End-to-end distributed tracing

Observability of all telemetry at scale

Distributed tracing

From browser and mobile apps to databases and individual lines of code, FusionReactor Application Performance Monitoring (APM) provides end-to-end distributed tracing. Using FusionReactor APM, you can monitor service dependencies and health metrics, reduce latency, and eliminate user errors by seamlessly correlating distributed traces with frontend and backend data.

End-to-end application performance monitoring

  • Monitor requests from RUM sessions to services, serverless functions, and databases
  • Automated injection of trace_ids allows you to view traces and logs in context
  • Distributed traces can be connected to infrastructure metrics, network calls, and live processes
  • Identify backend errors in synthetic API and browser tests
Distributed tracing, FusionReactor
Distributed tracing, FusionReactor

Control and visibility in real-time

  • Identify incidents faster with real-time visibility into ingested traces and service dependencies
  • Discover errors and latency outliers during active investigations with ML-based insights
  • Set custom retention filters based on tags to retain only the most important traces for your business
  • Using span-based metrics, you can set SLOs, track trends, and monitor KPIs

Performance monitoring for any stack, anywhere

  • Automatically instrument any application, on any host, container, serverless function, or PaaS, in just seconds
  • Integrate hundreds of third-party frameworks or libraries for unparalleled visibility into Java, .NET, PHP, Node.js, Ruby, Python, Go, or C++ code
  • With OpenTelemetry and OpenTracing, you can access vendor-neutral support
Distributed tracing, FusionReactor
Distributed tracing, FusionReactor

Identify root causes instantly with code-level insights

  • With always-on, low-overhead code profiling, you can optimize your production code and save on compute costs
  • Slow requests can be broken down by time spent in code on CPU, GC, lock contention, and I/O to reduce service latency
  • Analyze performance regressions caused by inefficient code using any timeframe and tag
  • Identify bottlenecks in your code by monitoring profile aggregations of services and endpoints