Efficient bulk deletes for multi dimensional clustered tables in DB2
Bibliographic record
Abstract
In data warehousing applications, the ability to efficiently delete large chunks of data from a table is very important. This feature is also known as Rollout or Bulk Deletes. Rollout is generally carried out periodically and is often done on more than one dimension or attribute. The ability to efficiently handle the updates of RID indexes while doing Rollouts is a well known problem for database engines and its solution is very important for data warehousing applications. DB2 UDB V8.1 introduced a new physical clustering scheme called Multi Dimensional Clustering (MDC) which allows users to cluster data in a table on multiple attributes or dimensions. This is very useful for query processing and maintenance activities including deletes. Subsequently, an enhancement was incorporated in DB2 UDB Viper 2 which allows for very efficient online rollout of data on dimensional boundaries even when there are a lot of secondary RID indexes defined on the table. This is done by the asynchronous updates of these RID indexes in the background while allowing the delete to commit and the table to be accessed. This paper details the design of MDC Rollout and the challenges that were encountered. It discusses some performance results which show order of magnitude improvements using it and the lessons learnt. 1.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".