Bibliographic record
Abstract
In the big data era, big data frameworks play a vital role in storing and processing large amounts of data, providing significant improvements in performance and availability. Spark is one of the most popular big data frameworks, providing high scalability and fault-tolerance with its unique in-memory engine. To hide the complex settings from users, Spark has approximately 200 configurable parameters in the execution engine. Default values assigned to the parameters provide initial ease of use. However, the default values are not the best setting for all workloads. In this work, we propose a general tuning algorithm named QST, Queen’s Spark Tuning, to help users with tuning Spark and to improve overall performance. First of all, we study Spark performance for a variety of workloads and identify 9 tunable parameters among more than 200 parameters that have significant impact on performance. Then, we propose QST, a general greedy iterative tuning algorithm for our set of 9 key parameters. By classifying Spark workloads as memory-intensive, shuffle-intensive or all-intensive, QST configures the parameters for each type of workload. We perform an experimental evaluation of QST using benchmark workloads and industry workloads. In our experiments, using QST significantly improves Spark performance. Overall, using QST yields an average speedup of 65% for our benchmark evaluation workloads and 57% for our industry evaluation workloads.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".