Performance of frameworks for declarative data fetching : An evaluation of Falcor and Relay+GraphQL

Abstract: With the rise of mobile devices claiming a greater and greater portion of internet traffic, optimizing performance of data fetching becomes more important. A common technique of communicating between subsystems of online applications is through web services using the REpresentational State Transfer (REST) architectural style. However, REST is imposing restrictions in flexibility when creating APIs that are potentially introducing suboptimal performance and implementation difficulties. One proposed solution for increasing efficiency in data fetching is through the use of frameworks for declarative data fetching. During 2015 two open source frameworks for declarative data fetching, Falcor and Relay+ GraphQL, were released. Because of their recency, no information of how they impact performance could be found. Using the experimental approach, the frameworks were evaluated in terms of latency, data volume and number of requests using test cases based on a real world news application. The test cases were designed to test single requests, parallel and sequential data flows. Also the filtering abilities of the frameworks were tested. The results showed that Falcor introduced an increase in response time for all test cases and an increased transfer size for all test cases but one, a case where the data was filtered extensively. The results for Relay+GraphQL showed a decrease in response time for parallel and sequential data flows, but an increase for data fetching corresponding to a single REST API access. The results for transfer size were also inconclusive, but the majority showed an increase. Only when extensive data filtering was applied the transfer size could be decreased. Both frameworks could reduce the number of requests to a single request independent of how many requests the corresponding REST API needed. These results led to a conclusion that whenever it is possible, best performance can be achieved by creating custom REST endpoints. However, if this is not feasible or there are other implementation benefits and the alternative is to resort to a "one-size-fits-all" API, Relay+GraphQL can be used to reduce response times for parallel and sequential data flows but not for single request-response interactions. Data transfer size can only be reduced if filtering offered by the frameworks can reduce the response size more than the increased request size introduced by the frameworks.