GraphQL

GraphQL's unified endpoint and flexible query structure can inadvertently expose sensitive data. Without strict authentication and authorization checks at the field level, unauthorized users can query restricted information. Field-level security is complex, error-prone, and can cause entire requests to fail.

Implementing schema federation or stitching across multiple services is complex, slow, and difficult to secure. Federation introduces fragility and inter-domain dependencies that are hard to manage and debug at scale.

Combining multiple schemas from different data sources into a single coherent schema is complex and time-consuming. Developers must carefully map data relationships and ensure schema consistency, especially in applications with many data sources.

Traditional HTTP caching mechanisms struggle with GraphQL because each unique query variation is treated as a distinct request. Parameterized queries (e.g., different $userId values) create cache misses. Additionally, query permutations can be exploited to spam server memory with cache entries.

Developers struggle to balance data retrieval efficiency. Requesting excessive data degrades performance, while requesting too little data causes multiple round trips to the server, impacting application speed and responsiveness.

Developers frequently forget to batch queries in GraphQL, resulting in N+1 query problems that cause slow performance and unnecessary database strain. Solutions like DataLoader are required to optimize query execution.

GraphQL queries can become increasingly complex as projects grow, with deeply nested queries and over-fetching of fields leading to poor performance, extensive database joins, and slow execution times. Query complexity assessment is difficult and clients can crater performance without guardrails.

Implementing flexible filtering in GraphQL requires awkward nested input types and bespoke filter designs for AND/OR logic, range filters, and custom operators. Filter inputs balloon in complexity with duplicate logic across different entity types and no cross-schema reuse.

GraphQL does not support map/table/dictionary return types or associative arrays keyed by dynamic values. Developers must use workarounds like custom scalars, key-value object arrays, or pre-transformed responses, adding unnecessary complexity to both schema and client logic.

Different components define their own ad hoc query shapes, resulting in fragmented chaos where the same resource is requested in dozens of slightly different structures. This defeats GraphQL's promise of reusability and creates brittle client logic with no unified contract.

GraphQL adds significant complexity to projects through schema management, type system duplication, tooling, and infrastructure costs. This overhead is not justified for small projects, simple APIs, or cases where use cases are well-known and static. 90% of typical API use cases can be handled by simpler alternatives.

Authorization code written for GraphQL contexts often cannot be reused in background jobs or HTML endpoints, because Dataloaders (the common solution to N+1 problems) are GraphQL-specific. This forces developers to implement separate authorization logic for different execution contexts.

GraphQL queries may not fetch sufficient data in a single request, necessitating additional round trips to enrich data and build complete models. This wastes bandwidth, consumes resources, adds latency, and increases complexity for API consumers.

In large, complex GraphQL applications, tracking how clients query the API is burdensome and requires significant engineering effort. Maintaining consistency and preventing the API from devolving into a REST-like structure as the product evolves demands considerable discipline.

Developers often forget to properly handle errors in GraphQL resolvers, returning null values instead of throwing errors. This prevents clients from knowing when something goes wrong.

Writing tests for GraphQL queries and mutations is challenging and time-consuming, requiring mocking of multiple data scenarios. Testing complexity increases significantly with complex query structures.

As applications grow, managing all types, queries, and mutations becomes messy and difficult to track. Developers must regularly review and update schemas to ensure everything aligns correctly.

Handling mutations that involve multiple entities and relationships is complex and difficult. Managing data consistency across mutations poses significant challenges for developers.

GraphQL pagination lacks standardized patterns, especially for multi-dimensional data structures with different requirements at different levels. Implementing pagination for large lists is cumbersome and each service may implement pagination differently.

GraphQL discourages breaking changes but provides no built-in tools to manage them, forcing developers who control all their clients to find workarounds and add needless complexity.

GraphQL returns HTTP 200 for most responses even when errors occur, making it difficult for clients to programmatically determine error nature. Errors are embedded in the response body without standardized error codes, forcing developers to implement custom error-handling logic and parse fragile error message strings.

Depending on the backend language and code generation approach, developers must manage two or more type systems (backend native types, GraphQL schema, generated client types). Code-first and schema-first approaches each have tradeoffs and friction points.

GraphQL forces developers to think about every possible query variation upfront and design schemas accordingly. Generated GraphQL APIs that auto-expose storage models violate information hiding, force business logic onto API consumers, and are hard to evolve due to tight coupling.

File uploads in GraphQL are significantly more complex than in REST APIs, requiring special handling on the server side. This adds substantial development overhead.

GraphQL has a significant learning curve compared to REST APIs. Developers must learn schema definitions, resolvers, and query structures, and debugging is more complex. Studies show developers find GraphQL challenging and time-consuming to learn with limited knowledge-base resources.

GraphQL requires developers to define their own validation rules for incoming data, resulting in significantly more work compared to frameworks that handle validation automatically.

Developers lack adequate GUI tools for visualizing how requests move through GraphQL resolvers. Understanding request routing and resolver execution flow is more difficult than with simpler protocols, creating a gap in observability.

Data structures often differ between the database and GraphQL responses. For example, a database field like `authorId` might be transformed into a nested object in GraphQL responses, creating misalignment and complicating data mapping.

Exposing each entity in a GraphQL data model requires repetitive code—type definitions, root query fields, resolvers, mutations, and more. Adding new resources to the application multiplies this boilerplate burden.

For simple APIs that don't require complex functionality, GraphQL makes things more complicated than necessary. REST API tools are more appropriate for such cases.