Dalton: Learned Partitioning for Distributed Data Streams
Summary: Dalton: an RL-based, lightweight partitioner for distributed streams that memoizes recent state to minimize per-tuple overhead and rapidly adapt to unknown, changing hot-key skews. Scales via cooperative learning across instances (no centralized bottleneck), achieving 1.3–6.7× higher throughput. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Eleni Zapridou (EPFL)
- 2. Ioannis Mytilinis (Oracle)
- 3. Anastasia Ailamaki (EPFL)
BibTeX Citation
@article{zapridou_vldb23,
title = {{Dalton: Learned Partitioning for Distributed Data Streams}},
author = {Zapridou, Eleni and Mytilinis, Ioannis and Ailamaki, Anastasia},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {3},
pages = {491--504},
doi = {10.14778/3570690.3570699},
url = {https://doi.org/10.14778/3570690.3570699},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,306 | Workload-Aware Incremental Reclustering in Cloud Data Warehouses | 2026 | SIGMOD | 5.093636e-05 |
| 11,065 | Learned Cost Models for Query Optimization: From Batch to Streaming Systems | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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