According to Postman’s 2023 State of the API Report, GraphQL has taken the place of SOAP as the third most popular API architectural style, following only REST and Webhooks. Whether you’re applying to be a developer, technical lead, data engineer, or technical product manager, these answers to some of the most common GraphQL interview questions will help you navigate the interview process with confidence.
GraphQL interview questions and answers: beginner
In this section, we’ll go over some of the most common beginner-level questions and answers about GraphQL. You might be asked these questions if you’re applying for a role as a frontend developer, web or mobile app developer, product manager, or UI/UX designer at an API-first company.
What is GraphQL?
GraphQL is both a query language and a server-side runtime for APIs that allows clients to request the exact data they need. Unlike traditional REST APIs that might return more information than you’re looking for, GraphQL offers a way to interact with data services that prevents data over- or under-fetching. Web and mobile applications often use GraphQL to enhance data retrieval and manipulation, leveraging its server-side runtime to resolve queries.
What is the difference between a mutation and a query in GraphQL?
In GraphQL, mutations are used to write or change data, while queries are used to read data. Queries are used for operations that do not have side effects, such as data retrieval, while mutations are used for operations that can modify data, such as those that create, update, or delete records.
What is a GraphQL schema?
A GraphQL schema describes the capabilities of a GraphQL server by defining a list of types and directives. It describes the types of data that can be queried and manipulated, the relationships between these types, and the queries and mutations that are available. The schema acts as a contract that specifies what information the client may request and how the server will respond.
What are scalar types in GraphQL?
Scalar types are basic atomic data types in GraphQL that represent single values. They include types like String for text, Int for integers, Float for floating-point numbers, Boolean for true or false values, and ID for unique identifiers. Scalars are used to represent the leaves of the GraphQL query tree, serving as the foundation for more complex data structures.
What is an exclamation point in GraphQL?
In GraphQL, an exclamation point (!) indicates that a field in a query or a field argument is non-nullable. This means that the field must contain a value and cannot be empty. When used with a field, it ensures that the server always returns a value that is not null. When used with a field argument, it indicates that the client must provide the argument and it cannot be left out.
What are resolvers in GraphQL?
In GraphQL, each schema field corresponds to a function known as the resolver. The resolver returns the value for a given field in an operation. Resolvers provide instructions on how to compute or retrieve data from the server or other sources. They are a crucial part of implementing GraphQL servers because they translate the fields in the schema to the real data sources—which might be databases, REST APIs, or other services.
When is GraphQL useful?
GraphQL is useful in situations where applications require efficient and precise data retrieval, real-time updates, and complex data relationships. It also functions well in situations where there are multiple clients, different data requirements, and the need to compile information from various sources. Additionally, GraphQL can be used as a layer to overcome the limitations of RESTful or SOAP APIs by providing a more flexible querying interface.
What are the key concepts of the GraphQL query language?
The key concepts of the GraphQL query language revolve around its schema-driven approach. GraphQL defines types and relationships in a schema, allowing clients to request precisely the data they need using queries. Mutations enable clients to modify data, while fields specify what data to retrieve. Arguments, aliases, and fragments enhance query flexibility, and variables make queries dynamic. Directives offer conditional execution, and introspection allows clients to explore a schema’s structure and capabilities, making GraphQL a powerful and versatile querying language.
GraphQL interview questions and answers: intermediate
This section includes some common GraphQL interview questions and answers at the intermediate level. You might be asked these questions if you’re applying for a role as a backend developer, DevOps engineer, technical product manager, or solutions architect.
What are variables in GraphQL, and how do you use them?
Variables in GraphQL are dynamic values that can be passed as arguments in queries or mutations, allowing for more flexible and reusable code. You define variables in your query or mutation and then pass the actual values when executing the request. With this method, you can write generic mutations or queries where the details are provided at runtime.
What is introspection in GraphQL, and how is it useful?
GraphQL introspection enables clients to ask the GraphQL server questions about the schema, including available types, fields, and directives. It’s useful for building client-side tools that need to understand the schema, and it supports auto-generating queries, documentation, and validating queries against the schema before sending them. As a result, GraphQL APIs are self-documenting and easier to explore and integrate.
How do you do authentication and authorization in GraphQL?
Authentication is usually handled outside of the GraphQL layer, typically through HTTP headers such as JSON Web Tokens (JWT). Authorization is implemented in GraphQL resolvers by checking permissions before returning data or performing mutations. Authentication verifies a user’s identity, while authorization determines their access rights to various parts of the GraphQL schema.
How do you do error handling in GraphQL?
Instead of using traditional HTTP status codes, errors are returned in the response alongside the data. These errors can be generated by the GraphQL server (for syntax or validation errors), as well as by resolvers (for business logic or runtime errors). Clients can then parse these errors and handle them accordingly, often using the error message and optional fields—like error codes or paths—to identify the nature and location of the error.
How do you handle and report errors in a production GraphQL API?
Errors in a production GraphQL API are handled by sending user-friendly error messages to the client and logging them to a monitoring system for analysis and alerting. Sensitive data is omitted for security reasons, but critical details about the error context—such as the query, variables, and user information—are recorded for debugging purposes. Additionally, operational errors are differentiated from developer errors in order to help with response and mitigation strategies.
What is the main difference between GraphQL and REST?
The main difference between GraphQL and REST is their approach to data retrieval. GraphQL allows clients to request exactly the data they need in a single query, reducing over-fetching and under-fetching, while REST typically uses predefined endpoints that return fixed data structures. GraphQL enables clients to aggregate data from multiple sources in a single request, but REST often requires multiple round-trips to different endpoints to gather all necessary data.
What are the advantages and disadvantages of GraphQL?
GraphQL has the advantages of efficient data fetching, customized client requests, and a strongly typed schema that makes API exploration and validation easier. But compared to conventional REST APIs, its disadvantages include a steeper learning curve, potentially intensive server-side processing, and complex query optimization. Additionally, GraphQL queries are dynamic, which can make caching more of a challenge.
How can you implement versioning in a GraphQL API without breaking existing clients?
GraphQL developers can successfully implement API versioning by using the schema extension, which allows them to add or change fields and types without affecting or deleting existing ones. This approach, known as “evolutionary” or “continuous” versioning, allows clients to continue using the original schema while new clients use the extended schema. Developers can also preserve backward compatibility by deprecating obsolete fields rather than deleting them and by using field aliases for major changes.
GraphQL interview questions and answers: advanced
We’ll now go over some advanced GraphQL questions. If you’re applying for a role as a GraphQL developer, senior full-stack developer, API architect, or performance engineer, you might be asked some of the more in-depth questions in this section.
What is batching in GraphQL, and what is its impact on performance?
Batching in GraphQL refers to the process of combining multiple queries or mutations into a single HTTP request, which reduces the number of network round trips. This approach can significantly improve performance by minimizing the latency and overhead associated with making multiple separate requests—particularly in scenarios with multiple concurrent data requirements. However, it requires careful management on the server side to efficiently resolve these batched requests without overloading the system; tools like DataLoader can help.
How can you optimize GraphQL queries for performance, especially when dealing with deeply nested data?
To optimize GraphQL queries for performance, use query depth limiting and complexity analysis to avoid costly database operations. You should also use efficient data loading techniques, such as batching and caching at the data fetching layer, to reduce database load. Additionally, consider implementing a persisted queries mechanism, which will store and efficiently retrieve frequently used or expensive queries. This approach will reduce the need for query parsing and validation on each request.
What are some security considerations and best practices when exposing a GraphQL API to the public internet?
Strong authentication and authorization procedures, input validation and sanitization, and limitations on query complexity and depth are essential when opening a GraphQL API to public access. It’s also important to use API monitoring and rate-limiting techniques to identify and stop abusive traffic patterns. Additionally, keep the GraphQL schema secure and don’t reveal sensitive data in error messages in order to protect against information leakage and potential exploitation.
Related: Don’t panic: a developer’s guide to building secure GraphQL APIs
How would you protect against common security vulnerabilities, like SQL injection or DDoS attacks, in a GraphQL API?
To protect a GraphQL API against SQL injection, use parameterized queries or prepared statements in database operations, and rigorously validate and sanitize all user inputs. To defend against DDoS attacks, implement rate limiting, query complexity analysis, and depth limiting to control the load on your server. As an added measure, use monitoring systems and Web Application Firewalls (WAFs) to identify and address suspicious traffic or activities.
What are the benefits and challenges of federated GraphQL schemas in a microservices architecture?
Federated GraphQL schemas in a microservice-based architecture allow different services to define their own part of the schema, enabling a scalable and modular approach that aligns well with microservice principles. By giving clients access to a single API gateway, this federation increases API usability and development efficiency. However, challenges include maintaining schema consistency across services, handling cross-cutting concerns like authorization and error handling, and ensuring efficient query execution without incurring significant inter-service communication overhead.
How can you create custom directives in GraphQL, and what are some use cases for them?
Custom directives in GraphQL can be defined in the schema language and implemented on the server side, typically in the GraphQL server configuration. These directives can be used to change how queries or mutations are executed; for example, they can be used to perform field-level transformations, enforce permissions, or implement custom business logic. Some use cases include logging, authentication, field deprecation, and dynamically changing query responses based on certain conditions or user roles.
What is the role of serverless functions in a serverless GraphQL architecture, and when might you use them?
A serverless GraphQL architecture eliminates the requirement for a dedicated, continuously operating server by using serverless functions to carry out the business logic of GraphQL resolvers. Incoming GraphQL requests are handled by these dynamically allocated functions, which enable cost-effective scaling based on request load. They are particularly useful for handling sporadic or unpredictable traffic, executing computationally intensive tasks, and integrating with other serverless services or APIs. These benefits make serverless functions a flexible and scalable backend solution.
How can you implement real-time updates in GraphQL using subscriptions?
The subscriptions feature in GraphQL enables clients to receive real-time data from the server over a persistent connection, usually through WebSockets, which is useful for implementing real-time updates. When a client subscribes to a specific event, the GraphQL server pushes updates to the client as the relevant data changes, keeping the client in sync. This is especially useful for features such as live chats, real-time notifications, or any scenario in which data must be updated in real time on the client side.
Conclusion
This article has covered a wide range of GraphQL topics, from the basics to more advanced strategies and concepts. GraphQL offers an incredibly flexible way to work with APIs, as demonstrated by the variety of questions we have answered. Whether you’re a developer, a product manager, or a user experience designer, GraphQL knowledge is critical to your success in the software industry today.
Technical review by Meenakshi Dhanani.
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