Robin Hood Hashing on the JVM10 Oct 2016
Robin Hood Hashing, despite its name, isn't a hashing technique, but rather a way to implement linear-probing hash tables.
In the classical linear probing implementation, whenever a hash table slot is already taken (due to hash collision), you probe ahead until you find an empty slot. When the hash table is highly loaded, this can lead to entries being stored far away from their preferred position.
Robin Hood Hashing features a notion of displacement. The displacement of an entry is the distance between its actual position and its preferred position. Furthermore, we call probe count the number of probed slots in order to find a free slot. When inserting an entry, the final probe count is the diplacement.
When inserting an entry A using Robin Hood Hashing, if you encounter an entry B whose displacement is smaller than the probe count, you insert A in B's position, and relocate B further in the table (following the same rules).
The result is that the displacements of all entries in the map tend to even out. The length of the longest displacement tends to increase as the load increase but very slowly (around 6 for a load of 0.9 — see this paper (pdf)).
The technique is actually quite old. It was first published in 1986. As for me, I first become aware of Robin Hood Hashing through a post by Sebastian Sylvan. Emmanuel Goossaert also has two posts (one, two) analyzing and then improving the technique. If you're looking for further information, I highly recommend these posts.
I was interesting in a more pragmatic question: could I use Robin Hood Hashing and get a boost in performance? In particular, how would it perform on the JVM, where I do most of my work these days?
I implemented two versions of the algorithm (source). The first versions stores entry objects in the table (hence requiring an extra indirection for each probed slot). The second version used two tables: the first one was an array of integers, where each table entry occupies two array entries. The first array entry is the hash, the second is the displacement. The second table is an array of objects. Here too, each table entry occupies two array entry. The first array entry is the key, the second is the value. The theory is that most of the time, when probing, you only need to check the hash and the displacement. Having those in the primary table avoids extra indirection (and hence cache misses).
Disappointingly enough, the performance delta between both techniques is too
small to be meaningful. In general however, the technique is almost as fast as
HashMap implementation. With some performance tweaking, I
believe it would be possible to close the gap. Be warned that my measurements
are very wonky. Nevertheless they gave me confidence enough to come to two
Robin Hood Hashing is practical and fast enough to rival separate-chaining hash table implementation (like Java's
HashMap) on the JVM.
However, it is not significantly faster either, and so it's probably not worth the trouble of maintaining my own implementation.
Note this says nothing about the relative performance of Robin Hood Hashing and separate-chaining on other platforms than the JVM.
And off course, you should totally perform you own measurements. You can get started from my own code. If anyone has more data to add to the discussion, make sure to share it in the comments.