pararun 0.1.0

A generic library for concurrent/parallel task execution with idempotent caching.

Pararun

Pararun is a lightweight, fault-tolerant Python library for concurrent and parallel task execution. It simplifies running tasks using asyncio, multiprocessing, or threading, with built-in support for persistent caching (idempotency), progress bars, and streaming large datasets.

Features

• 🚀 Unified API: Simple pr.map for parallel processing and pr.aio_map for async tasks.
• 💾 Idempotent Caching: Automatically skips processed items by checking a JSONL cache file. Perfect for resumable long-running jobs.
• 🌊 Streaming Support: Handles large datasets (generators) with controlled memory usage using backpressure.
• 📊 Progress Monitoring: Integrated tqdm progress bars.
• 🛡️ Fault Tolerance: Safely handles crashes by flushing results to disk periodically.

Installation

pip install pararun

Quick Start

1. Parallel Processing (CPU/IO Bound)

Use pr.map for blocking functions. It uses concurrent.futures implementation.

import pararun as pr
import time

def process_file(filename):
    time.sleep(0.1)  # Simulate blocking work
    return {"id": filename, "status": "done"}

# Works with Lists or Generators
files = (f"data_{i}.txt" for i in range(100))

# Result is saved to 'results.jsonl' automatically
pr.map(
    func=process_file,
    iterable=files,
    n_workers=4,
    cache_path="results.jsonl"
)

2. Async Processing (AsyncIO)

Use pr.aio_map for native async functions.

import pararun as pr
import asyncio

async def fetch_url(item):
    await asyncio.sleep(0.1) # Simulate network request
    return {"id": item["url"], "status": 200}

async def main():
    urls = [{"url": f"https://example.com/{i}"} for i in range(100)]
    
    await pr.aio_map(
        func=fetch_url,
        iterable=urls,
        n_workers=10,
        cache_path="async_results.jsonl"
    )

if __name__ == "__main__":
    asyncio.run(main())

Advanced Usage

Idempotency & Resuming

When cache_path is provided, pararun reads the file (if it exists) to verify which items have already been processed.

By default, it assumes the output items contain an "id" field. You can customize this field using the key_field parameter:

pr.map(..., cache_path="cache.jsonl", key_field="filename")

• Run 1: Process 50% of items -> Crash.
• Run 2: Point to same cache_path. pararun skips the first 50% and resumes from where it left off.

Streaming Large Datasets

pararun is designed to be memory efficient. It uses bounded queues (semaphores) to ensure that even if you pass a generator with 100M items, only n_workers * 2 items are held in memory at any time.

Development

Install dependencies and run tests:

# Install package in editable mode
pip install -e .

# Run tests
python -m pytest

License

MIT