What is GraphQL?
A GraphQL API is an application programming interface that utilizes GraphQL, a query language for APIs, to enable clients to request precisely the data they need. Unlike traditional REST APIs, which often require multiple requests to different endpoints to gather related data, GraphQL allows clients to define the structure of the data they require in a single query, resulting in a single response with only the requested fields.
Key characteristics and features of a GraphQL API include:
- Client-specified response: Clients control the data they receive, requesting only necessary fields, which minimizes over-fetching and under-fetching of data.
- Queries and mutations: Clients can use "queries" to fetch data and "mutations" to modify data (create, update, delete).
- Subscriptions: GraphQL also supports subscriptions for real-time data updates, allowing clients to receive push notifications when data changes.
- Hierarchical structure: Queries reflect the hierarchical nature of user interfaces, allowing for natural data requests.
- Self-documenting: GraphQL APIs can describe themselves, enabling tools and clients to introspect the schema and understand available types and capabilities.
- Strong typing: A GraphQL service defines a type system (schema) that ensures the validity of queries and predictable responses.
- Language and database neutrality: GraphQL can be implemented with any programming language and used with various database structures.
In this article:
1. Client-Specified Response
A core feature of GraphQL is that clients explicitly declare which data fields they require in a response by structuring their queries accordingly. This allows clients to avoid over-fetching (getting too much data) or under-fetching (needing additional requests), both of which are frequent pain points with REST APIs. Each query returns only the requested data, tailored to the shape specified by the client’s needs.
This declarative approach improves application performance, particularly on mobile and constrained networks, as clients reduce wasted bandwidth and parsing overhead. Client-specified queries mean changes to frontend requirements rarely require backend changes, improving agility and decoupling front-end and back-end development cycles. This enables a more efficient workflow and better end-user experiences.
2. Queries and Mutations
GraphQL defines two primary root types for operations: queries (for reading data) and mutations (for writing or modifying data). Queries fetch data according to the structure specified by the client while mutations allow the creation, updating, or deletion of data on the server. Both operation types use the same hierarchical syntax and are validated against the schema, ensuring predictable, consistent interactions.
Separation of reads and writes ensures clarity in API usage and helps teams enforce permissions and business logic at the appropriate granularity. As both queries and mutations are explicit in their intent, side effects are controlled and visible through the schema, making security reviews and impact assessments more straightforward. This leads to APIs that are both reliable and secure by design.
3. Subscriptions
In addition to queries and mutations, GraphQL provides subscriptions as a mechanism for real-time data updates. Subscriptions allow clients to specify which data they want to be notified about as it changes on the server, using a persistent connection (typically over WebSockets). When a relevant change occurs, the server pushes updates to all subscribed clients.
Subscriptions suit use cases that demand live data, including collaborative applications, chat systems, and real-time dashboards. Unlike polling endpoints with REST, subscriptions offer a more efficient and responsive alternative, reducing server and network loads. By making real-time APIs first-class citizens, GraphQL enables modern, interactive user experiences without additional low-level plumbing or custom socket protocols.
4. Hierarchical Structure
GraphQL queries mirror the hierarchical structure of the underlying data graph. Each query returns a predictable response structure based on relationships defined in the schema, such as users with their posts and comments, or products with reviews. This contrasts with REST, where responses often require multiple roundtrips and endpoint calls, and the returned data rarely matches the nested format needed by client applications.
Separation of reads and writes ensures clarity in API usage and helps teams enforce permissions and business logic at the appropriate granularity. As both queries and mutations are explicit in their intent, side effects are controlled and visible through the schema, making security reviews and impact assessments more straightforward. This leads to APIs that are both reliable and secure by design.
5. Self-Documenting
GraphQL schemas are inherently self-documenting due to their strongly-typed nature. Documentation is built directly into the schema via descriptive language for types and their fields, making it easier for developers to discover and explore available data. Tools like GraphiQL, Apollo Studio, and GraphQL Playground leverage this metadata to give interactive, real-time documentation and query building within developer environments.
This self-documenting ability lowers the barrier for onboarding new developers or third-party integrators, as the schema is always up-to-date with the API. Unlike REST APIs—where documentation frequently drifts out of sync with actual implementation—GraphQL APIs reliably reflect the current version and shape of the data, reducing confusion and integration errors.
6. Strong Typing
GraphQL APIs are defined by a schema written in the GraphQL schema definition language (SDL), which specifies all data types, fields, and their relationships. This strong typing helps catch errors early in the development process by validating queries and mutations against the schema, providing clear, actionable feedback before runtime. As a result, developers benefit from code completion, in-editor documentation, and automatic validation tooling.
Typed schemas enhance maintainability by clarifying the contract between client and server, reducing ambiguity about available data and types. Tooling and automation—including schema linting, breaking change detection, and static analysis—rely on this strong typing to ensure safe evolution of the API. These capabilities reduce bugs and improve the integration process as applications and teams scale.
7. Language and Database Neutrality
GraphQL is agnostic to both programming languages and backend data sources. Its specification does not mandate any specific database or storage system—a GraphQL server can aggregate data from SQL or NoSQL databases, microservices, REST APIs, or even in-memory stores. This abstraction makes GraphQL an excellent choice for modern architectures that integrate multiple disparate backends.
GraphQL server libraries exist for most popular languages, including JavaScript/TypeScript, Python, Java, Go, Ruby, and more. This ensures developers can adopt GraphQL without a wholesale change to their technology stack. Database neutrality introduces flexibility in system evolution, allowing data sources to change underneath the API without affecting front-end consumers—a critical advantage for long-term scalability.
GraphQL and REST both provide ways to build APIs, but they differ significantly in how they handle data retrieval and exposure.
REST APIs are built around discrete endpoints representing resources, where each endpoint returns fixed data structures. Clients often need to make multiple requests to different endpoints to gather related data, resulting in over-fetching, under-fetching, and increased network traffic.
GraphQL consolidates access to all resources under a single endpoint. Clients issue queries that specify the exact data structure and fields they need. This flexibility eliminates redundant requests and minimizes bandwidth usage. Instead of retrieving entire objects or unrelated fields, clients fetch only what is relevant for a given view or operation.
Here are a few more key differences:
- Change management: REST typically requires versioning to handle changes over time, as modifying responses or endpoints can break existing clients, while GraphQL handles change more gracefully via schema evolution. Fields can be deprecated while keeping the existing schema intact, supporting backward-compatible improvements without introducing new versions.
- Caching: Caching and tooling are handled differently. REST can leverage HTTP caching semantics directly due to its reliance on standard HTTP verbs and status codes. GraphQL requires more sophisticated client-side caching strategies, often integrated with tools like Apollo Client or Relay.
- Security: REST benefits from HTTP-level security controls and fine-grained endpoint permissions. GraphQL centralizes access through a single endpoint, requiring explicit field-level authorization and runtime query validation. This adds flexibility but increases the complexity of securing individual operations.
Complex and Nested Data Retrieval
GraphQL shines wherever data structures are deeply nested or have complex relationships. In domains like e-commerce, social media, and content management, clients frequently need multiple related resources: for instance, user profiles with posts, comments, and likes, or products with detailed attributes, reviews, and inventory status. Traditional REST APIs would need multiple endpoints and requests to fetch all this interconnected data.
GraphQL allows for expressing complex queries that traverse these relationships in one go. The server then assembles and returns the output in a nested structure matching the query, reducing latency and simplifying the client code. This makes front-end development more straightforward and speeds up feature delivery, as backend systems do not need to change to accommodate varying data-fetching needs.
Mobile and Web Applications
GraphQL’s ability to fetch exactly the required data in a single request is especially valuable for mobile and web applications, where excessive payload size can degrade user experience. Mobile clients often face fluctuating network conditions and strict power or bandwidth constraints. By minimizing over-fetching and under-fetching, GraphQL optimizes both data usage and battery performance, leading to smoother, more interactive applications.
Web developers benefit similarly. Client-driven queries enable rapid page rendering and dynamic UIs that fetch specific nested data for components as needed. This supports rich, personalized interfaces and lets teams iterate quickly—adjusting data needs on the client without repeatedly changing backend endpoints.
Microservices Aggregation
Microservices-based architectures often segment data across numerous backend systems, complicating frontend data retrieval. Without a unified API layer, clients might need to coordinate multiple requests to aggregate the required pieces, introducing operational complexity and performance bottlenecks. GraphQL solves this by aggregating data from many sources behind a single, unified schema.
Resolvers within GraphQL orchestrate the collection and composition of data from various services, databases, and APIs. This simplifies client logic and lets backend teams refactor or replace internal services without visibility to consumers. As a result, organizations can scale their microservices ecosystem while maintaining a consistent API contract with frontend applications.
Server-Side Rendering (SSR) and Static Site Generation (SSG)
Modern frameworks like Next.js or Gatsby rely on SSR and SSG to generate fast, SEO-friendly pages. These workflows frequently require gathering data from multiple sources in a precise format for rendering components on the server. GraphQL’s declarative queries map well to the needs of SSR/SSG, enabling developers to efficiently gather all necessary data in a single network request during the build or render process.
This improves page load times and SEO by embedding exactly the data needed for each view at generation time. The strong typing and introspective schema of GraphQL also help automate build processes, reducing errors compared to hand-assembling data from multiple REST endpoints. For modern content-heavy and dynamic sites, GraphQL optimizes both development speed and runtime performance.
Jeremie Ohayon
Jeremie Ohayon is a Senior Product Manager at Radware with 20 years of experience in application security and cybersecurity. Jeremie holds a Master's degree in Telecommunications, and has an abiding passion for technology and a deep understanding of the cybersecurity industry. Jeremie thrives on human exchanges and strives for excellence in a multicultural environment to create innovative cybersecurity solutions.
Tips from the Expert:
In my experience, here are tips that can help you better design, secure, and operate GraphQL APIs:
1. Harden the GraphQL gateway: Treat your GraphQL gateway as a choke point: apply Web Application Firewall (WAF) rules, schema-aware query validation, and IP reputation filtering before queries reach resolvers. This stops malformed or malicious queries early, reducing backend stress.
2. Use persisted queries in production: Instead of letting clients send arbitrary queries, enforce persisted queries (pre-approved and hashed). This blocks attackers from crafting abusive queries while boosting cacheability at the CDN layer.
3. Leverage field-level query cost weighting: Don’t just use depth limits. Assign cost multipliers to expensive fields (e.g., search or aggregation). This prevents attackers from bypassing protections with shallow but high-cost queries.
4. Deploy schema segmentation: Expose different schemas for internal services, trusted partners, and public clients. This limits attack surfaces and allows tighter control over sensitive operations.
5. Hide implementation-specific errors: Never leak resolver stack traces or database errors in GraphQL responses. Standardize error handling into opaque codes with contextual messages only where safe. This prevents attackers from fingerprinting your stack.
To understand how GraphQL APIs are structured and executed, let’s walk through a basic implementation using a schema, resolvers, and a query. These examples are adapted from the GraphQL documentation.
1. Define the Schema
The schema defines the types and fields that clients can query. Here’s a simple example of a schema that exposes a logged-in user’s name:
type Query {
me: User
}
type User {
name: String
}
This schema defines a Query type with a single field me, which returns a User. The User type has one field, name, of type String.
2. Write Resolver Functions
Resolvers are functions that tell GraphQL how to fetch the data for each field in the schema. Here's how you might implement them:
function resolveQueryMe(_parent, _args, context, _info) {
return context.request.auth.user;
}
function resolveUserName(user, _args, context, _info) {
return context.db.getUserFullName(user.id);
}
The resolveQueryMe function accesses the authenticated user from the request context. The resolveUserName function takes the user object and fetches the full name from the database using the user’s ID.
3. Run a Query
Once the server is running, a client can send a GraphQL query like this:
The response returned will look like:
{
"data": {
"me": {
"name": "Luke Skywalker"
}
}
}
This query structure mirrors the response shape, and the client receives only the requested data in a single request.
4. Evolving the Schema
As application requirements change, you can evolve your schema without breaking existing clients. For example, to offer more detailed name information:
type User {
fullName: String
nickname: String
name: String @deprecated(reason: "Use `fullName`.")
}
GraphQL tooling will alert developers to use the new fields, while the old field continues to function until it's safe to remove.
5. Practice with Queries
Interactive tools like GraphiQL or Apollo Studio let you practice writing and executing queries. Try modifying a query to fetch more fields:
{
hero {
name
appearsIn
id
}
}
This makes it easy to test, debug, and explore your schema without needing to inspect backend implementation details.
GraphQL can be secure, but it requires careful design and implementation. By default, GraphQL’s flexibility and introspection features present a broad attack surface. Each query must be validated and constrained to avoid resource abuse, and resolvers must enforce strict access controls to prevent unauthorized data exposure. Unlike REST, which has a more rigid structure, GraphQL’s dynamic nature makes it easier to overlook security gaps without comprehensive testing and monitoring.
Securing a GraphQL API involves multiple layers: disabling introspection in production for unauthenticated users, validating query depth and complexity, implementing field-level authorization, and sanitizing all inputs at the resolver layer. Rate limiting, audit logging, and anomaly detection are also essential to detect and mitigate threats early. With these practices in place, GraphQL can provide strong security guarantees, but it demands more active effort and tooling compared to traditional REST APIs.
1. Introspection and Schema Exposure
GraphQL APIs are self-documenting by nature, exposing schema introspection that helps developers build on top of them. However, if left unconstrained in production, introspection can reveal sensitive internal structures, deprecated fields, or operations that should remain hidden from attackers. This could aid malicious actors in crafting targeted queries or probing for vulnerabilities in business logic.
To reduce risk, disable or restrict introspection in production environments, especially for unauthenticated users. Consider serving different schemas based on user roles or toggling introspection via configuration flags or security-aware middleware. Regularly review schema visibility and audit public fields to mitigate inadvertent exposure.
2. Unrestricted Resource Consumption/DoS
GraphQL's flexibility comes with potential abuse vectors: clients can construct deeply nested, expensive queries or batch operations that stress compute, memory, or database resources. If unchecked, this can enable denial of service (DoS) attacks or degrade experience for legitimate users. Unlike REST, where endpoints are fixed and limits are easier to enforce, a single GraphQL endpoint must validate and bound every incoming query.
Mitigate these risks by setting maximum query complexity or depth limits using libraries like graphql-cost-analysis or server configurations. Apply timeouts, concurrency limits, and query whitelisting where feasible. Logging and monitoring query patterns help identify abuse before it impacts the service. Frontend and API teams should collaborate on query shaping and best practices to avoid unintentional heavy loads.
3. Injection Attacks
Although GraphQL's use of parameters reduces some risk compared to ad-hoc query build-up in REST, backend resolvers often interact with SQL, NoSQL, or third-party APIs, which pose injection risks if not sanitized. Inputs passed to resolvers can be manipulated by attackers to inject malicious code or commands—in SQL databases, this could lead to classic SQL injection vulnerabilities.
Prevent these attacks by consistently validating and sanitizing all inputs at the resolver level. Use parameterized queries or ORM features when accessing backends. Security reviews and automated testing of resolver codebases should be regular practices. Never interpolate user input directly into dynamic queries, regardless of perceived input trustworthiness; apply a defense-in-depth approach across your stack.
4. Broken Authentication and Authorization (BOLA)
BOLA vulnerabilities arise when APIs fail to enforce authorization consistently across operations, allowing users to access or mutate resources beyond their intended rights. GraphQL's flexible, nested querying can make it difficult to track which parts of a query should be subject to which authentication or authorization checks, increasing the risk of data leaks or privilege escalation.
Implement explicit, field-level authorization policies within resolvers to ensure every requested resource is validated against the user's roles or permissions. Automated authorization middlewares and attribute-based access control (ABAC) frameworks can reduce developer error. Regularly test and audit access controls with dynamic and static tooling to detect and patch gaps as your schema or business logic evolves.
Related content: Read our guide to API security.
1. Keep Queries Efficient and Bounded
GraphQL allows clients to request deeply nested fields and complex relationships in a single query, which can lead to significant strain on backend resources if not properly constrained. To avoid performance bottlenecks and denial-of-service vectors, enforce query cost limits and maximum depth using tools like graphql-depth-limit or graphql-cost-analysis. These tools calculate the resource cost of incoming queries and reject those exceeding configured thresholds.
Encourage developers to design queries around specific use cases rather than general-purpose data fetching. Avoid exposing fields that are rarely needed or expensive to resolve. Use field-level rate limiting or throttling for high-cost operations, and consider using persisted queries to limit clients to predefined query shapes. This not only protects the backend but also improves cacheability and predictability of requests.
2. Use Schema Stitching and Federation Carefully
Schema stitching and federation enable large teams to scale GraphQL development across microservices, but they introduce operational complexity and require clear architectural discipline. When using schema stitching, ensure that each stitched schema maintains clear ownership and follows consistent naming conventions to avoid conflicts. Be cautious about resolving dependencies across services, as stitched schemas can create tight coupling and increase fragility.
GraphQL federation, especially with Apollo Federation, introduces a declarative way to compose multiple subgraphs into a unified graph. Use it to maintain separation of concerns between services while exposing a cohesive API to clients. However, federation requires careful planning around shared types, ownership boundaries, and composition rules. Implement observability and monitoring at the gateway level to detect and debug cross-service issues effectively.
3. Document Schema Changes Clearly
A schema is the contract between the client and server, and changes to it must be managed carefully. Always use the @deprecated directive to signal when a field is no longer recommended for use. Provide a reason and guidance for alternatives so client developers know how to update their code. Avoid breaking changes unless absolutely necessary, and communicate these well in advance.
Use tools like GraphQL Inspector or Apollo Studio to track schema diffs over time and catch breaking changes before they reach production. Maintain a changelog that records modifications, additions, and deprecations to the schema, and automate notifications to frontend teams when changes occur. Clear schema documentation and evolution policies reduce the risk of regressions and support better coordination across teams.
4. Enforce Strong Authentication and Authorization
GraphQL’s nested nature means a single query can access multiple resources, each of which may have different access rules. Authentication (verifying user identity) and authorization (enforcing permissions) must be implemented rigorously at all levels. Authentication is usually handled via tokens (e.g., JWTs) passed in headers, but authorization logic often needs to be embedded in resolvers.
Avoid applying access control only at the top-level query; instead, implement field-level and type-level authorization to ensure each data access is verified against the user’s roles or attributes. Use libraries or middleware to enforce consistent access patterns, and avoid leaking sensitive metadata (like IDs or internal flags) in unauthorized responses. Conduct regular security audits to verify that no data can be accessed or mutated without appropriate permissions.
5. Monitor Performance and Apply Rate Limits
A single poorly constructed GraphQL query can affect backend performance significantly. Monitoring should be in place to track execution time, error rates, resolver latencies, and memory usage. Tools like Apollo Engine, Prometheus with custom resolvers metrics, or OpenTelemetry integrations help observe GraphQL workloads and identify problematic patterns or regressions.
Implement rate limiting using user identity, API keys, or IP addresses to prevent abuse. Layered limits—global, per-field, and per-client—offer fine-grained control over resource usage. Consider caching frequently accessed query results at the gateway or resolver level using tools like DataLoader or full response caches. Combine logging, alerting, and visualization to proactively address spikes in load, and ensure your system can scale predictably under varying traffic conditions.
Securing GraphQL APIs with Radware
Radware’s Cloud Application Protection Service provides holistic protection for modern application architectures, including GraphQL-based APIs. The service integrates multiple Radware technologies — Cloud WAF, API Protection, Bot Manager, and Client-Side Protection — within a unified cloud platform. Each module can operate independently or as part of a comprehensive, integrated defense against application-layer attacks.
For GraphQL APIs, the service delivers:
- Schema-aware inspection and query validation through its Web Application Firewall, protecting against injection, excessive query depth, and DoS-style resource exhaustion.
- API Protection that automatically discovers GraphQL endpoints, maps data flows, and enforces positive-security policies to prevent business-logic and authorization abuses.
- Bot Manager capabilities that distinguish legitimate clients from malicious automation, mitigating credential-stuffing, scraping, and abuse of introspection endpoints.
- Client-Side Protection that secures browser-side scripts and prevents sensitive data exfiltration via compromised third-party components.
- Centralized visibility and analytics, giving security teams real-time insight into GraphQL-specific threats and performance metrics.
Together, these modules enable organizations to confidently deploy GraphQL APIs while maintaining robust protection against OWASP-listed and emerging API-layer threats.