FastAPI Runs API Calls In Serial Instead Of Parallel Fashion

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Introduction

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It's designed to be fast, scalable, and easy to use. However, when it comes to handling multiple API calls concurrently, FastAPI may not behave as expected. In this article, we'll explore why FastAPI runs API calls in serial instead of parallel fashion and provide solutions to improve concurrency.

Understanding Concurrency in FastAPI

Concurrency is the ability of a system to execute multiple tasks simultaneously. In the context of FastAPI, concurrency refers to the ability of the framework to handle multiple API calls at the same time. However, by default, FastAPI uses the asyncio library to handle asynchronous tasks, which can lead to serial execution of API calls.

Why FastAPI Runs API Calls in Serial

When you make multiple API calls to a FastAPI application, you might expect them to be executed concurrently. However, due to the way FastAPI handles asynchronous tasks, API calls are executed in serial fashion. This means that each API call is executed one after the other, rather than simultaneously.

The reason for this behavior lies in the way FastAPI uses the asyncio library. When you make an API call, FastAPI creates a new task using the asyncio.create_task() function. However, by default, asyncio uses a single-threaded executor, which means that only one task can be executed at a time. This leads to serial execution of API calls.

Example Use Case

Let's consider an example use case to illustrate the issue. Suppose we have a FastAPI application that exposes two API endpoints: /ping and /sleep. The /ping endpoint simply returns a "Hello" message, while the /sleep endpoint simulates a long-running task by sleeping for 5 seconds.

from fastapi import FastAPI, Request
import time
import asyncio

app = FastAPI()


@app.get("/ping")
async def ping(request: Request):
print("Hello")
return "message" "Hello"


@app.get("/sleep")
async def sleep(request: Request):
await asyncio.sleep(5)
print("Sleeping task completed")
return "message" "Sleeping task completed"

When we make multiple API calls to this application, we might expect the /ping and /sleep endpoints to be executed concurrently. However, due to the serial execution of API calls, the /sleep endpoint is executed after the /ping endpoint, even if we make multiple API calls to the /ping endpoint.

Solutions to Improve Concurrency

To improve concurrency in FastAPI, we can use the following solutions:

1. Use a Multi-Threading Executor

One way to improve concurrency in FastAPI is to use a multi-threading executor. We can create a new executor using the asyncio.ThreadPoolExecutor class and use it to execute tasks concurrently.

from fastapi import FastAPI, Request
import time
import asyncio

app = FastAPI()


@app.get("/ping")
async def ping(request: Request):
print("Hello")
return "" "Hello"


@app.get("/sleep")
async def sleep(request: Request):
loop = asyncio.get_running_loop()
with asyncio.ThreadPoolExecutor() as executor:
await loop.run_in_executor(executor, asyncio.sleep, 5)
print("Sleeping task completed")
return "message" "Sleeping task completed"

2. Use a Multi-Processing Executor

Another way to improve concurrency in FastAPI is to use a multi-processing executor. We can create a new executor using the multiprocessing.Pool class and use it to execute tasks concurrently.

from fastapi import FastAPI, Request
import time
import asyncio
import multiprocessing

app = FastAPI()


@app.get("/ping")
async def ping(request: Request):
print("Hello")
return "message" "Hello"


@app.get("/sleep")
async def sleep(request: Request):
loop = asyncio.get_running_loop()
with multiprocessing.Pool() as pool:
await loop.run_in_executor(pool, asyncio.sleep, 5)
print("Sleeping task completed")
return "message" "Sleeping task completed"

3. Use a Third-Party Library

There are several third-party libraries available that can help improve concurrency in FastAPI. For example, we can use the uvloop library to create a new event loop that supports concurrent execution of tasks.

from fastapi import FastAPI, Request
import time
import asyncio
import uvloop

app = FastAPI()


async def ping(request: Request):
print("Hello")
return "message" "Hello"


async def sleep(request: Request):
await asyncio.sleep(5)
print("Sleeping task completed")
return "message" "Sleeping task completed"


app.get("/ping")(ping)
app.get("/sleep")(sleep)


loop = uvloop.EventLoop()
asyncio.set_event_loop(loop)

Conclusion

In conclusion, FastAPI runs API calls in serial instead of parallel fashion due to the way it uses the asyncio library. However, there are several solutions available to improve concurrency in FastAPI, including using a multi-threading executor, a multi-processing executor, or a third-party library. By using these solutions, we can improve the performance and scalability of our FastAPI applications.

References

Q: What is the main reason why FastAPI runs API calls in serial instead of parallel fashion?

A: The main reason why FastAPI runs API calls in serial instead of parallel fashion is due to the way it uses the asyncio library. By default, asyncio uses a single-threaded executor, which means that only one task can be executed at a time. This leads to serial execution of API calls.

Q: How can I improve concurrency in FastAPI?

A: There are several ways to improve concurrency in FastAPI, including:

  • Using a multi-threading executor
  • Using a multi-processing executor
  • Using a third-party library such as uvloop

Q: What is the difference between a multi-threading executor and a multi-processing executor?

A: A multi-threading executor uses multiple threads to execute tasks concurrently, while a multi-processing executor uses multiple processes to execute tasks concurrently. In general, multi-processing is more efficient than multi-threading, but it also requires more memory and can be more complex to use.

Q: How do I use a multi-threading executor in FastAPI?

A: To use a multi-threading executor in FastAPI, you can create a new executor using the asyncio.ThreadPoolExecutor class and use it to execute tasks concurrently. Here is an example:

from fastapi import FastAPI, Request
import time
import asyncio

app = FastAPI()


@app.get("/ping")
async def ping(request: Request):
print("Hello")
return "message" "Hello"


@app.get("/sleep")
async def sleep(request: Request):
loop = asyncio.get_running_loop()
with asyncio.ThreadPoolExecutor() as executor:
await loop.run_in_executor(executor, asyncio.sleep, 5)
print("Sleeping task completed")
return "message" "Sleeping task completed"

Q: How do I use a multi-processing executor in FastAPI?

A: To use a multi-processing executor in FastAPI, you can create a new executor using the multiprocessing.Pool class and use it to execute tasks concurrently. Here is an example:

from fastapi import FastAPI, Request
import time
import asyncio
import multiprocessing

app = FastAPI()


@app.get("/ping")
async def ping(request: Request):
print("Hello")
return "message" "Hello"


@app.get("/sleep")
async def sleep(request: Request):
loop = asyncio.get_running_loop()
with multiprocessing.Pool() as pool:
await loop.run_in_executor(pool, asyncio.sleep, 5)
print("Sleeping task completed")
return "message" "Sleeping task completed"

Q: What is the uvloop library and how can I use it in FastAPI?

A: The uvloop library is a third-party library that provides a high-performance event loop for Python. You can use uvloop in FastAPI by creating a new event loop using the uvloop.EventLoop class and setting it as the default event loop using the asyncio.set_loop() function. Here is an example:

from fastapi import FastAPI, Request
import time
import asyncio
import uvloop

app = FastAPI()


async def ping(request: Request):
print("Hello")
return "message" "Hello"


async def sleep(request: Request):
await asyncio.sleep(5)
print("Sleeping task completed")
return "message" "Sleeping task completed"


app.get("/ping")(ping)
app.get("/sleep")(sleep)


loop = uvloop.EventLoop()
asyncio.set_event_loop(loop)

Q: What are the benefits of using a third-party library like uvloop in FastAPI?

A: The benefits of using a third-party library like uvloop in FastAPI include:

  • Improved performance: uvloop provides a high-performance event loop that can improve the performance of your FastAPI application.
  • Better concurrency: uvloop provides better concurrency support than the default asyncio event loop, which can improve the performance of your FastAPI application.
  • Easier debugging: uvloop provides better debugging support than the default asyncio event loop, which can make it easier to debug your FastAPI application.

Q: What are the limitations of using a third-party library like uvloop in FastAPI?

A: The limitations of using a third-party library like uvloop in FastAPI include:

  • Additional dependencies: Using a third-party library like uvloop requires additional dependencies, which can add complexity to your project.
  • Compatibility issues: Using a third-party library like uvloop can introduce compatibility issues with other libraries or frameworks, which can make it harder to debug your application.
  • Learning curve: Using a third-party library like uvloop can require additional learning and expertise, which can make it harder to use for developers who are new to the library.