Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam!
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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?
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.
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