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rust-lang
hashbrown
Rust2019–2025
rust-lang / hashbrown

hashbrown

Rust port of Google's SwissTable hash map

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10Distinct people
10Mentions
0Explicit recommenders
3KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Rust port of Google's SwissTable hash map

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year10 dated mentions

Observed mentions: 2019-04-26 21:28:23 UTC to 2025-12-29 21:41:07 UTC.

01232019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC101
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC303
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC202
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101

What Hacker News says

10 sources
westurner·Dec 5, 2023
"Introduction to Datalog" re: Linked Data https://news.ycombinator.com/context?id=34808887 pyDatalog/examples/SQLAlchemy.py: https://github.com/baojie/pydatalog/blob/master/pyDatalog/ex... GH topics > datalog: https://github.com/topics/datalog datalog?l=rust: https://github.com/topics/datalog?l=rust ... Cozo, Crepe Crepe: https://github.com/ekzhang/crepe : > Crepe is a library that allows you to write declarative logic programs in Rust, with a Datalog-like syntax. It provides a procedural macro that generates efficient, safe code and interoperates seamlessly with Rust programs. Looks like there's not yet a Python grammar for the treeedb tree-sitter: https://github.com/langston-barrett/treeedb : > Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages. Looks like roxi supports n3,
Read the rest
which adds `=>` "implies" to the Turtle lightweight RDF representation: https://github.com/pbonte/roxi FWIW rdflib/owl-rl: https://owl-rl.readthedocs.io/en/latest/owlrl.html : > simple forward chaining rules are used to extend (recursively) the incoming graph with all triples that the rule sets permit (ie, the “deductive closure” of the graph is computed). ForwardChainingStore and BackwardChainingStore implementations w/ rdflib in Python: https://github.com/RDFLib/FuXi/issues/15 Fast CUDA hashmaps Gdlog is built on CuCollections. GPU HashMap libs to benchmark: Warpcore, CuCollections, https://github.com/NVIDIA/cuCollections https://github.com/NVIDIA/cccl https://github.com/sleeepyjack/warpcore /? Rocm HashMap DeMoriarty/DOKsparse: https://github.com/DeMoriarty/DOKSparse /? SIMD hashmap Google's SwissTable: https://github.com/topics/swisstable rust-lang/hashbrown: https://github.com/rust-lang/hashbrown CuPy has array but not yet hashmaps, or (GPU) SIMD FWICS? NumPy does SIMD: https://numpy.org/doc/stable/reference/simd/ google/highway: https://github.com/google/highway xtensor-stack/xsimd: https://github.com/xtensor-stack/xsimd GH topics > HashMap: https://github.com/topics/hashmap
In GDlog: A GPU-accelerated deductive engine
airstrike·Sep 18, 2023
Thanks, great point indeed. I am looking into this https://github.com/rust-lang/hashbrown The way I think about it -- rather naively, I suppose -- is that I care more about the references cells make to each other than the actual grid of cells displayed on a table. The latter feels more like a "view" of the data than an actual data structure? This also seems to align with the relative priority of (sorted from highest to lowest): figuring out the order of evaluation, calculating those evaluations, and finally displaying the results of the evaluation
In Memory-efficient enum arrays in Zig
foldr·Apr 12, 2023
I believe Rust uses hashbrown as the underlying implementation now. This just calculates the number of buckets based on the number of items requested: https://github.com/rust-lang/hashbrown/blob/009969a860290849... In the worst case all the items you insert will go in one bucket, and you'll have to rehash, which requires allocation. I'm not sure this is any different from Go in that respect. I suspect that the inherently probabilistic nature of a hashtable makes it impossible to guarantee capacity without allocating way more space than would be required in typical cases. If there is a clever way to avoid this it would certainly be interesting to read about it. Edit: Also, Go's hashtable implementation does not use linked lists (though it is a chaining implementation).
In Deduplicate a slice in Go, use sort or a map?
itamarst·Aug 5, 2021
I'm working on a memory profiler for Python that is fast enough to run in production (see link below), so I've ended up with some similar problems re performance and importance of testing. A few things the article talks about where one can maybe do even better: 1. likely()/unlikely() not being in stable Rust. This is true, but the hashbrown project has some hacked-up variants it claims work on stable: https://github.com/rust-lang/hashbrown/blob/bbda6e0077bafb75... 2. Rust not having fast thread locals. Same problem for me, so likewise did it in C with "initial-exec". But! If you use clang, you can get LTO across C and Rust, so you can get fast thread locals _and_ not have function call overhead. Basically need to use same version of Clang as Rust does (12 at the moment) and do a little song and dance in linker and compiler flags. See http
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s://matklad.github.io/2020/10/03/fast-thread-locals-in-r... 3. For testing these sort of things, being able to assert "this test touched this code path" is extremely useful. In my case, for example, I have different code paths for sampled and unsampled allocations, but from perspective of code calling malloc() everything should be identical. So how to tell if correct code path was used? Coverage marks are a great solution for this: https://ferrous-systems.com/blog/coverage-marks/ (The Python profiler, if anyone is interested: I've already released an open source memory profiler that tracks all allocations, https://pythonspeed.com/fil/. Unlike most memory profilers it tells you source-of-allocations at peak memory, which is key for data processing applications. The commercial production-grade version I'm now working on uses sampling, and will be even more focused on data processing batch applications; the goal is to have essentially no performance overhead so it can always be on.)
In Slitter: A slab allocator that trusts, but verifies

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