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An e-commerce company runs a microservices-based application across multiple services in Google Cloud. Following a recent feature release, operations engineers notice three distinct issues affecting production:
Which combination of Google Cloud Application Performance Management (APM) and observability tools should you implement to address each issue?
Use Cloud Logging queries to calculate distributed request duration, Cloud Monitoring dashboards to analyze process-level memory allocations, and Cloud Trace to aggregate application stack traces.
Use Cloud Trace to analyze inter-service request latency, Cloud Profiler to inspect CPU and memory usage at the code level, and Error Reporting to aggregate and alert on application crashes.
Use Cloud Profiler to map end-to-end distributed service latency, Cloud Trace to pinpoint in-memory code allocations, and Log Analytics to group unhandled exceptions.
Use Cloud Service Mesh access logs to measure database query latency, Cloud Audit Logs to track memory leaks, and Eventarc to trigger notifications on application crashes.
Use Cloud Logging queries to calculate distributed request duration, Cloud Monitoring dashboards to analyze process-level memory allocations, and Cloud Trace to aggregate application stack traces.
Use Cloud Trace to analyze inter-service request latency, Cloud Profiler to inspect CPU and memory usage at the code level, and Error Reporting to aggregate and alert on application crashes.
This solution combines Cloud Trace, Cloud Profiler, and Error Reporting, which together form the core Application Performance Management (APM) suite in Google Cloud. Each tool is purpose-built to target a specific dimension of distributed system performance and application reliability.
This approach directly aligns each operational symptom with its optimal diagnostic tool, enabling granular distributed latency tracing, code-level resource tuning, and centralized exception triage without requiring custom analytical infrastructure.
Use Cloud Profiler to map end-to-end distributed service latency, Cloud Trace to pinpoint in-memory code allocations, and Log Analytics to group unhandled exceptions.
Use Cloud Service Mesh access logs to measure database query latency, Cloud Audit Logs to track memory leaks, and Eventarc to trigger notifications on application crashes.
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