- name: < model name >
        timestamp_column: < timestamp column >
      - 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 >


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

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]     exclude_final_results: [SQL expression]