Debugging performance issues in a lagging distributed system requires a systematic, layered approach. Start by profiling the system end-to-end, using distributed tracing tools like Jaeger or Zipkin to pinpoint bottlenecks across services. Analyze metrics such as latency, throughput, and resource utilization to isolate problem areas. A unique strategy is to simulate load scenarios resembling real-world traffic using tools like k6 or Locust, enabling you to observe failure patterns. Prioritize addressing contention points, like database locks or network latency, and use circuit breakers to isolate failing components. This structured debugging ensures you identify root causes while maintaining system stability during fixes.

I believe its very important to have various metrics (e.g. latency, throughput etc.) emitted at different layers (e.g. client side latency, server processing latency, latency call latency) to identify the bottleneck component. For downstream latency, scaling could be the best solve. You can explore adding caching layer in between. If server processing is the cause then reviewing the resource utilisation should be the next steps. Tools like SAR provide resource level stats for a host. Onboarding to a profiler to diagnose memory and CPU usage and other application-level issues is an option. Besides running load test and scaling the system accordingly is critical for good CX and optimising the code should be part of the optimisation plan.

The first thing to do is look at the architecture for where large processing and communications are happening and what technologies are being used. Evaluate these points for likely culprits and start looking there at system metrics and logs. In distributed systems, look at the volume of communication between nodes and try to group high communication volume nodes together along with configuring them to use communication through memory vs. network, if possible. In processing, consider how the processes and threads interact, is the anything blocking or starving these. Also, insure there is enough processing margin to handle surges.

Without bulletproof metrics it is impossible to characterize your service's loads. After adding Prometheus metrics to one service, we found another group was "borrowing" our API's to run reports during the heaviest time periods. Secondly, metrics have to be easy for developers to add and in unlimited amounts. Systems that rely on an agent process ultimately compete with the service for CPU and memory resources. Alternatively, in-memory metric caches such as Prometheus have almost no overhead for additional metrics. This allows developers to add application specific metrics easily that are useful to both the engineering and product teams. Otherwise systems are usually limited to a smaller set of system metrics and API metrics.