Implementing a Simplified Garbage Collector in Python

Python has a sophisticated built-in garbage collector that automatically manages memory by reclaiming unused objects. While you can't truly "implement" Python's garbage collector from scratch within Python itself (as it's a core part of the interpreter), this challenge aims to simulate a fundamental garbage collection strategy. Understanding these principles can demystify how memory management works and how to write more memory-efficient Python code.

Problem Description

Your task is to create a simplified garbage collector simulation for a custom object system. You will simulate a reference counting garbage collector. This means that each object will have a count of how many references point to it. When this count drops to zero, the object is considered eligible for garbage collection.

You'll need to:

1. Define an Object class: This class will represent an item in our simulated memory. Each Object should have a unique identifier and a way to store references to other Objects.
2. Implement a GarbageCollector class: This class will manage a collection of Objects. It should provide methods to:
   • create_object(): Adds a new object to the collector's management.
   • add_reference(from_obj_id, to_obj_id): Increments the reference count of to_obj_id and records that from_obj_id now refers to it.
   • remove_reference(from_obj_id, to_obj_id): Decrements the reference count of to_obj_id and removes the reference from from_obj_id. If the reference count of to_obj_id becomes zero, it should be marked for collection.
   • collect(): Iterates through all managed objects, identifies those with a reference count of zero that haven't been collected yet, and removes them from management.
3. Track reference counts: Each Object should maintain its own reference count. The GarbageCollector will be responsible for updating these counts.
4. Simulate object creation and deletion: The create_object method should return a unique ID for the newly created object. When an object is collected, it should no longer be accessible through the GarbageCollector.

Key Requirements:

• Objects should be uniquely identifiable.
• References between objects must be managed.
• Reference counts must be accurately updated.
• The collect method must identify and reclaim objects with zero references.
• The GarbageCollector should keep track of all objects it's currently managing.

Expected Behavior:

• When create_object is called, a new object is created and added to the collector.
• add_reference increases the target object's reference count.
• remove_reference decreases the target object's reference count.
• If remove_reference causes an object's count to reach zero, that object becomes eligible for collection.
• collect will remove all eligible objects from the collector's management.

Edge Cases:

• Attempting to add a reference to a non-existent object.
• Attempting to remove a reference to a non-existent object.
• Removing a reference that wasn't established.
• An object referencing itself.

Examples

Example 1: Basic Creation and Collection

gc = GarbageCollector()
obj1_id = gc.create_object() # obj1_id = 0
obj2_id = gc.create_object() # obj2_id = 1

gc.add_reference(obj1_id, obj2_id) # obj2 reference count becomes 1

print(f"Managed objects before collection: {gc.get_managed_object_ids()}")
gc.collect()
print(f"Managed objects after collection: {gc.get_managed_object_ids()}")

gc.remove_reference(obj1_id, obj2_id) # obj2 reference count becomes 0

gc.collect()
print(f"Managed objects after collecting obj2: {gc.get_managed_object_ids()}")

Output:

Managed objects before collection: [0, 1]
Managed objects after collection: [0, 1]
Managed objects after collecting obj2: [0]

Explanation: Initially, two objects (IDs 0 and 1) are created and managed. obj1 references obj2, increasing obj2's count. When collect is first called, neither object has a reference count of zero, so nothing is collected. After obj1's reference to obj2 is removed, obj2's count drops to zero. The subsequent collect call reclaims obj2. obj1 remains because it wasn't explicitly removed and still has an implicit "root" reference from the collector's perspective until it's removed.

Example 2: Cyclic References

gc = GarbageCollector()
objA_id = gc.create_object() # objA_id = 0
objB_id = gc.create_object() # objB_id = 1

gc.add_reference(objA_id, objB_id) # objB ref count = 1
gc.add_reference(objB_id, objA_id) # objA ref count = 1

# At this point, objA has 1 reference, objB has 1 reference.
# Neither is collectible on its own.

# Let's imagine these are the only two objects created and they are
# no longer referenced externally by any "root" (which in this simulation
# are objects managed by the GC itself).
# To simulate this, we can remove the implicit "root" reference from the GC
# for objA and objB. In a real scenario, this would be when variables go out of scope.
# For this simulation, we'll assume the GC implicitly holds a root reference to
# all objects it manages until explicitly told otherwise, or we can
# simply try to collect when no external references exist *within the simulation*.

# In our simplified model, objects are only collected if their ref count hits zero.
# With cyclic references, their counts will never hit zero unless one of them
# is explicitly de-referenced in a way that breaks the cycle.

# For demonstration, let's manually simulate the removal of the "root" context
# for both objects. This is a simplification; a real GC uses root sets.
# Let's assume the GC's internal list of "roots" for these objects is cleared.
# Since we don't have an explicit root set in this prompt, we'll rely on
# the fact that nothing new is adding references, and we will manually
# break the cycle for demonstration.

# Let's simulate removal of the GC's implicit root reference for objA and objB.
# For THIS SIMULATION, we'll remove objA and objB from GC's *direct management*
# after they've formed a cycle.
# This is a hack to show that GC won't collect them if they are still referenced.

print(f"Managed objects before attempts to break cycle: {gc.get_managed_object_ids()}")
gc.collect() # Nothing will be collected due to cycle

print("Breaking cycle...")
gc.remove_reference(objA_id, objB_id) # objB ref count becomes 0
# Now objB is eligible for collection. When objB is collected, its
# reference to objA will be removed, making objA eligible too.

gc.collect()
print(f"Managed objects after breaking cycle and collecting: {gc.get_managed_object_ids()}")

Output:

Managed objects before attempts to break cycle: [0, 1]
Breaking cycle...
Managed objects after breaking cycle and collecting: []

Explanation: objA references objB, and objB references objA. Both have a reference count of 1. Without any external references and assuming the GC's root set is cleared (simulated here by how we proceed), they would normally be collected. However, because they reference each other, their counts never drop to zero. Only when objA's reference to objB is removed (simulating objA going out of scope and being cleaned up first, breaking the cycle), does objB's count drop to zero. The subsequent collect will reclaim objB. As objB is reclaimed, its reference to objA is also removed, making objA's count zero, and it too is collected in the same collect cycle (or the next one, depending on implementation order).

Constraints

• Object IDs will be non-negative integers starting from 0.
• The total number of objects managed by the GarbageCollector at any given time will not exceed 1000.
• The total number of references tracked between objects will not exceed 5000.
• Your implementation should be able to handle add_reference and remove_reference calls for object IDs that might not yet exist or have already been collected (though the behavior for non-existent IDs can be to ignore or raise an error, consistency is key).
• The collect() method should ideally complete within a reasonable time, proportional to the number of managed objects and references.

Notes

• This is a simplified model of reference counting. Real-world reference counting garbage collectors have optimizations and handle cycles using additional mechanisms (like mark-and-sweep for cycles).
• Consider how you will store the objects and their references. Dictionaries are often useful for mapping IDs to objects and for storing reference counts.
• Think about how to represent the "root" references – objects that the GC considers "alive" by default (like objects directly held by variables in your current scope). In this simulation, objects are implicitly "rooted" by the GC's management until they are explicitly removed from management or collected.
• When an object is "collected," it means it's no longer managed by the GarbageCollector and its memory is conceptually freed. You can simulate this by removing it from the GC's internal data structures.