Computer Programming

When you write programs with threads, multiple tasks can run at the same time. But inside each task, you might wonder: “Which thread is running this?” That’s where Python gives you a helpful tool. The threading module includes a simple function: current_thread(). This lets you check the identity of the thread currently running your code. […]

In Python, both processes and threads help you run multiple tasks at the same time. A process is like a separate program running on your computer, each with its own memory. A thread is a smaller task running inside a program, sharing the same memory with other threads. Using processes and threads makes your programs

Threads are great when you want parts of your program to run at the same time. But sometimes, these threads need to talk to each other — like one thread making things and another using them. This is where a Queue comes in. A Queue is a special tool that lets threads pass data to

When working with multiple threads, there are times when you want all threads to pause at a certain point and wait for each other before moving on. This ensures that no thread gets too far ahead, and everyone stays in sync. A Barrier in Python threading acts like a red light on the road. Each

Threads are like busy workers trying to use shared tools or enter a room. But sometimes, only a few workers can use the tool or fit in the room at the same time. Without control, things get messy and confusing. A Semaphore acts like a smart gatekeeper. It counts how many workers are inside and

Sometimes, threads need to pause and wait for something important to happen before moving on. Condition variables act like traffic lights for threads—they tell them when to stop and when it’s safe to go. In this article, we’ll learn how to use condition variables to make threads wait for a signal and then continue safely,

Threads are like busy helpers working together. Sometimes, they need to share the same tools — like a hammer or a paintbrush. If two helpers try to use the same tool at once, things get messy! Locks act like a tool locker that lets only one helper use a tool at a time, keeping things

Imagine your program is a busy workshop. It has little helpers — threads — that take care of different jobs. Some helpers are serious: they stay until every task is done. Others are more easygoing — they vanish quietly when the main shift ends. These are daemon threads. In Python, daemon threads are like night-shift

Imagine a bunch of kids trying to play with the same toy at the same time. One grabs it, another pulls, and suddenly it’s chaos — the toy’s broken, and everyone’s crying. Threads in Python can be just like those kids. They run at the same time and often need to use the same stuff

Think of threads as tiny workers inside your Python program. Each one has a job to do, like fetching water or delivering messages. Just like people, threads have a lifecycle—they start out new, get to work running their tasks, and then finish when their job is done. In this article, we’ll follow these little workers