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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]