Anomaly Tests configuration params
If your data set has a timestamp column that represents the creation time of a
field, it is highly recommended configuring it as a timestamp_column.
All anomaly detection tests:
— timestamp_column: column name
— where_expression: sql expression
— anomaly_sensitivity: int
— anomaly_direction: [both | spike | drop]
— ignore_small_changes:
spike_failure_percent_threshold: int
drop_failure_percent_threshold: int
— anomaly_exclude_metrics: [SQL expression]
Anomaly detection tests with timestamp_column:
— training_period: int
period: [hour | day | week | month]
count: int
— detection_period: int
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
dimension_anomalies, column_anomalies, all_columns_anomalies tests:
— dimensions: sql expression
volume_anomalies test:
— fail_on_zero: [true | false]
all_columns_anomalies test:
— column_anomalies: column monitors list
— exclude_prefix: string
— exclude_regexp: regex
dimension_anomalies test:
— exclude_final_results: [SQL where expression on fields value / average]
event_freshness_anomalies:
— event_timestamp_column: column name
— update_timestamp_column: column name