CC VS pottery

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.

Pottery - The page for open hash map reads "Documentation still needs to be written. In the meantime check out the examples."

"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).

The prototype of CC used this mechanism to provide a generic API for types instantiated via templates (so basically like other container libraries, but with an extendible-_Generic-based API laid over the top of the generated types). This approach has some significant advantages over the approach CC now uses, but I got a bit obsessed with eliminating the need to manually instantiate templates.

I think it's common for C programmers to roll their own. I did the same [0].

I went pretty deep into composable C templates to build mine so it's more powerful than most. The containers can handle non-bitwise-movable types with full C++-style lifecycle functions and such, and the sort algorithms can handle dynamic and non-contiguous arrays (they are powerful enough to implement qsort() [1], which is more than I can say for any other C sort templates I've seen.) My reasoning for the complexity at the time was that any powerful container library is going to be reasonably complex in implementation (as anyone who's looked at STL source code knows), so it just needs to be encapsulated behind a good interface.

I'm not so sure that's true anymore. These sorts of simpler libraries like the one linked here definitely seem to be more popular among C programmers. I think if people are using C, it's not just the C++ language complexity they want to get away from, but also the implementation complexity of libraries and such. There's a balance to be had for sure, and I think the balance varies from person to person, which is why no library has emerged as the de facto standard for containers in C.

[0]: https://github.com/ludocode/pottery

Yes! The include style of templates in C is way better than the old way of huge macros to instantiate code. The template code can look mostly like idiomatic C, it interacts way better with a debugger, it gives better compiler errors... everything about it is better and it's finally starting to become more popular.

I've open sourced my own C template library here:

https://github.com/ludocode/pottery

Not only does it use the #include style of templates, but it actually makes the templates composable. It takes this idea pretty far, for example having a lifecycle template that lets you define operations on your type like move, copy, destroy, etc. This way the containers can fully manage the lifecycles of your types even if they're not bitwise movable.

There's also this other more popular C template library, one that tries to more directly port C++ templates to C but with a lot less features:

https://github.com/glouw/ctl/

This article doesn't really make it clear but the merge sort discussion is specifically about glibc's implementation of qsort(). glibc's qsort() and Wine's qsort() are the only ones I know of that use merge sort to implement qsort(). Most implementations use quick sort.

I recently did my own benchmarking on various qsort()s since I was trying to implement a faster one. The various BSDs and macOS qsort() are all faster than glibc at sorting integers and they don't allocate memory:

https://github.com/ludocode/pottery/tree/master/examples/pot...

Of course sorting is much faster if you can inline the comparator so a templated sort algorithm is always going to be faster than a function that takes a function pointer. But this does not require C++; it can be done in plain C. The templated intro_sort from Pottery (linked above) is competitive with std::sort, as are the excellent swensort/sort templates:

https://github.com/swenson/sort