Load test your distributed system, not just its edges
Cascading failures hide between services. Test the full call graph under realistic traffic to find the bottleneck before your SREs do.
Challenges you face
Cascading failures
A slow downstream service causes queue buildup, thread exhaustion, and timeouts to ripple through the entire system.
Circuit breakers untested
Resilience patterns like retries, timeouts, and circuit breakers are configured but never validated under actual load.
Service-level blind spots
Testing the API gateway in isolation misses latency added by service mesh, serialisation, and inter-service auth.
How perfscale helps
End-to-end load injection
Drive load through the public API and let it propagate naturally through the service graph — no service-level stubbing.
Chaos-compatible
Run load tests while injecting failures with Chaos Monkey or Fault Injection to validate resilience patterns.
Multi-service scenarios
Write scripts that exercise read-heavy, write-heavy, and mixed workloads to match real traffic composition.
Retry storm detection
Identify when aggressive retry logic amplifies load during degraded conditions — a common failure mode in distributed systems.
Correlated metrics
Correlate perfscale results with your APM traces and infrastructure metrics to pinpoint the exact service causing latency.
Per-service regression tests
Run targeted load tests when a specific service is updated — catch performance regressions without a full-stack test.
What you get
- Validate circuit breakers and retries under load
- Find the bottleneck service in the call chain
- Test with realistic traffic distributions
- Works with gRPC, REST, and async message queues
- Correlates with Prometheus, Jaeger, and OpenTelemetry
- Supports Kubernetes-native deployments
- Test fan-out patterns and aggregation services
- Identify head-of-line blocking in connection pools
Find the weak link in your distributed system
Talk to us about setting up load testing for your microservices architecture.
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