Can you do concurrent programming in Python?

Can you do concurrent programming in Python?

Yes, Python does support concurrent programming through various means and libraries. Here are a few approaches:

Example:

import threading
def worker():
for i in range(10):
print(f"Thread {i}")
threads = []
for i in range(4):
thread = threading.Thread(target=worker)
threads.append(thread)
thread.start()
for thread in threads:
thread.join()

Example:

import multiprocessing
def worker():
for i in range(10):
print(f"Process {i}")
processes = []
for i in range(4):
process = multiprocessing.Process(target=worker)
processes.append(process)
process.start()
for process in processes:
process.join()

Example:

import asyncio
async def main():
print("Starting")
await asyncio.sleep(1)  # Simulate some work
print("Done")
loop = asyncio.get_event_loop()
loop.run_until_complete(main())

Example:

import concurrent.futures
def worker(x):
return x * 2
with concurrent.futures.ProcessPoolExecutor() as executor:
futures = [executor.submit(worker, i) for i in range(4)]
results = [f.result() for f in futures]
print(results)

Example:

from joblib import Parallel, delayed
def worker(x):
return x * 2
results = Parallel(n_jobs=4)(delayed(worker)(i) for i in range(4))
print(results)

In summary, Python provides various ways to perform concurrent programming, including threading, multiprocessing, asyncio, concurrent.futures, and joblib. Each approach has its own strengths and limitations, and the choice of which one to use depends on the specific requirements of your project.

Python concurrent futures

Here's an overview of Python concurrent futures:

Python provides a convenient way to handle concurrency using the concurrent.futures module, which was introduced in Python 3.2. This module allows you to easily parallelize and execute asynchronous tasks.

What is concurrent.futures?

The concurrent.futures module is a high-level interface for asynchronously executing callables. It provides a way to schedule the execution of multiple tasks concurrently and collect their results.

When should I use concurrent.futures?

You should consider using concurrent.futures in situations where you have:

How does concurrent.futures work?

The concurrent.futures module provides two main classes:

To use concurrent.futures, you create an instance of one of these classes and then submit tasks (functions) for execution using its submit() method. The tasks will be executed asynchronously, and the results can be collected using the result() or done() methods.

Here's a simple example:

import concurrent.futures
import time
def task(x):
print(f"Task {x} started")
time.sleep(2)
print(f"Task {x} finished")
with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
for i in range(5):
executor.submit(task, i)
print("Main thread finished")

In this example, we create a ThreadPoolExecutor with 3 worker threads and submit 5 tasks (functions) to be executed concurrently. The tasks print a message indicating when they start and finish.

Benefits of using concurrent.futures

Using concurrent.futures can bring several benefits:

Overall, concurrent.futures is a powerful and convenient way to handle concurrency in Python, making it easier for developers to write efficient and scalable programs.