An Empirical Study on Quality Issues of eBay's Big Data SQL Analytics Platform
Bibliographic record
Abstract
Big data SQL analytics platform has evolved as the key infrastructure for business data analysis. Compared with traditional costly commercial RDBMS, scalable solutions with open-source projects, such as SQL-on-Hadoop, are more popular and attractive to enter-prises. In eBay, we build Carmel, a company-wide interactive SQL analytics platform based on Apache Spark. Carmel has been serving thousands of customers from hundreds of teams globally for more than 3 years. Meanwhile, despite the popularity of open-source based big data SQL analytics platforms, few empirical studies on service quality issues (e.g., job failure) were carried out for them. However, a deep understanding of service quality issues and taking right mitigation are significant to the ease of manual maintenance efforts. To fill this gap, we conduct a comprehensive empirical study on 1,884 real-word service quality issues from Carmel. We summa-rize the common symptoms and identify the root causes with typical cases. Stakeholders including system developers, researchers, and platform maintainers can benefit from our findings and implications. Furthermore, we also present lessons learned from critical cases in our daily practice, as well as insights to motivate automatic tool support and future research directions.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".