Modeling and exploiting query interactions in database systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The typical workload in a database system consists of a mixture of multiple queries of different types, running concurrently and interacting with each other. Hence, optimizing performance requires reasoning about query mixes and their interactions, rather than considering individual queries or query types. In this paper, we show the significant impact that query interactions can have on workload performance. We present a new approach based on planning experiments and statistical modeling to capture the impact of query interactions. This approach requires no prior assumptions about the internal workings of the database system or the nature or cause of query interactions, making it portable across systems. As a concrete demonstration of the potential of capturing, modeling, and exploiting query interactions, we develop a novel interaction-aware query scheduler that targets report-generation workloads in Business Intelligence (BI) settings. Under certain assumptions, the schedule found by this scheduler is within a constant factor of optimal. An experimental evaluation with TPC-H queries on IBM DB2 demonstrates that our scheduler consistently outperforms (up to 4x) conventional schedulers that do not account for query interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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