Bibliographic record
Abstract
In this work we introduce a novel approach to the problem of cardinality estimation over multijoin queries. Our approach leveraging randomized hashing and data sketching to tighten these bounds beyond the current state of the art. We demonstrate that the bounds can be injected directly into the cost based query optimizer framework enabling it to avoid expensive physical join plans. We outline our base data structures and methodology, and how these bounds may be introduced to the optimizer's parameterized cost function as a new statistic for physical join plan selection. We demonstrate a complex tradeoff space between the tightness of our bounds and the size and complexity of our data structures. This space is not always monotonic as one might expect. In order combat this non-monotonicity, we introduce a partition budgeting scheme that guarantees monotonic behavior. We evaluate our methods on GooglePlus community graphs~\citegoogleplus, and the Join Order Benchmark (JOB)~\citeLeis:2015:GQO:2850583.2850594. In the presence of foreign key indexes, we demonstrate a $1.7\times$ improvement in aggregate (time summed over all queries in benchmark) physical query plan runtime compared to plans chosen by Postgres using the default cardinality estimation methods. When foreign key indexes are absent, this advantage improves to over $10\times$.
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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".