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

# Dimension Anomalies

The `elementary.dimension_anomalies` test counts rows grouped by given `dimensions` (columns/expressions). It monitors the frequency of values in the configured dimension over time and alerts on unexpected changes in the distribution. This test is best configured on low-cardinality fields.

### How it works

* If `timestamp_column` is configured, the distribution is collected per `time_bucket`.
  * If not, it counts the total rows per dimension.
* The test alerts on unexpected changes in the distribution of dimension values over time.

<Tabs>
  <Tab title="Models">
    ```yml theme={"system"}
    models:
      - name: < model name >
        config:
          elementary:
            timestamp_column: < timestamp column >
        tests:
          - elementary.dimension_anomalies:
              dimensions: < columns or sql expressions of columns >
              # optional - configure a where a expression to accurate the dimension monitoring
              where_expression: < sql expression >
              time_bucket: # Daily by default
                period: < time period >
                count: < number of periods >
    ```
  </Tab>

  <Tab title="Models example">
    ```yml theme={"system"}
    models:
      - name: login_events
        config:
          elementary:
            timestamp_column: "loaded_at"
        tests:
          - elementary.dimension_anomalies:
              dimensions:
                - event_type
                - country_name
              where_expression: "event_type in ('event_1', 'event_2') and country_name != 'unwanted country'"
              time_bucket:
                period: hour
                count: 4
              # optional - use tags to run elementary tests on a dedicated run
              tags: ["elementary"]
              config:
                # optional - change severity
                severity: warn

      - name: users
        # if no timestamp is configured, elementary will monitor without time filtering
        tests:
          - elementary.dimension_anomalies:
              dimensions:
                - event_type
              tags: ["elementary"]
    ```
  </Tab>
</Tabs>

### [​](https://docs.elementary-data.com/data-tests/anomaly-detection-tests/dimension-anomalies#test-configuration)Test configuration

```yaml theme={"system"}
tests:
  — elementary.dimension_anomalies:
    dimensions: sql expression
    timestamp_column: column name
    where_expression: sql expression
    anomaly_sensitivity: int
    anomaly_direction: [both | spike | drop]
    detection_period:
      period: [hour | day | week | month]
      count: int
    training_period:
      period: [hour | day | week | month]
      count: int
    time_bucket:
      period: [hour | day | week | month]
      count: int
    seasonality: day_of_week
    detection_delay:
      period: [hour | day | week | month]
      count: int
    ignore_small_changes:
      spike_failure_percent_threshold: int
      drop_failure_percent_threshold: int
    anomaly_exclude_metrics: [SQL expression]
```

<Info>
  **Important Notes**

  * **Required configuration**: `dimensions`
  * The test is best suited for low-cardinality fields.
  * If `timestamp_column` is not configured, the test will monitor without time filtering.
  * Tags can be used to run elementary tests on a dedicated run.
  * Severity can be optionally changed in the config section.
  * The `where_expression` can be used to refine the scope of dimension monitoring.
</Info>


## Related topics

- [Anomaly Tests Parameters](/integrations/elementary-data/anomaly-detection-tests/anomaly-tests-parameters.md)
- [Column Anomalies](/integrations/elementary-data/anomaly-detection-tests/column-anomalies.md)
- [Volume Anomalies](/integrations/elementary-data/anomaly-detection-tests/volume-anomalies.md)
- [Freshness Anomalies](/integrations/elementary-data/anomaly-detection-tests/freshness-anomalies.md)
- [Macros](/guides/dbt-fundamentals/configuring-your-dbt-project/macros.md)