Multithreading and Multiprocessing in Python

Python provides powerful tools to perform multiple tasks simultaneously using Multithreading and Multiprocessing. These concepts are widely used to improve performance and efficiency in real-world applications.

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Why Concurrency is Needed?

Concurrency helps when:

• Tasks take time (I/O operations)
• Multiple independent tasks need to run
• System resources must be utilized efficiently

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Multithreading in Python

Multithreading allows multiple threads to run within the same process. All threads share the same memory space.

Python uses the threading module to create and manage threads.

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Simple Multithreading Example

import threading
import time

def task():
    print("Task started")
    time.sleep(2)
    print("Task completed")

thread1 = threading.Thread(target=task)
thread2 = threading.Thread(target=task)

thread1.start()
thread2.start()

thread1.join()
thread2.join()

print("Main thread finished")

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Thread with Arguments

def print_numbers(name):
    for i in range(3):
        print(name, i)

t1 = threading.Thread(target=print_numbers, args=("Thread-1",))
t2 = threading.Thread(target=print_numbers, args=("Thread-2",))

t1.start()
t2.start()

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Global Interpreter Lock (GIL)

Python has a Global Interpreter Lock (GIL) which allows only one thread to execute Python bytecode at a time.

Because of GIL:

• Multithreading is best for I/O-bound tasks
• Not ideal for CPU-bound tasks

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Use Cases of Multithreading

• File reading and writing
• API calls
• Web scraping
• Automation testing (parallel browser actions)

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Multiprocessing in Python

Multiprocessing runs multiple processes, each with its own memory space. It bypasses the GIL and uses multiple CPU cores.

Python provides the multiprocessing module.

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Simple Multiprocessing Example

from multiprocessing import Process
import time

def task():
    print("Process started")
    time.sleep(2)
    print("Process completed")

p1 = Process(target=task)
p2 = Process(target=task)

p1.start()
p2.start()

p1.join()
p2.join()

print("Main process finished")

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Multiprocessing with Arguments

from multiprocessing import Process

def square(num):
    print(f"Square of {num} is {num*num}")

processes = []

for i in range(5):
    p = Process(target=square, args=(i,))
    processes.append(p)
    p.start()

for p in processes:
    p.join()

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Multithreading vs Multiprocessing

Multithreading	Multiprocessing
Uses threads	Uses processes
Shares memory	Separate memory
Affected by GIL	Not affected by GIL
Best for I/O-bound tasks	Best for CPU-bound tasks

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Multithreading in Automation Testing

def run_test(test_name):
    print("Running", test_name)

tests = ["LoginTest", "PaymentTest", "SearchTest"]

threads = []

for test in tests:
    t = threading.Thread(target=run_test, args=(test,))
    threads.append(t)
    t.start()

for t in threads:
    t.join()

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Multiprocessing in Automation Testing

from multiprocessing import Process

def run_test(test_name):
    print("Executing", test_name)

tests = ["ChromeTest", "FirefoxTest"]

for test in tests:
    p = Process(target=run_test, args=(test,))
    p.start()

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Best Practices

• Use multithreading for I/O-bound operations
• Use multiprocessing for CPU-intensive tasks
• Always use join() to avoid orphan threads/processes
• Avoid shared data issues

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Common Interview Questions

• What is the difference between threading and multiprocessing?
• What is GIL?
• When to use multithreading?
• When to use multiprocessing?
• Is multiprocessing faster than multithreading?

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Conclusion

Multithreading and multiprocessing help build high-performance Python applications. Choosing the right approach depends on whether the task is I/O-bound or CPU-bound.

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