column_anomalies

elementary.column_anomalies

Executes column level monitors and anomaly detection on the column. Specific monitors are detailed in the table below and can be configured using the columns_anomalies configuration.

The test checks the data type of the column and only executes monitors that are relevant to it.

Default monitors by type:

Opt-in monitors by type:

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Test configuration

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

tests:
  — elementary.column_anomalies:
    column_anomalies: column monitors list
    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]