Gorilla: A Fast, Scalable, In-Memory Time Series Database
Summary: Gorilla is an in-memory TSDB for massive monitoring workloads, exploiting recency and aggregate-query priorities with delta-of-delta and XOR compression. It trades limited write-side loss for regional failure tolerance, delivering 10× compression, 73× lower latency, and 14× higher throughput than HBase. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tuomas Pelkonen (Meta)
- 2. Paul Cavallaro (Meta)
- 3. Qi Huang (Meta)
- 4. Scott Franklin (Meta)
- 5. Justin Meza (Meta)
- 6. Justin Teller (Meta)
- 7. Kaushik Veeraraghavan (Meta)
BibTeX Citation
@article{pelkonen_vldb15,
title = {{Gorilla: A Fast, Scalable, In-Memory Time Series Database}},
author = {Pelkonen, Tuomas and Cavallaro, Paul and Huang, Qi and Franklin, Scott and Meza, Justin and Teller, Justin and Veeraraghavan, Kaushik},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {12},
pages = {1816--1827},
doi = {10.14778/2824032.2824078},
url = {https://doi.org/10.14778/2824032.2824078},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 50 of 77 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 32 | Hive - A Warehousing Solution Over a Map-Reduce Framework | 2009 | VLDB | 0.00050111008 |
| 190 | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases | 2001 | SIGMOD | 0.00026105472 |
| 1,352 | Scuba: Diving into Data at Facebook | 2013 | VLDB | 0.00011064595 |
| 1,764 | Fast Approximate Correlation for Massive Time-series Data | 2010 | SIGMOD | 9.8120315e-05 |
| 3,359 | VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases | 2004 | VLDB | 7.4898575e-05 |
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