Python Memory Leak Detector

This challenge requires you to implement a basic memory leak detection mechanism in Python. Memory leaks occur when a program allocates memory but fails to release it when it's no longer needed, leading to gradual depletion of available memory and potential program instability or crashes. Developing tools to identify these leaks is crucial for building robust and efficient applications.

Problem Description

Your task is to create a Python class, MemoryLeakDetector, that can track object allocations and help identify potential memory leaks. The detector should be able to:

1. Track Object Allocations: Record when new objects are created and store references to them.
2. Identify Unreferenced Objects: After a certain operation or a designated "check" period, identify objects that have been allocated but are no longer referenced by any active part of the program.
3. Report Potential Leaks: Provide a mechanism to list these unreferenced objects, along with information about their type and when they were allocated.

Key Requirements:

• The MemoryLeakDetector class should have methods to track_object(obj) and detect_leaks().
• track_object(obj) should accept any Python object and store a reference to it internally.
• detect_leaks() should analyze the tracked objects and return a list of objects that are no longer reachable from the program's global or local scopes.
• For each potential leak, the detector should store and report the object's type and a timestamp of its allocation.
• The detector should have a way to clear its tracking of objects that are confirmed to be no longer needed (e.g., through a release_object(obj) method, though for this challenge, focus on detecting unreferenced objects).

Expected Behavior:

When detect_leaks() is called, it should simulate the Python garbage collector's process to some extent. It should identify objects that are "orphaned" – meaning they exist in memory but have no active references pointing to them from anywhere else in the program.

Edge Cases to Consider:

• Objects created within loops that are subsequently destroyed.
• Objects held in data structures that are themselves released.
• Circular references (though a full solution for circular references is complex and beyond the scope of this basic detector; focus on simple unreferenced objects).

Examples

Example 1:

import time

detector = MemoryLeakDetector()

# Create an object and track it
obj1 = [1, 2, 3]
detector.track_object(obj1)
print(f"Tracked object: {obj1}")

# Make the object unreferenced by removing the direct reference
del obj1

# Allow some time for potential garbage collection (simulated)
time.sleep(0.1)

# Detect leaks
leaks = detector.detect_leaks()

print("Detected Leaks:")
for leak_info in leaks:
    print(f"- Type: {leak_info['type']}, Allocated At: {leak_info['allocated_at']:.2f}")

Output:

Tracked object: [1, 2, 3]
Detected Leaks:
- Type: <class 'list'>, Allocated At: 1678886400.123456 # (Timestamp will vary)

Explanation:

The obj1 list was created and tracked. After del obj1, the original reference was removed. When detect_leaks() was called, it found that the list object was no longer reachable and reported it as a potential leak.

Example 2:

import time

detector = MemoryLeakDetector()

class MyClass:
    def __init__(self, name):
        self.name = name

# Track multiple objects
obj2 = MyClass("A")
obj3 = MyClass("B")
detector.track_object(obj2)
detector.track_object(obj3)
print(f"Tracked: {obj2.name}, {obj3.name}")

# Keep one object referenced
referenced_obj = obj3
del obj2

time.sleep(0.1)

leaks = detector.detect_leaks()

print("\nDetected Leaks:")
for leak_info in leaks:
    print(f"- Type: {leak_info['type']}, Allocated At: {leak_info['allocated_at']:.2f}")

Output:

Tracked: A, B

Detected Leaks:
- Type: <class '__main__.MyClass'>, Allocated At: 1678886400.234567 # (Timestamp will vary)

Explanation:

obj2 and obj3 were tracked. obj2 was deleted, becoming unreferenced. obj3 was kept referenced by referenced_obj. Therefore, only obj2 is reported as a leak.

Example 3 (Edge Case - Object in a released container):

import time

detector = MemoryLeakDetector()

def create_and_lose_object():
    data = {"key": "value"}
    detector.track_object(data)
    print("Object created and tracked within function.")
    # 'data' goes out of scope here, but if not garbage collected immediately,
    # our detector should still find it if it's unreferenced.

create_and_lose_object()

time.sleep(0.1)

leaks = detector.detect_leaks()

print("\nDetected Leaks:")
for leak_info in leaks:
    print(f"- Type: {leak_info['type']}, Allocated At: {leak_info['allocated_at']:.2f}")

# To simulate a 'successful' release of an object that was tracked
# (though this is not a direct requirement for the detector itself to implement)
# In a real scenario, if 'data' was no longer needed, it would naturally become
# unreferenced.

Output:

Object created and tracked within function.

Detected Leaks:
- Type: <class 'dict'>, Allocated At: 1678886400.345678 # (Timestamp will vary)

Explanation:

The dictionary data was created and tracked within the function. When the function returned, the local reference data was gone. If Python's garbage collector hasn't cleaned it up yet, our detector should identify it as an unreferenced object if it was indeed created and tracked.

Constraints

• The MemoryLeakDetector class must be implemented in pure Python.
• The tracking mechanism should have a low overhead for typical operations.
• The detect_leaks() method should not rely on external libraries like gc for its core logic of identifying unreferenced objects, but it can use gc.get_referrers() or similar to help determine reachability (a hint).
• The solution should focus on detecting simple unreferenced objects, not complex scenarios like cycles that the standard Python garbage collector handles.

Notes

• To determine if an object is truly unreferenced, you'll need a way to inspect Python's object graph. Consider using gc.get_referrers() to find what objects are referencing a given object. If an object has no referrers from roots (like global variables, stack frames), it's a strong candidate for a leak.
• You'll need to store objects and their allocation timestamps. The time.time() function is suitable for timestamps.
• When reporting leaks, you should provide information about the object's type. The type(obj) function can be used for this.
• Be mindful of how you store tracked objects to avoid creating new references that would prevent them from being detected as leaks. You might consider using weak references internally if you want to store objects without preventing their garbage collection.