Category: Multithreading

In Python, when multiple threads are working concurrently with shared resources, it’s important to synchronize their access to maintain data integrity and program correctness. Synchronizing threads in python can be achieved using various synchronization primitives provided by the threading module, such as locks, conditions, semaphores, and barriers to control access to shared resources and coordinate the execution…

Daemon threads in Python are useful for running background tasks that are not critical to the program’s operation. They allow you to run tasks in the background without worrying about keeping track of them. Python provides two types of threads: non-daemon and daemon threads. By default, threads are non-daemon threads. This tutorial provides a detailed…

In Python, currently thread priority is not directly supported by the threadingmodule. unlike Java, Python does not support thread priorities, thread groups, or certain thread control mechanisms like destroying, stopping, suspending, resuming, or interrupting threads. Even thought Python threads are designed simple and is loosely based on Java’s threading model. This is because of Python’s Global Interpreter Lock…

In Python, the main thread is the initial thread that starts when the Python interpreter is executed. It is the default thread within a Python process, responsible for managing the program and creating additional threads. Every Python program has at least one thread of execution called the main thread. The main thread by default is a non-daemon…

A thread pool is a mechanism that automatically manages multiple threads efficiently, allowing tasks to be executed concurrently. Python does not provide thread pooling directly through the threading module. Instead, it offers thread-based pooling through the multiprocessing.dummymodule and the concurrent.futures module. These modules provide convenient interfaces for creating and managing thread pools, making it easier to perform concurrent task execution.…

Thread scheduling in Python is a process of deciding which thread runs at any given time. In a multi-threaded program, multiple threads are executed independently, allowing for parallel execution of tasks. However, Python does not have built-in support for controlling thread priorities or scheduling policies directly. Instead, it relies on the operating system’s thread scheduler.…

In Python, naming a thread involves assigning a string as an identifier to the thread object. Thread names in Python are primarily used for identification purposes only and do not affect the thread’s behavior or semantics. Multiple threads can share the same name, and names can be specified during the thread’s initialization or changed dynamically.…

In Python, joining the threads means using the join() method to wait for one thread to finish before moving on to others. This is useful in multithreaded programming to make sure some threads are completed before starting or continuing with other threads. By using the join() method, you can make sure that one thread has finished running before another…

In Python, starting a thread involves using the start() method provided by the Thread class in the threading module. This method initiates the thread’s activity and automatically calls its run() method in a separate thread of execution. Meaning that, when you call start() on each thread object (for example., thread1, thread2, thread3) to initiate their execution. Python to launch separate threads…

Creating a thread in Python involves initiating a separate flow of execution within a program, allowing multiple operations to run concurrently. This is particularly useful for performing tasks simultaneously, such as handling various I/O operations in parallel. Python provides multiple ways to create and manage threads.  In this tutorial, you will learn the basics of…