How HashMap Resolves Collisions And Its Limitations?

If you've ever used a HashMap in Java, you've already relied on one of the most important data structures in the language. But have you ever wondered what happens when two keys generate the same hash? That's where collision resolution comes in.

How HashMap Resolves Collisions

Java's HashMap uses a strategy called separate chaining:

Each key is converted into a hash.

That hash determines the bucket where the entry will be stored.

If two keys map to the same bucket, Java stores them in a linked list.

Starting from Java 8, if a bucket contains many entries, the linked list is converted into a Red-Black Tree, improving performance from O(n) to O(log n).

This hybrid approach makes HashMap both fast and robust.

Limitations of This Approach

Although powerful, the design has some constraints:

Hash collisions still cost performance: Even with trees, accessing entries in a heavily-collided bucket is slower than accessing unique buckets.

Poor hashCode() implementations break performance: If many objects generate the same hash, the map becomes unbalanced and lookups slow down.

Treeification only happens after thresholds:

- bucket size ≥ 8

- table capacity ≥ 64

Before that, it stays as a linked list.

Higher memory usage: Tree nodes are more complex and heavier than list nodes.

Why Collisions Aren't a Big Problem?

Collisions sound bad, but in HashMap they're expected and handled efficiently:

1 - HashMap is designed to deal with collisions using lists and trees.

2 - Average lookup time stays O(1) in real-world scenarios.

3 - Resizing and a good hashCode() keep collisions low.

4 - Treeification prevents worst-case slowdowns.

5 - In practice, collisions only matter when the hashing is really poor.

👉 To sum up

HashMap handles collisions using linked lists and Red-Black Trees, but it's not magic. A bad hashCode() method or too many collisions can still degrade performance.