What is the difference between asyncio and multithreading python ?

Python developers often need to make their programs concurrent to improve performance. The two main options for concurrency in Python are asyncio and multithreading. But what's the difference, and when should you use each one?

Asyncio Allows a Single Thread to Handle Multiple Tasks

Asyncio provides concurrency by allowing a single thread to handle multiple tasks efficiently. Here's a simple asyncio example:

import asyncio

async def task1():
    print('Task 1 running')
    await asyncio.sleep(1)
    print('Task 1 finished')

async def task2():
    print('Task 2 running')
    await asyncio.sleep(2)
    print('Task 2 finished')
    
async def main():
    await asyncio.gather(task1(), task2())

asyncio.run(main())

This schedules task1 and task2 to run concurrently, but they will be handled on the same thread in an efficient event loop instead of blocking.

Asyncio works great for I/O-bound tasks like network requests where your code is waiting for data. It avoids wasting resources on extra threaded contexts.

Threads Allow True Parallel Execution

Threading lets you spawn totally separate threads to achieve parallelism on multi-core systems:

import threading 

def task1():
    print('Task 1 running')
    threading.Event().wait(1) 
    print('Task 1 finished')

def task2():
    print('Task 2 running') 
    threading.Event().wait(2)
    print('Task 2 finished')  

t1 = threading.Thread(target=task1)
t2 = threading.Thread(target=task2)
t1.start(); t2.start() 

Now task1 and task2 literally run in parallel on separate CPU cores. This works well for CPU-bound processing.

However, threading has overheads from context switching and shared memory access to worry about. Asyncio avoids these issues.

Conclusion

Asyncio provides efficient concurrency for I/O-bound work on a single thread. Threading enables true parallelism across multiple cores, but has overhead from thread management. Choose the right tool for your use case!