Squall: Scalable Real-time Analytics
Summary: Squall is a cluster-based online query engine for complex real-time analytics, combining skew-resilient partitioning with adaptive operators. It contributes novel join algorithms and distills five years of systems experience. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Aleksandar Vitorovic (EPFL)
- 2. Mohammed Elseidy (EPFL)
- 3. Khayyam Guliyev (EPFL)
- 4. Khue Vu Minh (EPFL)
- 5. Daniel Espino (EPFL)
- 6. Mohammad Dashti (EPFL)
- 7. Yannis Klonatos (EPFL)
- 8. Christoph Koch (EPFL)
BibTeX Citation
@article{vitorovic_vldb16,
title = {{Squall: Scalable Real-time Analytics}},
author = {Vitorovic, Aleksandar and Elseidy, Mohammed and Guliyev, Khayyam and Minh, Khue Vu and Espino, Daniel and Dashti, Mohammad and Klonatos, Yannis and Koch, Christoph},
journal = {PVLDB},
series = {{VLDB} '16},
volume = {9},
number = {13},
doi = {10.14778/3007263.3007307},
url = {https://doi.org/10.14778/3007263.3007307},
year = {2016}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,607 | CLASH: A High-Level Abstraction for Optimized, Multi-Way Stream Joins over Apache Storm | 2019 | SIGMOD | 5.4026249e-05 |
| 11,181 | Low-Latency Adaptive Distributed Stream Join System Based on a Flexible Join Model | 2024 | SIGMOD | 5.093636e-05 |
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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 |
|---|---|---|---|---|
| 438 | DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views | 2012 | VLDB | 0.00018471721 |
| 843 | Processing Theta-Joins using MapReduce* | 2011 | SIGMOD | 0.00013666161 |
| 2,887 | Efficient Multi-way Theta-Join Processing Using MapReduce | 2012 | VLDB | 7.9952432e-05 |
| 2,892 | Scalable and Adaptive Online Joins | 2014 | VLDB | 7.9852178e-05 |
| 3,087 | How to Win a Hot Dog Eating Contest: Distributed Incremental View Maintenance with Batch Updates | 2016 | SIGMOD | 7.7702906e-05 |
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|---|---|---|---|---|
| 1 | 612 | Twitter Heron: Stream Processing at Scale | 2015 | SIGMOD |
| 2 | 939 | Starling: A Scalable Query Engine on Cloud Functions | 2020 | SIGMOD |
| 3 | 10,768 | Intra-Query Runtime Elasticity for Cloud-Native Data Analysis | 2025 | SIGMOD |
| 4 | 6,822 | JetScope: Reliable and Interactive Analytics at Cloud Scale | 2015 | VLDB |
| 5 | 3,532 | Scalable Distributed Stream Join Processing | 2015 | SIGMOD |
| 6 | 12,829 | Adaptive Distributed Query Processing | 2003 | VLDB |
| 7 | 12,112 | STORM: Spatio-Temporal Online Reasoning and Management of Large Spatio-Temporal Data | 2015 | SIGMOD |
| 8 | 220 | Storm @Twitter | 2014 | SIGMOD |
| 9 | 7,709 | Quill: Efficient, Transferable, and Rich Analytics at Scale | 2016 | VLDB |
| 10 | 2,407 | Squall: Fine-Grained Live Reconfiguration for Partitioned Main Memory Databases | 2015 | SIGMOD |