Not really a data structure per say, but just knowing LISP and the interesting structures it uses internally.

The results of LISP operations CAR, CDR, CADR and the other one I can’t remember now.

B trees are cool but not obscure necessarily. I didn’t learn about them in college. It sounds like binary tree and it’s similar but it’s different. It’s a data structure to take advantage of the way disk reads work.

I’ve been knee-deep in these lately so I’m a big fan

Theta sketches!

Do you want to approximately count a large volume of items, but save the state for later so you can UNION , INTERSECT and even DIFF them? Then Thetas are right for you!

Or basically anything in the Apache Datasketches lbrary.

Dewey decimal

Fibonacci heaps are pretty cool. Not used very often b/c they’re awful to implement, but better complexity than many other heaps.

Also Binary Lifting is closer to an algorithm than a data structure but it’s used in Competitive Programming a fair bit, and isn’t often taught: https://cp-algorithms.com/graph/lca_binary_lifting.html

And again closer to an algo tham a data structure, but Sum over Subsets DP in 3^n also has a cool little bit of math in it: https://cp-algorithms.com/algebra/all-submasks.html

Skew binary trees. They’re an immutable data structure combining the performance characteristics of lists (O(1) non-amortized push/pop) and b-trees (log(N) lookup and updates)
They use a sequence of complete trees, cleverly arranged using skew binary numbers so that adding an element never causes cascading updates.
In practice they’re superseded by relaxed radix balanced trees.

I personally don’t think it’s that obscure but I have never seen this used in production code that I didn’t write: the linked hash map or ordered hash map.

I’m also not sure if this is obscure, but Bloom Filters! It’s a structure that you can add elements to then ask it if it has seen the element before with the answer being either “no” or “probably yes”. There’s a trade-off between confidence of a “probably yes”, how many elements you expect to add, and how big the Bloom Filter is, but it’s very space and time efficient. And it uses hash functions which always make for a fun time.

The CSR (compressed sparse row) format is a very simple but efficient way of storing sparse matrices, meaning matrices with a large amount of zero entries, which should not all occupy memory. It has three arrays: one holds all non-zero entries in order, read row by row, the next array contains the column indices of each non-zero element (and therefore has the same length as the first array), the third array indices into the first array for the first element of each row, so we can tell where a new row starts.

On sparse matrices it has optimal memory efficiency and fast lookups, the main downside is that adding or removing elements from the matrix requires shifting all three arrays, so it is mostly useful for immutable data.

XOR lists are obscure and cursed but cool. And not useful on modern hardware as the CPU can’t predict access patterns. They date from a time when every byte of memory counted and CPUs didn’t have pipelines.

(In general, all linked lists or trees are terrible for performance on modern CPUs. Prefer vectors or btrees with large fanout factors. There are some niche use cases still for linked lists in for example kernels, but unless you know exactly what you are doing you shouldn’t use linked data structures.)

EDIT: Fixed spelling

Conflict free replicated data types, I don’t know if I’d call them obscure but they’re definitely cool and less often used. They’re for shared state across computers, like in collaborative apps

I came up with a kind of clever data type for storing short strings in a fixed size struct so they can be stored on the stack or inline without any allocations.
It’s always null-terminated so it can be passed directly as a C-style string, but it also stores the string length without using any additional data (Getting the length would normally have to iterate to find the end).
The trick is to store the number of unused bytes in the last character of the buffer. When the string is full, there are 0 unused bytes and the size byte overlaps the null terminator.
(Only works for strings < 256 chars excluding null byte)

Implementation in C++ here: https://github.com/frustra/strayphotons/blob/master/src/common/common/InlineString.hh

I get way more use out of Doubly Connected Edge Lists (DCEL) than I ever thought I would when I first learned about them in school.

When I want to render simple stuff to the screen, built-in functions like ‘circle’ or ‘line’ work. But for any shapes more complicated than that, I often find that it’s useful to work with the data in DCEL form.

skiplists are interesting data structures. The underlying mechanism is it’s a 2-dimensional probabilistic linked list with some associated height ‘h’ that enables skipping of nodes through key-value pairs. So, compared to a traditional linked list that uses a traversal method to search through all values stored. A skip list starts from the maxLevel/maxheight, determines if “next” points to a key greater than the key provided or a nullptr, and moves down to the level below it if it is. This reduces the time complexity from O(1) with a linked list to O(N) where N Is the maxLevel.

The reason behind why its probabilistic (in this case using a pseudo random number) is because its easier to insert and remove elements, otherwise (if you went with the idealized theoretical form) you would have to reconstruct the entire data structure each and every time you want to add/remove elements.

In my testing when adding 1,000,000 elements to a skiplist it reduced from 6s search with a linked list to less than 1s!

@protein

Finger Tree!

A persistent, purely functional workhorse. Amortized O(1) access at both ends, O(log n) concatenation/splitting.

It generalizes elegantly to build sequences, priority queues, and more. Powers Haskell’s main Data.Sequence. A functional programmer’s secret weapon.