Summary
This chapter covers using a Python library to visualize in-memory object graphs by counting live instances, tracking object growth, and identifying reference cycles to diagnose memory leaks.

General domain of usage
memory leak detection

`tracemalloc` and `memory_profiler` show how much memory is used. `objgraph` shows **what** is in memory – how many instances of each type exist, what references what, and where reference cycles are hiding. It is the go-to tool for diagnosing memory leaks.

This video provides a visual and narrated walkthrough of using the `objgraph` library for memory leak detection and object graph visualization in Python. You'll learn how to install `objgraph`, count objects by type, detect object growth over time, find reference cycles, and track specific object types. The video also compares `objgraph` with `tracemalloc`, highlighting when to use each tool. All demonstrations use practical code examples to reinforce the concepts.

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Cut all examples of code to a minimum. Show only the essential code. Show code no longer than 10 lines. Remove all possible pieces to demonstrate some code feature. You may show not full code that will not start, but still demonstrates some feature theoritacally.
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## Installation

```
pip install objgraph
```

## Counting Objects by Type

`objgraph.show_most_common_types()` lists the types with the most live instances:

import objgraph

# Creating some objects and checking what's in memory
class Invoice:
    def __init__(self, invoice_id, amount):
        self.invoice_id = invoice_id
        self.amount = amount

invoices = [Invoice(invoice_id, invoice_id * 250.0) for invoice_id in range(1000)]
cached_results = {"q1": list(range(500)), "q2": list(range(500))}

objgraph.show_most_common_types(limit=10)

This gives an instant overview of what is consuming memory.



## Detecting Growth

`objgraph.show_growth()` compares the current object counts to the previous call and shows only what grew:

import objgraph

class ReportSection:
    def __init__(self, section_id):
        self.section_id = section_id

objgraph.show_growth()  # Baseline snapshot

# Simulating a function that leaks objects
leaked_sections = []
for section_id in range(500):
    leaked_sections.append(ReportSection(section_id))

objgraph.show_growth()  # Shows what grew since the baseline

If `show_growth()` keeps showing the same type growing on every call, that type is leaking.

## Finding Reference Cycles

`objgraph.find_backref_chain()` traces the reference path from a specific object back to a root – showing exactly what is keeping it alive:

import objgraph
import gc

class Node:
    def __init__(self, label):
        self.label = label
        self.child = None

# Creating a cycle
node_a = Node("parent")
node_b = Node("child")
node_a.child = node_b
node_b.child = node_a  # Cycle

# Finding what keeps node_a alive
chain = objgraph.find_backref_chain(
    node_a,
    objgraph.is_proper_module
)
print(chain)

gc.collect()  # Collecting the cycle after inspection

## Tracking a Specific Type

`objgraph.by_type()` returns all live instances of a given type – useful for checking that a factory or cache is not accumulating objects:

import objgraph

class DataChunk:
    def __init__(self, chunk_id):
        self.chunk_id = chunk_id

chunks = [DataChunk(chunk_id) for chunk_id in range(5)]

live_chunks = objgraph.by_type("DataChunk")
print(f"Live DataChunk instances: {len(live_chunks)}")

for chunk in live_chunks:
    print(f"  chunk_id={chunk.chunk_id}")

What does `objgraph.show_growth()` report?

Master the skills needed to understand Python's memory management internals and performance optimization techniques. This course is designed for intermediate and advanced Python developers who want to understand how Python handles memory, write memory-efficient code, detect and fix memory leaks, and optimize performance in real-world applications.

Explore the essentials of Python's memory management, including reference counting, garbage collection, and memory allocation strategies.

Get hands-on experience with practical techniques for writing memory-efficient Python code, including specialized data structures and tools for controlling object lifecycle.

Master the skills needed to profile memory usage and detect memory leaks in Python applications.

Explore the essentials of advanced memory and performance optimization patterns, including object interning, memory inspection tools, and the GIL's impact on memory access.

Visualizing Object Graphs with objgraph

Installation

Counting Objects by Type

Detecting Growth

Finding Reference Cycles

Tracking a Specific Type

`objgraph` vs `tracemalloc`