions can sometimes lead to unexpected results. You might find yourself sending the entire generated data at once instead of streaming it, defeating the purpose of using StreamingResponse.
The Problem: Delayed Streaming
Let's imagine we want to create a FastAPI endpoint that generates a large CSV file, line by line, and streams it to the client. Our naive approach might look like this:
from fastapi import FastAPI, StreamingResponse
app = FastAPI()
@app.get("/csv")
async def get_csv():
def generate_csv():
for i in range(1000000):
yield f"row_{i}\n"
return StreamingResponse(generate_csv(), media_type="text/csv")
The intention is to stream each line generated by generate_csv() to the client. However, this code often ends up sending the entire CSV file at once, defeating the purpose of streaming.
The Cause: Eager Evaluation
The problem arises because Python generators are evaluated eagerly by default. When you call StreamingResponse(generate_csv(), ...) within the FastAPI route, the entire generator function is evaluated before the response is sent. This means that the generator's entire output is stored in memory before the response is sent, leading to a non-streamed response.
The Solution: Async Generators to the Rescue
To achieve true streaming, we need to leverage the power of asynchronous generators in Python. They allow us to generate values lazily, only when needed.
Here's the corrected version:
from fastapi import FastAPI, StreamingResponse
app = FastAPI()
@app.get("/csv")
async def get_csv():
async def generate_csv():
for i in range(1000000):
yield f"row_{i}\n"
return StreamingResponse(generate_csv(), media_type="text/csv")
By making generate_csv() an asynchronous generator (async def), we ensure that each line is generated on demand, leading to proper streaming.
Key Takeaways
- Always use async generators when working with
StreamingResponseto avoid premature evaluation of generators and ensure proper streaming. StreamingResponseis a valuable tool for efficient handling of large datasets and dynamic content in FastAPI.- Understanding the differences between regular generators and asynchronous generators is crucial for efficient and scalable API design.
Additional Resources
- FastAPI Documentation: Official documentation with extensive information on FastAPI.
- Async Generators in Python: An in-depth guide on using asynchronous generators in Python.
- Streaming Large Files with FastAPI: A FastAPI tutorial focusing on streaming large files.
By understanding the nuances of asynchronous generators and StreamingResponse in FastAPI, you can confidently build powerful and efficient APIs for handling large datasets and streaming content seamlessly.
