CC VS Generic-Data-Structures

With extendible macros, you could achieve the following:

In my example the table stores the hash codes themselves instead of the keys (because the hash function is invertible)

Oh, I see, right. If determining the home bucket is trivial, then the back-shifting method is great. The issue is just that it’s not as much of a general-purpose solution as it may initially seem.

“With a different algorithm (Robin Hood or bidirectional linear probing), the load factor can be kept well over 90% with good performance, as the benchmarks in the same repo demonstrate.”

I’ve seen the 90% claim made several times in literature on Robin Hood hash tables. In my experience, the claim is a bit exaggerated, although I suppose it depends on what our idea of “good performance” is. See these benchmarks, which again go up to a maximum load factor of 0.95 (Although boost and Absl forcibly grow/rehash at 0.85-0.9):

https://strong-starlight-4ea0ed.netlify.app/

Tsl, Martinus, and CC are all Robin Hood tables (https://github.com/Tessil/robin-map, https://github.com/martinus/robin-hood-hashing, and https://github.com/JacksonAllan/CC, respectively). Absl and Boost are the well-known SIMD-based hash tables. Khash (https://github.com/attractivechaos/klib/blob/master/khash.h) is, I think, an ordinary open-addressing table using quadratic probing. Fastmap is a new, yet-to-be-published design that is fundamentally similar to bytell (https://www.youtube.com/watch?v=M2fKMP47slQ) but also incorporates some aspects of the aforementioned SIMD maps (it caches a 4-bit fragment of the hash code to avoid most key comparisons).

As you can see, all the Robin Hood maps spike upwards dramatically as the load factor gets high, becoming as much as 5-6 times slower at 0.95 vs 0.5 in one of the benchmarks (uint64_t key, 256-bit struct value: Total time to erase 1000 existing elements with N elements in map). Only the SIMD maps (with Boost being the better performer) and Fastmap appear mostly immune to load factor in all benchmarks, although the SIMD maps do - I believe - use tombstones for deletion.

I’ve only read briefly about bi-directional linear probing – never experimented with it.

From your other comments, it seems like your knowledge of hash tables might be limited to closed-addressing/separate-chaining hash tables. The current frontrunners in high-performance, memory-efficient hash table design all use some form of open addressing, largely to avoid pointer chasing and limit cache misses. In this regard, you want to check our SSE-powered hash tables (such as Abseil, Boost, and Folly/F14), Robin Hood hash tables (such as Martinus and Tessil), or Skarupke (I've recently had a lot of success with a similar design that I will publish here soon and is destined to replace my own Robin Hood hash tables). Also check out existing research/benchmarks here and here. But we a little bit wary of any benchmarks you look at or perform because there are a lot of factors that influence the result (e.g. benchmarking hash tables at a maximum load factor of 0.5 will produce wildly different result to benchmarking them at a load factor of 0.95, just as benchmarking them with integer keys-value pairs will produce different results to benchmarking them with 256-byte key-value pairs). And you need to familiarize yourself with open addressing and different probing strategies (e.g. linear, quadratic) first.

CC (Full disclosure: I authored this one)

If you're using C23 or have typeof (so GCC or Clang), then yet another approach is to define a type that aliases the specified type if it is unique or otherwise becomes a "dummy" type. Here's what that looks like in CC:

I’d like to share version 1.0.3 of Convenient Containers (CC), my generic container library. The library was previously discussed here and here. As explained elsewhere,

Convenient Containers (CC) - I'm the author of this one.

"Using macros" is a broad description that covers multiple paradigms. There are libraries that use macros in combination with typed pointers and functions that take void* parameters to provide some degree of API genericity and type safety at the same time (e.g. stb_ds and, as you mentioned, my own CC). There are libraries that use macros (or #include directives) to manually instantiate templates (e.g. STC, M*LIB, and Pottery). And then there are libraries that are implemented entirely or almost entirely as macros (e.g. uthash).

Shameless plug: Take a look a my library Convenient Containers, which solves this exact problem within the (narrow) domain of data structures.

This GitHub repo: https://github.com/pavlosdais/Abstract-Data-Types is great! It uses void pointers and dynamic memory to store any kind of data type. Also, I'm currently working on a repository myself! Here's the link: https://github.com/saulvaldelvira/Generic-Data-Structures It also has the same approach, void pointers and dynamic memory, for versatility. Those are the ones I known. About documentation/Books, i don't really know about any that are focused on C. What I use for my repo is basically my notes for university. But the internet is huge! There are tons of Data Structures tutorials focused on C. Good Luck!

A lot of it is indented, e.g. linked_list.h begins: