> ## Documentation Index > Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt > Use this file to discover all available pages before exploring further. # dimension_anomalies Let our Slack chatbot create the anomaly test you need. `elementary.dimension_anomalies` The test counts rows grouped by given `dimensions` (columns/expressions). This test practically monitors the frequency of values in the configured dimension over time, and alerts on unexpected changes in the distribution. It is best to configure it on low-cardinality fields. If `timestamp_column` is configured, the distribution is collected per `time_bucket`. If not, it counts the total rows per dimension. ### Test configuration *Required configuration: `dimensions`* 
  
    data\_tests:
      -- elementary.dimension\_anomalies:
        arguments:
          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]
          exclude\_final\_results: \[SQL expression]
```yml Models theme={null} models: - name: < model name > config: elementary: timestamp_column: < timestamp column > data_tests: - elementary.dimension_anomalies: arguments: 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 > ``` ```yml Models example theme={null} models: - name: login_events config: elementary: timestamp_column: "loaded_at" data_tests: - elementary.dimension_anomalies: arguments: 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 config: # optional - use tags to run elementary tests on a dedicated run tags: ["elementary"] # optional - change severity severity: warn - name: users # if no timestamp is configured, elementary will monitor without time filtering data_tests: - elementary.dimension_anomalies: arguments: dimensions: - event_type config: tags: ["elementary"] ```