Proceedings of the 2nd Workshop on Simplicity in Management of Data
Bibliographic record
Abstract
Andrew will discuss engineering tradeoffs made when building Apache Data-Fusion, an open source and extensible query engine used as the basis of many commercial and open source projects.These decisions (mostly) favored simplicity and worked better than initially expected.He will cover the rationale for which parts of DataFusion use pre-existing standards such as Arrow and Parquet, and which parts are built "from scratch" such as vectorized hashing and normalized sort keys.He will also discuss Data-Fusion's design philosophy of extensible APIs paired with simple default implementations.Finally, he will offer lessons learned and enumerate some things that worked well and what could have been improved.Bio: Andrew Lamb has experience in environments from 2 developers in a VC's office, to large multinational corporations and distributed open source projects.He focuses on systems programming (e.g.databases), and platform engineering, and has paid leadership dues as both an architect and manager/VP.As a Staff Engineer at InfluxData, he works on InfluxDB 3.0's IOx Engine, a new timeseries database written in Rust.He is a Member of the Apache Software Foundation, and a member of, and past chair of the Apache Arrow PMC, and actively contributes to Apache Arrow DataFusion query engine and the Apache Arrow Rust implementation.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.054 | 0.017 |
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".