python-async 0.2.0

Thread-based parallelism for Python. Simple, explicit API for running tasks in parallel.

PyAsync

Thread-based parallelism for Python. Simple, explicit API for running tasks in parallel.

No magic, just straightforward parallel execution using threads.

Installation

pip install python-async

Quick Start

import pyasync
import requests

def fetch(url):
    return requests.get(url).json()

# Run 3 requests in parallel - takes ~1 second, not ~3 seconds!
results = pyasync.parallel(
    lambda: fetch("https://api.example.com/users/1"),
    lambda: fetch("https://api.example.com/users/2"),
    lambda: fetch("https://api.example.com/users/3")
)

API

parallel(*callables)

Run multiple functions in parallel threads. Returns results in order.

results = pyasync.parallel(
    lambda: requests.get("https://api1.com"),
    lambda: requests.get("https://api2.com"),
    lambda: requests.get("https://api3.com")
)
# All 3 run simultaneously!

background(callable)

Start a function in the background. Returns a Task.

task = pyasync.background(lambda: slow_operation())

# Do other work while it runs...
print("Working...")

# Get result when ready
result = task.result()

run(callable)

Run a single function in the thread pool.

result = pyasync.run(lambda: requests.get("https://api.com"))

Examples

Parallel Tasks

$ python examples/simple_parallel.py

=== Simple Parallel Tasks ===

[Task A] Starting...
[Task B] Starting...
[Task C] Starting...
[Task A] Done!
[Task C] Done!
[Task B] Done!

Results: ['Task A completed', 'Task B completed', 'Task C completed']
Total time: 2.01s (longest task was 2s)

Parallel API Calls

$ python examples/parallel_api_calls.py

=== Parallel API Calls ===

Fetching 3 users in parallel...
  - Leanne Graham ([e-mail])
  - Ervin Howell ([e-mail])
  - Clementine Bauch ([e-mail])

Fetching posts for each user...
  - Leanne Graham: 10 posts
  - Ervin Howell: 10 posts
  - Clementine Bauch: 10 posts

Parallel File Processing

$ python examples/parallel_files.py

=== Parallel File Processing ===

Processing 2 files in parallel...

File                 Size       Hash      
----------------------------------------
__init__.py          587        8c137857  
runtime.py           3363       0bca3ba5

Web Scraping

$ python examples/web_scraping.py

Scraping 5 URLs in parallel...

Total time:                    0.69s
Sequential would take:         1.23s
Speedup:                       1.8x faster

When to Use

Good for:

• Scripts making multiple HTTP requests
• Batch processing I/O operations
• Parallel file operations

Not for:

• Thousands of concurrent connections (use asyncio)
• CPU-bound tasks (use multiprocessing)

Testing

python -m unittest discover -s tests -v

License

MIT