Easy Async

Running multiple tasks at the same time can make programs faster and more efficient. Whether you're processing data, running independent operations, or automating tasks, easy_async provides simple helpers that make asynchronous execution easier to understand and use.

A small real-world example

Imagine you are building a tool that needs to perform several independent calculations. Instead of waiting for each function to finish one by one, you want to run them at the same time.

from py_simple import run_at_the_same_time_no_params

def download_file():
    return "File downloaded"

def update_database():
    return "Database updated"

results = run_at_the_same_time_no_params([
    download_file,
    update_database,
])

print(results)

Example output:

[
    ('download_file', 'File downloaded'),
    ('update_database', 'Database updated')
]

What happened?

run_at_the_same_time_no_params() runs multiple functions at the same time when they do not need any arguments.

It uses a thread pool internally to execute each function independently and collects all the results in a simple list.

If your functions need arguments, you can use:

from py_simple import run_at_the_same_time_with_params

def add(a, b):
    return a + b

def multiply(a, b):
    return a * b

results = run_at_the_same_time_with_params([
    (add, 2, 3),
    (multiply, 4, 5),
])

print(results)

Example output:

[
    ('add', 5),
    ('multiply', 20)
]

run_at_the_same_time_with_params() allows every function to receive its own arguments while still running all tasks together.

Why use these helpers?

Instead of manually creating thread pools and handling results every time, you can simply write:

run_at_the_same_time_no_params([
    task_one,
    task_two,
])

or:

run_at_the_same_time_with_params([
    (task_one, value),
    (task_two, value),
])

These helpers keep asynchronous code simple, readable, and beginner-friendly while providing consistent error handling when tasks fail.