Min Check

The min check validates the minimum value of a numeric column. This check helps ensure data quality by validating that the smallest values in your dataset meet expectations.

Configuration

Parameter	Required	Description
name	Yes	Unique name for the check
dataset	Yes	Table name or SQL query
type	Yes	Must be min
measure	Yes	Column name to find minimum
condition	Yes	Comparison operator
threshold	Yes	Value to compare against
dimensions	No	Group by columns
filter	No	WHERE clause conditions

Examples

Basic Min Check

- name: minimum_order_amount
  dataset: orders
  type: min
  measure: order_amount
  condition: ge
  threshold: 1.0

This check ensures the smallest order is at least $1.

Min with Filter

- name: min_product_price
  dataset: products
  type: min
  measure: price
  condition: gt
  threshold: 0
  filter: status = 'active'

This check validates that all active products have a price greater than 0.

Min by Dimensions

- name: min_salary_by_department
  dataset: employees
  type: min
  measure: salary
  dimensions: [department]
  condition: ge
  threshold: 30000

This check ensures minimum salary in each department is at least $30K.

Min with Time Dimension

- name: daily_min_temperature
  dataset: sensor_readings
  type: min
  measure: temperature
  condition: gt
  threshold: -40
  time_dimension:
    name: reading_date
    granularity: day

This check validates daily minimum temperature is above -40°.

Use Cases

• Data Quality: Ensure no unreasonably low values
• Business Rules: Validate minimum acceptable values
• Range Validation: Check lower bounds of data ranges
• Outlier Detection: Identify potentially problematic minimum values
• Compliance: Ensure minimum standards are met

Generated SQL

The min check generates SQL like:

SELECT MIN(order_amount)
FROM orders
WHERE status = 'completed'

With dimensions:

SELECT department, MIN(salary)
FROM employees
GROUP BY department

Example Results

✓ minimum_order_amount: 5.50 (≥ 1.0)
✗ min_product_price: 0.00 (> 0)
✓ min_salary_by_department_engineering: 65000 (≥ 30000)
✓ min_salary_by_department_sales: 35000 (≥ 30000)

Data Types

The measure column should be numeric or comparable. Common types include:

• INTEGER
• DECIMAL / NUMERIC
• FLOAT / DOUBLE
• DATE / TIMESTAMP
• TIME

NULL Handling

• MIN() ignores NULL values automatically
• If all values are NULL, MIN() returns NULL
• The check will fail if MIN() returns NULL (unless specifically handled)

Common Patterns

Price Validation

- name: positive_prices
  dataset: products
  type: min
  measure: price
  condition: gt
  threshold: 0

Date Range Validation

- name: recent_data
  dataset: transactions
  type: min
  measure: transaction_date
  condition: ge
  threshold: '2024-01-01'

Performance Metrics

- name: minimum_response_time
  dataset: api_logs
  type: min
  measure: response_time_ms
  condition: le
  threshold: 5000  # Max acceptable minimum response time

Performance Tips

1. Indexes: Ensure the measure column has appropriate indexes
2. Partitioning: Use partitioning for time-based queries
3. Filters: Apply filters to reduce data scanning
4. Statistics: Keep table statistics updated for query optimization

Related Checks

• Max - Maximum value validation
• Sum - Sum aggregation validation
• Numeric - Custom numeric expressions