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arXiv · 2602.23289

Workload-Aware Incremental Reclustering in Cloud Data Warehouses

Yipeng Liu·Renfei Zhou·Jiaqi Yan·Huanchen Zhang
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Abstract

Modern cloud data warehouses store data in micro-partitions and rely on metadata (e.g., zonemaps) for efficient data pruning during query processing. Maintaining data clustering in a large-scale table is crucial for effective data pruning. Existing automatic clustering approaches lack the flexibility required in dynamic cloud environments with continuous data ingestion and evolving workloads. This paper advocates a clean separation between reclustering policy and clustering-key selection. We introduce the concept of boundary micro-partitions that sit on the boundary of query ranges. We then present WAIR, a workload-aware algorithm to identify and recluster only boundary micro-partitions most critical for pruning efficiency. WAIR achieves near-optimal (with respect to fully sorted table layouts) query performance but incurs significantly lower reclustering cost with a theoretical upper bound. We further implement the algorithm into a prototype reclustering service and evaluate on standard benchmarks (TPC-H, DSB) and a real-world workload. Results show that WAIR improves query performance and reduces the overall cost compared to existing solutions.

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Yipeng Liu, Renfei Zhou, Jiaqi Yan, Huanchen Zhang. 2026-02-26. Workload-Aware Incremental Reclustering in Cloud Data Warehouses. https://doi.org/10.1145/3802127

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