January 8, 2026
When hash maps hash it out
Lessons from Hash Table Merging
Hash map merge meltdown has devs yelling “just use the built‑in” vs “salt it and pray”
TLDR: A dev found merging big hash maps can be 10x slower if you do it naively; they suggest salting, preallocating, or changing the loop. Commenters split between “use the built‑in merge in Rust” and “tune it yourself,” with side debates over probing details and even a code suffix typo—because of course.
A simple task—merging two giant “phonebooks for computers” called hash maps—turned into a 10x slowdown, and the internet did what it does best: argue loudly and hilariously. The author tested popular C++ and Rust libraries and found that when you merge in a predictable order, the table can clog like a bad traffic jam. Their fixes? Add a “salt” to shuffle things, pre‑reserve space, or change up the loop order. But the comments stole the show.
One camp cheered the detective work—“great catch!”—while warning that marching through data in the same order the table stores it is a recipe for gridlock. Another camp, led by Rust voices, basically said: stop hand‑rolling, just use the built‑in method. In Rust, HashMap::extend exists for a reason—let the pros optimize it. Meanwhile, optimization aficionados pitched curveballs like “Fibonacci hashing” (yes, the golden ratio made a cameo) to spread out the traffic even better.
Then came the drama seasoning: a pedant pointed out a tiny “ULL” typo in the code (and somehow that became a whole mini‑thread), and another commenter corrected the headline claim—those fancy tables aren’t “plain” linear probing; they probe in chunks. Cue memes about golden ratios, salted hashes, and the eternal clash: DIY micro‑tuning vs “trust the library”
Key Points
- •Merging large hash maps showed >10× slowdown compared to the expected O(N) behavior.
- •Benchmark setup used splitmix64 to generate 3N random numbers, with N between 7 and 31 million.
- •Tests merged 2N-key map into an N-key map and recorded timing on an M4 Pro system.
- •Libraries evaluated include Abseil, Boost, unordered_dense, khashl, Rust standard HashMap, and hashbrown, focusing on linear probing approaches.
- •Three proposed fixes for efficient merging: salted hash function, preallocation, and non-linear iteration.