Understanding dbt and Full Refresh

Introduction to dbt

dbt (data build tool) is an open-source data engineering platform designed to help teams manage data pipelines. It provides a unified interface for building, testing, and maintaining data models, making it easier to collaborate on projects and track changes.

One of the key features of dbt is its ability to handle incremental builds, allowing users to only rebuild files that have changed since the last run. This helps reduce the computational resources required for large datasets and ensures data accuracy by avoiding unnecessary recalculations.

Understanding Full Refresh

A full refresh in dbt refers to a complete rebuild of all models without any incremental updates. This can be useful when starting a new project, updating dependencies, or migrating from an old schema to a new one.

However, running full refreshes manually for each model can become cumbersome, especially when working with large and interconnected datasets.

The Question: Cascading Full Refresh Downstream

Background on Model Selection in dbt

When you run dbt run with the --select option, it selects specific models to be rebuilt. By default, dbt uses a graph-based selection process to determine which models should be updated based on their dependencies.

The graph operators, including the + operator, are used to specify how models should be connected and refreshed.

The Problem: Incorrect Operator Placement

In the original question, the user is running dbt run --full-refresh --select models/folder/my_model.sql+ --profiles-dir .. This command includes an additional + operator at the end of the model name.

However, according to the dbt documentation, placing the + operator at the end of a model selector selects only child models. The user wants to run all parent models of my_model, but is getting incorrect results due to the incorrect placement of the + operator.

The Solution: Correct Placement of Operator

To run all parent models of my_model, the correct placement of the + operator is at the beginning of the model selector. This tells dbt to select all parents of the selected model, effectively cascading the full refresh down the dependency chain.

The corrected command would be:

dbt run --full-refresh --select +my_model --profiles-dir .

This command will rebuild my_model and all its parent models that have not been updated since the last run.

Additional Considerations

If you also want to update child models of my_model, you can add another + operator at the end of the model selector. This would tell dbt to select all child models as well.

The corrected command for this scenario would be:

dbt run --full-refresh --select +my_model+ --profiles-dir .

This approach ensures that all dependent models are rebuilt, including parent and child models.

Best Practices

When working with complex data pipelines and multiple dependencies between models, it’s essential to understand how the graph operators work.

Here are some best practices to keep in mind:

• Always consult the dbt documentation for the most up-to-date information on using graph operators.
• Use + operator at the beginning of a model selector to select all parents of the selected model.
• Use another + operator at the end of a model selector to select child models.

By following these guidelines, you can ensure that your data pipeline is properly refreshed and updated when needed.

Conclusion

In this article, we discussed how dbt uses graph operators to manage dependencies between models. We also examined an example where the user was trying to run a full refresh on a model with dependent models, but encountered issues due to incorrect operator placement.

By understanding the correct usage of + operator and following best practices for model selection, you can ensure that your data pipeline is properly refreshed and updated when needed.

Remember to consult the dbt documentation for more information on using graph operators and managing dependencies between models.

Last modified on 2023-08-01