Optimizing Druid with Roaring bitmaps
Bibliographic record
Abstract
In the current Big Data era, systems for collecting, storing and efficiently exploiting huge amounts of data are continually introduced, such as Hadoop, Apache Spark, Dremel, etc. Druid is one of theses systems especially designed to manage such data quantities, and allows to perform detailed real-time analysis on terabytes of data within sub-second latencies. One of the important Druid's requirements is fast data filtering. To insure that, Druid makes an extensive use of bitmap indexes. Previously, we introduced a new compressed bitmap index scheme called Roaring bitmap that has shown interesting results when compared to the bitmap compression scheme adopted by Druid: Concise. Since, Roaring bitmap has been integrated to Druid as an indexing solution. In this work, we produce an extensive series of experiments in order to compare Roaring bitmap and Concise time-space performances when used to accelerate Druid's OLAP queries and other kinds of operations Druid realizes on bitmaps, like: retrieving set bits from bitmaps, computing bitmap complements, aggregating several bitmaps with logical ORs and ANDs operations. Roaring bitmap has shown to improve up to ≈ 5× analytical queries response times under Druid compared to Concise.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".