Can GraphQL Replace REST? A Study of Their Efficiency and Viability
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.
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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 | 0.027 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".