Two Bits Are Better Than One: making bloom filters 2x more accurate
Hacker News
February 17, 2026
AI-Generated Deep Dive Summary
Bloom filters are probabilistic data structures designed to quickly answer membership queries with high efficiency but at the risk of false positives. The article discusses an optimization technique where using two bits per hash instead of one doubles the accuracy, significantly reducing false results. This improvement is achieved through lightweight hashing and a double bit setting, which enhances reliability without sacrificing speed.
The optimized bloom filters are applied in critical database operations such as hash joins and storage engine filtering. In hash joins, the filter reduces unnecessary lookups by first checking if elements likely exist before probing the hash table. For storage engines, particularly column stores, this technique minimizes decompression of unused data, leading to a 9x reduction in processing time.
Adaptive filtering further enhances efficiency by monitoring performance metrics and adjusting bloom filter
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Originally published on Hacker News on 2/17/2026