# **Multithreading vs Multiprocessing**
Multithreading and multiprocessing are two key approaches to achieving concurrency in Python. This note compares their characteristics, use cases, and limitations to help you choose the best option for your application.
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## **Multithreading**
### **What It Is**
- A threading-based approach where multiple threads run within the same process.
- Threads share memory, enabling efficient communication.
### **Advantages**
- **Low Overhead**: Threads are lightweight compared to processes.
- **Shared Memory**: No need for complex inter-thread communication.
- **Good for I/O-Bound Tasks**: Ideal for tasks involving I/O operations like file reading/writing, network communication.
### **Limitations**
- **Global Interpreter Lock (GIL)**: In CPython, only one thread executes Python bytecode at a time, limiting parallelism for CPU-bound tasks.
- **Race Conditions**: Shared memory access can lead to race conditions without proper synchronization.
### **Example**
```python
import threading
def task(name):
print(f"Task {name} is running")
threads = [threading.Thread(target=task, args=(i,)) for i in range(5)]
for t in threads:
t.start()
for t in threads:
t.join()
```
---
## **Multiprocessing**
### **What It Is**
- A process-based approach where each process runs independently with its own memory space.
- Allows true parallel execution by leveraging multiple CPU cores.
### **Advantages**
- **True Parallelism**: Bypasses the GIL, making it suitable for CPU-bound tasks.
- **Fault Isolation**: Crashes in one process do not affect others.
- **Scalable**: Efficient for computationally intensive workloads.
### **Limitations**
- **High Overhead**: Processes require more memory and initialization time than threads.
- **Inter-Process Communication (IPC)**: Data sharing between processes requires explicit mechanisms like queues or pipes.
### **Example**
```python
from multiprocessing import Process
def task(name):
print(f"Task {name} is running")
processes = [Process(target=task, args=(i,)) for i in range(5)]
for p in processes:
p.start()
for p in processes:
p.join()
```
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## **Comparison Table**
| Feature | Multithreading | Multiprocessing |
|----------------------|------------------------------------|---------------------------------|
| **Concurrency Type** | Cooperative multitasking (GIL) | True parallelism |
| **Memory Sharing** | Shared memory | Separate memory |
| **Overhead** | Lower | Higher |
| **Best For** | I/O-bound tasks | CPU-bound tasks |
| **Fault Isolation** | Poor (one thread crash affects all)| Good (process isolation) |
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## **When to Use Multithreading**
- Your task is I/O-bound and requires minimal computational power.
- You need lightweight concurrency with shared data.
- Example: Web scraping, network requests, or file I/O.
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## **When to Use Multiprocessing**
- Your task is CPU-bound and involves heavy computation.
- You need true parallelism to leverage multiple cores.
- Example: Data processing, image rendering, or machine learning.
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## **Best Practices**
1. Choose multithreading for I/O-bound tasks and multiprocessing for CPU-bound tasks.
2. Avoid overloading the system with too many threads or processes—optimize concurrency levels.
3. Combine both approaches when handling hybrid workloads (e.g., asyncio for I/O and multiprocessing for computation).
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## **Explore Next**
- [[Hybrid Concurrency Models]]: Combining threads, processes, and asyncio.
- [[Thread vs Asyncio]]: Choosing between threading and asynchronous programming.
- [[Concurrency Performance Optimization]]: Techniques to improve system throughput.
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This note explains multithreading vs multiprocessing with practical examples and is formatted for direct use in Obsidian.