Efficient bulk deletes for multi dimensional clustered tables in DB2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it