Memory Allocation in Python: Understanding Arenas, Pools, and Blocks

Python handles memory behind the scenes, but understanding it can make your code faster. Learn about arenas, pools, and blocks with…

Memory Allocation in Python: Understanding Arenas, Pools, and Blocks

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Python manages memory efficiently using an internal allocation system that prevents frequent calls to the operating system. This system is designed to optimize performance, reduce fragmentation, and ensure memory reuse where possible.

In this post, we’ll break down Python’s memory allocation process using real-world examples, official documentation references, and practical code snippets.

1️⃣ Python’s Memory Allocation Strategy

When you create objects in Python, memory is allocated in multiple layers:

Blocks → Small memory units assigned to individual objects.
Pools → Groups of blocks of similar sizes.
Arenas → Large memory regions that hold multiple pools.

How This Works in Practice

Imagine you’re running a web server that handles thousands of user sessions. Instead of allocating memory for each session separately, Python groups similar-sized objects together, reducing overhead and improving efficiency.

2️⃣ Blocks: The Smallest Unit of Memory

A block is the fundamental unit of memory allocation. When you create an object, Python assigns it a block. If the object is small (≤512 bytes), it gets stored in a pre-allocated block to avoid repeated memory requests.

Example: Memory Allocation for Integers

x = 100  
y = 100

print(id(x), id(y))  # Same memory address

Since integers between -5 and 256 are interned , Python reuses the same block for x and y.

📄 Reference: Python Integer Objects

3️⃣ Pools: Grouping Blocks for Efficiency

A pool is a collection of blocks of the same size. Instead of allocating memory for each new object, Python keeps pools ready for reuse.

🔹 Objects smaller than 512 bytes are stored in pools.
🔹 Each pool contains blocks of the same size (8, 16, 32 bytes, etc.).

Example: Why Pools Matter in a Web API

Consider an API that processes user data:

def process_request(user_data):  
    user = {"name": user_data["name"], "age": user_data["age"]}    
    return user

Each request creates a new dictionary , leading to multiple memory allocations.

Better Approach: Using Object Caching (Reusing Memory)

Instead of creating a new object each time, we can reuse objects efficiently:

import functools

@functools.lru_cache(maxsize=1000)  
def get_user_template():  
    return {"name": "", "age": 0}

def process_request(user_data):  
    user = get_user_template().copy()  
    user["name"] = user_data["name"]  
    user["age"] = user_data["age"]  
    return user

This approach reduces memory allocation by using a pre-allocated object structure.

📄 Reference: Python Memory Management

4️⃣ Arenas: The Backbone of Python’s Memory System

An arena is a large 256 KB memory region that contains multiple pools. Instead of allocating small pieces of memory individually, Python requests a large block from the OS and distributes it as needed.

from multiprocessing import shared_memory  
import numpy as np

# Create shared memory  
shm = shared_memory.SharedMemory(create=True, size=1024)

# Assign a NumPy array to this memory block  
array = np.ndarray((256,), dtype=np.int32, buffer=shm.buf)  
array[:] = np.arange(256)  
print("Shared Memory Name:", shm.name)

# Cleanup  
shm.close()  
shm.unlink()

Here, multiple processes can access shared memory without duplication , reducing memory usage.

📄 Reference: PEP 3118 — Revising the buffer protocol

5️⃣ Putting It All Together: Memory Optimization in a FastAPI Server

A real-world example of Python’s memory allocation system is a FastAPI-based authentication service that manages sessions.

Bad Approach: Creating New Session Objects for Each Request

from fastapi import FastAPI

app = FastAPI()  
sessions = {}

@app.get("/session/{user_id}")  
def get_session(user_id: str):  
    sessions[user_id] = {"user_id": user_id, "data": {}}  
    return sessions[user_id]

Each request allocates new memory , leading to high memory usage.

Optimized Approach: Using a Memory Pool for Sessions

from fastapi import FastAPI  
from multiprocessing import Manager

app = FastAPI()  
session_manager = Manager()  
sessions = session_manager.dict()

@app.get("/session/{user_id}")  
def get_session(user_id: str):  
    if user_id not in sessions:  
        sessions[user_id] = {"user_id": user_id, "data": {}}  
    return sessions[user_id]

By using a shared memory dictionary , Python reuses objects instead of allocating new ones for each request.

📄 Reference: FastAPI Performance Guide

Key Takeaways

Blocks are the smallest memory units, allocated for objects.
Pools group blocks of the same size, preventing fragmentation.
Arenas allocate large memory chunks to reduce OS calls.
Optimized memory usage is crucial for high-performance applications.

📄 Further Reading:

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By Kfir Gisman on March 10, 2025.

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Memory Allocation in Python: Understanding Arenas, Pools, and Blocks