Performance Evaluation of Yahoo! S4: A First Look
Bibliographic record
Abstract
Processing large data sets has been dominated recently by the map/reduce programming model [1], originally proposed by Google and widely adopted through the Apache Hadoop1 implementation. Over the years, developers have identified weaknesses of processing data sets in batches as in MapReduce and have proposed alternatives. One such alternative is continuous processing of data streams. This is particularly suitable for applications in online analytics, monitoring, financial data processing and fraud detection that require timely processing of data, making the delay introduced by batch processing highly undesirable. This processing paradigm has led to the development of systems such as Yahoo! S4 [2] and Twitter Storm.2 Yahoo! S4 is a general-purpose, distributed and scalable platform that allows programmers to easily develop applications for processing continuous unbounded streams of data. As these frameworks are quite young and new, there is a need to understand their performance for real time applications and find out the existing issues in terms of scalability, execution time and fault tolerance. We did an empirical evaluation of one application on Yahoo! S4 and focused on the performance in terms of scalability, lost events and fault tolerance. Findings of our analyses can be helpful towards understanding the challenges in developing stream-based data intensive computing tools and thus providing a guideline for the future development.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".