As always, benchmarks should be taken with a grain of salt. Always measure for your workload.
Below are the benchmark results from the conc-map-bench benchmarking harness under varying workloads. All benchmarks were run on a AMD Ryzen 9 9950X processor, using ahash and the mimalloc allocator.
 |
 |
 |
 |
 |
 |
As mentioned in the performance section of the guide, papaya is optimized read-heavy workloads. As expected, it outperforms all competitors in the read-heavy benchmark. An important guarantee of papaya is that reads never block under any circumstances. This is crucial for providing consistent read latency regardless of write concurrency. However, it falls short in update-heavy workloads due to allocator pressure and the overhead of memory reclamation, which is necessary for lock-free reads. If your workload is write-heavy and you do not benefit from any of papaya's features, you may wish to consider an alternate hash-table implementation.
Additionally, papaya does a lot better in terms of latency distribution due to incremental resizing and the lack of bucket locks. Comparing histograms of insert latency between papaya and dashmap, we see that papaya manages to keep tail latency lower by a few orders of magnitude. Some latency spikes are unavoidable due to the allocations necessary to maintain a large hash-table, but the distribution is much more consistent (notice the scale of the y-axis).
