Native Cloud Object Storage in Db2 Warehouse: Implementing a Fast and Cost-Efficient Cloud Storage Architecture
Bibliographic record
Abstract
Database systems built on traditional storage subsystems typically store their data in small blocks referred to as data pages (commonly sized in a multiple of 4KB for historical reasons). These traditional storage subsystems, for example network attached block storage, were designed for efficient random-access I/O patterns at the block level, and the block size is usually configurable by the application based on its needs. For large scale analytic databases in cloud environments, these traditional storage subsystems are not cost effective when compared to cloud object storage, and database systems that exploit them risk becoming uncompetitive. This paper describes the modernization of the storage architecture of Db2 Warehouse, a traditional full feature and high-performance database system with 3 decades of development, to exploit the new paradigm of cost-effective storage for the cloud. We discuss a solution based on the integration of LSM trees as part of the storage subsystem, that enables Db2 Warehouse to efficiently store data pages within object storage, and through the application of special techniques to minimize read and write latencies as well as all of the amplification factors (write, read, and storage), achieve not only storage cost savings, but also higher performance. Further, by retaining the traditional data page format, we are able to avoid significantly re-architecting the database kernel and thereby retain the substantial capabilities and optimizations of the existing system.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".