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Answer: Enable managed collection on every GKE cluster. Query the metrics in Cloud Monitoring.
To design a highly-available solution for collecting and querying both domain-specific workload metrics and GKE default metrics across all clusters with minimal operational overhead, the best approach is to leverage Google's managed services. Option D suggests enabling managed collection on every GKE cluster and querying the metrics in Cloud Monitoring. This approach minimizes operational overhead as it uses Google's managed services for both collection and querying, ensuring high availability and scalability without the need for manual setup and maintenance of additional components like Prometheus or Thanos. Cloud Monitoring is designed to work seamlessly with GKE, providing a unified platform for monitoring metrics across all clusters. Option A and C involve setting up and managing Prometheus and Thanos, which increases operational overhead. Option B suggests querying metrics in BigQuery, which is not optimized for real-time monitoring and querying of metrics like Cloud Monitoring is.
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Design a highly-available solution to collect and query both domain-specific workload metrics and default GKE metrics across multiple clusters in a project while minimizing operational overhead. What is the recommended approach?
A
Use Prometheus operator to install Prometheus in every cluster and scrape the metrics. Configure remote-write to one central Prometheus. Query the central Prometheus instance.
B
Enable managed collection on every GKE cluster. Query the metrics in BigQuery.
C
Use Prometheus operator to install Prometheus in every cluster and scrape the metrics. Ensure that a Thanos sidecar is enabled on every Prometheus instance. Configure Thanos in the central cluster. Query the central Thanos instance.
D
Enable managed collection on every GKE cluster. Query the metrics in Cloud Monitoring.
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