PERFORMA MICROFRAMEWORK PHP PADA REST API MENGGUNAKAN METODE LOAD TESTING
Bibliographic record
Abstract
The need for flexibility in application development by minimizing the constraints of a server production environment makes n-tier architectures increasingly used. This architecture is implemented in the form of middleware or often called API. REST API is an API architecture that is currently the most widely used for middleware-based application development. PHP is an easy-to-use programming language for developing REST APIs with its microframework. Each microframework has features such as database connection handling, URL routing, and performance is the top one. The large number of PHP microframeworks with different performance issues makes the selection of API-based application development cores very important. Especially if it is projected to handle data exchange or client requests in large numbers. As a basis for selection, performance testing needs to be carried out on each microframework to determine its suitability for API-based application development. Performance testing uses the load testing method and develops the popular PHP microframework as a basis for creating test applications. The microframework includes FatFree, Lumen, Phalcon-micro, and Slim. Testing is carried out systematically using a test plan that has been designed for performance testing needs. The focus is to analyze the RPS (Request Per Seconds) and latency to the percentile that the wrk2 test tool generates on a predetermined test type
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.193 | 0.122 |
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".