Back to papers
Gorilla: A Fast, Scalable, In-Memory Time Series Database
Summary: Facebook's Gorilla is an in-memory time-series DB tuned for high-availability writes and fast aggregate analysis of monitoring data. Delta-of-delta timestamps and XORed floats yield ~10x compression, enabling memory-resident storage with 73x latency reduction and 14x throughput over HBase-backed TSDBs, while tolerating some write loss.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 11060
- Venue
- VLDB
- Year
- 2015
- Pagerank
- 0.00028826544
- Overall Rank
- 156 | 98.92%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 25 of 75 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 10,175 |
Improving LZ4 for Effective Compression and Efficient Query |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,303 |
Morphing-based Compression for Data-centric ML Pipelines |
2026 |
VLDB |
5.1725247e-05 |
| 10,321 |
CLaP - State Detection from Time Series |
2026 |
VLDB |
5.1725247e-05 |
| 10,333 |
DeXOR: Enabling xor in Decimal Space for Streaming Lossless Compression of Floating-point Data |
2026 |
VLDB |
5.1725247e-05 |
| 10,393 |
LCP: Enhancing Scientific Data Management with Lossy Compression for Particles |
2025 |
SIGMOD |
5.1725247e-05 |
| 10,588 |
Streaming Time Series Subsequence Anomaly Detection: A Glance and Focus Approach |
2025 |
VLDB |
5.1725247e-05 |
| 10,616 |
Approximation-First Timeseries Query At Scale |
2025 |
VLDB |
5.1725247e-05 |
| 10,622 |
QPET: A Versatile and Portable Quantity-of-Interest-Preservation Framework for Error-Bounded Lossy Compression |
2025 |
VLDB |
5.1725247e-05 |
| 10,675 |
Déjà Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse |
2025 |
VLDB |
5.1725247e-05 |
| 10,682 |
Improving Time Series Data Compression in Apache IoTDB |
2025 |
VLDB |
5.1725247e-05 |
| 10,708 |
LogLite: Lightweight Plug-and-Play Streaming Log Compression |
2025 |
VLDB |
5.1725247e-05 |
| 10,748 |
Beyond Compression: A Comprehensive Evaluation of Lossless Floating-Point Compression |
2025 |
VLDB |
5.1725247e-05 |
| 10,786 |
Goku: A Schemaless Time Series Database for Large Scale Monitoring at Pinterest |
2025 |
VLDB |
5.1725247e-05 |
| 10,799 |
Demonstration of ModelarDB: Model-Based Management of High-Frequency Time Series Across Edge, Cloud, and Client |
2025 |
VLDB |
5.1725247e-05 |
| 10,858 |
LiquidCache: Efficient Pushdown Caching for Cloud-Native Data Analytics |
2025 |
VLDB |
5.1725247e-05 |
| 10,940 |
High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation |
2024 |
SIGMOD |
5.1725247e-05 |
| 11,091 |
Lindorm-UWC: An Ultra-Wide-Column Database for Internet of Vehicles |
2024 |
VLDB |
5.1725247e-05 |
| 11,093 |
Simple (yet Efficient) Function Authoring for Vectorized Engines |
2024 |
VLDB |
5.1725247e-05 |
| 11,136 |
Scalable Model-Based Management of Massive High Frequency Wind Turbine Data with ModelarDB |
2024 |
VLDB |
5.1725247e-05 |
| 11,149 |
Raising the Level of Abstraction for Time-State Analytics With the Timeline Framework |
2023 |
CIDR |
5.1725247e-05 |
| 11,178 |
Grouping Time Series for Efficient Columnar Storage |
2023 |
SIGMOD |
5.1725247e-05 |
| 11,194 |
ForestTI: A Scalable Inverted-Index-Oriented Timeseries Management System with Flexible Memory Efficiency |
2023 |
SIGMOD |
5.1725247e-05 |
| 11,491 |
Toto - Benchmarking the Efficiency of a Cloud Service |
2021 |
SIGMOD |
5.1725247e-05 |
| 11,578 |
An Evaluation of Methods of Compressing Doubles |
2020 |
SIGMOD |
5.1725247e-05 |
| 11,764 |
Prioritizing Attention in Fast Data: Principles and Promise |
2017 |
CIDR |
5.1725247e-05 |
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.
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 10,682 |
Improving Time Series Data Compression in Apache IoTDB |
2025 |
VLDB |
5.1725247e-05 |
| 11,342 |
Scalable Time Series Compound Infrastructure |
2022 |
SIGMOD |
5.1725247e-05 |
| 3,059 |
Managing Large Dynamic Graphs Efficiently |
2012 |
SIGMOD |
7.8670526e-05 |
| 9,666 |
Lindorm TSDB: A Cloud-native Time-series Database for Large-scale Monitoring Systems |
2023 |
VLDB |
5.2956801e-05 |
| 11,194 |
ForestTI: A Scalable Inverted-Index-Oriented Timeseries Management System with Flexible Memory Efficiency |
2023 |
SIGMOD |
5.1725247e-05 |
| 1,533 |
Chimp: Efficient Lossless Floating Point Compression for Time Series Databases |
2022 |
VLDB |
0.00010501771 |
| 3,081 |
Monarch: Google’s Planet-Scale In-Memory Time Series Database |
2020 |
VLDB |
7.8444659e-05 |
| 8,830 |
TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications |
2023 |
VLDB |
5.4272648e-05 |
| 6,253 |
Heracles: An Efficient Storage Model and Data Flushing for Performance Monitoring Timeseries |
2021 |
VLDB |
6.0004089e-05 |
| 10,786 |
Goku: A Schemaless Time Series Database for Large Scale Monitoring at Pinterest |
2025 |
VLDB |
5.1725247e-05 |