> ## Documentation Index
> Fetch the complete documentation index at: https://docs.chronosphere.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Reduce cardinality

export const TUsageAnalyzer = () => <>
    Telemetry Usage Analyzer
  </>;

When you're first using Chronosphere Observability Platform, or a new app or service
comes online, you might see cardinality spikes. Cardinality spikes can occur when:

* A metric or group of metrics has unexpectedly large numbers of labels.
* A process or service creates many similarly named metrics.

<Tip>
  For more information about cardinality, see
  [What is high cardinality in observability?](https://chronosphere.io/learn/what-is-high-cardinality/)
  on the Chronosphere Blog.
</Tip>

Cardinality spikes can cause storage and licensing issues. To reduce cardinality, or
data storage for less important metrics:

1. Find a problematic metric or label.
2. Review that metric or label's usage.
3. Decide what to do with it (drop, rollup).

## Find a metric and inspect the associated labels

Observability Platform provides the following tools to help you understand the impact of
metric growth, identify problematic metrics and labels, and assess the impact of
existing aggregation rules:

* The [Metric Growth dashboard](/observe/dashboards/managed-dashboards#metric-growth)
  can includes metrics and labels that have recently increased in cardinality.
* The [Live Telemetry Analyzer](/investigate/analyze/telemetry-analyzer) provides real-time
  insight into current incoming metrics. Sort metrics by **Unique value** to find
  potential high cardinality.
* The [Aggregation Rules UI](/control/shaping/shape-metrics/reduce-cardinality/aggregation-rules)
  visualizes existing shaping rules and how they affect your environment. Review
  these rules to understand their impact.

If you want to reduce the cardinality of a metric, you must first understand the
targeted metric and its associated labels.

If you have administrative privileges:

1. In the navigation menu, click **<Icon icon="shield-user" /> Go to Admin**
   and then select
   **<Icon icon="microscope" /> Analyzers <span aria-label="and then">></span> Live Telemetry**.
2. The analyzer defaults to `_name_`. Sort **Label values** by name, or add a
   **label filter**.
3. In the **Labels** section, inspect the incoming label keys. The **Unique Values**
   column shows how many distinct values are incoming for a given label
   (cardinality), and **Appears In** shows how frequently that label is attached to
   the metric.

When the number of unique values for a metric is high, that label contributes
significantly to the cardinality for the metric.

## Review metric and label usage

After identifying a high-cardinality label, you need to understand whether this
label is meaningful, or if it can be safely removed.

To verify dropping a label is safe, use the
[<TUsageAnalyzer />](/investigate/analyze/usage#high-volume-low-utility-metrics)
to review each label's **Utility score**. This score provides insight into which
labels users [find important](/investigate/analyze/usage#usage-patterns).

## Remove the identified label

If you identify a label that isn't used in any dashboards or alerts, consider
reducing or removing the label using these methods:

* Create [drop, mapping, or rollup rules](/control/shaping/shape-metrics/rules#reduce-stored-metrics)
  to reduce stored metrics by aggregating, downsampling, or dropping unneeded metric
  data.
* Use the [Recommendations](/control/shaping/shape-metrics/reduce-cardinality/recommendations) page
  to identify metrics and labels with no usage or utility over the past 30 days.
  Apply the suggested recommendations to reduce the impact on persisted writes and
  persisted cardinality.
* When ingesting Google Cloud metrics, use
  [filters and aggregations](/ingest/metrics-traces/gcp#control-incoming-metrics).

### Validation

For rollup rules, preview the [shaping impact](/control/shaping/shape-metrics/reduce-cardinality/shaping-impact) to
review and confirm your changes before deleting metrics and labels that still matter.

## Post validation tasks

Return to the Live Telemetry Analyzer and search for your metric. If you've used a
rollup rule, it can take some time before your rolled up metric appears.

For rolled up metrics, it often makes sense to drop raw data if that data isn't
needed. This reduces cardinality and data storage requirements.

After you've validated your rule, apply the rule using your selected method.