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lookml-tests

lookml-tests

github.com/looker-open-sourceTesting & quality43146 installsUpdated 2w ago
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About lookml-tests

lookml-tests is a development skill for teams validating LookML models and data assumptions. It covers key integrity, measure accuracy, and business logic, with guidance on organizing tests and limiting query scans.

Key features

  1. 01Organize test files by Explore
  2. 02Build tests with queries and assertions
  3. 03Check primary-key uniqueness
  4. 04Validate measures and business rules
  5. 05Limit scans with filters

Use cases

  1. 01Detect duplicate keys caused by joins
  2. 02Check measures such as revenue against expectations
  3. 03Test calculation rules and null constraints

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/looker-open-source/looker-skills --skill lookml-tests

After installing, type/lookml-testsin Claude Code to confirm it loaded.

Details

From the project README

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.

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

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

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

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").

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