Why coroutines are better than threads in python?

Mar 25, 2024 ยท 2 min read

Threads allow Python developers to execute multiple flows of execution concurrently within a process. However, threads come with overhead from context switching that can limit performance and scalability. Coroutines in Python provide a lightweight alternative for concurrent programming without this overhead.

What are Coroutines?

Coroutines are specialized functions that can have their execution paused and resumed. This allows them to yield control when idle or blocking, letting other coroutines run instead of blocking a thread.

import asyncio

async def coroutine_example():
    print('coroutine started')
    await asyncio.sleep(1)
    print('coroutine resumed')

They provide an async/await-based approach for non-blocking concurrent code. The asyncio module in Python manages switching between coroutines automatically.

Why Coroutines are Better

1. Low Overhead

Threads require CPU time to save and restore execution contexts when switching between threads. Coroutines avoid this by only switching when a coroutine explicitly yields control. This makes them extremely lightweight.

2. Simplicity

Coroutine-based concurrence is often simpler to write and reason about compared to multi-threaded code which must synchronize access to shared state to avoid race conditions.

3. Scale Better

The lower overhead of coroutines allows a Python process to have thousands of simultaneous coroutines with little performance impact. Threads are much more resource intensive.

Concurrency Scenarios Suited for Coroutines

Coroutines shine for I/O bound workloads and concurrent tasks involving network calls, file I/O, interfacing with APIs, web scraping and more. They simplify writing responsive asynchronous code.

For CPU heavy parallel processing involving calculations or matrix operations, multiprocessing may be better suited. But for most network and I/O centric apps, coroutines provide an easier path to concurrency.

Key Takeaways

  • Coroutines enable lightweight concurrency without overhead of threads
  • Simplicity from async/await syntax and not needing locks on shared state
  • Scale efficiently to thousands of simultaneous coroutines
  • Ideal for I/O bound workloads involving networking and asynchronous code
  • By leveraging coroutines, Python developers can write highly concurrent code that is simple, efficient and scalable. The async/await syntax makes coroutines an appealing option for modern application development needs.

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