Working with the Metrics Data explorer

Metrics view

Select the Metrics tab in the Usage Analysis page.

Use this view to detect metrics that are heavily influenced by the chosen label or that have excessive label variations.

At the top-level, you can find charts that summarize metrics ingestion and trends for the selected time range. Use these charts to understand how your metric volume and cardinality change over time:

• The Total samples chart that shows the metric data points that are ingested in the selected time range.

  Use this chart to spot ingestion spikes or drops that might indicate deployment issues, misconfigured scrapers, or unexpected increases in data volume.

• The Total Cardinality chart shows unique time series in the selected time range.

  Use this chart to detect sudden growth in time-series count, often caused by new labels, unexpected label values, or high-cardinality dimensions such as pod_name or operation.

These charts help you correlate ingestion patterns, identify high-impact changes quickly, and prioritize where to reduce cardinality or remove unnecessary variations.

The Metrics view shows how the selected label is used across different metrics. For each metric, you can view the following information:

• In the Volume column, you can see the volume of metrics.
• In the % Volume column, you can see the fraction of total metrics volume.
• In the Samples column, you can see the number of data points collected.
• In the % Samples column, you can see the fraction of data points collected.
• In the Dimensions column, you can see the count of unique label names.
• In the Variations column, you can see the count of unique variations. A variation represents the unique label combinations for a metric.
• In the Cardinality column, you can see the count of unique time series.
• In the % Cardinality column, you can see the fraction of total series cardinality.
• In the Last Ingested column, you can see the timestamp of the latest ingestion for that metric.
• In the Status column, you can see whether the metric is active or disabled.
• In the Action column, you can see whether the metric is blocked.

When you click on a metric, a page opens with detailed information about the metric.

Labels view

Select the Labels tab in the Usage Analysis page.

Use this view to analyze how a specific label contributes to metric usage, variation, and value diversity.

You can use the Labels tab to analyze label-level impact on storage and cardinality, for example:

• List all labels attached to the metric
• Show each label's usage, cardinality, and unique value count
• Highlight labels driving high series counts

Watch for labels with many unique values such as pod_name or operation. They often inflate cost.

• You can select a label to display detailed information such as the metrics that have that label.

• If you select Live values, you can see the list of values identified for the chosen label in the selected time range.

• You can review the table to compare values and identify which ones generate the most variations. This helps determine whether specific label values are responsible for cardinality growth and where to focus optimization efforts.

Query analyzer

Select the Queries tab in the Usage Analysis page.

Use the Query Analyzer to gain insight into how metrics are used across your environment, explore query patterns, and track usage frequency. Use it periodically to identify query patterns across different sources in one place, along with their usage and success rates, to help clean up unused metrics.

For example, you can use the Query Analyzer to:

• Identify high-value metrics versus rarely used or unused ones.
• Detect redundant queries that increase cost or cardinality.
• Understand which dashboards, alerts, or APIs use a given metric.
• Correlate high-usage queries with ingestion data for cost analysis.
• Before removing a metric, verify that no critical dashboards or alerts depend on it.

Consider the following information:

• Supports PromQL queries only.
• Location shows where a mmetric is being used and queries.
• Can only analyze one day at a time, not a range of days.

In this tab, you can view the following information:

• Query pattern: Normalizes queries by replacing variable values with $var to group similar queries and reduce label noise.
• # Times used: Displays the number of times each query pattern has run on the selected date.
• Status: Shows success and failure percentages for query executions, helping identify errors or invalid queries.
• Location: Lists the source of the query, such as a Dashboard, alerts.
• Date selection: View query activity for a specific date using the date selector.

You can filter query patterns by keyword or location.

Consider the following information when you interpret the data:

• When the value of # Times used is high, the metric is actively queried and likely important. Keep the metrics and monitor its performance.
• When the # Times used is low or zero, the metric is rarely or never used. Review dashboards and alerts to confirm whether you can drop the metric.
• When the # Times used for a Frequent location such as Alerts is high, the metric is operationally critical. Prioritize retention and optimization.
• WWhen the # Times used for a Frequent location such as Alerts is low, you might be able to drop this metrics. Investigate label usage and aggregation strategies.