The Concurrent Programming Paradigm is a design approach where multiple computations execute independently and may run simultaneously to improve performance. In Python, concurrency allows multiple tasks to make progress without necessarily executing at the same instant.
Key Concepts in Concurrency:
- Concurrency: Multiple tasks start, run, and complete in overlapping time periods (but not necessarily at the same time).
- Parallelism: Tasks are executed exactly at the same time using multiple CPU cores.
- Threading vs Multiprocessing: Threads share memory, while processes have separate memory spaces.
- Asynchronous Programming: Tasks don’t block each other while waiting for input/output (I/O).
Concurrency Models in Python
Python provides several ways to handle concurrency:
| Model | Module | Best Use Case |
|---|---|---|
| Threads (Preemptive Concurrency) | threading | I/O-bound tasks (e.g., network requests, file I/O) |
| Processes (Parallel Execution) | multiprocessing | CPU-bound tasks (e.g., data processing, calculations) |
| Async I/O (Cooperative Concurrency) | asyncio | High-performance I/O-bound tasks (e.g., web scraping, database queries) |
See also: