TiLT: A Time-Centric Approach for Stream Query Optimization and Parallelization
Bibliographic record
Abstract
Stream processing engines (SPEs) are widely used for large scale streaming analytics over unbounded time-ordered data streams. Modern day streaming analytics applications exhibit diverse compute characteristics and demand strict latency and throughput requirements. Over the years, there has been significant attention in building hardware-efficient stream processing engines (SPEs) that support several query optimization, parallelization, and execution strategies to meet the performance requirements of large scale streaming analytics applications. However, in this work, we observe that these strategies often fail to generalize well on many real-world streaming analytics applications due to several inherent design limitations of current SPEs. We further argue that these limitations stem from the shortcomings of the fundamental design choices and the query representation model followed in modern SPEs. To address these challenges, we first propose TiLT, a novel intermediate representation (IR) that offers a highly expressive temporal query language amenable to effective query optimization and parallelization strategies. We subsequently build a compiler backend for TiLT that applies such optimizations on streaming queries and generates hardware-efficient code to achieve high performance on multi-core stream query executions. We demonstrate that TiLT achieves up to 326× (20.49× on average) higher throughput compared to state-of-the-art SPEs (e.g., Trill) across eight real-world streaming analytics applications. TiLT source code is available at https://github.com/ampersand-projects/tilt.git.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".