Skip to content

Max count

Data quality rule that verifies if a data quality check reading is lesser or equal a maximal value.

Parameters

This checks has one parameter that should be configured for each alert:

  • low:
    rule threshold for a low severity (1) alert
    • max_value: float
      maximal accepted value for the actual_value returned by the sensor (inclusive)
  • medium:
    rule threshold for a medium severity (2) alert
    • max_value: float
      maximal accepted value for the actual_value returned by the sensor (inclusive)
  • high:
    rule threshold for a high severity (3) alert
    • max_value: float
      maximal accepted value for the actual_value returned by the sensor (inclusive)

Example

The following example shows how to implement max_count rule for a check.

The assigned severity depends on sensor result (see YAML configuration below):

  • result <= 12.0, the check is passed: valid result
  • 12.0 < result <= 24.0 the severity is low (1)
  • 24.0 < result <= 36.0 the severity is medium (2)
  • 36.0 < result, the severity is high (3)
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
# yaml-language-server: $schema=https://cloud.dqo.ai/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  target:
    schema_name: test_data
    table_name: string_dates
  time_series: null
  columns:
    dates:
      type_snapshot:
        column_type: STRING
        nullable: true
      checks:
        validity:
          date_type_percent:
            rules:
              max_count:
                low:
                  max_value: 12.0
                medium:
                  max_value: 24.0
                high:
                  max_value: 36.0