- name: < model name >
      - elementary.volume_anomalies:
          timestamp_column: < timestamp column >
          where_expression: < sql expression >
          time_bucket: # Daily by default
            period: < time period >
            count: < number of periods >


Monitors the row count of your table over time per time bucket (if configured without timestamp_column, will count table total rows).

Upon running the test, your data is split into time buckets (daily by default, configurable with the time bucket field), and then we compute the row count per bucket for the last training_period days (by default 14).

The test then compares the row count of each bucket within the detection period (last 2 days by default, configured as detection_period), and compares it to the row count of the previous time buckets.

The test will only run on completed time buckets, so if you run it with daily buckets in the middle of today, the test would only count yesterday as a complete bucket. If there were any anomalies during the detection period, the test will fail.

Test configuration

No mandatory configuration, however it is highly recommended to configure a timestamp_column.

tests:   — elementary.volume_anomalies:     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     fail_on_zero: [true | false]     ignore_small_changes:       spike_failure_percent_threshold: int       drop_failure_percent_threshold: int     detection_delay:       period: [hour | day | week | month]       count: int     anomaly_exclude_metrics: [SQL expression]