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80 GraphQL Interview Questions

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1. Can you explain what GraphQL is and why it's used?

GraphQL is a query language for APIs that gives clients the power to ask for exactly what they need and nothing more. Unlike traditional REST APIs, where you have to make several requests to different endpoints to fetch related data, GraphQL allows you to aggregate all of the data you need in a single request. This makes apps using GraphQL efficient and fast, significantly reducing the amount of data that needs to be transferred over the network. It also makes your code more maintainable and easier to evolve over time. Since GraphQL schemas are strongly typed, it offers meaningful error messages, excellent developer tools and enables powerful features like automatic UI updates. It's used by teams of all sizes and different domains, from small startups to large enterprises, and across all platforms (web, mobile, IoT etc.).

2. How does GraphQL compare to REST?

REST and GraphQL serve the same purpose - they are both used to build APIs that enable applications to communicate with each other. However, they have fundamental differences in the way they approach this goal. In REST, you structure the API around resources, which are accessed through different endpoints. Each endpoint returns a fixed structure of data, which can lead to overfetching or underfetching - you might get more data than you need, or have to make separate requests to get the complete data.

On the other hand, GraphQL is built around a flexible schema system. Instead of making multiple requests to different endpoints, you make a single request to a GraphQL server describing exactly what data you need. The server then responds with a JSON object where these requirements are precisely fulfilled. This ability to fetch all necessary data in one request makes GraphQL a more efficient choice, leading to faster and more responsive applications. Furthermore, because the data requirements are defined by the client, GraphQL eliminates the underfetching and overfetching problem distinct to REST.

3. What are the key components of GraphQL?

GraphQL's architecture consists primarily of three key components: Schema, Resolvers, and Queries or Mutations.

The Schema is the heart of a GraphQL service. It specifies the capabilities of the API including the types of data, how they relate to each other, and the operations that the clients can perform—be it fetching data (queries), changing data (mutations), or listening for data changes (subscriptions).

Resolvers are the functions that satisfy requests for a specific field on a type. When a field is executed, the corresponding resolver is called to produce the next value. If the field is a scalar type, the resolver is responsible for returning a value with a corresponding type.

Queries and Mutations are how the clients interact with the data. A Query is used when you want to read or fetch some data, whereas a Mutation is used when you want to write or change some data. Clients specify exactly what they need, sending a string to the server (a request) that gets validated against the schema before the server, in turn, provides a response - efficiently giving the client just what they asked for and nothing more.

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4. What does it mean to say that GraphQL is strongly typed?

Being strongly typed, in terms of GraphQL, means that every piece of data that's exchanged through the API, and every operation that a client can do, is defined in the GraphQL schema. The schema defines the types of data available, their characteristics, and their relations. When you're working with a GraphQL API, you always know what data structure to expect, without needing to guess or infer from the data itself.

This attribute brings several benefits. For one, type safety mitigates potential bugs as the API will validate the shape and data type of incoming queries and mutations against the schema definition. It means that if a client tries to send data that doesn't match the predefined schema, the request will be rejected outright. Furthermore, strong typing is fundamental to GraphQL's introspective nature. Because everything is precisely defined, tools can leverage this information to provide excellent developer experiences - things like autocompletion, type checking, automated documentation, and more.

5. How is caching implemented in GraphQL?

In GraphQL, caching isn't directly built into the specification but rather, it's often handled on the client-side with libraries like Apollo Client or Relay.

Apollo Client, for example, provides a normalized cache out of the box, automatically caching query results, which helps save on network calls for identical requests. It stores every object by its ID and type, making sure that all parts of your UI are updated and in sync with any possible changes in your data.

On the server-side, literally caching isn't common because GraphQL queries can be so varied, making caching at the query level ineffective. However, some server-side solutions exist like DataLoader, a utility developed by Facebook, which helps with caching and batching data loads.

Rather than literal caching, many GraphQL servers employ approaches like query complexity or depth limiting to prevent resource abuse. Persistent queries are another technique where a complex operation is saved on the server side, which can then be called by clients passing a simple identifier.

It's also possible to place a caching layer at the HTTP level, or directly in the data fetching logic in resolvers. As usual, caching strategies are highly dependent on your specific use cases and data requirements.

6. How is validation handled in GraphQL?

Validation in GraphQL is quite robust and is performed at several stages. Firstly, when a server receives a request from a client, the GraphQL execution engine will validate the request against the schema. The schema, being a strong type system, acts as a contract between the client and the server. Any request that doesn't adhere to this contract will be deemed invalid. This includes checks to see if the fields requested actually exist and if they're the correct type.

Secondly, GraphQL automatically checks for syntax errors in the incoming query. If there are any issues with the syntax, it sends a precise error message back to the user, pointing out what went wrong and where.

Lastly, GraphQL also has the ability to handle more advanced validation like directives which can be used within queries and mutations to include or skip fields under certain conditions.

Some developers may also choose to implement further validation logic inside their resolver functions, guarding against invalid values or unauthorized actions. For example, checking if an argument passed into a mutation falls within an expected range.

Basically, GraphQL, with its strong typing system and relative schemas, provides robust tools for verifying the validity of data during exchanges between the client and the server.

7. How do you optimize a GraphQL application?

Optimizing a GraphQL application involves a combination of strategies. Here are a few key ones:

Firstly, use DataLoader or a similar tool to batch and cache database requests. This tool helps minimize the number of database requests needed by caching duplicate requests and batching others where applicable.

Secondly, be mindful of the costs of your queries. Some GraphQL servers support the concept of query complexity calculation, essentially determining how "expensive" the query is before it is run. Implementing a maximum allowed query complexity can prevent overly complex and potentially harmful queries from running.

Thirdly, use pagination to limit the amount of data that's returned and processed at once. Without it, fetching a large list of items can slow down your app or potentially crash it. Constraints can be placed on list fields to paginate the output and return manageable chunks of data at a time.

Lastly, manage configuration properly. This can mean deploying your GraphQL server as a schema stitching or data gateway to optimize data fetching from multiple services, or applying the right settings on your GraphQL server for caching and performance.

Remember, optimization strategies will widely depend on the details of your specific use cases, the data requirements, the architecture, and the server environments you're working with. So it's essential to continually monitor the performance and adjust accordingly.

8. What is a GraphQL subscription and how does it work?

A GraphQL subscription is a type of operation that allows a client to subscribe to specific data changes. It essentially provides real-time functionality by pushing data to the clients proactively, as opposed to queries and mutations where the client has to initiate the request.

This works by maintaining a persistent connection between the server and the client, typically using WebSockets. When an event happens that alters the data the client is subscribed to, the server pushes an update to the client using this connection.

For instance, think of a chat application. A client would 'subscribe' to new messages in a chat room. Then, whenever a new message is posted, instead of the client polling for updates or refreshing, the server will push the new message to the client in real-time.

It's important to note that subscriptions are more complex to implement compared to queries and mutations since they require maintaining a persistent connection and keeping track of who is subscribed to what data. The level of support for subscriptions varies across different GraphQL server implementations and you might also need to consider things like scalability and connection management.

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9. Can you explain how a GraphQL query works?

10. How do you handle errors in GraphQL?

11. How is GraphQL schema defined?

12. Can you provide an example of a GraphQL mutation?

13. What is a GraphQL resolver?

14. What is the difference between Query and Mutation in GraphQL?

15. Explain how GraphQL reduces the amount of data transferred.

16. How does authorization work in GraphQL?

17. What are GraphQL directives?

18. What are the challenges with using GraphQL?

19. What are some best practices for using GraphQL in a production environment?

20. How is version control managed in GraphQL?

21. Can you discuss Apollo and its relation to GraphQL?

22. What is a GraphQL interface, and when would you use it?

23. How can you handle pagination in GraphQL?

24. What are some problems that can arise from over-fetching/under-fetching data in GraphQL?

25. How do you design a GraphQL schema for complex systems?

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