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Answer: Export the Prometheus metrics and use Cloud Monitoring to view them as external metrics. Configure Cloud Monitoring to create log-based metrics from the logs, and correlate them with the Prometheus data.
Option D is the correct choice because it effectively uses Google Cloud Monitoring to handle both Prometheus metrics and log-based metrics from Cloud Logging. By exporting Prometheus metrics as external metrics to Cloud Monitoring and creating log-based metrics from the logs, you can correlate these data sources within the same tool. Cloud Monitoring allows real-time alerting and dashboards, which addresses the requirement for real-time alerts. This approach minimizes costs by leveraging existing services without exporting data to additional storage or processing systems like Bigtable or BigQuery, which could incur higher expenses.
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You deployed an application on Google Kubernetes Engine and noticed performance degradation. Logs are being written to Cloud Logging, and you're using a Prometheus sidecar for metrics. How can you correlate metrics and log data to troubleshoot the issue while sending real-time alerts cost-effectively?
A
Create custom metrics from the Cloud Logging logs, and use Prometheus to import the results using the Cloud Monitoring REST API.
B
Export the Cloud Logging logs and the Prometheus metrics to Cloud Bigtable. Run a query to join the results, and analyze in Google Data Studio.
C
Export the Cloud Logging logs and stream the Prometheus metrics to BigQuery. Run a recurring query to join the results, and send notifications using Cloud Tasks.
D
Export the Prometheus metrics and use Cloud Monitoring to view them as external metrics. Configure Cloud Monitoring to create log-based metrics from the logs, and correlate them with the Prometheus data.
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