Performance Evaluation and Optimization of Multi-dimensional Indexes in Hive
Bibliographic record
Abstract
Apache Hive has been widely used for big data processing over large scale clusters by many companies. It provides a declarative query language called HiveQL. The efficiency of filtering out query-irrelevant data from HDFS closely affects the performance of query processing. This is especially true for multi-dimensional, high-selective, and few columns involving queries, which provides sufficient information to reduce the amount of bytes read. Indexing (Compact Index, Aggregate Index, Bitmap Index, DGFIndex, and the index in ORC file) and columnar storage (RCFile, ORC file, and Parquet) are powerful techniques to achieve this. However, it is not trivial to choosing a suitable index and columnar storage based on data and query features. In this paper, we compare the data filtering performance of the above indexes with different columnar storage formats by conducting comprehensive experiments using uniform and skew TPC-H data sets and various multi-dimensional queries, and suggest the best practices of improving multi-dimensional queries in Hive under different conditions.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".