Python Async Synchronization: How Semaphore, Lock, and Queue Work

Python's asyncio allows coroutines to run concurrently by pausing at await points, but this can create problems when multiple coroutines access shared resources simultaneously. Although only one coroutine executes at any given instant, a paused coroutine can resume to find that another has already modified the shared data it was working with, causing race conditions. To address this, Python provides asynchronous synchronization primitives — Semaphore, Lock, and Queue — which coordinate how coroutines execute rather than when they execute. A Semaphore limits the number of coroutines that can enter a section of code at once, while a Lock ensures only one coroutine at a time accesses a critical section such as a shared bank balance. These primitives are essential in real-world backend applications where uncontrolled concurrency can overload external APIs, corrupt shared data, or leave queued jobs without a structured way to be processed.