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

Inheritance in Python

  Learning Sections          show Inheritance in Python Inheritance is a fundamental concept in object-oriented programming (OOP) that allows a class to inherit attributes and methods from another class. The class that inherits is called the child class or subclass, and the class being inherited from is called the parent class or superclass. Basic Inheritance In Python, a child class inherits from a parent class by specifying the parent class in parentheses after the child class name. Example: class Animal : def __init__ ( self , name ): self . name = name def speak ( self ): raise NotImplementedError ( "Subclass must implement this method" ) class Dog ( Animal ): def speak ( self ): return "Woof!" class Cat ( Animal ): def speak ( self ): return "Meow!" # Create instances of Dog and Cat dog = Dog ( "Buddy" ) cat = Cat ( "Whiskers" ...
Read more

Introduction to OOPs in Python

  Learning Sections          show Introduction to Object-Oriented Programming (OOP) Object-Oriented Programming (OOP) is a programming paradigm that organizes software design around objects rather than actions and data rather than logic. It revolves around the concept of "objects", which are instances of classes. These objects encapsulate data, in the form of attributes or properties, and behaviors, in the form of methods or functions. OOP promotes modularity, reusability, and extensibility in software development. Key Concepts of OOP: Class: A class is a blueprint or template for creating objects. It defines the attributes (data) and methods (functions) that will characterize any object instantiated from that class. Object: An object is an instance of a class. It is a concrete realization of the class blueprint, containing actual values instead of placeholders for attributes. Encapsulation: Encapsulation is ...
Read more

Generators in Python

  Learning Sections          show Generators in Python Generators are a special type of iterator in Python that allow you to iterate over a sequence of items without storing them all in memory at once. They are useful for generating large sequences of data on-the-fly, or for processing data in a memory-efficient manner. Creating Generators In Python, generators are created using generator functions or generator expressions: # Generator function def my_generator ( n ): for i in range ( n ): yield i # Generator expression my_generator = ( i for i in range ( 10 )) A generator function uses the yield keyword to yield values one at a time, while a generator expression creates an anonymous generator. Iterating Over Generators You can iterate over the values produced by a generator using a for loop: for value in my_generator ( 5 ): print ( value ) This w...
Read more