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When GraphQL wins in a GraphQL vs. REST performance comparison

Delve into the fundamental benefits and intricate features of GraphQL, and see just why it can serve as a much better alternative to REST in many scenarios.

Faced with unchecked data growth, developers welcome anything that can speed up data processing. GraphQL aims to fill that need because it simplifies API build processes and delineates a query process clearly.

GraphQL is an open standard query language that specifies how clients can consume an API. It increases facility for designing back-end resources versus representational state transfer (REST) architectures, and also simplifies front-end application builds by precisely defining the data gathering process.

There are numerous flexibility benefits to GraphQL, as well as some advantages when compared with REST services.

GraphQL benefits

GraphQL's refined data retrieval process and clearly defined structure enables a high degree of flexibility with the data. GraphQL passes each query through a single endpoint for data retrieval, which solves the problem of overfetching or underfetching data -- an issue common to RESTful APIs that affects their performance. Each GraphQL client is designed to specify exact data parameters to the server.

Since GraphQL substantially reduces the number of endpoints, services can acquire all the needed data with one call, and can format this information in JSON for fast retrieval. GraphQL is also transport-layer agnostic, so the API server can exchange data over any protocol, including HTTP, HTTPS and WebSockets, and TCP.

Three key GraphQL features explained

Refined data retrieval: Developers define how the client accesses data via the GraphQL schema definition language. With an explicitly defined schema, front-end and back-end development teams can work independently, and can ensure continuous resource updates by simply submitting changes via the appropriate source code repositories.

Defined structure: GraphQL's explicit structure enables users to define requests, initiate auto-completion within an integrated development environment and automatically generate API documents. With GraphQL, developers can work in a declarative style and select specific data or designate the required operations to be performed.

Introspection: GraphQL adherents use dynamic introspection to query the type system and gain detailed information about GraphQL schema. Developers can run introspection queries to learn about the available types of GraphQL schema. They can also view those different introspection schemas based on preset business privacy rules and access control features.

GraphQL vs. REST performance

In general, GraphQL's appeal comes from increased efficiency when compared with REST for APIs. RESTful services frequently return large amounts of unusable data mixed in with relevant information, usually the result of multiple server queries. These inefficiencies also increase the time it takes for a client to return all the required data.

However, while the GraphQL vs. REST performance contest usually leans toward the former, there are certain development scenarios where it's better to use REST APIs. For example, RESTful services can make effective use of HTTP caching mechanisms, whereas GraphQL doesn't support browser and mobile caching to expedite calls.

GraphQL query steps
GraphQL enables developers to input specific, customized queries that output only relevant, desired data.

REST APIs can use HTTP status codes to detect errors and ease the API monitoring process. By contrast, GraphQL can make it difficult to handle these errors and to monitor the APIs. Finally, RESTful services simplify the SQL log process. Developers find it relatively easy to optimize SQL log queries with REST versus with GraphQL's dynamic approach.

Because GraphQL acts as an intermediary to help query the data it receives from various data sources, it has become a standard for API building. By dictating exactly what data is required and giving more control to the client than REST APIs do, GraphQL solves the problem of random data returns that's seen in RESTful service use cases. Like anything else, you should use it in the right scenarios to get high performance with minimal tradeoffs.

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