Skipping-oriented partitioning for columnar layouts
Why this work is in the frame
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Bibliographic record
Abstract
As data volumes continue to grow, modern database systems increasingly rely on data skipping mechanisms to improve performance by avoiding access to irrelevant data. Recent work [39] proposed a fine-grained partitioning scheme that was shown to improve the opportunities for data skipping in row-oriented systems. Modern analytics and big data systems increasingly adopt columnar storage schemes, and in such systems, a row-based approach misses important opportunities for further improving data skipping. The flexibility of column-oriented organizations, however, comes with the additional cost of tuple reconstruction. In this paper, we develop Generalized Skipping-Oriented Partitioning (GSOP), a novel hybrid data skipping framework that takes into account these row-based and column-based tradeoffs. In contrast to previous column-oriented physical design work, GSOP considers the tradeoffs between horizontal data skipping and vertical partitioning jointly. Our experiments using two public benchmarks and a real-world workload show that GSOP can significantly reduce the amount of data scanned and improve end-to-end query response times over the state-of-the- art techniques.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it