Understanding LookML Validation Rules for Consistent Data Models

LookML, Looker’s data modeling language, is a powerful tool for building customized data models that deliver accurate, actionable insights. However, like any complex coding language, LookML can be prone to errors or misconfigurations. To help ensure the integrity and consistency of your LookML models, Looker provides built-in validation rules that check your LookML code for common mistakes, inefficiencies, and best practice violations.

In this blog post, we’ll explore LookML validation rules, why they matter, and how to use them to maintain high-quality data models in Looker. (Ref: Implementing Unit Testing for LookML Models)

LookML validation rules are automated checks built into the Looker platform that validate the syntax and structure of your LookML code. These rules are designed to identify common errors or issues that can affect the functionality or performance of your models.

Validation rules check for things like:

1. Syntax errors
2. Incorrect or missing parameters
3. Incorrect dimension or measure definitions
4. Performance issues in your queries
5. Compliance with Looker’s best practices for data modeling

The main purpose of these validation rules is to prevent errors from making their way into production and to help data engineers and developers build scalable, high-performance data models with minimal manual intervention.

Why Are LookML Validation Rules Important?

1. Ensures Accuracy: LookML models are often complex, with relationships, joins, and calculations spread across different views and explores. Validation rules ensure that all components are working together as expected, which minimizes the risk of errors or miscalculations.
2. Improves Data Quality: By checking for errors such as undefined fields, redundant joins, or incompatible data types, validation rules help maintain high-quality, accurate datasets. This ensures that the reports and dashboards based on those datasets are reliable.
3. Enhances Code Quality: LookML validation rules enforce best practices for coding and organization. This improves the readability, maintainability, and scalability of the code, making it easier for teams to collaborate on LookML models.
4. Boosts Development Efficiency: Automated validation saves time by quickly identifying errors during development, reducing the need for manual debugging. This lets developers focus more on building high-value models rather than troubleshooting small mistakes.
5. Prevents Performance Issues: LookML validation rules also flag potential performance issues, such as inefficient queries or missing indexes. By catching these early, you can optimize your models before they impact the user experience.

Common LookML Validation Rules and How They Help

1. Syntax and Structural Errors

LookML has a specific syntax that needs to be followed. Validation rules check for common syntax errors, such as:

• Misspelled keywords (e.g., dimension vs. dimenson)
• Missing or mismatched parentheses, brackets, or quotation marks
• Incorrect indentation or alignment

By catching these errors early, validation rules ensure that LookML models are structurally sound and adhere to Looker’s expectations.

2. Undefined Fields

If you reference a field that has not been defined or improperly referenced, LookML validation rules will flag this as an error. For instance, if a measure or dimension is used without being defined in the model, Looker will indicate that the field is missing or unrecognized.

3. Redundant Joins or Loops

Validation rules also check for redundant joins or circular dependencies in your LookML models. For example, if a join is defined multiple times or if two views are joined in a way that creates a loop, Looker will flag this as a potential issue. Redundant joins can lead to performance degradation and incorrect query results, making this validation rule crucial for maintaining clean, efficient models.

4. Performance Optimization Warnings

LookML validation rules may warn you about performance issues related to your queries. For example:

• If a derived table (PDT) is too complex and could lead to slow query performance
• If queries are missing essential indexes or filters
• If the data model is likely to return an unnecessarily large dataset that will slow down your dashboards

By identifying these issues early, you can adjust your model to prevent performance bottlenecks.

5. Missing or Incorrect Parameters

LookML models often include parameters, such as filters, which may need to be defined for the model to function properly. If any parameters are missing, incorrectly defined, or improperly referenced, Looker will flag this as an error.

6. Best Practice Violations

Looker’s built-in rules also check for adherence to best practices. For example, LookML validation might warn you about:

• Using sql_always_where or sql_always_filter incorrectly
• Missing descriptions for fields, measures, or dimensions
• The use of type: string for large text fields when type: text would be more appropriate

By following these best practices, you can ensure that your LookML models are both efficient and user-friendly.

How to Use LookML Validation Rules

1. Activate Validation in the Looker IDE

In the Looker development environment, LookML validation is automatically enabled as you write code. Errors and warnings will appear in the IDE, helping you identify issues quickly.

When working on LookML models, look for any validation warnings or errors indicated by color-coded highlights or messages within the Looker interface. These messages provide specific details about what needs to be corrected.

2. Utilize the LookML Validator Tool

You can run the LookML Validator tool to perform a broader validation of your model. This will check for any structural, syntactical, or performance-related issues across all your LookML files, even if the issue isn’t directly affecting the model you’re working on.

3. Correct Errors Promptly

Once an error or warning is identified, make the necessary changes promptly. For example, if you receive an error about an undefined field, define that field or remove the reference. If a performance issue is flagged, consider optimizing the query or adding filters to limit the amount of data being processed.

4. Collaborate with Team Members

Validation rules help teams work more collaboratively by standardizing model development and ensuring that code is consistent. LookML validation is especially important in large teams where multiple developers are working on the same codebase. By using validation to enforce best practices, you reduce the likelihood of errors or conflicts during development.

Best Practices for LookML Validation

• Run Regular Validation Checks: Regularly validate your LookML models, especially after making significant changes. This ensures that new issues are caught early.
• Address Warnings as Well as Errors: Warnings can often highlight areas for improvement, even if they aren’t critical errors. Addressing these will improve the quality and performance of your models.
• Review and Fix Redundant Joins and Complex Queries: Redundant joins can drastically impact performance, so always check for these potential problems and simplify your models as needed.
• Test Models in Different Scenarios: Simulate different scenarios, such as large datasets or high concurrency, to ensure your models perform efficiently in all conditions.

Final Thoughts

LookML validation rules are an essential tool for building reliable, high-performing, and maintainable Looker models. By understanding how to use these rules effectively, data engineers and developers can avoid common pitfalls, adhere to best practices, and ensure the integrity of their LookML models.

Implementing robust validation processes will not only help prevent errors but will also streamline your workflow, improve collaboration, and ultimately provide more accurate insights to end users. Leverage LookML validation rules to elevate your data models and ensure that your Looker projects are always top-notch.

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