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An e-commerce company runs a microservices-based checkout system deployed across Google Kubernetes Engine (GKE) and Cloud Run. During peak shopping events, end users report intermittent latency spikes that violate response time Service Level Objectives (SLOs), accompanied by sudden increases in cluster compute consumption. The operations team needs to pinpoint which specific inter-service calls introduce the delays and identify the exact application functions consuming excessive CPU and memory in production.
Which observability strategy should you implement to diagnose these performance bottlenecks?
Instrument the microservices with Cloud Trace to capture distributed request flows across services, and deploy Cloud Profiler agents to identify resource-intensive functions and call stacks in production.
Enable Active Assist Recommender on the clusters and configure Horizontal Pod Autoscaling (HPA) using standard CPU utilization thresholds.
Create an aggregated sink in Cloud Logging to export application logs to BigQuery, and build Looker Studio reports to analyze function call stacks and memory allocations.
Configure Cloud Monitoring custom dashboards with high-frequency metric collection intervals, and enable GKE usage metering to isolate function-level performance bottlenecks.
Instrument the microservices with Cloud Trace to capture distributed request flows across services, and deploy Cloud Profiler agents to identify resource-intensive functions and call stacks in production.
Cloud Trace is a distributed tracing system that collects latency data from applications and displays the flow of requests across multiple network boundaries and microservices in near real time. Cloud Profiler is a continuous, statistical, low-overhead profiling tool that captures CPU utilization and memory allocations directly from production workloads, mapping resource consumption directly to specific source code functions.
Using Cloud Trace alongside Cloud Profiler provides complete vertical observability. Cloud Trace isolates the problematic microservice in the distributed transaction, while Cloud Profiler pinpoints the exact method or loop causing CPU exhaustion and latency inside that service.
Enable Active Assist Recommender on the clusters and configure Horizontal Pod Autoscaling (HPA) using standard CPU utilization thresholds.
Create an aggregated sink in Cloud Logging to export application logs to BigQuery, and build Looker Studio reports to analyze function call stacks and memory allocations.
Configure Cloud Monitoring custom dashboards with high-frequency metric collection intervals, and enable GKE usage metering to isolate function-level performance bottlenecks.
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