Klink: Progress-Aware Scheduling for Streaming Data Systems
Bibliographic record
Abstract
Modern stream processing engines (SPEs) process large volumes of events propagated at high velocity through multiple queries. To improve performance, existing SPEs generally aim to minimize query output latency by minimizing, in turn, the propagation delay of events in query pipelines. However, for queries containing commonly used blocking operators such as windows, this scheduling approach can be inefficient. Watermarks are events popularly utilized by SPEs to correctly process window operators. Watermarks are injected into the stream to signify that no events preceding their timestamp should be further expected. Through the design and development of Klink, we leverage these watermarks to robustly infer stream progress based on window deadlines and network delay, and to schedule query pipeline execution that reflects stream progress. Klink aims to unblock window operators and to rapidly propagate events to output operators while performing judicious memory management. We integrate Klink into the popular open source SPE Apache Flink and demonstrate that Klink delivers significant performance gains over existing scheduling policies on benchmark workloads for both scale-up and scale-out deployments.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".