Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2602.05925

Adaptive Hashing: Faster Hash Functions with Fewer Collisions

Gábor Melis
View original source ↗Read source document ↗Export metadata ↓

Abstract

Hash tables are ubiquitous, and the choice of hash function, which maps a key to a bucket, is key to their performance. We argue that the predominant approach of fixing the hash function for the lifetime of the hash table is suboptimal and propose adapting it to the current set of keys. In the prevailing view, good hash functions spread the keys ``randomly'' and are fast to evaluate. General-purpose ones (e.g. Murmur) are designed to do both while remaining agnostic to the distribution of the keys, which limits their bucketing ability and wastes computation. When these shortcomings are recognized, one may specify a hash function more tailored to some assumed key distribution, but doing so almost always introduces an unbounded risk in case this assumption does not bear out in practice. At the other, fully key-aware end of the spectrum, Perfect Hashing algorithms can discover hash functions to bucket a given set of keys optimally, but they are costly to run and require the keys to be known and fixed ahead of time. Our main conceptual contribution is that adapting the hash table's hash function to the keys online is necessary for the best performance, as adaptivity allows for better bucketing of keys \emph{and} faster hash functions. We instantiate the idea of online adaptation with minimal overhead and no change to the hash table API. The experiments show that the adaptive approach marries the common-case performance of weak hash functions with the robustness of general-purpose ones.

Explore related subjects

cs.DS

Keep this discovery

BibTeXRIS

Gábor Melis. 2026-02-05. Adaptive Hashing: Faster Hash Functions with Fewer Collisions. https://doi.org/10.5281/zenodo.10991321

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS↗
arXiv2026

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS↗