Why Hash Tables Collide: Swiss Tables, Robin Hood Hashing, and CPU Cache Lines
Every introductory computer science course teaches hash tables the exact same way: Take a key, run it through a hash function. Compute index = hash % table_size . If two keys land on the same index, append the new entry to a linked list at that bucket (separate chaining). If you inspect the standard libraries of modern production runtimes (Rust's std::collections::HashMap , Google's Abseil C++…
Modern hash tables have moved away from linked lists due to hardware limitations. The memory hierarchy consists of L1, L2, and L3 caches, along with main memory, each with increasing latency. Linked lists trigger cache misses when traversing nodes, causing significant delays. Open addressing and tombstones were introduced as alternatives, but they have their own drawbacks like primary clustering and inefficient deletion.
Robin Hood Hashing addresses these issues by redistributing keys to minimize probe lengths, resulting in faster lookups. Swiss Tables, used by Google's Abseil library and Rust's HashMap, implement this strategy and have become the industry standard for high-performance hash tables.
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