Is Python asynchronous or synchronous?

Python is generally considered a synchronous programming language - when you execute a line of code, the next line waits for the first to finish before executing. However, Python does provide asynchronous capabilities through asyncio and other libraries. So when is async useful and how can you leverage it in Python?

The Difference Between Asynchronous and Synchronous

To understand asyncio, let's quickly define synchronous vs asynchronous:

# Synchronous
import time
print('Start')
time.sleep(2) 
print('End')

# Asynchronous
import asyncio

async def main():
    print('Start')
    await asyncio.sleep(2)  
    print('End')

asyncio.run(main())

So Python itself runs synchronously, but asyncio provides infrastructure to write asynchronous code.

When to Use Asynchronous Programming

The main benefit of async is that while one part of the program waits for I/O (like network requests), other parts can continue executing. This enables concurrency - multiple things happening at the same time.

Some examples where async programming shines:

However, async introduces overhead and complexity. It's best to start simple and only utilize when:

Async IO - asyncio

The asyncio module is Python's official library for async IO. Some key concepts:

Coroutines - Functions that can pause execution without blocking the thread. Defined with async def:

async def fetch_data():
   print('fetching!')
   await asyncio.sleep(2)
   print('done!')

Event loop - Runs and schedules coroutines. asyncio.run() creates a loop:

asyncio.run(fetch_data())

Awaitable - Objects that can be awaited on within coroutines, like delays or I/O operations.

We await on awaitables to pause coroutines instead of blocking:

await asyncio.sleep(2)
data = await fetch_url('example.com') 

Asyncio handles switching between coroutines transparently as they execute and await.

Concurrency Patterns with Asyncio

Here are some common patterns that leverage asyncio concurrency:

Parallelize Independent Tasks

Run unrelated coroutines concurrently:

async def fetch_url(url):
   # fetch and return response

urls = ['url1', 'url2', 'url3']

async def main():
   tasks = []
   for url in urls:
      task = asyncio.create_task(fetch_url(url))  
      tasks.append(task)
   
   await asyncio.gather(*tasks)

asyncio.run(main()) 

Producer/Consumer Queues

A queue lets one coroutine produce data for another to consume asynchronously:

queue = asyncio.Queue()

async def producer():
   for i in range(5):
      await queue.put(i)
   await queue.put(None) # Signal we're done

async def consumer():
   while True:
      item = await queue.get()
      if item is None:
         break
      print(f'Consumed {item}')

asyncio.run(asyncio.gather(producer(), consumer()))

This enables concurrent data processing pipelines.

Async Context Managers

Asyncio provides async versions of common context managers like files and locks:

async with aiofiles.open('file.txt') as f:
   contents = await f.read()

The async context handles opening/closing the file without blocking.

Going Further with Asyncio

Here are some tips to leverage asyncio further:

And some things to watch out for:

In Summary

While Python executes synchronously by default, the asyncio module enables asynchronous I/O for improved concurrency:

Asyncio does take some re-thinking for asynchronous code design. But when used properly, it enables efficient concurrent programs in Python.