Gmp Podmonitoring Scrape Interval Labeldrop

What Managed Service for Prometheus Metric Relabeling Is

In Google Cloud Managed Service for Prometheus (GMP), managed collection relies on Kubernetes Custom Resources such as PodMonitoring and ClusterPodMonitoring to define scraping targets and ingestion configurations. The metricRelabeling section implements standard Prometheus relabeling rules (metric_relabel_configs) applied to scraped samples immediately before they are ingested into the global Monarch datastore.

How It Meets the Requirements

• Adjusting Scraping Interval: Updating spec.endpoints[].interval to 30s reduces the collection frequency from 12 samples per minute to 2 samples per minute per time series, resulting in an immediate 83.3% reduction in baseline sample ingestion rate.
• Filtering High-Cardinality Dimensions: Setting action: labeldrop with regex: user_id in the metricRelabeling pipeline removes the user_id label from the scraped metric samples prior to storage. This aggregates the remaining dimensions and collapses millions of ephemeral time series into a small set of persistent streams.
• Zero Code Changes: Applying this transformation directly in the Kubernetes operator configuration eliminates the need to refactor, recompile, or redeploy application source code.

Key Advantages and Capabilities

• Sample-Based Cost Optimization: Because GMP bills primarily on the number of metric samples ingested, combining lower scrape intervals with label dropping yields dramatic financial savings.
• Pre-Ingestion Filtering: Metric relabeling occurs in memory on the local collector daemon before network transit and storage ingestion, preventing billable writes.
• Granular Control: Allows fine-grained manipulation of Prometheus metrics using upstream actions like keep, drop, labeldrop, and labelkeep.
• Declarative GitOps Management: PodMonitoring resources can be versioned and deployed automatically via standard Kubernetes CI/CD and Terraform workflows.

Why This Is Optimal

This configuration directly targets the two core drivers of observability costs in sample-metered environments: sample ingestion frequency and active time series cardinality. Modifying the PodMonitoring custom resource is the native, Google-recommended approach for managed collection on GKE.