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Harness the power of Python threading for parallel task execution and enhanced program performance.

## Python Threading: How to Use threading.TIMEOUT_MAX

Threading in Python is a technique used to perform multiple tasks simultaneously. By using threading, multiple tasks can be processed in parallel, thus improving the performance of a program. The `threading` module in Python allows the creation and management of threads.

### 1. Definition
Threading in Python allows for the execution of multiple tasks concurrently. It enables parallel processing of tasks, which can enhance a program’s performance. The `threading` module is used in Python to create and manage threads. Each thread represents an independent operation within a program.

### 2. Usage
To use threading in Python, the `threading` module must be imported. Tasks can be specified, and necessary information can be passed to the threads. Threads are then started to execute the tasks.

Example:
“`python
import threading

def my_function():
# Define the task to be executed in the thread
print(“Hello, I’m running in a thread!”)

# Create and start a thread
thread = threading.Thread(target=my_function)
thread.start()
“`

### 3. Daemon Threads
Daemon threads in Python perform auxiliary tasks to assist the main threads. They automatically terminate when the main thread ends. Daemon threads are suitable for tasks related to the application’s lifecycle, such as automatic saving, garbage collection, file monitoring, etc.

### 4. Pros and Cons of Threading
#### Pros:
– Enhances program performance by executing tasks in parallel
– Saves time by processing multiple tasks simultaneously
– Allows tasks from different threads to run concurrently

#### Cons:
– Synchronization issues can occur between threads accessing the same resources
– Thread management can be complex, considering priorities and states
– Overhead may occur when creating and managing threads, consuming system resources

### 5. Types of Threads in Python
– `threading.active_count()`: Returns the number of alive Thread objects
– `threading.current_thread()`: Returns the current Thread object for the caller’s control thread
– `threading.TIMEOUT_MAX`: Maximum timeout value allowed for blocking functions

### 6. Thread Objects
Thread objects in Python represent threads of execution. They have methods for starting, running, and joining threads. Key methods include `start()`, `run()`, `join()`, `name`, `is_alive()`, `daemon`, etc.

### 7. Fork and Join
– **Fork**: Main thread creating sub-threads
– **Join**: Main thread waiting for all threads to finish before proceeding

Example:
“`python
import threading
import time

class Worker(threading.Thread):
def __init__(self, name):
super().__init__()
self.name = name

def run(self):
print(“Sub thread start “, threading.currentThread().getName())
time.sleep(5)
print(“Sub thread end “, threading.currentThread().getName())

print(“Main thread start”)
t1 = Worker(“1”)
t1.start()
t2 = Worker(“2”)
t2.start()
t1.join()
t2.join()
print(“Main thread post job”)
print(“Main thread end”)
“`

In this example, the main thread waits for sub-threads `t1` and `t2` to finish their tasks before proceeding.

### References
– [Python Threading Documentation](https://docs.python.org/ko/3.9/library/threading.html)
– [Understanding Threading in Python](https://miki3079.tistory.com/50)
– [Practical Thread Usage in Python](https://mechacave.tistory.com/2)
– [Explanation of Daemon Threads](https://sharonprogress.tistory.com/324)
– [Threading in Python (Korean)](https://wikidocs.net/82581)

By utilizing Python’s threading capabilities effectively, developers can enhance the performance and efficiency of their programs. Threading allows for parallel execution of tasks, leading to optimized resource utilization and improved responsiveness. Remember to handle synchronization and management of threads carefully to avoid potential issues.