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A head-to-head GraphQL vs. REST performance faceoff
Both GraphQL and REST aim to simplify API development processes, but which one is right for your project? Find out by examining this API design style faceoff.
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 to increase performance.
Since 2000, representational state transfer (REST) has become the dominant web API standard that's both language- and platform-agnostic. A RESTful architecture uses the stateless HTTP protocol to enable communications between applications, servers and clients. As the de facto standard for deploying APIs, it enables developers to easily configure web caches and design complex queries, such as retrieving data to meet specific operational needs.
By contrast, GraphQL conducts queries in a single request and precisely defines the data. Originally created by Facebook in 2012, and open sourced in 2015, GraphQL greatly reduces the number of endpoints in an API.
Let's take a closer look GraphQL vs. REST performance, their advantages, limitations and why one is better than the other.
The ability to loosely couple application systems helps ease web development over time. However, the growing reliance on mobility exposes key limitations of RESTful approaches. These include the use of multiple endpoints, overfetching/underfetching of data and API versioning, all of which can affect application performance and hamper long-term maintenance.
GraphQL stands out as a better alternative and includes three major components:
- Schemas. Define all API operations.
- Queries. Request data from sources.
- Resolvers. Ease communications between schema and sources.
Schemas validate API requests, auto-complete within integrated development environments and auto-generate API docs. As a result, developers know the structure of the returned results and the exact type of data to be retrieved in advance. These features not only improve queries, they also provide greater API understanding and ease development.
GraphQL also has three key advantages that set it apart from REST:
1. 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. This also helps ensure continuous resource updates by simply submitting changes via the appropriate source code repositories.
2. 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.
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.
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, in addition to effective HTTP caching mechanisms, a REST API provides many endpoints enabling developers to more easily configure their web cache to match certain URL patterns. In general, by providing multiple endpoints, REST APIs make it easier for developers to create complex queries that can meet specific deployment needs.
When making REST API calls, the process is the same, whether the call is simple or complex. For example, an application utilizes route handlers, which are specific URL functions, to make a REST API call. Once a server receives that request, it retrieves both the HTTP verb (i.e., GET) and the URL path. The server finds a specific function for the API by matching the HTTP verb with the path, executes that function, and produces a result. At execution, the API library serializes the result using a response code and header, and sends that result to the client. Also, 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 API errors and to adequately monitor them. 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.
GraphQL vs. REST performance
In general, GraphQL's appeal comes from increased efficiency. 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.
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.
Unlike multiple endpoints in REST, GraphQL sends all queries to one endpoint (i.e., HTTP POST). Moreover, GraphQL schemas are static: While the query language enables developers to define schemas for validation and inspection, GraphQL doesn't cover dynamic features, such as runtime modifications or dynamic type definitions.
As a result, developers must rely on workarounds or custom server implementations, which only increase development time and resource consumption.
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 trade-offs.