Understanding Python's Garbage Collection Mechanism(Python Core in Action 22)
Learn how Python manages memory with garbage collection, reference counting, and how to handle circular references and memory leaks with objgraph.
Welcome to the "Python Core in Action" Series
It’s well-known that modern computers are based on the Turing machine architecture.
The essence of the Turing architecture is an infinitely long tape, which corresponds to today’s memory systems.
Over time, engineering has evolved to include registers, volatile memory (RAM), and permanent storage (hard drives).
This stems from a basic trade-off: faster memory is more expensive per unit.
Thus, efficiently using every bit of high-speed memory is always a key part of system design.
Now, let’s look at Python.
When a Python program runs, it allocates memory for temporary variables. After processing, results are saved to permanent storage. If the data size is too large and memory management is poor, an out-of-memory (OOM) error may occur, causing the system to terminate the program.
For servers, memory management is even more crucial to avoid memory leaks. What is a memory leak?
A memory leak doesn’t mean a security breach; it means the program didn’t release memory it no longer needs.
It also doesn’t mean memory physically disappears, but rather, that the program lost control over allocated memory, wasting resources.
So how does Python handle this? Specifically, how does Python reclaim memory that’s no longer needed?