Conquer Memory Leaks: A Guide to Dominator Trees and Retained Heap Size

Understanding dominator trees and retained heap size is crucial for effectively analyzing heap dumps and pinpointing memory leaks. Let’s delve deeper into these concepts with examples:

Dominator Tree:

Imagine the heap as a network of objects connected by references. A dominator tree visually represents this network, with objects as nodes and edges showing dominance relationships. Here’s how it works:

• Dominator: Object A is considered a dominator of object B if any path from the application’s root (think: starting point) to B must necessarily pass through A.
• Dominator Tree Structure: The tree is constructed such that each object has a single parent dominator (except the root, which has none). This creates a hierarchical structure where “higher” objects dominate “lower” ones.

Example:

Consider a simple scenario where your application has a Customer object that holds a reference to an Order object. In the dominator tree:

• The root node could be the main application object.
• The Customer object would be a child of the root, dominated by the application itself.
• The Order object would be a child of the Customer object, dominated by it since any reference to the Order must go through the Customer.

Dominator Tree Analysis:

By analyzing the dominator tree, you can identify objects that are preventing others from being garbage collected. For instance, if a long-lived object (like a static variable holding unnecessary data) dominates a large subtree, all the objects in that subtree will remain in memory even if they’re no longer needed.

Retained Heap Size:

Retained heap size goes beyond the memory footprint of a single object. It captures the total amount of memory consumed by an object and all the objects it directly or indirectly references through its fields.

Example:

Continuing from the previous scenario:

• The Customer object itself might have some memory overhead for its own data members.
• The retained heap size of the Customer object would include not just its own memory but also the memory used by the Order object it references.

Retained Heap Size Analysis:

By looking at objects with a large retained heap size in the dominator tree, you can prioritize your investigation. Objects with a high retained size but weak justification for being long-lived (e.g., unnecessary static caches) are potential culprits for memory leaks.

Combining Dominator Tree and Retained Heap Size:

These two concepts work together. The dominator tree helps you navigate the relationships between objects, while the retained heap size reveals the overall memory impact of those relationships. By analyzing both, you can pinpoint the root cause of memory leaks – objects that are dominators of large subtrees with a significant retained heap size, but no clear reason to persist.