Why Async Python Improves Application Performance

Async Python allows developers to write non-blocking, event-driven code to improve application performance. This means Python can execute other parts of the app while waiting on long-running I/O-bound tasks like network requests or file I/O.

Avoid Blocking the Main Thread

Python executes code linearly on a single thread by default. So a blocking task holds up further execution:

import time

def long_task():
  time.sleep(5) # blocks for 5 seconds

print('First') 
long_task() 
print('Second')

This prints First and then pauses for 5 seconds before printing Second.

Async code uses cooperative multitasking so other functions can run while awaiting I/O-bound ops:

import asyncio

async def long_task():
  await asyncio.sleep(5)
  print('long task complete')

async def main():
  print('First')
  asyncio.create_task(long_task())
  print('Second')

asyncio.run(main())

Now both First and Second print immediately while long_task runs in the background.

Improved Throughput

Async allows processing multiple requests concurrently:

async def handle_request(request):
  response = ... # process request
  return response

async def main():
  tasks = []
  for request in requests:
    tasks.append(asyncio.create_task(handle_request(request)))
  
  responses = await asyncio.gather(*tasks)
  return responses

Processing each request asynchronously improves throughput compared to a synchronous approach.

Compatibility Considerations

Async code requires understanding asyncio concepts like await, async/await, event loops and coroutines. Most Python frameworks now support async, but some legacy libraries may need compatibility layers.

The benefits can be substantial for I/O-intensive apps like web servers, scrapers and CLI tools. But sync code is still simpler for CPU-bound tasks. Evaluate your specific use case before deciding.