An enterprise analytics team purchased 200 dedicated BigQuery slots through a reservation. To avoid saturation, they want proactive notification whenever the reservation's slot utilization stays above 90 percent for at least ten minutes while leaving non-critical workloads untouched. They prefer a managed solution and want to avoid writing custom code or scheduled queries. Which approach best meets the requirement?
Create a Cloud Monitoring metric-threshold alert on bigquery.googleapis.com/reservation/slot_utilization, filtered to the reservation and set to trigger when the value is ≥ 0.9 for ten consecutive minutes.
Define a Cloud Billing budget at 90 percent of expected monthly spend and rely on its e-mail notifications to warn the team.
Schedule a BigQuery job every ten minutes that queries INFORMATION_SCHEMA.JOBS_BY_PROJECT for slot usage and publishes the result to a Pub/Sub topic linked to Cloud Monitoring.
Export BigQuery Audit Logs to Pub/Sub, run a streaming Dataflow pipeline that calculates slot utilization, and send alerts from a Cloud Function when the threshold is exceeded.
Cloud Monitoring exposes the metric bigquery.googleapis.com/reservation/slot_utilization for each reservation. A metric-threshold alerting policy can be configured to watch this metric, filter on the specific reservation, aggregate over a ten-minute window, and fire when the value is ≥ 0.9. This produces notifications without any custom data collection or scheduled jobs.
Billing budget alerts track spend, not slot usage, so they cannot detect capacity saturation. Scheduled INFORMATION_SCHEMA queries or log-based pipelines would work but require custom code and maintenance, which the team wants to avoid. Exporting Audit Logs to Pub/Sub and processing them with Dataflow is similarly complex and unnecessary when a native metric already exists.
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