Technology Expertise

GraphQL
Development.

Expert GraphQL development with deep production experience. From architecture decisions to performance optimization, I help teams build systems that scale.

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Expertise Level

GraphQL developer since the GitHub API v4 launch (2017). Built schemas serving 10M+ queries daily, implemented Federation gateways composing 8 subgraphs, and optimized resolver performance using DataLoader patterns and query analysis. Experience with both schema-first (SDL) and code-first approaches.

When to Use GraphQL

Mobile applications where minimizing network payload and round trips directly impacts battery and data usage

Frontend-heavy teams that need to iterate on data requirements without backend changes

Applications with complex, interconnected data where REST would require multiple round trips

Building public APIs where clients have diverse data needs you can't predict

Microservices architectures using Apollo Federation to compose a unified graph from multiple services

Real-time features requiring subscriptions with typed, selective data streaming

Internal tools where rapid iteration matters more than caching simplicity

Best Practices

Use DataLoader for batching and caching database fetches within a single request

Implement query complexity analysis to reject expensive queries before execution

Design schema with domain modeling principles... don't expose internal implementation details

Use code-first schema generation (TypeGraphQL, Pothos) for type safety between schema and resolvers

Implement field-level authorization, not just query-level... some fields may be restricted

Use persisted queries in production to improve performance and security (no arbitrary queries)

Leverage fragments for reusable field selections in client code

Common Pitfalls to Avoid

N+1 query problem... naive resolvers fetch data per-item instead of batching; always use DataLoader

Allowing unbounded queries... without depth limiting and complexity analysis, clients can DoS your server

Over-fetching at the resolver level... resolving fields that aren't requested wastes database queries

Not understanding the performance implications of resolver chains... each field can trigger a database call

Using GraphQL for simple CRUD where REST would be simpler with better HTTP caching

Ignoring persisted queries for production... parsing and validating queries on every request is expensive

Exposing database schema directly... GraphQL schema should model domain concepts, not tables

Ideal Project Types

Mobile applications with complex data needs

Dashboard and analytics platforms

E-commerce with complex product relationships

Social platforms with interconnected data

Developer APIs and platforms

Federated microservices architectures

Complementary Technologies

Apollo Server/Client (full-stack GraphQL)

Pothos or TypeGraphQL (code-first schemas)

DataLoader (N+1 prevention)

GraphQL Code Generator (type generation)

Apollo Federation (microservices composition)

urql (lightweight alternative to Apollo Client)

Real-World Example

Case Study

A marketplace platform I built demonstrates GraphQL's strengths and pitfalls. The schema modeled products, sellers, reviews, and orders... highly interconnected data where REST would require N+1 endpoints or bloated nested responses. I implemented Apollo Federation with three subgraphs: catalog (products, categories), users (profiles, authentication), and orders (transactions, payments). The gateway composes these into a unified schema, but each team owns their subgraph independently. The key optimization was DataLoader everywhere: a query for 50 products with their sellers and average ratings batches into three database queries instead of 150. I implemented query complexity limits (max cost: 1000) calculated from field weights... fetching all products with all reviews could cost millions, so we reject those queries. For the mobile app, persisted queries reduced payload size by 60% (sending a hash instead of the full query string) and allowed us to deprecate fields without breaking older app versions. The subscription system handles real-time order updates using Redis pub/sub behind GraphQL subscriptions, delivering updates to 10K concurrent connections.

Ready to Build?

Let's discuss your
GraphQL project.

Whether you're starting fresh, migrating an existing system, or need architectural guidance, I can help you build with GraphQL the right way.

Talk through your stack

GraphQLDevelopment.

●Expertise Level

●When to Use GraphQL

●Best Practices

●Common Pitfalls to Avoid

●Ideal Project Types

●Complementary Technologies

●Real-World Example

Let's discuss yourGraphQL project.

GraphQLDevelopment.

●Expertise Level

●When to Use GraphQL

●Best Practices

●Common Pitfalls to Avoid

●Ideal Project Types

●Complementary Technologies

●Real-World Example

Let's discuss yourGraphQL project.

GraphQL
Development.

Expertise Level

When to Use GraphQL

Best Practices

Common Pitfalls to Avoid

Ideal Project Types

Complementary Technologies

Real-World Example

Let's discuss your
GraphQL project.

GraphQL
Development.

Expertise Level

When to Use GraphQL

Best Practices

Common Pitfalls to Avoid

Ideal Project Types

Complementary Technologies

Real-World Example

Let's discuss your
GraphQL project.