Tabular: Efficiently Building Efficient Indexes
Bibliographic record
Abstract
Concurrent indexes are hard to build by requiring complex, careful yet error-prone processes of design and implementation. As prior work has observed, modeling indexes as transactional tables can largely ease programming. The developer only needs to write single-threaded logic without worrying about concurrency or persistence, which are transparently supported by ACID table operations. However, this was deemed infeasible due to high overheads caused by the underlying OLTP engine. In this paper, we argue that by adapting recent OLTP techniques which have been shown to deliver unprecedented performance, this idea is now feasible. We propose Tabular, a new library for building efficient indexes by modeling indexes as ACID tables which provide concurrency and persistence transparently. We elaborate the design of Tabular and its use cases. Our evaluation shows that compared to hand-crafted ones, indexes built using Tabular provide competitive performance with improved programming efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".