Memory Management in Python
Memory management is one of those behind-the-scenes systems that Python developers often take for granted. But understanding how Python handles memory can help you write faster, more efficient, and less error-prone code—especially as your applications grow.
In this third article in the “Road to 10x - Python Mastery” series, we’ll unpack how memory is allocated, what garbage collection actually means in Python, why memory leaks can still happen, and what tools you can use to spot issues early.
How Python Allocates Memory
Python handles memory management automatically, which is one reason it’s so popular for beginners. But under the hood, it’s more complex than it seems.
Every variable in Python is a reference to an object stored in memory. When you write x = 5, Python creates an integer object in memory and binds the name x to it.
All Python objects and data structures are stored in a private heap—a region of memory reserved exclusively for the Python interpreter. This heap is managed by Python’s memory manager, which handles allocation and deallocation through its own mechanisms and interfaces with lower-level system allocators like malloc() in C.
To understand how Python allocates memory, it's helpful to think in terms of stack vs. heap which are two sections in memory.
Stack vs. Heap
So What Happens When You Write x = 5?
Let's break it down:
5 is an object. Python treats everything as an object—even integers. The integer 5 already exists in memory thanks to something known as the small integer cache.
x is a reference. x is a variable name stored in the current function’s stack frame. It points to the 5 object on the heap.
Understanding this helps explain subtle details like two variables pointing to the same object. So two variables assigned the value 5 will point to the same object on the heap. This can be verified by using the “is” keyword to check if “x is y” which will return True.
Reference Counting & Garbage Collection
Before we dive into Python’s memory management specifics, let’s quickly cover what garbage collection means in programming. Garbage collection is the process of automatically identifying and freeing memory that is no longer in use—so developers don’t have to do it manually. This helps prevent memory leaks and keeps applications running efficiently. In other languages, this responsibility of allocating and freeing memory falls on the developer. What that would involve in practice is
However Python abstracts this away and uses reference counting as its core memory management strategy. Every object in Python has an internal counter that tracks how many references point to it.
When you do something like:
a = [1, 2, 3]
b = a
Both a and b refer to the same list object, so its reference count is 2.
If both a and b go out of scope, the reference count drops to 0—meaning nothing is using the object anymore—and Python immediately frees up that memory.
Reference counting is simple and fast, but it has one big limitation: it can’t handle circular references.
Consider two objects that reference each other:
class Node:
def init(self):
self.ref = None
a = Node()
b = Node()
a.ref = b
b.ref = a
Even if a and b go out of scope, they’re still referencing each other, so their reference counts never hit zero. This creates a memory leak—unless Python can detect it.
The Cyclic Garbage Collector
To handle these tricky cases, Python includes a cyclic garbage collector. It periodically looks for groups of objects that reference each other but are otherwise unreachable from the rest of the program.
This collector is based on a generational model:
This design is based on the assumption that short-lived objects are more common than long-lived ones, which is generally true in real-world programs.
Memory Pools and PyMalloc
When you write Python code, you’re often creating lots of small objects—integers, strings, lists, functions, and so on. If Python had to ask the operating system for memory every time one of these was created or destroyed, it would be painfully slow.
To solve this, Python uses a specialised memory allocator called PyMalloc. This allocator is optimised for small objects (specifically, anything smaller than 512 bytes), which make up the vast majority of Python objects.
Every time a program asks the OS for memory (e.g., with malloc() in C), it’s a relatively slow and expensive operation. So python avoids this by pre-allocating chunks of memory called "arenas", then breaking those down into "pools" and "blocks" for actual object storage. Here's the hierarchy:
This structure lets Python recycle memory very efficiently for objects of the same size, without having to go back to the OS every time.
Object Reuse and Interning
To further improve performance, Python sometimes reuses objects rather than creating new ones. In a process called “interning”, objects are reused rather than recreated. Interning saves memory and speeds up comparisons.
Why This Matters
Why Memory Leaks Still Happen
Even though Python manages memory for you with reference counting and garbage collection, memory leaks can still happen. They’re just a bit sneakier than in lower-level languages.
A Python memory leak doesn’t usually crash your program. Instead, your app slowly starts using more memory than expected. Over time, that can hurt performance—or even take down long-running services.
Common Causes of Memory Leaks in Python
Here are a few patterns that frequently lead to leaks, even in clean-looking code:
1. Reference Cycles with del()
When two or more objects reference each other, they form a cycle. Normally, Python’s garbage collector can detect and clean these up. But if one of the objects defines a del() method (a custom destructor), Python can’t easily determine the correct order to destroy them — so it leaves them alone.
class Node:
def init(self):
self.ref = None
def __del__(self):
print("Node deleted")
a = Node()
b = Node()
a.ref = b
b.ref = a
If this cycle never gets broken manually, the memory stays allocated.
2. Globals and Caches That Hold References Forever
Storing objects in global variables, module-level lists, or in-memory caches (like a dict or lru_cache) is fine—but if you never clear those containers, the memory never gets released.
cache = {}
def expensive_lookup(key):
if key in cache:
return cache[key]
result = compute_result(key)
cache[key] = result
return result
Over time, if cache keeps growing and nothing removes unused keys, your program may hoard memory unnecessarily.
3. Closures and Lambdas That Capture Variables
Closures and lambdas can accidentally hold onto references longer than intended. If a function returns another function that captures a local variable, that variable won’t be garbage collected—even if the original function has returned.
def wrapper():
large_data = load_big_dataset()
def inner():
return large_data[:10]
return inner
If inner is stored somewhere (e.g., in a list or global), then large_data stays in memory too.
4. Long-Lived Containers
Sometimes we add things to a list, dict, or set for convenience—and just never clean it up.
Unless you explicitly remove them from the data structure, this list will grow forever.
Tools to Catch Memory Leaks
Here are a few tools that can help you identify leaks in real projects:
Moving onto Pythonic Code
So far in this series, we’ve peeled back the layers of Python’s internals—starting with the execution model, exploring the Global Interpreter Lock (GIL), and now diving into memory management. Each of these topics builds a foundation for writing faster, more efficient, and more predictable code.
In the next article, we’ll shift our focus to writing truly Pythonic code—code that’s not just syntactically correct, but also optimised to work with Python’s internals, not against them. Pythonic code takes advantage of how Python is designed under the hood—its memory model, its execution patterns, and its philosophy—to be both elegant and performant.
We’ll explore:
Now that we understand how Python runs, it’s time to start writing code that runs beautifully in it. See you in the next one!