ERMIA: Fast Memory-Optimized Database System for Heterogeneous Workloads
Summary: ERMIA is a memory-optimized DB system built from scratch to support heterogeneous workloads. It uses snapshot isolation for coordination and can provide serializability, delivering scalable performance and preserving high read-mostly throughput where OCC-based systems falter. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kangnyeon Kim (University of Toronto)
- 2. Tianzheng Wang (University of Toronto)
- 3. Ryan Johnson (LogicBlox)
- 4. Ippokratis Pandis (Amazon)
BibTeX Citation
@inproceedings{kim_sigmod16,
title = {{ERMIA: Fast Memory-Optimized Database System for Heterogeneous Workloads}},
author = {Kim, Kangnyeon and Wang, Tianzheng and Johnson, Ryan and Pandis, Ippokratis},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2882905},
url = {https://dl.acm.org/doi/10.1145/2882903.2882905},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 13 of 63 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 29 of 29 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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