DW vs OLTP Performance Optimization in the Cloud on PostgreSQL (A Case Study)
Bibliographic record
Abstract
This case study shows the performance issues and solutions for a data warehouse (DW) performing well to serve industrial partners in improving customer data retrieval performance. An online transaction processing (OLTP) relational database and a DW were deployed in PostgreSQL and tested against each other. Several test cases were carried out with the DW, including indexing and creating pre-aggregated tables, all guided by in-depth analysis of EXPLAIN plans. Queries and DW design were continually improved throughout testing to ensure that the OLTP and DW were compared equally. Seven queries (requested by the industrial client) were used to thoroughly test different performance aspects concerning client feedback and the complexity of requests for all areas the DW might cover. On average, the data warehouse showed a one to three magnitudes increase in query execution performance, with the highest calibre results coming in at 2,493 times faster than the OLTP. All test cases showed an increase in performance over the OLTP. Additionally, the data contained in the DWtook up 24% less storage space than the OLTP. The results here indicate a promising direction to take business analytics with data warehousing, as customers will experience significant cost savings and a reduction in time to receive desired results from their data storage platforms in the cloud. The work in this case study is a continuation of previous work in a much larger project concerning integrating database technologies with machine learning to improve natural language processing solutions as a cost-saving measure for utilities consumers.
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".