Skip to main content

Multithreading in Python

 


Learning Sections          show

Multithreading in Python

Multithreading is a programming technique used to run multiple threads (smaller units of process) concurrently within a single process. It allows for parallel execution of tasks and can significantly improve the performance of applications, especially those involving I/O-bound operations.

Creating Threads

You can create and start a new thread by using the threading module in Python:


import threading

def print_numbers():
    for i in range(10):
        print(i)

t1 = threading.Thread(target=print_numbers)
t1.start()
t1.join()  # Wait for the thread to complete
Using Thread Pools

The concurrent.futures module provides a high-level interface for asynchronously executing callables. The ThreadPoolExecutor is particularly useful for managing a pool of threads:


from concurrent.futures import ThreadPoolExecutor

def task(n):
    print(n * "Hello ")

with ThreadPoolExecutor(3) as executor:
    executor.map(task, range(5))
Thread Synchronization

Threads often need to communicate and share data. Python provides several primitives to handle synchronization, such as locks, events, conditions, and semaphores:


import threading

lock = threading.Lock()

def safe_print(msg):
    with lock:
        print(msg)

t1 = threading.Thread(target=safe_print, args=("Hello from Thread 1",))
t2 = threading.Thread(target=safe_print, args=("Hello from Thread 2",))

t1.start()
t2.start()
t1.join()
t2.join()
Using Queues

The queue module provides a thread-safe FIFO implementation that can be used to safely pass data between threads:


import queue
import threading

q = queue.Queue()

def producer():
    for i in range(5):
        q.put(i)
        print("Produced", i)

def consumer():
    while not q.empty():
        item = q.get()
        print("Consumed", item)

t1 = threading.Thread(target=producer)
t2 = threading.Thread(target=consumer)

t1.start()
t2.start()
t1.join()
t2.join()
Daemon Threads

Daemon threads run in the background and are automatically terminated when all non-daemon threads have completed. They are useful for background tasks that should not block the program from exiting:


import threading
import time

def background_task():
    while True:
        print("Running in the background")
        time.sleep(2)

t = threading.Thread(target=background_task)
t.daemon = True
t.start()

print("Main thread exiting")
Labels:

Popular posts from this blog

MultiProcessing in Python

  Learning Sections          show MultiProcessing in Python Multiprocessing in Python involves using the multiprocessing module to run multiple processes concurrently, taking advantage of multiple CPU cores. This module provides a higher level of concurrency than threading and is especially useful for CPU-bound tasks. Creating Processes You can create and start a new process by using the multiprocessing module: import multiprocessing def print_numbers (): for i in range ( 10 ): print ( i ) p1 = multiprocessing.Process ( target = print_numbers ) p1 . start () p1 . join () # Wait for the process to complete Using Process Pools The multiprocessing module provides a Pool class, which allows you to manage a pool of worker processes: from multiprocessing import Pool def square ( n ): return n * n with Pool ( 4 ) as pool : result = pool.map ( square , range (...
Read more

Learn Python

  Learning Sections Introduction to Python Comment, escape sequence and print statement in Python Variables and Data Types in Python Typecasting in Python User input in Python String slicing and operations on string in Python String methods in Python If else conditional statements in Python Match case statement in Python For loops in Python While loops in Python Break and continue statement in Python Functions in Python Function Arguments in Python introduction to lists in Python List methods in Python Tuples in Python Operations on tuple in Python f strings in Python Docstrings in Python Recursion in Python Sets in Python Set methods in Python Dictionaries in Python for Loop with else in Python Exception Handling in Python Finally keyword in Python Raising custom errors in Python Short hand if else statements Enumerate Function in Python Virtual Environment in Python How import works in Python if __nam...
Read more

Instance variables vs Class variables in Python

  Learning Sections          show Instance variables vs Class variables in Python In Python, instance variables are variables that are bound to instances of a class, while class variables are variables that are bound to the class itself. Each instance of the class has its own copy of instance variables, while class variables are shared among all instances of the class. Instance variables are defined within methods of a class using the self keyword, while class variables are defined directly within the class body. Example: class Car : # Class variable wheels = 4 # Instance method to initialize instance variables def __init__ ( self , color ): # Instance variable self . color = color # Create instances of the Car class car1 = Car ( 'red' ) car2 = Car ( 'blue' ) # Access instance variables print ( car1 . color ) # Output: 'red' print ( car2 . color ) # Output: 'blue' # Acc...
Read more