VIP Hashing - Adapting to Skew in Popularity of Data on the Fly
Summary: VIP Hashing is an in-memory hash table that non-blockingly learns access skew and dynamically rearranges layout, with sensing to avoid unnecessary learning overhead. It adapts online to workload changes, improving fetch throughput and DuckDB TPC-H performance. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Aarati Kakaraparthy (University of Wisconsin)
- 2. Jignesh M. Patel (University of Wisconsin)
- 3. Brian P. Kroth (Microsoft)
- 4. Kwanghyun Park (Microsoft)
BibTeX Citation
@article{kakaraparthy_vldb22,
title = {{VIP Hashing - Adapting to Skew in Popularity of Data on the Fly}},
author = {Kakaraparthy, Aarati and Patel, Jignesh M. and Kroth, Brian P. and Park, Kwanghyun},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {10},
pages = {1978--1990},
doi = {10.14778/3547305.3547306},
url = {https://doi.org/10.14778/3547305.3547306},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,416 | Is Perfect Hashing Practical for OLAP Systems? | 2024 | CIDR | 5.5331654e-05 |
| 10,777 | Succinct and Fast Tiny Pointer Hash Tables | 2026 | VLDB | 4.9793485e-05 |
| 11,538 | A Universal Sketch for Estimating Heavy Hitters and Per-Element Frequency Moments in Data Streams with Bounded Deletions | 2024 | SIGMOD | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 40 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00046284649 |
| 71 | DuckDB: an Embeddable Analytical Database | 2019 | SIGMOD | 0.00037720227 |
| 212 | The LRU-K Page Replacement Algorithm For Database Disk Buffering | 1993 | SIGMOD | 0.00024767296 |
| 361 | Design and Evaluation of Main Memory Hash Join Algorithms for Multi-core CPUs | 2011 | SIGMOD | 0.00020006406 |
| 546 | Faster: A Concurrent Key-Value Store with In-Place Updates | 2018 | SIGMOD | 0.00016590738 |
| 1,283 | A Seven-Dimensional Analysis of Hashing Methods and its Implications on Query Processing | 2016 | VLDB | 0.00011209209 |
| 3,028 | Autoscaling Tiered Cloud Storage in Anna | 2019 | VLDB | 7.7377953e-05 |
| 4,008 | Automating Distributed Tiered Storage Management in Cluster Computing | 2020 | VLDB | 6.8591707e-05 |
| 9,003 | Entropy-Learned Hashing: Constant Time Hashing with Controllable Uniformity | 2022 | SIGMOD | 5.2392472e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,482 | Meep Hashing: Ultrafast and Compact Minimal Perfect Hashing for Practical Large-Scale Lookup Systems | 2026 | SIGMOD |
| 2 | 9,003 | Entropy-Learned Hashing: Constant Time Hashing with Controllable Uniformity | 2022 | SIGMOD |
| 3 | 4,144 | Memory-Contention Responsive Hash Joins | 1994 | VLDB |
| 4 | 361 | Design and Evaluation of Main Memory Hash Join Algorithms for Multi-core CPUs | 2011 | SIGMOD |
| 5 | 4,890 | Can Learned Models Replace Hash Functions? | 2023 | VLDB |
| 6 | 8,241 | Pea Hash: A Performant Extendible Adaptive Hashing Index | 2023 | SIGMOD |
| 7 | 10,468 | HotHash: Hotness-Aware Consistent Hashing for Cloud Databases | 2026 | SIGMOD |
| 8 | 7,814 | Analyzing Vectorized Hash Tables Across CPU Architectures | 2023 | VLDB |
| 9 | 1,283 | A Seven-Dimensional Analysis of Hashing Methods and its Implications on Query Processing | 2016 | VLDB |
| 10 | 1,254 | Handling Data Skew in Multiprocessor Database Computers Using Partition Tuning | 1991 | VLDB |