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Metadata-Version: 2.4
Name: multitasking
Version: 0.0.12
Summary: Non-blocking Python methods using decorators
Home-page: https://github.com/ranaroussi/multitasking
Author: Ran Aroussi
Author-email: ran@aroussi.com
License: Apache
Keywords: multitasking multitask threading async
Platform: any
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Description-Content-Type: text/x-rst
License-File: LICENSE.txt
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license
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Dynamic: summary
MultiTasking: Non-blocking Python methods using decorators
==========================================================
|Python version| |PyPi version| |PyPi status| |PyPi downloads|
|CodeFactor| |Star this repo| |Follow me on twitter|
--------------
**MultiTasking** is a lightweight Python library that lets you convert
your Python methods into asynchronous, non-blocking methods simply by
using a decorator. Perfect for I/O-bound tasks, API calls, web scraping,
and any scenario where you want to run multiple operations concurrently
without the complexity of manual thread or process management.
✨ **What's New in v0.0.12**
----------------------------
- 🎯 **Full Type Hint Support**: Complete type annotations for better
IDE support and code safety
- 📚 **Enhanced Documentation**: Comprehensive docstrings and inline
comments for better maintainability
- 🔧 **Improved Error Handling**: More robust exception handling with
specific error types
- 🚀 **Better Performance**: Optimized task creation and management
logic
- 🛡️ **Code Quality**: PEP8 compliant, linter-friendly codebase
Quick Start
-----------
.. code:: python
import multitasking
import time
@multitasking.task
def fetch_data(url_id):
# Simulate API call or I/O operation
time.sleep(1)
return f"Data from {url_id}"
# These run concurrently, not sequentially!
for i in range(5):
fetch_data(i)
# Wait for all tasks to complete
multitasking.wait_for_tasks()
print("All data fetched!")
Basic Example
-------------
.. code:: python
# example.py
import multitasking
import time
import random
import signal
# Kill all tasks on ctrl-c (recommended for development)
signal.signal(signal.SIGINT, multitasking.killall)
# Or, wait for tasks to finish gracefully on ctrl-c:
# signal.signal(signal.SIGINT, multitasking.wait_for_tasks)
@multitasking.task # <== this is all it takes! 🎉
def hello(count):
sleep_time = random.randint(1, 10) / 2
print(f"Hello {count} (sleeping for {sleep_time}s)")
time.sleep(sleep_time)
print(f"Goodbye {count} (slept for {sleep_time}s)")
if __name__ == "__main__":
# Launch 10 concurrent tasks
for i in range(10):
hello(i + 1)
# Wait for all tasks to complete
multitasking.wait_for_tasks()
print("All tasks completed!")
**Output:**
.. code:: bash
$ python example.py
Hello 1 (sleeping for 0.5s)
Hello 2 (sleeping for 1.0s)
Hello 3 (sleeping for 5.0s)
Hello 4 (sleeping for 0.5s)
Hello 5 (sleeping for 2.5s)
Hello 6 (sleeping for 3.0s)
Hello 7 (sleeping for 0.5s)
Hello 8 (sleeping for 4.0s)
Hello 9 (sleeping for 3.0s)
Hello 10 (sleeping for 1.0s)
Goodbye 1 (slept for 0.5s)
Goodbye 4 (slept for 0.5s)
Goodbye 7 (slept for 0.5s)
Goodbye 2 (slept for 1.0s)
Goodbye 10 (slept for 1.0s)
Goodbye 5 (slept for 2.5s)
Goodbye 6 (slept for 3.0s)
Goodbye 9 (slept for 3.0s)
Goodbye 8 (slept for 4.0s)
Goodbye 3 (slept for 5.0s)
All tasks completed!
Advanced Usage
==============
Real-World Examples
-------------------
**Web Scraping with Concurrent Requests:**
.. code:: python
import multitasking
import requests
import signal
signal.signal(signal.SIGINT, multitasking.killall)
@multitasking.task
def fetch_url(url):
try:
response = requests.get(url, timeout=10)
print(f"✅ {url}: {response.status_code}")
return response.text
except Exception as e:
print(f"❌ {url}: {str(e)}")
return None
# Fetch multiple URLs concurrently
urls = [
"https://httpbin.org/delay/1",
"https://httpbin.org/delay/2",
"https://httpbin.org/status/200",
"https://httpbin.org/json"
]
for url in urls:
fetch_url(url)
multitasking.wait_for_tasks()
print(f"Processed {len(urls)} URLs concurrently!")
**Database Operations:**
.. code:: python
import multitasking
import sqlite3
import time
@multitasking.task
def process_batch(batch_id, data_batch):
# Simulate database processing
conn = sqlite3.connect(f'batch_{batch_id}.db')
# ... database operations ...
conn.close()
print(f"Processed batch {batch_id} with {len(data_batch)} records")
# Process multiple data batches concurrently
large_dataset = list(range(1000))
batch_size = 100
for i in range(0, len(large_dataset), batch_size):
batch = large_dataset[i:i + batch_size]
process_batch(i // batch_size, batch)
multitasking.wait_for_tasks()
Pool Management
---------------
MultiTasking uses execution pools to manage concurrent tasks. You can
create and configure multiple pools for different types of operations:
.. code:: python
import multitasking
# Create a pool for API calls (higher concurrency)
multitasking.createPool("api_pool", threads=20, engine="thread")
# Create a pool for CPU-intensive tasks (lower concurrency)
multitasking.createPool("cpu_pool", threads=4, engine="process")
# Switch between pools
multitasking.use_tag("api_pool") # Future tasks use this pool
@multitasking.task
def api_call(endpoint):
# This will use the api_pool
pass
# Get pool information
pool_info = multitasking.getPool("api_pool")
print(f"Pool: {pool_info}") # {'engine': 'thread', 'name': 'api_pool', 'threads': 20}
Task Monitoring
---------------
Monitor and control your tasks with built-in functions:
.. code:: python
import multitasking
import time
@multitasking.task
def long_running_task(task_id):
time.sleep(2)
print(f"Task {task_id} completed")
# Start some tasks
for i in range(5):
long_running_task(i)
# Monitor active tasks
while multitasking.get_active_tasks():
active_count = len(multitasking.get_active_tasks())
total_count = len(multitasking.get_list_of_tasks())
print(f"Progress: {total_count - active_count}/{total_count} completed")
time.sleep(0.5)
print("All tasks finished!")
Configuration & Settings
========================
Thread/Process Limits
---------------------
The default maximum threads equals the number of CPU cores. You can
customize this:
.. code:: python
import multitasking
# Set maximum concurrent tasks
multitasking.set_max_threads(10)
# Scale based on CPU cores (good rule of thumb for I/O-bound tasks)
multitasking.set_max_threads(multitasking.config["CPU_CORES"] * 5)
# Unlimited concurrent tasks (use carefully!)
multitasking.set_max_threads(0)
Execution Engine Selection
--------------------------
Choose between threading and multiprocessing based on your use case:
.. code:: python
import multitasking
# For I/O-bound tasks (default, recommended for most cases)
multitasking.set_engine("thread")
# For CPU-bound tasks (avoids GIL limitations)
multitasking.set_engine("process")
**When to use threads vs processes:**
- **Threads** (default): Best for I/O-bound tasks like file operations,
network requests, database queries
- **Processes**: Best for CPU-intensive tasks like mathematical
computations, image processing, data analysis
Advanced Pool Configuration
---------------------------
Create specialized pools for different workloads:
.. code:: python
import multitasking
# Fast pool for quick API calls
multitasking.createPool("fast_api", threads=50, engine="thread")
# CPU pool for heavy computation
multitasking.createPool("compute", threads=2, engine="process")
# Unlimited pool for lightweight tasks
multitasking.createPool("unlimited", threads=0, engine="thread")
# Get current pool info
current_pool = multitasking.getPool()
print(f"Using pool: {current_pool['name']}")
Best Practices
==============
Performance Tips
----------------
1. **Choose the right engine**: Use threads for I/O-bound tasks,
processes for CPU-bound tasks
2. **Tune thread counts**: Start with CPU cores × 2-5 for I/O tasks, CPU
cores for CPU tasks
3. **Use pools wisely**: Create separate pools for different types of
operations
4. **Monitor memory usage**: Each thread/process consumes memory
5. **Handle exceptions**: Always wrap risky operations in try-catch
blocks
Error Handling
--------------
.. code:: python
import multitasking
import requests
@multitasking.task
def robust_fetch(url):
try:
response = requests.get(url, timeout=10)
response.raise_for_status()
return response.json()
except requests.exceptions.Timeout:
print(f"⏰ Timeout fetching {url}")
except requests.exceptions.RequestException as e:
print(f"❌ Error fetching {url}: {e}")
except Exception as e:
print(f"💥 Unexpected error: {e}")
return None
Resource Management
-------------------
.. code:: python
import multitasking
import signal
# Graceful shutdown on interrupt
def cleanup_handler(signum, frame):
print("🛑 Shutting down gracefully...")
multitasking.wait_for_tasks()
print("✅ All tasks completed")
exit(0)
signal.signal(signal.SIGINT, cleanup_handler)
# Your application code here...
Troubleshooting
===============
Common Issues
-------------
**Tasks not running concurrently?** Check if youre calling
``wait_for_tasks()`` inside your task loop instead of after it.
**High memory usage?** Reduce the number of concurrent threads or switch
to a process-based engine.
**Tasks hanging?** Ensure your tasks can complete (avoid infinite loops)
and handle exceptions properly.
**Import errors?** Make sure youre using Python 3.6+ and have installed
the latest version.
Debugging
---------
.. code:: python
import multitasking
# Enable task monitoring
active_tasks = multitasking.get_active_tasks()
all_tasks = multitasking.get_list_of_tasks()
print(f"Active: {len(active_tasks)}, Total: {len(all_tasks)}")
# Get current pool configuration
pool_info = multitasking.getPool()
print(f"Current pool: {pool_info}")
Installation
============
**Requirements:** - Python 3.6 or higher - No external dependencies!
**Install via pip:**
.. code:: bash
$ pip install multitasking --upgrade --no-cache-dir
**Development installation:**
.. code:: bash
$ git clone https://github.com/ranaroussi/multitasking.git
$ cd multitasking
$ pip install -e .
Compatibility
=============
- **Python**: 3.6+ (type hints require 3.6+)
- **Operating Systems**: Windows, macOS, Linux
- **Environments**: Works in Jupyter notebooks, scripts, web
applications
- **Frameworks**: Compatible with Flask, Django, FastAPI, and other
Python frameworks
API Reference
=============
Decorators
----------
- ``@multitasking.task`` - Convert function to asynchronous task
Configuration Functions
-----------------------
- ``set_max_threads(count)`` - Set maximum concurrent tasks
- ``set_engine(type)`` - Choose “thread” or “process” engine
- ``createPool(name, threads, engine)`` - Create custom execution pool
Task Management
---------------
- ``wait_for_tasks(sleep=0)`` - Wait for all tasks to complete
- ``get_active_tasks()`` - Get list of running tasks
- ``get_list_of_tasks()`` - Get list of all tasks
- ``killall()`` - Emergency shutdown (force exit)
.. _pool-management-1:
Pool Management
---------------
- ``getPool(name=None)`` - Get pool information
- ``createPool(name, threads=None, engine=None)`` - Create new pool
Performance Benchmarks
======================
Heres a simple benchmark comparing synchronous vs asynchronous
execution:
.. code:: python
import multitasking
import time
import requests
# Synchronous version
def sync_fetch():
start = time.time()
for i in range(10):
requests.get("https://httpbin.org/delay/1")
print(f"Synchronous: {time.time() - start:.2f}s")
# Asynchronous version
@multitasking.task
def async_fetch():
requests.get("https://httpbin.org/delay/1")
def concurrent_fetch():
start = time.time()
for i in range(10):
async_fetch()
multitasking.wait_for_tasks()
print(f"Concurrent: {time.time() - start:.2f}s")
# Results: Synchronous ~10s, Concurrent ~1s (10x speedup!)
Contributing
============
We welcome contributions! Heres how you can help:
1. **Report bugs**: Open an issue with details and reproduction steps
2. **Suggest features**: Share your ideas for improvements
3. **Submit PRs**: Fork, create a feature branch, and submit a pull
request
4. **Improve docs**: Help make the documentation even better
**Development setup:**
.. code:: bash
$ git clone https://github.com/ranaroussi/multitasking.git
$ cd multitasking
$ pip install -e .
$ python -m pytest # Run tests
Legal Stuff
===========
**MultiTasking** is distributed under the **Apache Software License**.
See the `LICENSE.txt <./LICENSE.txt>`__ file in the release for details.
Support
=======
- 📖 **Documentation**: This README and inline code documentation
- 🐛 **Issues**: `GitHub Issues <https://github.com/ranaroussi/multitasking/issues>`__
- 🐦 **Twitter**: [@aroussi](https://twitter.com/aroussi)
Changelog
=========
**v0.0.12-rc** - ✨ Added comprehensive type hints throughout the
codebase - 📚 Enhanced documentation with detailed docstrings and inline
comments - 🔧 Improved error handling with specific exception types - 🚀
Optimized task creation and pool management logic - 🛡️ Made codebase
fully PEP8 compliant and linter-friendly - 🧹 Better code organization
and maintainability
**v0.0.11** (Latest) - Previous stable release
--------------
**Happy Multitasking! 🚀**
*Please drop me a note with any feedback you have.*
**Ran Aroussi**
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