GraphQL vs REST for Blockchain Data

Over-fetching eliminated: Clients request only the fields they need (e.g., { block { number, timestamp, transactions { hash } } }). This reduces payload size by 60-80% for complex queries versus a generic REST /block endpoint. This matters for building responsive dApps and mobile wallets where bandwidth and speed are critical.

Unified interface: One endpoint (e.g., /graphql) replaces dozens of REST routes. Paired with a schema (like The Graph's subgraph schemas), it provides auto-completion, validation, and clear documentation. This matters for developer velocity and integrating complex data from protocols like Uniswap or Aave.

Universal standard: Every engineer understands REST. Tools like Etherscan's API, Alchemy's NFT API, and Infura's standard endpoints are battle-tested. No need to learn a query language or manage a schema. This matters for rapid prototyping, simple scripts, or when team familiarity is a priority.

Built-in HTTP caching: Leverage CDNs, browser caches, and standard HTTP headers (Cache-Control, ETag) out-of-the-box. Mature monitoring tools (e.g., Datadog, Prometheus) have native REST support. This matters for high-traffic applications like block explorers or data aggregators where predictable performance is key.

Precise Data Retrieval: Fetch only the fields you need (e.g., { block { timestamp, transactions { hash } } }). This reduces payload size by 60-80% for complex nested queries compared to REST, directly lowering bandwidth costs and client-side processing time. Critical for high-frequency frontends and mobile dApps where data efficiency impacts user experience and cost.

Unified Interface: A single endpoint (e.g., The Graph's subgraph endpoint) replaces multiple REST routes. Paired with a strict schema (GraphQL SDL), this enables auto-completion, validation, and clear contracts between frontend and backend. Essential for rapid prototyping and teams using TypeScript or tools like Apollo Client to build complex applications like DeFi dashboards or NFT explorers.

HTTP Native Caching: Leverages standard HTTP caching layers (CDNs, browser cache) via status codes and headers. Each resource URL (e.g., /api/v1/block/123456) is a cacheable entity. This provides predictable performance and reduces load on indexers like Alchemy or Infura for frequently accessed, static data (e.g., historical token metadata). The go-to for simple, cache-heavy read operations.

Ubiquitous Support: Every programming language, load balancer, and monitoring tool (Datadog, Prometheus) is built for REST. No N+1 Query Problem—a common GraphQL pitfall where a resolver triggers excessive database calls. This results in more straightforward performance profiling and scaling. Ideal for high-throughput, simple queries or integrating with legacy enterprise systems and monitoring stacks.

Complex dApp Frontends requiring real-time, nested data (e.g., a dashboard showing user's portfolio, pending TXs, and governance votes). Rapid Iteration where frontend data needs change frequently without backend modifications. Ecosystems like The Graph, where the subgraph standard provides indexed blockchain data via GraphQL.

High-Volume, Simple Reads where HTTP caching can be maximized (e.g., fetching current ETH price or token icons). Microservices & Webhooks that follow standard HTTP semantics for interoperability. Direct RPC Wrappers where the API structure mirrors node RPC methods (e.g., eth_getBlockByNumber) from providers like QuickNode or Chainstack.