AutoComp: Automated Data Compaction for Log-Structured Tables in Data Lakes
Summary: Automates compaction for log-structured tables in data lakes to curb small files and metadata bloat. AutoComp is scalable, LinkedIn-informed, integrates with OpenHouse, enabling multi-objective data-layout optimizations. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Anja Gruenheid (Microsoft)
- 2. Jesús Camacho-Rodríguez (Microsoft)
- 3. Carlo Curino (Microsoft)
- 4. Raghu Ramakrishnan (Microsoft)
- 5. Stanislav Pak (LinkedIn)
- 6. Sumedh Sakdeo (LinkedIn)
- 7. Lenisha Gandhi (LinkedIn)
- 8. Sandeep K. Singhal (LinkedIn)
- 9. Pooja Nilangekar (University of Maryland)
- 10. Daniel J. Abadi (University of Maryland)
BibTeX Citation
@inproceedings{gruenheid_sigmod25,
title = {{AutoComp: Automated Data Compaction for Log-Structured Tables in Data Lakes}},
author = {Gruenheid, Anja and Camacho-Rodríguez, Jesús and Curino, Carlo and Ramakrishnan, Raghu and Pak, Stanislav and Sakdeo, Sumedh and Gandhi, Lenisha and Singhal, Sandeep K. and Nilangekar, Pooja and Abadi, Daniel J.},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3724430},
url = {https://dl.acm.org/doi/10.1145/3722212.3724430},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,485 | PTO: A Workload-driven Predictive Table Optimizer for Lakehouse Systems | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,485 | PTO: A Workload-driven Predictive Table Optimizer for Lakehouse Systems | 2026 | SIGMOD |
| 2 | 2,788 | BtrBlocks: Efficient Columnar Compression for Data Lakes | 2023 | SIGMOD |
| 3 | 10,536 | Active Data Lakes: Regaining Physical Data Independence Without Losing Interoperability | 2026 | VLDB |
| 4 | 9,836 | Towards Functional Decomposition of Storage Formats | 2025 | CIDR |
| 5 | 8,467 | LST-Bench: Benchmarking Log-Structured Tables in the Cloud | 2024 | SIGMOD |
| 6 | 5,983 | Adaptive and Robust Query Execution for Lakehouses at Scale | 2024 | VLDB |
| 7 | 1,580 | Compaction management in distributed key-value datastores | 2015 | VLDB |
| 8 | 520 | Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores | 2020 | VLDB |
| 9 | 7,780 | Petabyte-Scale Row-Level Operations in Data Lakehouses | 2024 | VLDB |
| 10 | 4,457 | Analyzing and Comparing Lakehouse Storage Systems | 2023 | CIDR |