GraphQL vs. REST API
According to a couple of resources, GraphQL appears to be the impertinent little upstart coming to smash up the RESTful party.
They often declare that REST APIs are too inflexible and have become far too outdated for modern applications, whereas GraphQL is a modern and less constrained method, and therefore the future of designing and manipulating APIs.
There is a case for both sides of the argument. Ultimately, however, both REST and GraphQL have their places in API development, data querying and manipulation. In fact, the decision really needs to be based on the specific needs and constraints of a project.
The reality is that they actually approach data querying from different perspectives. REST is an architectural concept for application communication, whereas GraphQL is a specification, an API query language, and a set of tools.
It is for this reason that they can, and probably should, be used in conjunction with each other in any organisation’s architecture. Each method can fetch data from backend systems, but they approach this in different ways, helping to solve different problems.
So, how should organisation’s be using REST and/or GraphQL?
In this article, we will explain what they are, explore the fundamental differences between them, present the arguments for using each method, as well as highlight some of their use cases to try and help you make an informed decision.
What is REST API?
REST (Representational State Transfer) is an architectural style for designing networked applications. It is based on a set of constraints that help create a scalable and robust system. REST APIs have been widely used for more than a decade, and they follow several key principles:
- Stateless: Each request from a client to the server must contain all the information necessary to understand and fulfil the request. The server should not store any client state between requests.
- Client-Server: REST enforces a separation between the client and server, allowing them to evolve independently. This separation is crucial for scalability.
- Uniform Interface: REST APIs are designed around a set of uniform constraints, such as using standard HTTP methods like GET, POST, PUT and DELETE. Resources are identified by URIs (Uniform Resource Identifiers).
- Resource-Based: Resources are the key abstractions in REST. These can be physical objects, digital objects, or abstract concepts, and they are represented by URIs.
- Stateless Communication: REST APIs use stateless communication, meaning each request from a client to the server should contain all the information needed to understand and fulfil the request.
What is GraphQL?
GraphQL is a query language for APIs, as well as a server-side runtime for executing those queries by specifying the types and fields of data clients can request. GraphQL was developed by Facebook and released as an open-source project in 2015. It offers the following key features:
- Hierarchical Structure: In GraphQL, the shape of the response is determined by the structure of the query. Clients can request nested data with a single query, avoiding over-fetching or under-fetching.
- Single Endpoint: Unlike REST, which often requires multiple endpoints for different resources, GraphQL has a single endpoint for all interactions. This reduces the complexity of API requests.
- Strongly Typed: GraphQL enforces a strongly typed system, allowing you to define the structure of data through a schema. This enhances the consistency and validation of data.
- Real-time Capabilities: GraphQL supports real-time data updates through subscriptions, making it suitable for applications requiring live or near-real-time data.
- Client-Defined Queries: Clients have control over the data they receive, preventing the need for multiple versions of the API and enabling front-end developers to specify the data requirements.
Comparison of REST and GraphQL
Let’s delve into the fundamental differences between REST and GraphQL to help you understand their respective strengths and use cases.
Data Fetching
Data fetching via REST and GraphQL represents two distinct approaches, each with its own set of characteristics.
With a resource-oriented design, RESTful APIs establish a well-defined structure for retrieving data. Specific endpoints are predefined for distinct resources, enabling clients to obtain data through requests to these endpoints.
While this approach may sometimes result in either over-fetching or under-fetching of data, it offers simplicity and predictability in the data retrieval process. RESTful APIs are especially suitable for scenarios where the data model closely aligns with the API structure.
In contrast, GraphQL excels in providing meticulous control over data fetching. Clients have the ability to specify the precise fields and nested relationships they require in a single query, eliminating the need for multiple requests to various endpoints.
This capability effectively mitigates issues related to over-fetching and under-fetching, allowing clients to retrieve only the necessary data efficiently. GraphQL proves advantageous in situations where optimising network efficiency and minimising payload size are crucial, offering flexibility and precision in data retrieval.
The choice between REST and GraphQL hinges on specific project requirements and the trade-offs between simplicity and control over data retrieval.
This final statement is going to be applicable to all the following features and characteristics.
Over-fetching and Under-fetching
As discussed above, one of the main discussion points when comparing REST with GraphQL is over-fetching and under-fetching, with both methods addressing them in different ways.
RESTful APIs, with their fixed endpoints, may result in over-fetching, where clients retrieve more data than necessary, or under-fetching, where additional requests are needed to gather required information.
However, REST mitigates this by allowing clients to use query parameters to tailor requests and specify the desired fields. This flexibility empowers clients to optimise data retrieval, but it may still lead to occasional over-fetching due to the predefined nature of endpoints.
GraphQL, on the other hand, provides a more refined solution to the over-fetching and under-fetching challenge. Clients can precisely request the data they need, eliminating the risk of over-fetching entirely.
Additionally, GraphQL’s ability to retrieve nested data in a single query minimises the need for multiple requests, addressing the issue of under-fetching. This streamlined control over data retrieval makes GraphQL particularly advantageous in scenarios where minimising unnecessary data transfer over the network is a priority.
Versioning
Versioning is a crucial aspect of API design, ensuring compatibility and smooth transitions as applications evolve.
REST commonly employs versioning through URI (Uniform Resource Identifier) paths or custom headers. For example, adding a version number to the endpoint or using headers are common REST versioning strategies.
This approach allows for clear and explicit version identification. One notable benefit is that it ensures backward compatibility, meaning that existing clients can continue to interact with older versions of the API while newer clients can access the latest version.
This versioning strategy in REST helps maintain a stable interface for existing users while allowing for incremental updates.
GraphQL handles versioning in a different manner. It adopts a more flexible approach where clients explicitly request the fields they need, reducing the need for version-specific endpoints.
This results in a smoother transition for clients during updates, as they can choose to adopt new fields or features at their own pace. GraphQL’s introspection capabilities also enable clients to dynamically discover available API features, enhancing the adaptability of the system over time.
Response Size
In API development, effectively managing response size is addressed differently by REST and GraphQL, each offering unique advantages to meet varying needs.
RESTful APIs provide a direct means of controlling response size through resource endpoints. Clients can customise their requests by specifying desired fields using query parameters. This ensures that the response only includes essential data, optimising bandwidth usage and reducing payload size.
REST’s predictability and endpoint definitions contribute to efficient data retrieval, making it well-suited for scenarios where controlling response size is paramount.
GraphQL, meanwhile, empowers clients to precisely request the specific data they need, allowing for finely-tuned control over response size. Clients can retrieve only the required fields, eliminating the risk of over-fetching data.
This feature is particularly beneficial in situations where minimising data transfer over the network is a priority, as clients can avoid unnecessary payload and gain efficiency in communication.
Number of Requests
Managing the number of requests is a critical consideration in API design.
RESTful APIs typically require multiple requests to different endpoints to gather all the necessary data, due to their stateless nature.
While this may lead to a higher number of requests, it can be beneficial in scenarios where clients want to retrieve data from multiple resources or when adhering to a more traditional client-server communication model.
REST’s straightforward approach to endpoint design provides clarity in understanding the interactions between clients and servers.
The opposite is true for GraphQL, which excels in minimising the number of requests needed to retrieve data.
With GraphQL, clients can specify the exact data they need in a single request, avoiding the need for multiple endpoints. This feature, known as ‘batching’, allows clients to fetch related data in a more streamlined manner, reducing the overall number of requests and therefore enhancing network efficiency.
GraphQL’s ability to consolidate requests makes it beneficial in situations where minimising the number of interactions is crucial for performance optimisation.
Complexity and Nesting
Unsurprisingly, REST and GraphQL approach complexity and nesting in API design via different methods, each offering benefits in different contexts.
RESTful APIs tend to have a more straightforward structure. Endpoints are designed to represent resources, and the relationships between these resources are established through well-defined URIs.
While this simplicity can be an advantage for clarity and ease of understanding, it may lead to over-fetching of data or multiple requests to different endpoints to retrieve related information.
This simplicity can be beneficial in scenarios where a clear separation of concerns is desired, and the API structure aligns closely with the underlying data model.
GraphQL, however, excels in managing complexity and nesting by providing clients with the flexibility to specify the structure of the response.
Clients can request the exact data that they need, helping to avoid over-fetching and under-fetching issues commonly associated with REST.
Additionally, GraphQL’s ability to nest queries allows clients to retrieve related data in a single request, reducing the need for multiple round-trips.
This feature is advantageous in scenarios where minimising the number of requests and efficiently managing nested data structures are critical, providing a more tailored and efficient approach to data retrieval.
Error Handling
Error handling is another crucial aspect of API design.
RESTful APIs commonly use HTTP status codes to communicate the outcome of a request, making it a standardised and widely understood method for error signalling.
Each status code conveys specific information about the success or failure of the request, providing clarity to developers.
Additionally, RESTful APIs often include error details in the response body, offering more granular information about the nature of the error. This standardised approach simplifies error handling for developers, fostering better understanding and quicker resolution of issues.
GraphQL responses, however, always have a top-level ‘errors’ field that contains an array of error objects, each providing information about a specific issue. This consolidated approach ensures that even in the presence of errors, the main response structure remains intact.
This means that GraphQL’s approach to error handling is more consistent and predictable, as clients can anticipate where to find error information in every response.
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The Argument for REST
One of the primary reasons to choose REST is its simplicity and ease of adoption.
RESTful APIs follow a straightforward architectural style, relying on standard HTTP methods such as GET, POST, PUT and DELETE. This simplicity makes it easier for developers to understand, implement and debug.
Additionally, the RESTful principles align closely with the HTTP protocol, using its features like caching and statelessness. This simplicity and alignment with existing web standards make REST a pragmatic choice, especially for projects where a quick development cycle and minimal learning curve are crucial.
Scalability is another key consideration favouring REST in certain scenarios.
RESTful APIs inherently promote statelessness, meaning that each request from a client contains all the information needed for the server to fulfil it. This characteristic facilitates better scalability since servers do not need to store session data between requests.
As a result, RESTful architectures can easily scale horizontally, distributing the load across multiple servers without the need for complex session management.
REST’s flexibility in data fetching also plays a significant role in its favour.
In REST, endpoints are predefined, and clients have control over the data they retrieve by specifying the desired fields through query parameters. This flexibility allows clients to optimise data fetching based on their specific needs, reducing unnecessary data transfer over the network.
Although GraphQL offers a more detailed approach to data retrieval, REST’s flexibility remains valuable, particularly in scenarios where bandwidth or latency considerations are critical.
RESTful APIs are also well-suited for scenarios where a clear separation of concerns between the client and server is required. The resource-oriented nature of REST enables the creation of well-defined endpoints, each representing a specific resource, which can be easily understood and navigated.
This separation simplifies maintenance, improves documentation and promotes collaboration between frontend and backend teams.
While GraphQL has gained popularity for its ability to provide more in-depth control over data retrieval, REST remains a solid choice for many applications due to its simplicity, scalability and flexibility.
Depending on the project’s requirements, development team expertise, and considerations such as ease of adoption and scalability, REST can be the more pragmatic choice, offering a robust and well-established foundation for building web APIs.
The Argument for GraphQL
GraphQL has definitely emerged as a convincing alternative to RESTful APIs, offering distinct advantages that make it a preferred choice in certain scenarios.
As we have discussed, one of the key strengths of GraphQL is its flexibility in data retrieval.
Unlike REST, where endpoints dictate the structure of the response, GraphQL allows clients to specify the exact fields and nested relationships they need in a single query. This control over data retrieval minimises over-fetching, addressing the common REST problem where clients receive more data than required.
Additionally, GraphQL empowers clients to request only the information they need, optimising the payload size and reducing the number of requests necessary to retrieve the desired data.
Efficiency is another area where GraphQL can be beneficial.
With REST, clients often need to make multiple requests to different endpoints to gather related data. GraphQL, however, supports batching, enabling clients to request multiple resources in a single query.
This batching capability minimises the number of round-trips between the client and server, reducing latency and enhancing the overall efficiency of data retrieval. As a result, GraphQL is particularly advantageous in scenarios where network performance is a critical factor.
Like REST, it can be argued that GraphQL also cultivates a more collaborative development process between frontend and backend teams.
In REST, changes to the API often require modifications to existing endpoints or the creation of new ones. This can lead to versioning challenges and hinder the evolution of the API.
With its introspection capabilities and type system, GraphQL allows clients to discover and adapt to changes dynamically. Clients can request new fields or features without necessitating changes to the server or affecting existing functionality.
This dynamic adaptability streamlines development workflows and promotes smoother collaboration between frontend and backend teams.
GraphQL’s real-time capabilities further contribute to its appeal.
Through subscriptions, clients can receive real-time updates when data changes on the server, eliminating the need for clients to poll for updates repeatedly. This feature is particularly beneficial in applications where real-time interaction and data synchronisation are essential.
GraphQL offers a powerful alternative to REST by providing flexibility, efficiency and improved collaboration between development teams.
Its ability to empower clients with precise control over data retrieval, support batching for efficient queries, dynamic adaptability to changes, and real-time capabilities positions GraphQL as an excellent choice for modern API development, especially in scenarios where optimised data fetching and flexibility in response structure are paramount.
Use Cases
Having taken all the pros and cons of each method into consideration, what are the actual use cases for them both within API development?
Use Cases for REST:
- Public APIs: REST APIs are a suitable choice for public APIs, where versioning and a well-defined structure may be more crucial.
- Caching: REST APIs work well with caching strategies, as the structure of the response remains consistent.
- Simple Queries: In scenarios where simple queries are sufficient, REST can be a straightforward choice.
- Limited Client Control: When you want to maintain control over the data structure and limit the flexibility of client queries.
Use Cases for GraphQL:
- Complex Queries: In applications with complex data requirements and relationships, where the ability to fetch nested data efficiently is essential.
- Dynamic Data: When the data structure is dynamic or subject to change, GraphQL’s flexibility is extremely beneficial.
- Real-time Updates: For applications that require real-time updates, such as eCommerce processes, payments, social media feeds and collaborative tools.
- Microservices Integration: In microservices architectures, where multiple services with varying data structures need to be queried in a single request.
To REST or GraphQL?
As we have mentioned throughout this article, the choice between REST and GraphQL ultimately depends on the specific requirements of an organisation’s project and requirements.
However, whilst they have distinct advantages and use cases, they can also be used together to create a powerful combination that offers flexibility, improved data retrieval and simplified error handling.
Adopting GraphQL where it adds the most value, organisations can modernise their APIs without discarding existing infrastructure.
This hybrid approach can be achieved by adding an integration layer between the two, enabling organisation’s to exploit the strengths of both technologies, thus creating a more efficient and developer-friendly ecosystem.
Whether you are building a new application or enhancing an existing one, exploring the synergy between GraphQL and REST can open up exciting possibilities for development teams.
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