Graph Operators

Learn how to use dbt graph operators with the --select flag to navigate your project's execution graph, targeting specific models and their dependencies for precise execution in data pipelines.

Graph operators in dbt are special syntax used with the --select flag to target specific parts of your project's execution graph (DAG). This graph represents the dependencies between your models, with each model as a node and the dependencies as edges.

The graph operators allow you to navigate this execution graph and select subsets of your project's resources. They're like secret codes to tell dbt exactly which models you want to work with, whether it's running, testing, or listing them.

Operators

Wildcard Operator (*)

Run all models in a schema.

dbt run --select my_schema.*

Path Operator

No special character needed, just use the path.

dbt run --select models/staging

Parent/Child Operator (+)

The plus before a model name selects the model and its parents. The plus after a model name selects the model and its children.

dbt run --select +final_model
dbt run --select parent_model+

Exclusion Operator (@)

Select parents or children, without the original model.

dbt run --select @model_name
dbt run --select model_name@

Selection Operator (,)

Run multiple models.

dbt run --select model1,model2,model3

Intersection Operator (,)

Get the intersection of multiple selectors.

dbt run --select tag:nightly,staging.*

Pro Tips

• Combine operators for laser-focused selection:

dbt run --select tag:nightly,+final_model

• Use dbt ls to preview your selection:

dbt ls --select tag:nightly,+final_model

• Refresh two generations of parents and all children of critical models:

dbt run --select +2tag:critical+

• Test everything related to final reports except the reports themselves:

dbt test --select @tag:final_report@

• Remember, the order of operators matters, as dbt processes them from left to right.

With these graph operators, you can create powerful, precise dbt commands to execute exactly the models you need in your data transformation pipelines.