Understanding dbt Model Selection: A Deep Dive into +dbtModel vs dbtModel+”


Let’s dive into the difference between +dbtModel and dbtModel+ in dbt (data build tool). These two are configurations of model selection syntax in dbt, which is a powerful tool used for transforming and modeling data in the modern analytics stack.


The + in dbt’s model selection syntax allows you to include additional models that are upstream (models that feed data into your selected model) or downstream (models that receive data from your selected model) from the model you’ve selected.

+dbtModel: Selects the specified model and all of its downstream dependencies.

dbtModel+: Selects the specified model and all of its upstream dependencies.


Imagine a scenario where we have three dbt models:

freshers_in_raw_data: This model takes in raw data from source and cleans it.

freshers_in_transformed_data: This model takes the output from freshers_in_raw_data and performs transformations.

freshers_in_aggregated_data: This model aggregates data from freshers_in_transformed_data.

The dependency looks like this:


Now, let’s see how our two configurations would work in this example:

+freshers_in_transformed_data: If you were to run dbt with this selector, it would build or run freshers_in_transformed_data and its downstream model, which is freshers_in_aggregated_data.

freshers_in_transformed_data+: Using this selector would build or run freshers_in_transformed_data and its upstream model, which is freshers_in_raw_data.

Practical Application:

Imagine you’ve made a change to the freshers_in_transformed_data model and you want to ensure that:

All models that depend on it still work. You’d use +freshers_in_transformed_data to rebuild freshers_in_transformed_data and then freshers_in_aggregated_data to ensure everything downstream still functions.

The change you made doesn’t break due to some issue in the upstream data. In this case, you’d use freshers_in_transformed_data+ to rebuild freshers_in_raw_data first, and then freshers_in_transformed_data to ensure everything upstream is functioning as expected.

Understanding when to use +dbtModel and dbtModel+ and recognizing their advantages can help ensure efficient and effective data modeling and transformation within dbt. Here’s a breakdown:

When to use:

+dbtModel (Downstream Selection):

Scenario: When you’ve made a change in a specific model and need to understand the impact on all the models that rely on it.

Example: Ifyou’ve made a change in freshers_in_transformed_data and want to see how it affects all the subsequent models that rely on this transformed data.

dbtModel+ (Upstream Selection):

Scenario: When you’re considering changes or optimizations in a specific model, and you want to ensure that all the prerequisite models (i.e., source models) still feed into your selected model correctly.

Example:Before changing freshers_in_transformed_data, you’d want to ensure that its source, freshers_in_raw_data, is functioning correctly and supplying the expected data.


+dbtModel (Downstream Selection):

Efficient Testing: Ensures that modifications in one model don’t inadvertently break dependent models.

Impact Analysis: Allows for understanding the cascading effects of changes, which is crucial when rolling out updates in production. This helps in ensuring data integrity across a chain of transformations.

Targeted Operations: Instead of rebuilding the entire dbt project, you can focus on a subset of models, saving time and computational resources.

dbtModel+ (Upstream Selection):

Root Cause Analysis: If a model is not returning the expected results, this selection helps identify if the issue lies with the model itself or one of its upstream dependencies.

Data Lineage Verification: Before making changes, it’s beneficial to verify that all upstream data sources and transformations are correct. This ensures that your base data is accurate.

Dependency Management: Ensures that prerequisite models are functioning correctly, guaranteeing that any changes or optimizations you make are based on correct data.

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