Inter-Process Communication (IPC) means the ways that separate processes talk or share data with each other. Since each process runs independently, they need special tools to exchange information safely. Python’s multiprocessing.Queue is one such tool. It allows multiple processes to send and receive data in a safe, organized way, without data getting lost or mixed […]
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In multiprocessing, processes often need to work together and coordinate their actions to avoid conflicts or to wait for certain events. This is where synchronization tools come in handy. One important synchronization primitive is the Condition Variable. A Condition Variable lets one or more processes pause execution until another process signals them to continue. It
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Python’s multiprocessing module allows us to run multiple processes at the same time, making full use of multiple CPU cores. But when these processes need to share access to a limited resource — like printers, database connections, or even toys on a playground — we need a way to control how many processes can use
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Python’s multiprocessing module lets you run multiple processes at the same time. Each process has its own unique ID called a Process ID (PID). Also, every process (except the very first one) has a Parent Process ID (PPID), which tells you which process created it. Knowing the PID and PPID helps you track and manage
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When working with Python’s multiprocessing, processes often need to share and update the same data. Without proper control, they can step on each other’s toes—corrupting the data or causing unexpected behavior. That’s where synchronization tools like locks come in. A special kind of lock called RLock (Reentrant Lock) is designed for cases where a single
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When working with multiple processes in Python, you may run into situations where different parts of your program try to use or change the same resource—like a file, a number, or a list. This can lead to unexpected results if those processes clash while working at the same time. This is where synchronization comes in.
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Python is great at doing many things at once, especially when it comes to handling multiple processes. With the multiprocessing module, you can run different parts of your program at the same time — like having different animals in a zoo each doing their own thing, without waiting for one another. But here’s the catch:
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When working with parallel programming in Python, especially using the multiprocessing module, it’s important to understand how new processes are created. Python supports different ways to start a process, known as start methods. Each method controls how the new process begins and what it inherits from the parent. In this article, we’ll focus on the
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Process pools are a way to manage many worker processes in Python without having to create and control each one yourself. Instead of starting processes one by one, you create a pool that handles them for you. This makes running many tasks in parallel easier and cleaner. Executors are a higher-level tool built on top
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A daemon process in Python is a background process that runs alongside your main program. Unlike regular processes, a daemon process automatically stops as soon as the main program ends. This makes daemon processes perfect for tasks that need to run quietly in the background without keeping the program alive. For example, you might use
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