Predictable Database Systems
Making database behavior, latency, resource use, and uncertainty more predictable.
Can a data system explain and bound how it will behave under changing workloads?
Research on performance variability, workload behavior, physical design, diagnosis, scheduling, and data locality as parts of predictable system behavior.
The problem
Data systems can deliver good average performance while remaining difficult to diagnose, size, or trust under workload changes and tail-latency pressure.
The approach
Measure sources of variance, model workload-resource relationships, diagnose anomalies, and design mechanisms that remain robust under uncertainty.
Research on performance variability, workload behavior, physical design, diagnosis, scheduling, and data locality as parts of predictable system behavior.
Main contributions
- Research framing and system design
- Methods, implementation, and empirical evaluation
- Open research artifacts and scholarly dissemination
Publications
Identifying the Major Sources of Variance in Transaction Latencies: Towards More Predictable Databases
CoRR 2016 · Computing Research Repository
Project ↗
Cite
@article{79a739c2-69af-4e7a-9fab-9e1ca9ca9730,
title = {Identifying the Major Sources of Variance in Transaction Latencies: Towards More Predictable Databases},
author = {Jiamin Huang and Barzan Mozafari and Thomas F. Wenisch},
journal = {Computing Research Repository},
year = {2016}
}Statistical Analysis of Latency Through Semantic Profiling
EuroSys 2017 · ACM European Conference on Computer Systems
Paper ↗Code ↗Project ↗
Cite
@inproceedings{593a1faa-d447-439a-aca4-9dc5a037630e,
title = {Statistical Analysis of Latency Through Semantic Profiling},
author = {Jiamin Huang and Barzan Mozafari and Thomas F. Wenisch},
booktitle = {ACM European Conference on Computer Systems},
year = {2017},
doi = {10.1145/3064176.3064179}
}DMon: Efficient Detection and Correction of Data Locality Problems Using Selective Profiling
OSDI 2021 · USENIX Symposium on Operating Systems Design and Implementation
Paper ↗Project ↗
Cite
@inproceedings{bd35a4d0-2624-465c-aabb-0cf0d78b3147,
title = {DMon: Efficient Detection and Correction of Data Locality Problems Using Selective Profiling},
author = {Tanvir Ahmed Khan and Ian Neal and Gilles Pokam and Barzan Mozafari and Baris Kasikci},
booktitle = {USENIX Symposium on Operating Systems Design and Implementation},
year = {2021}
}Contention-Aware Lock Scheduling for Transactional Databases
PVLDB 2018 · Proceedings of the VLDB Endowment
Paper ↗Technical Report ↗Project ↗
Cite
@article{5d2dbe3f-c6e6-40c8-9a82-9f8e042ef4a2,
title = {Contention-Aware Lock Scheduling for Transactional Databases},
author = {Boyu Tian and Jiamin Huang and Barzan Mozafari and Grant Schoenebeck},
journal = {Proceedings of the VLDB Endowment},
year = {2018},
doi = {10.1145/3187009.3177740}
}SnappyData: A Unified Cluster for Streaming, Transactions and Interactice Analytics
CIDR 2017 · Conference on Innovative Data Systems Research
Paper ↗Project ↗
Cite
@inproceedings{e6de4a52-07a5-47c5-8196-ace46d2a49c2,
title = {SnappyData: A Unified Cluster for Streaming, Transactions and Interactice Analytics},
author = {Barzan Mozafari and Jags Ramnarayan and Sudhir Menon and Yogesh Mahajan and Soubhik Chakraborty and Hemant Bhanawat and Kishor Bachhav},
booktitle = {Conference on Innovative Data Systems Research},
year = {2017}
}DBSherlock: A Performance Diagnostic Tool for Transactional Databases
SIGMOD 2016 · ACM SIGMOD International Conference on Management of Data
Paper ↗Code ↗Data ↗Project ↗
Cite
@inproceedings{a5460b19-8bff-4fd2-ae5e-d0ce7477454b,
title = {DBSherlock: A Performance Diagnostic Tool for Transactional Databases},
author = {Dong Young Yoon and Ning Niu and Barzan Mozafari},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2016},
doi = {10.1145/2882903.2915218}
}SnappyData: A Hybrid Transactional Analytical Store Built On Spark
SIGMOD 2016 · ACM SIGMOD International Conference on Management of Data
Paper ↗Project ↗
Cite
@inproceedings{cea59a03-38c4-4acb-bd2d-a41ecabe154a,
title = {SnappyData: A Hybrid Transactional Analytical Store Built On Spark},
author = {Jags Ramnarayan and Barzan Mozafari and Sumedh Wale and Sudhir Menon and Neeraj Kumar and Hemant Bhanawat and Soubhik Chakraborty and Yogesh Mahajan and Rishitesh Mishra and Kishor Bachhav},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2016},
doi = {10.1145/2882903.2899408}
}CliffGuard: A Principled Framework for Finding Robust Database Designs
SIGMOD 2015 · ACM SIGMOD International Conference on Management of Data
Paper ↗Code ↗Project ↗Project ↗
Cite
@inproceedings{a55995d1-caca-4dbc-a138-c382c0a1e41c,
title = {CliffGuard: A Principled Framework for Finding Robust Database Designs},
author = {Barzan Mozafari and Eugene Zhen Ye Goh and Dong Young Yoon},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2015},
doi = {10.1145/2723372.2749454}
}DBSeer: Pain-free Database Administration through Workload Intelligence
PVLDB 2015 · Proceedings of the VLDB Endowment
Paper ↗Project ↗
Cite
@article{7c01b9d8-7064-406d-8eed-815b804063b2,
title = {DBSeer: Pain-free Database Administration through Workload Intelligence},
author = {Dong Young Yoon and Barzan Mozafari and Douglas P. Brown},
journal = {Proceedings of the VLDB Endowment},
year = {2015},
doi = {10.14778/2824032.2824130}
}DBSeer: Resource and Performance Prediction for Building a Next Generation Database Cloud
CIDR 2013 · Conference on Innovative Data Systems Research
Paper ↗Project ↗
Cite
@inproceedings{dbc38671-6184-4ab9-ba11-eb567fddba7b,
title = {DBSeer: Resource and Performance Prediction for Building a Next Generation Database Cloud},
author = {Barzan Mozafari and Carlo Curino and Samuel Madden},
booktitle = {Conference on Innovative Data Systems Research},
year = {2013}
}