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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)

Paper ID
13494
Venue
VLDB
Year
2023
Pagerank
5.3189314e-05
Overall Rank
9,128 | 37.38%
DOI
10.14778/3570690.3570699

Incoming Non-self Citations Over Time

Authors

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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