lookml-tests

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Standards and best practices for writing LookML tests to ensure data integrity, accuracy, and logic validation.

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lookml-testingdata-integritydata-validationbest-practicessemantic-modeling

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LookML Testing Standards

Testing is critical for maintaining trust in data. LookML tests allow us to verify that our semantic model behaves as expected and that the underlying data conforms to our assumptions.

1. File Organization

  • Location: Define tests in 
    tests/[explore_name].test.lkml
    .
  • One Suite Per Explore: Each file should contain all the test definitions for a specific Explore.
  • Naming Convention: 
    [explore_name].test.lkml
    (e.g., 
    orders.test.lkml
    ).

2. Test Structure

Each test consists of an 
explore_source
query and an 
assert
statement.
lookml
test: [test_name] {
  explore_source: [explore_name] {
    column: [column_name] { field: [view_name].[field_name] }
    filters: {
      field: [view_name].[field_name]
      value: "[value]"
    }
  }

  assert: [assertion_name] {
    expression: ${[view_name].[field_name]} [operator] [value] ;;
  }
}

3. Types of Tests

A. Integrity Checks (Critical)

Verify that Primary Keys remain unique after joins. This is the best defense against "fanout" errors caused by incorrect 
one_to_many
join definitions.
Example: Primary Key Uniqueness
lookml
test: orders_pk_is_unique {
  explore_source: orders {
    column: order_id {}
    column: count {}
    # Limit to recent data to save costs/time if table is large
    filters: {
      field: orders.created_date
      value: "last 7 days"
    }
  }

  assert: order_id_is_unique {
    expression: ${orders.count} = 1 ;;
  }
}

B. Accuracy Tests

Validate specific measure values against known constants or expectations.
Example: Revenue is Positive
lookml
test: revenue_is_positive {
  explore_source: orders {
    column: total_revenue {}
    filters: {
      field: orders.created_date
      value: "yesterday"
    }
  }

  assert: revenue_greater_than_zero {
    expression: ${orders.total_revenue} >= 0 ;;
  }
}

C. Business Logic Validation

Ensure calculations behave as expected. For example, checking that 
gross_margin
is never greater than 
revenue
or that 
lifetime_orders
is never NULL for an active user.
Example: Logic Check
lookml
test: margin_less_than_revenue {
  explore_source: orders {
    column: total_revenue {}
    column: total_margin {}
  }
  
  assert: margin_is_valid {
    expression: ${orders.total_margin} <= ${orders.total_revenue} ;;
  }
}

4. Best Practices

  • Descriptive Extensions: Use informative names for tests (
    orders_pk_is_unique
    ) and assertions (
    order_id_is_unique
    ).
  • Performance: Use filters (e.g., 
    last 7 days
    ) to limit the scan size for large tables, unless verifying full history is required.
  • Model Inclusion: Ensure 
    test
    files are included in the model file (e.g., 
    include: "/tests/*.test.lkml"
    ).