Don't Thrash: How to Cache Your Hash on Flash
Summary: Introduces quotient filters, Bloom-filter alternatives with contiguous access, deletion, resizing, and merging. Buffered and cascade variants optimize SSD workloads, achieving substantially faster insertions and lookups than prior flash-resident Bloom filters. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Michael A. Bender (State University of New York at Stony Brook; Tokutek, Inc.)
- 2. Martin Farach-Colton (Rutgers University; Tokutek, Inc.)
- 3. Bradley C. Kuszmaul (Massachusetts Institute of Technology; Tokutek, Inc.)
- 4. Dzejla Medjedovic (State University of New York at Stony Brook)
- 5. Richard P. Spillane (State University of New York at Stony Brook)
- 6. Rob Johnson (State University of New York at Stony Brook)
- 7. Pablo Montes (State University of New York at Stony Brook)
- 8. Erez Zadok (State University of New York at Stony Brook)
- 9. Russell Kraner (VCORE Solutions LLC)
- 10. Pradeep Shetty (State University of New York at Stony Brook)
BibTeX Citation
@article{bender_vldb12,
title = {{Don't Thrash: How to Cache Your Hash on Flash}},
author = {Bender, Michael A. and Farach-Colton, Martin and Kuszmaul, Bradley C. and Medjedovic, Dzejla and Spillane, Richard P. and Johnson, Rob and Montes, Pablo and Zadok, Erez and Kraner, Russell and Shetty, Pradeep},
journal = {PVLDB},
series = {{VLDB} '12},
volume = {5},
number = {11},
pages = {1627--1638},
doi = {10.14778/2350229.2350252},
url = {https://doi.org/10.14778/2350229.2350252},
year = {2012}
}Incoming Citations (Sorted by Pagerank)
Showing 36 of 36 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,075 | Cache-Oblivious String B-trees | 2006 | PODS | 0.00012157749 |
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|---|---|---|---|---|
| 1 | 2,245 | Cache-Efficient Aggregation: Hashing Is Sorting | 2015 | SIGMOD |
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| 3 | 3,285 | Performance-Optimal Filtering: Bloom Overtakes Cuckoo at High Throughput | 2019 | VLDB |
| 4 | 6,938 | A Shifting Bloom Filter Framework for Set Queries | 2016 | VLDB |
| 5 | 1,846 | Morton Filters: Faster, Space-Efficient Cuckoo Filters via Biasing, Compression, and Decoupled Logical Sparsity | 2018 | VLDB |
| 6 | 1,665 | SkimpyStash: RAM Space Skimpy Key-Value Store on Flash-based Storage | 2011 | SIGMOD |
| 7 | 11,887 | New Wine in an Old Bottle: Data-Aware Hash Functions for Bloom Filters | 2022 | VLDB |
| 8 | 2,073 | A General-Purpose Counting Filter: Making Every Bit Count | 2017 | SIGMOD |
| 9 | 973 | FlashStore: High Throughput Persistent Key-Value Store | 2010 | VLDB |
| 10 | 6,195 | Prefix Filter: Practically and Theoretically Better Than Bloom | 2022 | VLDB |