Metric Usage Analysis

The Metrics Usage Analyzer gives you a detailed view of your ingested metrics and labels. Use it to identify high-volume or high-cardinality metrics, understand ingestion patterns, detect inefficient time series, and optimize your data pipeline.

This tool works with pre-aggregated statistics updated from the ingestion pipeline. Variations, labels, and trends reflect real-time ingestion data.

By regularly using the Metrics Usage Analyzer, observability teams can:

Detect wasteful or unused metrics
Reduce cardinality
Control ingestion volume

Open the usage analysis tab

Navigate to Settings > Usage & Plans > Metric Data.
The Usage Analysis tab opens by default.
Use the three sub-tabs to explore:
- Metrics: Ingested metrics with usage, cardinality, and samples
- Labels: All labels attached to metrics
- Blocked: Metrics blocked from ingestion

Review top-level usage

At the top of the Metrics sub-tab you can see:

Total Metric Usage Units: Billing units generated by metrics
Fraction of Total Usage: Share of total data attributed to metrics

Note

Metric usage is billed in units. Select Show in Data Usage to compare with the overall Data Usage dashboard.

Explore the metrics table

The Metrics table lists usage details per metric. Key columns include:

Usage: Data volume in GB and billing units
% Usage: Share of total usage
Samples: Total datapoints ingested

Note

Sample counts might differ from PromQL results because Coralogix applies a 15-second deduplication window.

Dimensions: Unique label keys
Variations: Unique combinations of label keys
Cardinality: Unique time series per metric
% Cardinality: Share of total series cardinality
Last Ingested: Timestamp of the most recent ingestion event for the metric

Note

Variations reflect label set combinations. For example, (host, region) versus (host, app, region).

Drill into a specific metric

Overview tab

The Overview tab visualizes daily ingestion trends and metric activity.

Panels

Metric Unit Usage Per Day: Billing units ingested per day
Variation Unit Usage Per Day: Usage trends per variation
Label Unit Usage Per Day: Unit consumption per label

Display Modes

Toggle between:

Unit Usage
Data Volume
Sample Count
Cardinality

Insights

Detect ingestion spikes or drops
Identify inactive metrics
Compare usage trends over time

Variations

The Variations tab breaks down how label combinations affect a metric’s volume and cardinality.

Use this view to identify which label sets consume the most storage and where to optimize.

Total Labels: Lists all label keys found across the metric’s variations.

Examples:

__name__, process_tags_k8s_pod_name, operation, grpcStatusCode, serviceVersion, transactionRoot.

Shows the total number of labels (for example, 20 labels)
Displays each label as a visual tag
Includes both application and infrastructure labels (for example, Kubernetes pod_name, deployment_name)

Note

High label diversity increases unique series, storage, and billing. Review regularly to optimize. Metrics with multiple variations (different label combinations) require extra care at query time. Always use specific label filters in your queries to target the correct variation and avoid unexpected or inconsistent results.

Variations table

Each row represents a unique label set (variation). Hover to view the full label list.

Columns:

Variation: Label combination
Labels: Labels used versus total (for example, 18/20)
Usage: Data volume and units
% Usage: Share of metric usage
Samples: Datapoints ingested
Cardinality: Unique series generated
% Cardinality: Share of total series

Example:

The main variation uses 18/20 labels, consumes 3.33 GB (99.4%), and generates 8,688 series.
Two smaller variations use < 0.01 U each but still add thousands of series.

Note

Focus optimization on high-volume or high-cardinality variations.

Variation label overview

The Variations view highlights which labels are used in a selected variation.

Highlighted labels: labels included in the selected variation
Faded labels:labels that exist on the metric but don’t apply to this variation

This helps you see how label sets differ across variations and quickly identify which labels contribute to extra cardinality.

Note

When you see multiple variations for a metric, always filter by label in your queries to get accurate results.

Use the Variations view to:

Search by label key (for example, namespace=prod)
Compare high-cardinality versus high-usage sets
Drill down into label-level impact
Optimize redundant or low-value combinations

Best Practice: Aim for one variation per metric to simplify queries and reduce cardinality.

Labels tab

The Labels tab analyzes label-level impact on storage and cardinality.

Lists all labels attached to the metric
Shows each label’s usage, cardinality, and unique value count
Highlights labels driving high series counts

Note

Watch for labels like pod_name or operation with many unique values; they often inflate cost.

Explore label details

From the Labels tab, click any label row (for example, operation or host_name) to open its detailed view.

This view helps you analyze how a specific label contributes to metric usage, variation, and value diversity.

The label details page includes two sub-tabs:

Metrics: Lists all metrics associated with the selected label
Live values: Displays recent values for that label

Use this view to understand which metrics and label values drive data volume and cardinality.

Metrics tab

The Metrics tab shows how the selected label is used across different metrics.

For each metric, you can review:

Usage: Data volume attributed to the label
% Usage: Relative share of total usage
Unique Values: Count of distinct label values
Last Ingested: Timestamp of the latest ingestion for that metric

Example: Selecting the operation label lists metrics such as cx_service_catalog_service_duration_cx_bucket and latency_ms_sum, showing how each contributes to total usage.

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

Live values tab

The Live values tab lets you explore the actual values for a selected label.

Displays a sample of up to 1,000 values at a time. You can narrow the results using the search bar to filter by specific strings or patterns.
“Live” refers to a best-effort view of the last 24 hours of data. The list reflects values recently observed in your environment rather than a complete historical record.
Values are aggregated across all metrics that include the selected label, not limited to a single metric. Use this view to identify active label values and verify that expected data is still being ingested.

Note

Only 1,000 values are shown. Not all values are displayed.

Use this view to:

Inspect label values to understand what data is associated with a label.
Spot noise or garbage values in high-cardinality labels that may indicate misconfigured instrumentation.
Confirm label consistency, such as checking that values follow expected formats or naming patterns (for example, region: us-east-1, not region: useast).

Example: If you select the label operation, you might see hundreds of API operation names such as: GET /items, POST /checkout, grpc.health.v1.HealthCheck, etc.

Block noisy or unused metrics

In the Action column of the table, select Block to stop ingesting a metric.

Note

Blocking reduces both data volume and cost by preventing storage and queries of low-value metrics.

Unblock metrics when needed

Open the Blocked tab.
Select Unblock for any metrics you want to resume ingesting.

Investigate label usage

In the Labels tab, you can:

Search for a specific label (for example, task_id)
View all metrics that use the label
Investigate cardinality and value distribution for that label

Permissions

You must have the METRICS.DATA-ANALYTICS#HIGH:READ permission to access this section. For more information, see Create Roles and Permissions.

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