[2607.28892] Succinct and Fast Tiny Pointer Hash Tables

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arXiv:2607.28892 (cs)

[Submitted on 30 Jul 2026]

Title:Succinct and Fast Tiny Pointer Hash Tables

Authors:Xilin Tang (Cornell University), Yuqi Mai (Cornell University), William Kuszmaul (Carnegie Mellon University), Alex Conway (Cornell Tech)<br>View a PDF of the paper titled Succinct and Fast Tiny Pointer Hash Tables, by Xilin Tang (Cornell University) and 3 other authors

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Abstract:Hash tables sit on the critical path of many systems, yet modern designs still force a trade-off between fast operations and high memory overhead. We revisit this trade-off and present Tiny Pointer Hash Tables (TPHT), a family of practical hash tables that make two ideas from theory work at system scale: compressing pointers down to a byte, and encoding keys compactly so less metadata is needed. We engineer these ideas into two complementary designs. Chained-TPHT targets maximal space savings, and is to the best of our knowledge the first simple and practical succinct hash table design, achieving a footprint less than the total data size with constant-time operations. Flattened-TPHT targets latency, organizing data to keep the common case within a single cache miss while retaining strong space efficiency. Both variants support dynamic resizing without global pauses and integrate cleanly with 64-bit keys and values. Across YCSB and microbenchmarks, TPHT advances the latency-space Pareto frontier: Chained-TPHT reaches 105.4% space efficiency, and Flattened-TPHT achieves 83.4% space efficiency with up to 89.3% higher throughput than strong baselines. Together, these results show that techniques primarily known in theory can be turned into production-ready hash tables that meaningfully reduce memory use while delivering state-of-the-art performance.

Comments:<br>Comments: 16 pages, 13 figures. Author-prepared arXiv version of the PVLDB paper. Source code and experimental artifacts: this https URL . Standalone implementation for direct use: this https URL

Subjects:

Data Structures and Algorithms (cs.DS)

Cite as:<br>arXiv:2607.28892 [cs.DS]

(or<br>arXiv:2607.28892v1 [cs.DS] for this version)

https://doi.org/10.48550/arXiv.2607.28892

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arXiv-issued DOI via DataCite

Journal reference:<br>Proc. VLDB Endow. 19(9) (2026) 2168-2182

Related DOI:

https://doi.org/10.14778/3819518.3819542

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DOI(s) linking to related resources

Submission history<br>From: Xilin Tang [view email]<br>[v1]<br>Thu, 30 Jul 2026 23:19:05 UTC (4,478 KB)

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