Comparative Performance Analysis of PostgreSQL High Availability Database Clusters through Containment
Bibliographic record
Abstract
The enormous amount of data flow has made Relation Database Management System the most important and popular tools for persistence of data. While open-source RDBMS systems are not as widely used as proprietary systems like Oracle databases, but over the years, systems like PostgreSQL have gained massive popularity. Highavailability database clusters (also known as HA clusters or failover clusters) are groups of computers that support server applications that can be reliably utilized with a minimum of down-time. This article is an attempt to set a benchmark of PostgreSQL high-availability databases in comparing the performance of same-containment keepalivedrepmgr clusters against cross-containment HAProxy-PgBouncer clusters. The result shows that our cross-containment HAProxy-PgBouncer is still a significantly better performer with load balancing, healthcheck and its throughputs are improved from 0.346% to 9.454% in compared to keepalived-repmgr. Also we would like to present I/O activities and CPU usage percentages between the two kinds of PostgreSQL HA clusters.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".