A while back I had to perform an entity resolution task on several million entities.

We arrived at a point in the algorithm where we had a long list of connected components by ids that needed to be reduced into a mutually independent set of components, e.g. ({112, 531, 25}, {25, 238, 39, 901}, {43, 111}, ...)

After much head banging about working out way to do this that wouldn't lead to an out-of-memory error, we found the Disjoint Set Forest Data Structure and our prayers were answered.

I was very grateful for it!

Exactly the same for me. We had a defect management system, and a defect can be connected to others as dupes. We'd have whole chains of dupes. We used union find to identify them.

One example is connected component analyis on images. In one linear scan of an image (2d or 3d) you can find all connected blobs in an image.

I used it to find and track islands of connected polygons in 3D meshes.

a textbook example of an algorithm that works very well with union-find is the kruskal's algorithm to find the minimum weight spanning tree given a graph. Using union-find improves the time complexity of the algorithm from O(V²) to O(ElogV).

This happens because kruskal's algorithm essentially selects the cheapest edge not already included in our spanning tree that won't cause a cycle. So union-find is able to speed up this potential cycle check which would otherwise be naively quadratic.