SparkPerf: A Benchmarking Framework for Evaluating the Performance of Spark Data Analytics Projects
Bibliographic record
Abstract
Machine learning is quickly becoming an integral part of modern business. Significant effort goes into developing and deploying machine learning models for estimation, economic forecasting, and client interaction. This effort includes planning for tools and platforms to be used in training and validating these models, as well as allocating resources, time, and budget for these tasks. However, this planning remains largely dependent on human acumen and is expensive to determine in a systematic fashion with automated tools. Benchmarking is the process of efficiently running experiments to determine a system's performance requirements, among others, in order to aid planning and resource allocation. Benchmarking intelligent and data-intensive systems remains in its infancy and does not cover fully realistic or particular case studies. Furthermore, existing approaches often focus on one-time batch evaluations, which may be inadequate for workloads requiring continuous processing over extended periods. In this work, we propose SparkPerf, a benchmarking tool specifically designed for machine learning applications deployed with Apache Spark, supporting both batch and transactional workloads. Unlike existing solutions, SparkPerf incorporates automated workload generation with synthetic data, providing a more comprehensive evaluation. Additionally, SparkPerf focuses on longitudinal transactional workloads, which represent a more realistic class of case studies for enterprises, and it offers high customizability, allowing users to test their own applications with their own, synthetically augmented, datasets. Our experiments demonstrate the benchmark's reliability, consistency, portability, and customizability.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".