Debug GraphQL Queries in Production with OpenTelemetry: A Step-by-Step Guide

Updated at: 08/07/2025

Debugging GraphQL queries in production can be a nightmare. This step-by-step guide shows you how to leverage OpenTelemetry's powerful tracing capabilities to pinpoint performance bottlenecks and errors in your live GraphQL environment. Learn to instrument your queries, visualize traces, and effectively troubleshoot issues, saving valuable time and improving your application's stability. Let's dive in!

Step-by-Step Instructions

  1. Instrument GraphQL Service

    • Instrument your GraphQL service with OpenTelemetry to obtain distributed traces. Use appropriate instrumentation libraries for your GraphQL framework (e.g., nodejs instrumentation in the example).
    • Export spans to the OpenTelemetry collector.
    Export spans to the OpenTelemetry collector. Export spans to the OpenTelemetry collector.
    Instrument GraphQL Service

  2. Visualize Traces with Jaeger

    • Use Jaeger to visualize end-to-end distributed traces. Identify problem areas by examining spans and their relationships.
    Use Jaeger to visualize end-to-end distributed traces. Identify problem areas by examining spans and their relationships.
    Visualize Traces with Jaeger

  3. Collect and Analyze RED Metrics

    • Utilize Jaeger's (or a similar tool's) built-in metrics (or configure a span metrics connector) to collect RED metrics (Rate, Errors, Duration).
    • Analyze RED metrics in a dashboard (e.g., Jaeger dashboard connected to Prometheus) to pinpoint performance bottlenecks or error spikes. Focus on error rates and latency.
    Analyze RED metrics in a dashboard (e.g., Jaeger dashboard connected to Prometheus) to pinpoint performance bottlenecks or error spikes. Focus on error rates and latency. Analyze RED metrics in a dashboard (e.g., Jaeger dashboard connected to Prometheus) to pinpoint performance bottlenecks or error spikes. Focus on error rates and latency.
    Collect and Analyze RED Metrics

  4. Debug Upstream Errors

    • For Upstream errors (errors originating from external services called by your GraphQL API), use tracing to identify the failing external service and HTTP status code.
    For Upstream errors (errors originating from external services called by your GraphQL API), use tracing to identify the failing external service and HTTP status code.
    Debug Upstream Errors

  5. Debug Resolver Errors

    • For Resolver errors (errors within your GraphQL resolvers), note that GraphQL often returns a 200 HTTP status code even with errors. Add custom attributes to your spans (e.g., `graphql.error.message`) to capture detailed error information within the spans.
    For Resolver errors (errors within your GraphQL resolvers), note that GraphQL often returns a 200 HTTP status code even with errors. Add custom attributes to your spans (e.g., `graphql.error.message`) to capture detailed error information within the spans.
    Debug Resolver Errors

  6. Identify N+1 Query Issues

    • Identify N+1 query problems by checking for a large number of downstream HTTP calls resulting from a single GraphQL query. Jaeger's dependency graphs can assist in this.
    Identify N+1 query problems by checking for a large number of downstream HTTP calls resulting from a single GraphQL query. Jaeger's dependency graphs can assist in this.
    Identify N+1 Query Issues
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Tips

  • GraphQL's 200 HTTP status code doesn't always indicate success; examine the response body for errors.
  • A high average number of outgoing requests per GraphQL query (seen in Prometheus) often signals an N+1 problem.

Common Mistakes to Avoid

1. Incorrect Instrumentation Placement

Reason: Tracing spans are not placed correctly around the GraphQL resolvers, leading to incomplete or inaccurate tracing data.
Solution: Ensure OpenTelemetry instrumentation is properly wrapped around the relevant resolver functions to capture the entire execution flow.

2. Missing or Incomplete Context Propagation

Reason: Trace context isn't properly propagated between services or across different parts of the GraphQL execution pipeline, resulting in disconnected traces.
Solution: Use OpenTelemetry's context propagation mechanisms (e.g., baggage, context propagation headers) to maintain trace continuity across service boundaries and asynchronous operations.

3. Ignoring or Misinterpreting Error Handling

Reason: Errors aren't properly captured or handled in the tracing pipeline, making debugging difficult, and leading to missing error details in traces.
Solution: Implement proper error handling within OpenTelemetry spans to capture relevant error information and ensure that errors are associated with the correct spans.

FAQs

Why use OpenTelemetry for debugging GraphQL queries in production instead of other tools?
OpenTelemetry provides standardized, vendor-neutral tracing. This means your traces are compatible with numerous backends (e.g., Jaeger, Zipkin), offering flexibility and avoiding vendor lock-in. It also offers richer context and deeper insights into your query execution than many alternative solutions, allowing for more effective debugging.
What if I don't have control over the GraphQL server to instrument it with OpenTelemetry?
If you can't directly instrument the server, you might explore using a network proxy that intercepts and traces GraphQL requests. However, this approach adds complexity and might not capture all the necessary context. Direct server-side instrumentation is always preferred for optimal results.