Perfect.
DAY 4 is where you stop being a Python user and start thinking like the Python runtime.

Most senior engineers fail interviews here — because this topic is invisible but critical.

🧠 DAY 4 — Python Memory Management & Garbage Collection

(CPython Internals — no myths, only reality)

🔑 CORE IDEA OF DAY 4

Python memory management is NOT magic.
It is deterministic, reference-based, and sometimes misleading.

Python uses multiple memory management strategies, layered together.

1️⃣ Big Picture: Python Memory Architecture

At a high level:

CPU Stack (frames, local refs)
        ↓
Heap (Python objects)
        ↓
Memory Allocator (pymalloc)
        ↓
OS (malloc / free)

2️⃣ Stack vs Heap (Python Perspective)

🔹 Stack

• Function call frames
• Local variable references
• Short-lived
• Automatically cleaned

🔹 Heap

• All Python objects live here
• Managed by Python
• Garbage-collected

⚠️ Python variables NEVER live on the heap — objects do.

3️⃣ Reference Counting (PRIMARY GC MECHANISM)

Every Python object maintains:

ob_refcnt  → number of references

Example

a = []
b = a

Memory:

[]  → refcount = 2

Now:

del a

[] → refcount = 1

When:

refcount == 0 → object destroyed immediately

✅ This is why CPython memory cleanup feels “instant”.

4️⃣ Why del Is NOT Object Deletion (INTERVIEW TRAP)

a = [1, 2, 3]
del a

❌ Object deleted?
✅ Name removed

The object is deleted only if refcount becomes 0.

5️⃣ The BIG PROBLEM: Cyclic References

Reference counting cannot handle cycles.

Example

a = []
a.append(a)

Memory:

a ──▶ list
       ▲
       └── refers to itself

Refcount never reaches 0 ❌

➡️ This is where Python’s cyclic garbage collector steps in.

6️⃣ Python Cyclic Garbage Collector (gc module)

Python uses generational GC:

Why generations?

Most objects die young.

7️⃣ How Cyclic GC Works (Simplified)

1. Periodically pauses execution
2. Finds unreachable cycles
3. Breaks reference chains
4. Frees memory

⚠️ GC is not deterministic
⚠️ GC pauses execution (small but real cost)

8️⃣ When Python DOES NOT Free Memory

Critical Truth (Production systems!)

Even after object deletion:

• Memory may NOT return to OS
• Python keeps it for reuse

Why?

• Performance optimization
• Memory fragmentation avoidance

➡️ This is why long-running Python services appear to “leak” memory

9️⃣ __del__ Is Dangerous (VERY IMPORTANT)

class A:
    def __del__(self):
        print("deleted")

Problems:

• Unpredictable timing
• GC may skip cycles with __del__
• Can cause memory leaks

❌ Avoid in production systems.

10️⃣ gc Module — Look Inside the Engine

import gc

gc.get_threshold()
gc.get_count()
gc.collect()

You can:

• Disable GC (dangerous)
• Force GC (rarely needed)
• Inspect tracked objects

11️⃣ Memory Pools & pymalloc (CPython Optimization)

CPython uses:

• pymalloc for small objects (<512 bytes)
• Memory pools & arenas

Why?

• Faster allocation
• Less fragmentation

This is why Python memory usage ≠ OS memory usage.

12️⃣ Why Python Is NOT Good for Low-Level Memory Control

Python prioritizes:
✔ Safety
✔ Speed of development
✔ Predictability

🔥 INTERVIEW TRAPS (DAY 4)

Q1

a = []
b = a
del a
print(b)

✔ Output: []
Why? Object still referenced.

Q2

import gc
gc.disable()

a = []
a.append(a)
del a

✔ Memory leak risk
Cycle cannot be collected.

Q3

Does Python have memory leaks?

✅ Yes (in long-running processes, cycles, C extensions)

🧠 MENTAL MODEL TO REMEMBER

Before worrying about memory:

1. Count references
2. Check cycles
3. Understand lifetime
4. Remember: Python optimizes for speed, not RAM release

📝 DAY 4 ASSIGNMENT (IMPORTANT)

1️⃣ Predict without running:

import gc
gc.disable()

a = []
b = a
a.append(b)

del a
del b

Will memory free? Why?

2️⃣ Explain clearly:

• Difference between reference counting and GC
• Why CPython needs both

3️⃣ Think like interviewer:

Why does Python prefer reference counting over pure tracing GC?

🔜 DAY 5 PREVIEW

DAY 5 — Built-in Data Types (Big Picture & Object Model)

We’ll answer:

• Why everything is an object
• type, object, None
• Truthiness rules
• Singleton behavior

When ready, say 👉 “START DAY 5”