Development guidelines

When you’re prototyping your use case and fine-tuning the underlying data model, your priority is iteration speed. dbt has many features that can help speed up development, like node selection and model preview. Before you start, we recommend getting familiar with how these features work with the dbt-materialize adapter to make the most of your development time.

Node selection

By default, the dbt-materialize adapter drops and recreates all models on each dbt run invocation. This can have unintended consequences, in particular if you’re managing sources and sinks as models in your dbt project. dbt allows you to selectively run specific models and exclude specific materialization types from each run using node selection.

Exclude sources and sinks

You can manually exclude specific materialization types using the exclude flag in your dbt run invocations. To exclude sources and sinks, use:

dbt run --exclude config.materialized:source config.materialized:sink

YAML selectors

Instead of manually specifying node selection on each run, you can create a YAML selector that makes this the default behavior when running dbt:

# YAML selectors should be defined in a top-level file named selectors.yml
selectors:
  - name: exclude_sources_and_sinks
    description: >
      Exclude models that use source or sink materializations in the command
      invocation.      
    default: true
    definition:
      union:
        # The fqn method combined with the "*" operator selects all nodes in the
        # dbt graph
        - method: fqn
          value: "*"
        - exclude:
            - 'config.materialized:source'
            - 'config.materialized:sink'

Because default: true is specified, dbt will use the selector’s criteria whenever you run an unqualified command (e.g. dbt build, dbt run). You can still override this default by adding selection criteria to commands, or adjust the value of default depending on the target environment. To learn more about using the default and exclude properties with YAML selectors, check the dbt documentation.

Run a subset of models

You can run individual models, or groups of models, using the select flag in your dbt run invocations:

dbt run --select "my_dbt_project_name"   # runs all models in your project
dbt run --select "my_dbt_model"          # runs a specific model
dbt run --select "my_model+"             # select my_model and all downstream dependencies
dbt run --select "path.to.my.models"     # runs all models in a specific directory
dbt run --select "my_package.some_model" # runs a specific model in a specific package
dbt run --select "tag:nightly"           # runs models with the "nightly" tag
dbt run --select "path/to/models"        # runs models contained in path/to/models
dbt run --select "path/to/my_model.sql"  # runs a specific model by its path

For a full rundown of selection logic options, check the dbt documentation.

Model results preview

To debug and preview the results of your models without materializing the results, you can use the dbt show command:

dbt show --select "model_name.sql"

23:02:20  Running with dbt=1.7.7
23:02:20  Registered adapter: materialize=1.7.3
23:02:20  Found 3 models, 1 test, 4 seeds, 1 source, 0 exposures, 0 metrics, 430 macros, 0 groups, 0 semantic models
23:02:20
23:02:23  Previewing node 'model_name':
| col                  |
| -------------------- |
| value1               |
| value2               |
| value3               |
| value4               |
| value5               |

By default, the dbt show command will return the first 5 rows from the query result (i.e. LIMIT 5). You can adjust the number of rows returned using the --limit n flag.

It’s important to note that previewing results compiles the model and runs the compiled SQL against Materialize; it doesn’t query the already-materialized database relation (see dbt-core #7391).

Unit tests

Minimum requirements: dbt-materialize v1.8.0+

To validate your SQL logic without fully materializing a model, as well as future-proof it against edge cases, you can use unit tests. Unit tests can be a quicker way to iterate on model development in comparison to re-running the models, since you don’t need to wait for a model to hydrate before you can validate that it produces the expected results.

1. As an example, imagine your dbt project includes the following models:

   Filename: models/my_model_a.sql

   SELECT
     1 AS a,
     1 AS id,
     2 AS not_testing,
     'a' AS string_a,
     DATE '2020-01-02' AS date_a
   

   Filename: models/my_model_b.sql

   SELECT
     2 as b,
     1 as id,
     2 as c,
     'b' as string_b
   

   Filename: models/my_model.sql

   SELECT
     a+b AS c,
     CONCAT(string_a, string_b) AS string_c,
     not_testing,
     date_a
   FROM {{ ref('my_model_a')}} my_model_a
   JOIN {{ ref('my_model_b' )}} my_model_b
   ON my_model_a.id = my_model_b.id
   

2. To add a unit test to my_model, create a .yml file under the /models directory, and use the unit_tests property:

   Filename: models/unit_tests.yml

   unit_tests:
     - name: test_my_model
       model: my_model
       given:
         - input: ref('my_model_a')
           rows:
             - {id: 1, a: 1}
         - input: ref('my_model_b')
           rows:
             - {id: 1, b: 2}
             - {id: 2, b: 2}
       expect:
         rows:
           - {c: 2}
   

   For simplicity, this example provides mock data using inline dictionary values, but other formats are supported. Check the dbt documentation for a full rundown of the available options.

3. Run the unit tests using dbt test:

   dbt test --select test_type:unit
   
   12:30:14  Running with dbt=1.8.0
   12:30:14  Registered adapter: materialize=1.8.0
   12:30:14  Found 6 models, 1 test, 4 seeds, 1 source, 471 macros, 1 unit test
   12:30:14
   12:30:16  Concurrency: 1 threads (target='dev')
   12:30:16
   12:30:16  1 of 1 START unit_test my_model::test_my_model ................................. [RUN]
   12:30:17  1 of 1 FAIL 1 my_model::test_my_model .......................................... [FAIL 1 in 1.51s]
   12:30:17
   12:30:17  Finished running 1 unit test in 0 hours 0 minutes and 2.77 seconds (2.77s).
   12:30:17
   12:30:17  Completed with 1 error and 0 warnings:
   12:30:17
   12:30:17  Failure in unit_test test_my_model (models/models/unit_tests.yml)
   12:30:17
   
   actual differs from expected:
   
   @@ ,c
   +++,3
   ---,2
   

   It’s important to note that the direct upstream dependencies of the model that you’re unit testing must exist in Materialize before you can execute the unit test via dbt test. To ensure these dependencies exist, you can use the --empty flag to build an empty version of the models:

   dbt run --select "my_model_a.sql" "my_model_b.sql" --empty
   

   Alternatively, you can execute unit tests as part of the dbt build command, which will ensure the upstream depdendencies are created before any unit tests are executed:

   dbt build --select "+my_model.sql"
   
   11:53:30  Running with dbt=1.8.0
   11:53:30  Registered adapter: materialize=1.8.0
   ...
   11:53:33  2 of 12 START sql view model public.my_model_a ................................. [RUN]
   11:53:34  2 of 12 OK created sql view model public.my_model_a ............................ [CREATE VIEW in 0.49s]
   11:53:34  3 of 12 START sql view model public.my_model_b ................................. [RUN]
   11:53:34  3 of 12 OK created sql view model public.my_model_b ............................ [CREATE VIEW in 0.45s]
   ...
   11:53:35  11 of 12 START unit_test my_model::test_my_model ............................... [RUN]
   11:53:36  11 of 12 FAIL 1 my_model::test_my_model ........................................ [FAIL 1 in 0.84s]
   11:53:36  Failure in unit_test test_my_model (models/models/unit_tests.yml)
   11:53:36
   
   actual differs from expected:
   
   @@ ,c
   +++,3
   ---,2