Can GraphQL Replace REST? A Study of Their Efficiency and Viability
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
Representational State Transfer (REST) has traditionally been the standard web service architectural style for API creation. However, its popularity has been challenged with the introduction of GraphQL, an open source query language for APIs introduced by Facebook, in 2015. The latter has been quickly adopted by GitHub, Shopify, Airbnb, Twitter and more online portals are joining the list. In some instances, GraphQL has been adopted as an alternative architectural style or has been used in conjunction with REST.While GraphQL promises a considerable improvement over REST, much remains unexplored with respect to its efficiency and feasibility in its application. The goal of this paper is to determine viability of using GraphQL over REST for API architecture from quantitative and qualitative perspectives. A custom API client on GitHub is constructed to check on the response times and the corresponding magnitude of difference between REST and GraphQL. Thereafter, the paper surveyed employees of GitHub to understand software developers' educated opinion and perceptions about REST and GraphQL based on their practical experience with APIs. The results show that both API paradigms have their benefits and weaknesses, and one cannot replace the other, at least in the near future.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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