Learn GraphQL - Common Issues & Debugging Techniques

#Introduction

Every real application has bugs — what separates senior engineers is the ability to find and fix them quickly. Episode 45 equips you with GraphQL troubleshooting and debugging skills systematically.

We'll cover common problems, debugging tools, how to debug performance, error investigation, and production debugging techniques.

#Common Problems

#N+1, Circular Dependencies, and Memory Leaks

The most common problems in GraphQL:

• N+1 queries: you conquered these in episode 9 — the sign: repeated query logs with the same pattern.
• Circular dependencies: when types reference each other (User and Post) without query limits; usually not a bug, but can confuse tracing — limit depth.
• Memory leaks: cached query results without limits, or subscription listeners that are never cleaned up — growing slowly until the server weakens.
• Slow queries: resolvers without indexes or batching.
• Authentication failures: unparsed tokens, different secrets between environments.
• Cache inconsistencies: Redis or client caches not invalidated after mutations.

Detect a memory leak with --inspect

node --inspect dist/index.js

#Debugging Tools

#Apollo Studio and Chrome DevTools

• Apollo Studio: popular operations, errors, and per-resolver tracing (episode 24) — the first place to check production issues.
• Chrome DevTools Network tab: see GraphQL requests, duration, and raw responses from the client side.
• GraphQL Playground / Sandbox: easily retest queries while investigating.
• Structured logging: Pino (episode 24) with requestId to trace a single request across logs.

To see the raw GraphQL request from the terminal, use curl http://localhost:4000/ -H "Content-Type: application/json" -d '{"query":"{ posts { id } }"}'.

#Distributed Tracing

For problems spanning many services, OpenTelemetry (episode 24) shows a complete trace: a span per resolver, per database query, and per external call. Traces answer the "where did the time go" question precisely.

#Performance Debugging

#Identifying Slow Resolvers

Systematic steps for a slow query:

1. Measure total duration in Apollo Studio.
2. Look at per-field tracing — which resolver is the slowest.
3. Inspect the database queries that resolver produces.
4. Optimize: indexes, DataLoader, or caching.

Simple resolver timing

const slowResolver = async (_, args, ctx) => {
  const start = performance.now();
  const result = await ctx.db.users.findMany();
  ctx.logger.info({ ms: performance.now() - start }, "query users");
  return result;
};

#Profiling and Load Testing

For deeper analysis, use the Node profiler (--prof) or tools like clinic.js. Load testing with k6 or artillery simulates real traffic and finds breaking points before users experience them.

#Error Investigation

#Reading Stack Traces and Reproducing

When an error appears, investigate with discipline:

1. Read the stack trace from logs or Sentry (episode 24).
2. Correlate with requestId and the failed operation.
3. Reproduce with the same query in the Sandbox — narrow down the variables.
4. Debug the resolver by adding logs or breakpoints.
5. Inspect the context: what user, what environment, what data came in.

Log errors with context

async function resolver(_, args, ctx) {
  try {
    return await heavyOperation();
  } catch (err) {
    ctx.logger.error(
      { err, args, userId: ctx.user?.id },
      "heavyOperation gagal"
    );
    throw err;
  }
}

The key to fast investigation: context-rich logs. Errors without context force guessing; errors with context immediately point the way.

#Production Debugging

#Log Analysis and Incident Response

Production debugging differs from development:

• Log analysis: centralized log aggregation, searching by requestId and timestamp.
• Metrics interpretation: duration histograms, error rates, and latency peaks give early signals.
• Incident response: triage (how severe, who's affected), communication, temporary mitigation, then a permanent fix.
• Post-mortem: document the timeline, root cause, and prevention steps — every incident is a lesson.

journalctl -u api-graphql | grep "requestId=abc123"

#Conclusion

Key takeaways:

• N+1, circular dependencies, memory leaks, and cache inconsistencies are the most common problems.
• Apollo Studio, Chrome DevTools, and logging with requestId are the main tools.
• Performance debugging starts with resolver tracing, then database optimization.
• Context-rich errors drastically speed up investigation.
• Production debugging uses log analysis, metrics, and incident response.
• Every incident produces a post-mortem and prevention steps.

In the next episode, episode 46, you'll learn about team workflows and collaboration — schema ownership and governance, development workflows with feature branches, frontend-backend contracts with mocking, quality assurance with schema linting, and knowledge sharing with ADRs and documentation. GraphQL in your team will run professionally!