How Reliable are Streams? End-to-End Processing-Guarantee Validation and Performance Benchmarking of Stream Processing Systems
Bibliographic record
Abstract
Stream processing systems (SPSs) provide processing guarantees to ensure reliability under failure. However, no related work exists that empirically validates these guarantees. In this paper, we present PGVal, a tool that can end-to-end validate guarantees of SPSs. Additionally, we introduce new metrics for SPSs, such as reliability, reliable throughput, and failure cost, in addition to a refined definition of latency that results in improved measurements. We benchmark three popular SPSs, namely Kafka Streams, Apache Storm , and Apache Flink. Our results show that the reliability of SPSs depends on many characteristics, such as data rate, data partitions, processing topology, and parallelism factor. An SPS configuration may not continue to provide reliable outputs when any of these characteristics vary. PGVal can also inject faults into SPSs to observe their impact on reliability and performance. We provide a comprehensive failure model for fault-tolerance benchmarking of SPSs and report on the impact of faults on the reliability and performance of SPSs. Our experiments show that SPSs' reliability and performance drop varies by fault. Lastly, we provide suggestions to increase the reliability and performance of these systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".