For working professionals
For fresh graduates
More
13. Print In Python
15. Python for Loop
19. Break in Python
23. Float in Python
25. List in Python
27. Tuples in Python
29. Set in Python
53. Python Modules
57. Python Packages
59. Class in Python
61. Object in Python
73. JSON Python
79. Python Threading
84. Map in Python
85. Filter in Python
86. Eval in Python
96. Sort in Python
101. Datetime Python
103. 2D Array in Python
104. Abs in Python
105. Advantages of Python
107. Append in Python
110. Assert in Python
113. Bool in Python
115. chr in Python
118. Count in python
119. Counter in Python
121. Datetime in Python
122. Extend in Python
123. F-string in Python
125. Format in Python
131. Index in Python
132. Interface in Python
134. Isalpha in Python
136. Iterator in Python
137. Join in Python
140. Literals in Python
141. Matplotlib
144. Modulus in Python
147. OpenCV Python
149. ord in Python
150. Palindrome in Python
151. Pass in Python
156. Python Arrays
158. Python Frameworks
160. Python IDE
164. Python PIP
165. Python Seaborn
166. Python Slicing
168. Queue in Python
169. Replace in Python
173. Stack in Python
174. scikit-learn
175. Selenium with Python
176. Self in Python
177. Sleep in Python
179. Split in Python
184. Strip in Python
185. Subprocess in Python
186. Substring in Python
195. What is Pygame
197. XOR in Python
198. Yield in Python
199. Zip in Python
As Python continues its upward trajectory in the tech domain, the knowledge of multithreading is indispensable for professionals eager to extract maximum efficiency from their Python applications. This concept allows professionals to harness the power of concurrent execution, leading to optimized application performance and efficient CPU resource utilization. Aimed at those familiar with Python's basics, this tutorial delves into the intricate details of multithreading in Python.
Multithreading, a facet of multiprocessing, serves as a potent tool to optimize CPU usage. It encompasses the concurrent running of multiple threads, allowing for a judicious resource allocation and an accelerated execution of tasks, primarily in I/O-bound scenarios.
In Python programming, a process is perceived as a self-sufficient unit of execution. Distinct from threads, processes are quite robust, equipped with their own dedicated memory space, ensuring that the resources they utilize aren't easily tampered with by other processes. This makes processes stable and less prone to interference. When a program is initiated, a process is born, guiding the execution. Each process possesses a unique process ID, distinguishing it from others and affirming its autonomy.
Python's threading capability is often hailed as a significant boon for optimizing the efficiency of applications. It offers an avenue for concurrent operations, especially in scenarios where parallelization can boost performance without necessitating separate memory spaces as processes do.
In the computing ecosystem, a thread is visualized as a diminutive of a process. It is a lighter execution unit that operates within the confines of a parent process. Multiple threads can coexist within a single process, executing concurrently and enhancing the throughput of applications.
Multithreading is a technique in Python that allows you to execute multiple threads concurrently within a single process. Each thread represents an independent unit of execution that can run in parallel with other threads. Python's threading module is commonly used for implementing multithreading. Here's an example and some explanations:
Here is an example of multithreading in Python:
Code:
import threading
import time
# Function to simulate a task
def worker_function(thread_id):
print(f"Thread {thread_id} started.")
time.sleep(2) # Simulate some work
print(f"Thread {thread_id} finished.")
# Create multiple threads
threads = []
for i in range(3):
thread = threading.Thread(target=worker_function, args=(i,))
threads.append(thread)
# Start the threads
for thread in threads:
thread.start()
# Wait for all threads to finish
for thread in threads:
thread.join()
print("All threads have finished.")
In this example, three threads are created, each simulating work using the worker_function. The threads execute concurrently, and the main program waits for all threads to finish using the join() method.
Explanation:
We import the threading module to work with threads and the time module to simulate some work. The worker_function is defined to simulate a task that each thread will execute. It takes a thread_id parameter to identify the thread. We create three threads and add them to the threads list. Each thread is assigned the worker_function as its target, and a unique thread_id is passed as an argument.
We start each thread using the start() method. This initiates their execution. To ensure that the main program waits for all threads to finish, we use the join() method for each thread. This blocks the main program until each thread has completed. Finally, we print "All threads have finished" to indicate that all threads have completed their tasks.
Multithreading can be useful for tasks that can be parallelized to improve program performance, such as concurrent data processing, I/O operations, or running multiple tasks simultaneously.
Python's concurrent.futures module provides a ThreadPoolExecutor class that allows you to easily create and manage a pool of threads for concurrent execution of tasks. This is an alternative to the lower-level threading module and provides a higher-level interface for working with threads. Here's an example of using ThreadPoolExecutor:
Code:
import concurrent.futures
# Function to simulate a task
def worker_function(thread_id):
print(f"Thread {thread_id} started.")
return f"Thread {thread_id} result"
# Create a ThreadPoolExecutor with 3 threads
with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
# Submit tasks to the executor
futures = [executor.submit(worker_function, i) for i in range(3)]
# Retrieve results as they become available
for future in concurrent.futures.as_completed(futures):
result = future.result()
print(result)
print("All threads have finished.")
In this example, the ThreadPoolExecutor efficiently manages a pool of threads, executes tasks concurrently, and returns results as they become available. This approach simplifies the management of threads and is particularly useful for parallelizing tasks in a controlled and scalable way.
Explanation:
We import the concurrent.futures module, which contains the ThreadPoolExecutor class. The worker_function is defined to simulate a task. It takes a thread_id parameter and returns a result. We create a ThreadPoolExecutor with a specified number of maximum workers (in this case, 3). Tasks are submitted to the executor using the executor.submit() method. Each submit call schedules the execution of the worker_function with a specific thread_id.
We collect the resulting futures (representing the asynchronous results of the tasks) in a list called futures. We use concurrent.futures.as_completed(futures) to iterate through the futures as they are completed. The result() method retrieves the result of each future. Finally, we print the results and indicate when all threads have finished.
Python's support for multithreading offers several perks:
The judicious deployment of multithreading can make or break application performance.
Multithreading has undeniably transformed how developers approach concurrency in their applications. By efficiently leveraging CPU resources and optimizing execution times, multithreading offers a competitive edge in high-performance computing. It's vital, however, to understand its nuances and implement it judiciously. Overuse or misuse can lead to complications such as race conditions or deadlocks.
For professionals eager to elevate their Python skills, mastering multithreading becomes imperative. Moreover, as applications become complex and user demands increase, a deep comprehension of concurrency principles will be a cornerstone of robust software development. For those committed to continuous learning, platforms like upGrad provide courses that delve into advanced topics like these, ensuring professionals stay ahead in their careers.
1. Multithreading vs. multiprocessing in Python?
Multithreading involves multiple threads operating within one process, utilizing shared memory. It's suitable for tasks that require quick context switches. Multiprocessing, however, employs separate processes with individual memory spaces, ensuring true parallelism. This becomes especially vital for CPU-intensive operations where bypassing Python's Global Interpreter Lock (GIL) becomes crucial.
2. Advantages and disadvantages of multithreading in Python?
Multithreading offers numerous advantages such as parallel execution, improved application responsiveness, and efficient CPU and memory utilization. However, it's not without pitfalls. Drawbacks include potential race conditions due to shared memory access and the complexities in synchronization, which can introduce unexpected behaviors
3. What is the Python thread class?
Python's threading module provides a Thread class, used to create and handle threads. By instantiating the Thread class and providing a target function, one can spawn a new thread. The class offers various methods like start(), join(), and is_alive() to manage and monitor thread execution.
Take our Free Quiz on Python
Answer quick questions and assess your Python knowledge
Author
Talk to our experts. We are available 7 days a week, 9 AM to 12 AM (midnight)
Indian Nationals
1800 210 2020
Foreign Nationals
+918045604032
1.The above statistics depend on various factors and individual results may vary. Past performance is no guarantee of future results.
2.The student assumes full responsibility for all expenses associated with visas, travel, & related costs. upGrad does not provide any a.