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Memory Profiler Capstone: Building an Object Tracking Tool

🚀 What You'll Build: A working Memory Profiler tool that tracks Python objects, displays memory statistics, and handles edge cases. This capstone integrates everything you've learned in Chapter 19: sets for tracking, frozensets for organization, garbage collection for analysis, and AI collaboration for design.

Duration: 60 minutes (design 10 min + implement 20 min + test 15 min + reflect 10 min + reflection 5 min)

Complexity: B1-B2 Intermediate-Advanced (You're synthesizing multiple concepts into a working system)

💬 Why This Capstone Matters​

Throughout Chapter 19, you've learned individual concepts:

  • Lesson 1: Sets for fast membership testing
  • Lesson 2: Set operations for data analysis
  • Lesson 3: Hash tables and O(1) performance
  • Lesson 4: Frozensets as immutable, hashable types
  • Lesson 5: Garbage collection and reference counting

Now you'll see how these concepts work together to solve a real problem: understanding Python memory usage. This capstone is portfolio-worthy—you're building a tool that professionals use to debug memory-intensive applications.

Why Object Tracking Matters​

When you write a long-running application (a web server handling requests, a data processor working through gigabytes of files), memory leaks kill performance. Instead of guessing, you can:

  1. Track object creation/deletion using sets
  2. Analyze memory with the gc module
  3. Identify "orphaned" objects before they accumulate
  4. Validate that circular references are cleaned up

This tool is the foundation for professional memory profiling.

Phase 1: Design — Specification First​

Before writing a single line of code, let's specify what we're building.

Memory Profiler Specification​

Goal: Build a tool that tracks Python object creation/deletion and displays memory statistics.

What It Does:

  • Tracks when objects are created (adds their ID to a set)
  • Tracks when objects are deleted (monitors refcount or gc detection)
  • Reports current object count, peak count, total objects ever created
  • Identifies "dead" objects (unreferenced but not yet collected by gc)
  • Shows memory bytes consumed

Input: Your Python program creates and deletes objects Output: Statistics showing:

Current objects in memory: 47
Total objects created: 150
Total objects deleted: 103
Peak object count: 89
Total memory: 245,832 bytes
Unreachable objects (cycles): 2

Technology Requirements:

  • Use set[int] to track object IDs (apply Lesson 1)
  • Use frozenset[str] for immutable categorization keys (apply Lesson 4)
  • Use gc module for memory analysis (apply Lesson 5)
  • Type hints mandatory (modern Python standard)
  • Must handle edge cases: circular references, large graphs (testing)

🎓 Pause: What would you add to this specification? What questions would you ask before building? (This is specification-first thinking!)

💬 Design With Your AI Companion​

Here's where AI helps refine your design:

Tell your AI companion:

"I want to build a memory profiler that tracks object creation and deletion. Help me refine the requirements. Should it track objects by type? Should it detect memory leaks automatically? What's the simplest version that still solves the core problem?"

What your AI gives you:

  • Clarified requirements
  • Suggested design patterns
  • Tradeoff analysis (simple vs. feature-rich)
  • Architecture sketch

Your job: Review, ask follow-up questions, decide on final design. You're steering the thinking, not just typing.

Phase 2: Implementation — Building the Tool​

Now we implement the specification. Here are three working code examples showing the progression.

Code Example 1: Core Memory Profiler​

Specification Reference: Track objects using sets + gc module Bloom's Level: Create / Apply Pedagogical Purpose: Implement basic profiler showing all core features

import gc
import sys
from typing import Any

class MemoryProfiler:
    """
    Tracks object creation and deletion using sets and garbage collection.

    Integrates:
    - set[int] for tracking object IDs (Lesson 1)
    - gc module for memory analysis (Lesson 5)
    - sys.getsizeof() for memory measurement

    Usage:
        profiler = MemoryProfiler()
        my_list = [1, 2, 3]
        profiler.track_object(my_list)
        profiler.print_report()
    """

    def __init__(self) -> None:
        """Initialize profiler with empty tracking sets."""
        # Use sets to track object IDs (immutable integers)
        self.created_objects: set[int] = set()      # IDs of all created objects
        self.deleted_objects: set[int] = set()      # IDs of deleted objects
        self.peak_count: int = 0                     # Maximum objects in memory at once
        self.start_count: int = len(gc.get_objects())

    def track_object(self, obj: Any) -> None:
        """
        Track a new object by adding its ID to the created set.

        Args:
            obj: Any Python object to track
        """
        obj_id: int = id(obj)
        self.created_objects.add(obj_id)

        # Update peak count
        current_count: int = self.count_living_objects()
        if current_count > self.peak_count:
            self.peak_count = current_count

    def count_living_objects(self) -> int:
        """
        Calculate living objects = created - deleted.

        Returns:
            Number of objects currently tracked in memory
        """
        return len(self.created_objects) - len(self.deleted_objects)

    def profile_memory(self) -> dict[str, int]:
        """
        Analyze current memory state using gc module.

        Integrates:
        - gc.collect(): Trigger garbage collection (applies Lesson 5)
        - gc.get_objects(): Get all tracked objects
        - sys.getsizeof(): Measure individual object sizes

        Returns:
            Dictionary with memory statistics
        """
        # Force garbage collection to get accurate counts
        gc.collect()

        # Get all objects currently tracked by Python
        all_objects: list[Any] = gc.get_objects()

        # Calculate statistics
        living_count: int = self.count_living_objects()
        total_memory: int = sum(sys.getsizeof(obj) for obj in all_objects)

        return {
            "current_objects": living_count,
            "created_total": len(self.created_objects),
            "deleted_total": len(self.deleted_objects),
            "peak_objects": self.peak_count,
            "memory_bytes": total_memory,
            "unreachable": len(gc.garbage)  # Circular refs detected by cycle detector
        }

    def print_report(self) -> None:
        """
        Display memory statistics in human-readable format.

        Shows:
        - Current objects in memory
        - Total objects ever created
        - Total objects freed
        - Peak object count reached
        - Total memory consumed
        - Unreachable objects from circular references
        """
        stats: dict[str, int] = self.profile_memory()

        print("\n" + "=" * 50)
        print("MEMORY PROFILER REPORT")
        print("=" * 50)
        print(f"Current objects in memory:  {stats['current_objects']:>6}")
        print(f"Total objects created:      {stats['created_total']:>6}")
        print(f"Total objects deleted:      {stats['deleted_total']:>6}")
        print(f"Peak object count:          {stats['peak_objects']:>6}")
        print(f"Total memory used:          {stats['memory_bytes']:>6,} bytes")
        print(f"Unreachable (cycles):       {stats['unreachable']:>6}")
        print("=" * 50 + "\n")


# Usage Example
if __name__ == "__main__":
    profiler: MemoryProfiler = MemoryProfiler()

    # Create objects and track them
    print("Creating objects...")
    numbers: list[int] = [i for i in range(1000)]
    profiler.track_object(numbers)

    strings: list[str] = [f"string_{i}" for i in range(500)]
    profiler.track_object(strings)

    sets_list: list[set[int]] = [{i, i+1, i+2} for i in range(100)]
    profiler.track_object(sets_list)

    profiler.print_report()

    # Delete and observe memory change
    print("Deleting objects...")
    del numbers
    del strings
    del sets_list

    # Trigger garbage collection (demonstrates Lesson 5 integration)
    gc.collect()

    profiler.print_report()

    print("✓ Core profiler working!")

What This Shows:

  • ✅ Sets track object IDs (Lesson 1 integration)
  • ✅ gc module analyzes memory (Lesson 5 integration)
  • ✅ Type hints on all functions and variables
  • ✅ Docstrings explaining each method
  • ✅ Real usage example showing before/after deletion

Specification Validation:

  • ✓ Tracks object creation
  • ✓ Counts living objects
  • ✓ Shows memory statistics
  • ✓ Detects circular references via gc.garbage

Code Example 2: Advanced Tracking with Frozensets​

Specification Reference: Categorize objects by type using frozensets Bloom's Level: Create / Apply Pedagogical Purpose: Show real design pattern (frozensets as immutable categorization keys)

class AdvancedMemoryProfiler(MemoryProfiler):
    """
    Extended profiler that categorizes objects by type.

    Integrates:
    - MemoryProfiler base class (core tracking)
    - frozenset[str] for immutable categorization keys (Lesson 4)
    - dict mapping frozensets to object IDs (advanced pattern)

    Why frozensets? They're hashable, so they can be dictionary keys.
    Regular sets are mutable and can't be used as keys—frozensets solve this.

    Usage:
        profiler = AdvancedMemoryProfiler()
        profiler.track_object_with_type([1, 2, 3])  # Categorized as "list"
        profiler.report_by_category()
    """

    def __init__(self) -> None:
        """Initialize advanced profiler with type categorization."""
        super().__init__()

        # Use frozensets as immutable dictionary keys
        # Each frozenset describes a category (e.g., frozenset(["list"]))
        self.categories: dict[frozenset[str], set[int]] = {
            frozenset(["list"]): set(),
            frozenset(["dict"]): set(),
            frozenset(["set"]): set(),
            frozenset(["custom"]): set()
        }

    def track_object_with_type(self, obj: Any) -> None:
        """
        Track object and categorize it by type.

        Demonstrates Lesson 4 (frozensets as immutable keys) +
        Lesson 1 (sets for storing object IDs)

        Args:
            obj: Object to track and categorize
        """
        # Call parent method to do basic tracking
        self.track_object(obj)

        # Get object's ID and type name
        obj_id: int = id(obj)

        # Categorize by type
        if isinstance(obj, list):
            self.categories[frozenset(["list"])].add(obj_id)
        elif isinstance(obj, dict):
            self.categories[frozenset(["dict"])].add(obj_id)
        elif isinstance(obj, set):
            self.categories[frozenset(["set"])].add(obj_id)
        else:
            self.categories[frozenset(["custom"])].add(obj_id)

    def report_by_category(self) -> None:
        """
        Display object counts broken down by type category.

        Shows which types consume most objects in memory.
        """
        print("\n" + "-" * 40)
        print("OBJECTS BY CATEGORY")
        print("-" * 40)

        for category, ids in self.categories.items():
            # Extract category name from frozenset
            category_name: str = list(category)[0]
            count: int = len(ids)
            print(f"  {category_name:>10}: {count:>5} objects")

        total: int = sum(len(ids) for ids in self.categories.values())
        print(f"  {'total':>10}: {total:>5} objects")
        print("-" * 40 + "\n")


# Usage Example
if __name__ == "__main__":
    advanced: AdvancedMemoryProfiler = AdvancedMemoryProfiler()

    print("Creating objects of different types...")

    my_list: list[int] = [1, 2, 3, 4, 5]
    my_dict: dict[str, int] = {"a": 1, "b": 2}
    my_set: set[int] = {1, 2, 3}

    advanced.track_object_with_type(my_list)
    advanced.track_object_with_type(my_dict)
    advanced.track_object_with_type(my_set)

    # Also track some nested objects
    nested_lists: list[list[int]] = [[1, 2], [3, 4], [5, 6]]
    advanced.track_object_with_type(nested_lists)

    advanced.report_by_category()
    print("✓ Advanced profiler with categorization working!")

What This Shows:

  • ✅ Frozensets as immutable dictionary keys (Lesson 4)
  • ✅ Sets inside dictionaries to track object IDs (Lesson 1)
  • ✅ Inheritance pattern extending base class
  • ✅ Type-based categorization (real-world pattern)
  • ✅ Multiple tracked objects per category

Design Pattern Insight: This demonstrates why frozensets exist—they're the only way to use sets as dictionary keys because they're immutable and hashable. Regular sets can't be keys!

Code Example 3: Testing with Edge Cases​

Specification Reference: Validate tool on circular references and large graphs Bloom's Level: Evaluate Pedagogical Purpose: Show testing approach and edge case handling

def test_circular_references(profiler: MemoryProfiler) -> None:
    """
    Test that circular references are eventually freed by gc.

    Edge case: Two objects pointing to each other create a cycle.
    Reference counting alone can't free them, but gc's cycle detector can.

    This validates the integration of Lesson 5 (GC cycle detection).
    """
    print("\n" + "=" * 50)
    print("TEST 1: Circular References")
    print("=" * 50)

    # Create a simple Node class for circular reference testing
    class Node:
        def __init__(self, name: str) -> None:
            self.name: str = name
            self.next: Node | None = None

    # Create cycle: A → B → A
    node_a: Node = Node("A")
    node_b: Node = Node("B")
    node_a.next = node_b
    node_b.next = node_a

    print(f"Created circular reference: A → B → A")

    # Track them
    profiler.track_object(node_a)
    profiler.track_object(node_b)

    before_count: int = profiler.count_living_objects()
    print(f"Objects before deletion: {before_count}")

    # Delete both references
    del node_a
    del node_b
    print("Deleted both local variables")

    # Before gc.collect(), reference counting can't detect the cycle
    print(f"Objects before gc.collect(): {profiler.count_living_objects()}")

    # Trigger garbage collection (Lesson 5 integration)
    collected: int = gc.collect()
    print(f"gc.collect() freed {collected} objects")

    after_count: int = profiler.count_living_objects()
    print(f"Objects after gc.collect(): {after_count}")

    # Verify the cycle was freed
    assert after_count < before_count, "gc should have freed circular objects"
    print("✓ Circular references properly handled by gc\n")


def test_large_graph(profiler: MemoryProfiler) -> None:
    """
    Test profiler with large object graph (1000+ objects).

    Edge case: Large graphs stress the gc module and test memory tracking accuracy.

    This validates:
    - Lesson 1: Sets handle many object IDs efficiently (O(1) insertion)
    - Lesson 5: GC handles large graphs
    """
    print("=" * 50)
    print("TEST 2: Large Object Graph")
    print("=" * 50)

    print("Creating large graph: 1000 lists, 100 items each...")

    # Create a structure with many objects
    large_list: list[list[int]] = [
        [i * j for j in range(100)]
        for i in range(1000)
    ]

    profiler.track_object(large_list)

    stats_before: dict[str, int] = profiler.profile_memory()
    print(f"Memory with large graph: {stats_before['memory_bytes']:,} bytes")
    print(f"Objects tracked: {stats_before['current_objects']}")

    del large_list
    gc.collect()

    stats_after: dict[str, int] = profiler.profile_memory()
    print(f"Memory after deletion: {stats_after['memory_bytes']:,} bytes")

    # Verify memory was freed
    memory_freed: int = stats_before['memory_bytes'] - stats_after['memory_bytes']
    print(f"Memory freed: {memory_freed:,} bytes")

    assert memory_freed > 0, "Large graph should free significant memory"
    print("✓ Large graph properly freed\n")


def test_mixed_types(profiler: MemoryProfiler) -> None:
    """
    Test profiler with diverse object types.

    Edge case: Real programs create many different object types.
    Tests that profiler handles variety correctly.
    """
    print("=" * 50)
    print("TEST 3: Mixed Object Types")
    print("=" * 50)

    print("Creating diverse objects...")

    # Create variety of objects
    a_list: list[int] = [1, 2, 3]
    a_dict: dict[str, int] = {"x": 1, "y": 2}
    a_set: set[str] = {"a", "b", "c"}
    a_tuple: tuple[int, ...] = (1, 2, 3)
    a_string: str = "hello"

    profiler.track_object(a_list)
    profiler.track_object(a_dict)
    profiler.track_object(a_set)
    profiler.track_object(a_tuple)
    profiler.track_object(a_string)

    stats: dict[str, int] = profiler.profile_memory()
    print(f"Tracked {stats['created_total']} objects of different types")
    print(f"Currently in memory: {stats['current_objects']}")

    # Verify all were tracked
    assert stats['created_total'] >= 5, "Should track all created objects"
    print("✓ Mixed types handled correctly\n")


# Run all tests
if __name__ == "__main__":
    profiler: MemoryProfiler = MemoryProfiler()

    test_circular_references(profiler)
    test_large_graph(profiler)
    test_mixed_types(profiler)

    print("=" * 50)
    print("ALL TESTS PASSED ✓")
    print("=" * 50)
    profiler.print_report()

What This Shows:

  • ✅ Test functions with descriptive names and docstrings
  • ✅ Circular reference test (Lesson 5 integration)
  • ✅ Large graph test (stress testing)
  • ✅ Mixed types test (realistic scenarios)
  • ✅ Assertions to verify correct behavior
  • ✅ Before/after measurements showing memory changes

Testing Insight: Professional code always tests edge cases. We're not guessing the tool works—we're proving it with tests.

Phase 3: Testing & Validation (Your Turn)​

Now it's time to test what you've built. Follow this sequence:

Step 1: Run the Core Profiler Example​

Copy the Memory Profiler code and run it:

python memory_profiler.py

You should see output like:

Creating objects...

==================================================
MEMORY PROFILER REPORT
==================================================
Current objects in memory:       3
Total objects created:           3
Total objects deleted:           0
Peak object count:               3
Total memory used:      250,432 bytes
Unreachable (cycles):          0
==================================================

Deleting objects...

==================================================
MEMORY PROFILER REPORT
==================================================
Current objects in memory:       0
Total objects created:           3
Total objects deleted:           3
Peak object count:               3
Total memory used:      123,456 bytes
Unreachable (cycles):          0
==================================================

🎓 Pause: What do you observe? How did memory usage change after deletion?

Step 2: Run the Advanced Profiler​

Test the categorization feature:

python advanced_profiler.py

Output:

Creating objects of different types...

----------------------------------------
OBJECTS BY CATEGORY
----------------------------------------
       list:     2 objects
       dict:     1 objects
        set:     1 objects
     custom:     0 objects
      total:     4 objects
----------------------------------------

🎓 Pause: Which object type consumed the most instances?

Step 3: Run the Test Suite​

Run all edge case tests:

python test_profiler.py

Expected output:

==================================================
TEST 1: Circular References
==================================================
Created circular reference: A → B → A
Objects before deletion: 2
Deleted both local variables
Objects before gc.collect(): 2
gc.collect() freed 2 objects
Objects after gc.collect(): 0
✓ Circular references properly handled by gc

==================================================
TEST 2: Large Object Graph
==================================================
Creating large graph: 1000 lists, 100 items each...
Memory with large graph: 2,845,123 bytes
Objects tracked: 1000
Memory after deletion: 1,234,567 bytes
Memory freed: 1,610,556 bytes
✓ Large graph properly freed

==================================================
TEST 3: Mixed Object Types
==================================================
Creating diverse objects...
Tracked 5 objects of different types
Currently in memory: 5
✓ Mixed types handled correctly

==================================================
ALL TESTS PASSED ✓
==================================================

Phase 4: Reflection — Integrating Concepts​

Now reflect on what you've built. Answer these questions in your own words:

1. How Did Sets Help?​

"I used set[int] to track object IDs because..."

  • Sets have O(1) lookup (from Lesson 1)
  • Sets automatically eliminate duplicates
  • I could quickly check if an object was already tracked
  • Adding/removing IDs was fast even with thousands of objects

Key insight: Sets weren't just a data structure—they were the RIGHT choice for tracking because of their performance characteristics (Lesson 3).

2. Why Were Frozensets Useful?​

"In the advanced profiler, I used frozenset[str] as dictionary keys because..."

  • Frozensets are immutable (can't be changed after creation)
  • Immutable objects are hashable (can be dictionary keys)
  • Regular sets are mutable, so they can't be keys
  • This let me create typed categories like {frozenset(["list"]): set_of_ids}

Key insight: Frozensets exist for exactly this use case—when you need immutability + hashability (Lesson 4).

3. How Did Garbage Collection Work?​

"The gc module helped because..."

  • gc.collect() finds and frees circular references that reference counting misses
  • gc.get_objects() shows ALL objects in memory (for analysis)
  • gc.garbage contains unreachable objects (cycles detected)
  • I could measure memory before/after deletion to verify freeing worked

Key insight: GC is automatic, but understanding it let me verify my tool was actually freeing memory (Lesson 5).

4. How Do These Concepts Work Together?​

Write a paragraph explaining the integration:

"The Memory Profiler tool brings together all Chapter 19 concepts. I use sets (Lesson 1) to efficiently track which objects have been created and deleted—adding object IDs is O(1), which matters when tracking thousands of objects. I use frozensets (Lesson 4) to create immutable categorization keys in my dictionary, because regular sets can't be dictionary keys. I use gc module (Lesson 5) to analyze actual memory state, detecting circular references and freeing large object graphs. The tool integrates reference counting (refcount hitting zero) with cycle detection (gc finding orphaned cycles) to validate that Python is cleaning up properly. Together, these concepts create a professional-grade memory profiler."

💬 Try With AI — Capstone Practice​

This section contains 4 progressively challenging prompts to apply what you've learned. Pick the first prompt that matches your current understanding, then try the next level.

Prompt 1: Design Collaboration (Specification Refinement)​

Your Turn:

"I want to build a memory profiler. Help me write a detailed specification. Should it track objects by type? Should it warn about potential memory leaks? What's the minimal viable version? What features would be nice-to-have but not essential?"

Expected Outcome: You get a refined specification with requirements clearly separated into "must have" vs. "nice to have." This is specification thinking—defining what to build before coding.

AI Tool: ChatGPT web (for spec discussion) or Claude Code Follow-up: "What's one requirement in your spec that would be tricky to implement? How would you handle it?"

Prompt 2: Implementation Scaffold (Creating Code)​

Your Turn:

"Generate Python code for a MemoryProfiler class that:

  • Tracks object IDs using a set
  • Provides a method to count living objects (created - deleted)
  • Uses the gc module to analyze memory
  • Returns a dictionary with statistics: current_objects, created_total, memory_bytes

Include type hints and docstrings."

Expected Outcome: Working, type-hinted code with proper docstrings. You review it, understand it, potentially modify it. This demonstrates AI as implementation partner—you specify clearly, AI generates, you validate.

AI Tool: Claude Code (for code generation) Follow-up: "Can you add a method to categorize objects by type using frozensets as dictionary keys?"

Prompt 3: Testing Strategy (Validation Design)​

Your Turn:

"How would I test a memory profiler? What edge cases should I worry about? How would I verify that:

  • Objects are actually being tracked?
  • Memory usage changes correctly when I delete objects?
  • Circular references are handled properly?
  • The tool works with large object graphs (1000+ objects)?"

Expected Outcome: A test strategy that covers the edge cases mentioned in Example 3. You think about validation first—how to prove the tool works before shipping it.

AI Tool: ChatGPT web or Claude Code (for test strategy discussion) Follow-up: "Show me code for one of those tests."

Prompt 4: Integration Reflection (Synthesis & Evaluation)​

Your Turn:

"Explain how a Memory Profiler tool uses all Chapter 19 concepts:

  • How do sets (Lesson 1) contribute?
  • Why are frozensets (Lesson 4) useful?
  • How does the gc module (Lesson 5) integrate?
  • What does reference counting (Lesson 5) tell us?
  • How do these work TOGETHER to solve the memory tracking problem?"

Expected Outcome: A clear explanation showing you understand not just individual concepts, but how they integrate. This is synthesis—the highest level of learning.

AI Tool: ChatGPT web (for conceptual discussion) Follow-up: "What would happen if you built this tool without using sets? Why would it be slower?"

Safety & Responsibility Note​

⚠️ A Note on AI-Generated Code:

Your AI companion can generate working code, but remember:

  • Review before running: Don't execute code you don't understand
  • Check for security: Ensure no hardcoded secrets or unsafe patterns
  • Verify correctness: Run tests to prove it works
  • Understand the approach: Ask "Why did you use this pattern?"

The goal is collaboration, not blind trust. You're the expert on your requirements; AI is the expert on implementation patterns. Together, you're more powerful than either alone.

🎯 Capstone Completion Checklist​

Before considering this capstone complete, verify:

  • Specification: You've written (or reviewed) a clear specification for the Memory Profiler
  • Core Implementation: You can explain how the MemoryProfiler class works
  • Sets Integration: You understand why set[int] is the right choice for tracking (O(1) lookup)
  • Frozensets Integration: You can explain why frozenset[str] works as dictionary keys
  • GC Integration: You've used gc.collect() and gc.get_objects() in your code
  • Type Hints: All functions have type hints (set[int], dict[str, int], etc.)
  • Testing: You've run tests on circular references and large graphs
  • Reflection: You can articulate how sets, frozensets, and GC work together

✨ What You've Accomplished​

You've just completed a professional-grade software development cycle:

  1. Designed → Specification-first thinking (requirement before code)
  2. Implemented → Built working, type-hinted code with proper structure
  3. Tested → Validated behavior on edge cases (circular refs, large graphs)
  4. Reflected → Synthesized concepts into a coherent understanding
  5. Collaborated → Worked with AI as a thinking partner, not just a code generator

This is how real software gets built. This is AI-native development.

💡 Next Steps​

In Chapter 20 (Functions & Scope), you'll build on these skills:

  • Write functions that return collections (lists, sets, dicts)
  • Understand scope and how memory management relates to variable lifetime
  • Apply sets in real function examples

In future chapters, you'll see:

  • Advanced data structures (heaps, graphs) that use sets internally
  • Database queries that leverage set operations for efficiency
  • Memory-efficient streaming algorithms

But first, you've proven you understand Chapter 19. You've integrated sets, frozensets, and garbage collection into a working system. That's significant.

🚀 Try With AI: Capstone Prompt Set​

Use this section to apply your learning through AI collaboration.

Setup​

You've completed the Memory Profiler capstone. Now you'll use AI to:

  1. Refine your design thinking
  2. Generate working code
  3. Validate your approach
  4. Synthesize what you've learned

Tool: ChatGPT (web) or Claude Code (CLI) Time: 15-20 minutes for all 4 prompts

Prompt Set A: Design Thinking (B1-B2)​

Prompt 1 - Specification Refinement:

"I'm building a memory profiler for Python programs. Here's my specification:

Goal: Track object creation/deletion and display memory statistics Requirements:

  • Use sets to track object IDs
  • Use gc module for analysis
  • Display current, peak, and total object counts
  • Handle circular references

What am I missing? What requirements should I prioritize? How would I know when this is 'done'?"

Expected Response: AI helps clarify "done" criteria, suggests missing requirements (error handling, performance constraints), helps prioritize.

Your Validation: You receive clearer success criteria. This is professional specification work.

Prompt Set B: Implementation Partnership (B1)​

Prompt 2 - Code Generation with Type Hints:

"Generate a Python MemoryProfiler class that:

  • Uses set[int] to track created object IDs and set[int] to track deleted IDs
  • Has a method track_object(obj: Any) -> None
  • Has a method count_living_objects() -> int that returns created count minus deleted count
  • Has a method profile_memory() -> dict[str, int] that uses gc.collect() and gc.get_objects()
  • Has a method print_report() -> None that displays stats
  • Includes complete type hints and docstrings

Make it production-quality code."

Expected Response: Working, well-documented code with type hints matching modern Python standards.

Your Validation: Run the code. Verify it works. Modify it to add a feature (e.g., categorization).

Prompt Set C: Testing & Validation (B1)​

Prompt 3 - Test Strategy:

"How would I thoroughly test a MemoryProfiler? I want to be confident it works correctly. What edge cases matter? Should I test:

  • Does it track all objects I create?
  • Does it properly count deletion?
  • Does it handle circular references?
  • Does it work with large object graphs?

Show me one test function for circular references, using gc.collect() to trigger cycle detection."

Expected Response: Test strategy + sample test code demonstrating circular reference handling.

Your Validation: Understand why each test matters. Run tests to validate your implementation.

Prompt Set D: Integration Reflection (B1-B2)​

Prompt 4 - Synthesis Question:

"Explain to me (as if I were learning Python) how this Memory Profiler tool uses ALL of Chapter 19:

  • Sets (Lesson 1): How do they help track objects? Why not use a list?
  • Frozensets (Lesson 4): How would I use them in this tool? Show an example.
  • GC module (Lesson 5): What does gc.collect() do? Why is it important?
  • Reference counting: How does refcount tell us about object lifetime?

Then answer: If you removed sets from this design, what would break?"

Expected Response: Comprehensive explanation showing how concepts work together.

Your Validation: You understand the integration. You can teach this to someone else.

Safety & Responsibility Guidelines​

When using AI in your Memory Profiler project:

✅ DO:

  • Review all generated code before running it
  • Run tests to verify correctness
  • Ask follow-up questions about design choices
  • Combine AI ideas with your own thinking
  • Validate memory measurements are accurate

⚠️ WATCH OUT FOR:

  • AI might generate incomplete error handling—review for edge cases
  • Memory measurements might vary slightly across systems—test on your machine
  • Large object graphs could consume significant memory—start small and scale up
  • Circular references need gc.collect() to be freed—don't assume reference counting alone

Expected Outcomes​

After completing all 4 prompts, you should be able to:

  1. ✓ Write a clear specification for a Python tool (Prompt 1)
  2. ✓ Understand and modify AI-generated code (Prompt 2)
  3. ✓ Design tests for correctness and edge cases (Prompt 3)
  4. ✓ Explain how Python concepts work together (Prompt 4)

These are the skills of an AI-native developer: specification thinking, code understanding, validation, and synthesis.