What is the GIL and how does it affect performance?

The Python Global Interpreter Lock, or GIL, is a mutex that protects access to Python objects at a low level. This architectural feature guarantees that only one operating system thread can execute Python bytecode at any given time. The introduction of GIL at the time was driven by the need for safe and simple memory management, specifically reference counting on objects, which protected the interpreter from data races and crashes.

The main impact of the GIL on performance is that it artificially limits multi-threaded applications on multi-core processors. If a developer creates a program that performs intensive calculations using the standard threading module, all these threads will take turns fighting for the right to acquire the GIL. Due to the overhead of constant context switching, the overall execution speed of such a program may even be lower than that of sequential single-threaded code. Threads in Python physically cannot run in parallel on different processor cores when it comes to executing language instructions.

At the same time, the GIL practically does not hinder performance in I/O-bound tasks. When a thread performs a file read operation, a network request, or a database query, it voluntarily releases the GIL while waiting for a response. At this point, the operating system hands over control to another thread, which allows it to efficiently handle many network connections simultaneously without system idle time.

To overcome GIL limitations in CPU-bound tasks, time-tested approaches and engineering solutions are applied.

Understanding the nature of the GIL saves the developer from architectural mistakes at the system design stage. Knowing that threads will not speed up computations, a specialist will immediately choose the right tool for scaling the application and avoid wasting time on inefficient optimization.