These notes are the result of some research that I was asked to do for my job on GraphQL.

What Kinds of Problems Is GraphQL Good at Solving?

  • Provides a single, uniform API as opposed to multiple REST APIs for each service.
  • Avoids making multiple round-trip network calls to get data from various REST services, allowing a single GraphQL query to aggregate the data efficiently.
    • This is particularly useful for clients with variable network connections.
    • GraphQL allows passing arguments for nested objects to facilitate this process.
  • Returns exactly the data requested, eliminating the need for data parsing and validation on the client side. With REST, entire data objects are returned even if only a specific field is required.
  • Operates on a type system, making the API essentially a schema where clients use the same types.
    • No versioning is needed; fields can simply be deprecated.
    • Allows passing arguments to scalar fields for backend data transformations.

Potential Client Questions

  1. How does GraphQL improve data retrieval efficiency compared to REST?
    • Provides a uniform API so clients need only make a single query to retrieve their desired data.
    • In a microservice environment, this eliminates the need for multiple requests to different services, thus reducing latency and simplifying frontend code.
    • By retrieving exactly what is requested, GraphQL eliminates unnecessary data parsing on the client side.
    • Reduces over-fetching, avoiding large, unnecessary payloads.
  2. How does GraphQL handle changes in data requirements from the frontend?
    • Offers high flexibility, allowing frontend developers to modify queries as needed without backend adjustments.
    • Reminiscent of Postel’s Law but shifts the onus of tolerating changes from the client to the server side, enhancing efficiency.
    • Deprecates fields rather than versioning the API, simplifying backend management.
  3. Can GraphQL be integrated with any backend system?
    • Sits as an additional layer in front of the existing backend, requiring only access to data storage.
  4. Should I use a schema-first approach or code-first approach?
    • Schema-first pros include collaboration friendliness, clear API documentation, and tooling support, but with cons like initial overhead and less flexibility.
    • Code-first (resolver-first) pros include development agility and lower learning curve, but cons include potentially looser API design and less clarity in the API contract.
  5. What are security implications of using GraphQL?
    • Highlights include the need for multi-layered resolver security, protection against attacks on underlying APIs, custom type validation, rate limiting, query timeouts, and limiting query depth to prevent denial of service attacks.
    • Advises disabling introspection in production and emphasizes standard API monitoring and testing practices.
  6. How does GraphQL handle real-time data?
    • Uses Subscriptions, typically implemented via WebSockets, inherent in all GraphQL servers.
  7. Can I do the equivalent of a POST/PUT in REST with GraphQL?
    • Yes, through mutations, GraphQL supports data modification operations in addition to data retrieval and real-time updates.
  8. What is the learning curve for our team to adopt GraphQL?
    • Varies based on API design experience but can be mitigated through a code-first approach and leveraging GraphQL’s introspection, typing, and community resources.
  9. What are some common pitfalls when adopting GraphQL and how can they be mitigated?
    • Addresses efficiency, security, and performance optimization, recommending tools, libraries, and best practices to navigate these challenges.
  10. How does GraphQL affect mobile clients in terms of performance and data usage?
    • Minimizes data transfer and combines requests, benefiting mobile users with limited data plans or slower network connections.

Cautions and Considerations

  • Suggests a pragmatic approach to adopting GraphQL based on project complexity and evolving needs.
    • Small, specific applications might not initially require GraphQL, benefiting from simpler solutions or direct HTML responses via technologies like htmx.
    • Recommends considering GraphQL as the need for data aggregation or complex data retrieval grows, especially in projects expected to involve mobile clients.

Project Scenario: E-commerce Platform


An e-commerce platform experiences significant growth, resulting in a sprawling architecture with multiple microservices managing products, user profiles, orders, and reviews. The frontend requires data from all these services to render pages, leading to multiple REST API calls for a single page view. This results in:

  • Over-fetching: Retrieving more data than necessary, wasting bandwidth and processing power.
  • Under-fetching: The need for additional round-trip calls to render a complete page, increasing load times.
  • Complex Frontend Logic: Managing multiple API calls and data aggregation on the client side complicates frontend development and maintenance.


Implementing GraphQL as the data query language for the platform’s APIs addresses these challenges by:

  • Single Data Fetch: Allowing the frontend to query all required data through a single GraphQL query, regardless of which services possess the data. This reduces the number of network requests.
  • Precise Data Retrieval: Enabling the frontend to specify exactly what data it needs for a particular view, eliminating over-fetching and under-fetching.
  • Simplified Frontend Logic: Reducing the complexity of data management on the client side, as the frontend no longer needs to coordinate calls to multiple REST endpoints and aggregate their responses.


  • Improved Performance: The reduction in the number of network requests and the precise fetching of data significantly reduce the application’s load times, improving user experience.
  • Efficient Bandwidth Use: By avoiding over-fetching, the application uses bandwidth more efficiently, which is particularly beneficial for mobile users or those with limited data plans.
  • Simplified Development: Frontend developers can focus on building features rather than managing data fetching logistics, speeding up the development process. The backend is also simplified as it no longer needs to cater to specific frontend data requirements with bespoke endpoints.
  • Scalability: As the platform grows, adding new features or services becomes easier. The GraphQL schema can evolve to include new types and fields without impacting existing queries, facilitating backward compatibility and reducing the need for versioning.