G3root VS hashtable-benchmarks

Since the blog post mentioned a PR to replace linear probing with Robin Hood, I just wanted to mention that I found bidirectional linear probing to outperform Robin Hood across the board in my Java integer set benchmarks:

https://github.com/senderista/hashtable-benchmarks/blob/mast...

https://github.com/senderista/hashtable-benchmarks/wiki/64-b...

https://homes.cs.washington.edu/~magda/papers/wang-cidr17.pd...

I'm most interested in developing high-performance database engines in low-level languages, but open to any challenging systems programming project. I've been working in C++ for the last 3 years, but have written nontrivial projects in Rust and Java as well (e.g., https://github.com/senderista/rotated-array-set, https://github.com/senderista/hashtable-benchmarks). I would enjoy using Rust or Zig on a new project, but I consider the project itself to be much more important than the language it's written in. I am not interested in cryptocurrency, adtech, or fintech projects.

Thanks for the details on your benchmarks. I would like sometime to extend BLP to a more generic setting; as I said I think any trick used with RH would also work with BLP. I just used an integer set because that's all I needed for my use case and it was easy to implement several different approaches for benchmarking. As you note, it favors use cases where the hash function is cheap (or invertible) and elements are cheap to move around.

About your question on load factors: no, the benchmarks are measuring exactly what they claim to be. The hash table constructor divides max data size by load factor to get the table size (https://github.com/senderista/hashtable-benchmarks/blob/mast...), and the benchmark code instantiates each hash table for exactly the measured data set size and load factor (https://github.com/senderista/hashtable-benchmarks/blob/mast...).

I can't explain the peaks around 1M in many of the plots; I didn't investigate them at the time and I don't have time now. It could be a JVM artifact, but I did try to use JMH "best practices", and there's no dynamic memory allocation or GC happening during the benchmark at all. It would be interesting to port these tables to Rust and repeat the measurements with Criterion. For more informative graphs I might try a log-linear approach: divide the intervals between the logarithmically spaced data sizes into a fixed number of subintervals (say 4).

I think "bidirectional linear probing" is an underrated approach (and much simpler): https://github.com/senderista/hashtable-benchmarks/blob/master/src/main/java/set/int64/BLPLongHashSet.java

I will probably never get around to porting my bidirectional linear probing integer hash set from Java to C++, but I hope someone can try adapting BLP to general C++ hashmaps and hashsets, because it significantly outperforms Robin Hood in my benchmarks.

https://homes.cs.washington.edu/~magda/papers/wang-cidr17.pd...

I'm most interested in developing high-performance database engines in low-level languages, but open to any challenging systems programming project. I've been working in C++ for the last 2 years, but have written nontrivial projects in Rust and Java as well (e.g., https://github.com/senderista/rotated-array-set, https://github.com/senderista/hashtable-benchmarks). I would enjoy using Rust or Zig on a new project, but I consider the project itself to be much more important than the language it's written in. I am not interested in cryptocurrency, adtech, or fintech projects.