adding-dbt-unit-test

In software programming, unit tests validate small portions of your functional code, and they work much the same way in dbt. dbt unit tests allow you to validate your SQL modeling logic on a small set of static inputs _before_ you materialize your full model in production. dbt unit tests enable test-driven development, benefiting developer efficiency and code reliability.

Unit tests allow enforcing that all the unit tests for a model pass before it is materialized (i.e. dbt won't materialize the model in the database if any of its unit tests do not pass).

You should unit test a model:

• Adding Model-Input-Output scenarios for the intended functionality of the model as well as edge cases to prevent regressions if the model logic is changed at a later date.
• Verifying that a bug fix solves a bug report for an existing dbt model.

More examples:

• When your SQL contains complex logic:

- Regex

- Date math

- Window functions

- case when statements when there are many whens

- Truncation

- Complex joins (multiple joins, self-joins, or joins with non-trivial conditions)

• When you're writing custom logic to process input data, similar to creating a function.
• Logic for which you had bugs reported before.
• Edge cases not yet seen in your actual data that you want to be confident you are handling properly.
• Prior to refactoring the transformation logic (especially if the refactor is significant).
• Models with high "criticality" (public, contracted models or models directly upstream of an exposure).

Cases we don't recommend creating unit tests for:

• Built-in functions that are tested extensively by the warehouse provider. If an unexpected issue arises, it's more likely a result of issues in the underlying data rather than the function itself. Therefore, fixture data in the unit test won't provide valuable information.

- common SQL spec functions like min(), etc.

dbt unit test uses a trio of the model, given inputs, and expected outputs (Model-Inputs-Outputs):

1. model - when building this model
2. given inputs - given a set of source, seeds, and models as preconditions
3. expect output - then expect this row content of the model as a postcondition

Workflow

1. Choose the model to test

Self explanatory -- the title says it all!

2. Mock the inputs

• Create an input for each of the nodes the model depends on.
• Specify the mock data it should use.
• Specify the format if different than the default (YAML dict).

- See the "Data formats for unit tests" section below to determine which format to use.

• The mock data only needs include the subset of columns used within this test case.

Tip: Use dbt show to explore existing data from upstream models or sources. This helps you understand realistic input structures. However, always sanitize the sample data to remove any sensitive or PII information before using it in your unit test fixtures.

```shell

# Preview upstream model data

dbt show --select upstream_model --limit 5

```

3. Mock the output

• Specify the data that you expect the model to create given those inputs.
• Specify the format if different than the default (YAML dict).

- See the "Data formats for unit tests" section below to determine which format to use.

• The mock data only needs include the subset of columns used within this test case.

Suppose you have this model:

```sql

-- models/hello_world.sql

select 'world' as hello

```

Minimal unit test for that model:

```yaml

# models/_properties.yml

unit_tests:

- name: test_hello_world

# Always only one transformation to test

model: hello_world

# No inputs needed this time!

# Most unit tests will have inputs -- see the "real world example" section below

given: []

# Expected output can have zero to many rows

expect:

rows:

- {hello: world}

```

Run the unit tests, build the model, and run the data tests for the hello_world model:

```shell

dbt build --select hello_world

```

This saves on warehouse spend as the model will only be materialized and move on to the data tests if the unit tests pass successfully.

Or only run the unit tests without building the model or running the data tests:

```shell

dbt test --select "hello_world,test_type:unit"

```

Or choose a specific unit test by name:

```shell

dbt test --select test_is_valid_email_address

```

Excluding unit tests from production builds

dbt Labs strongly recommends only running unit tests in development or CI environments. Since the inputs of the unit tests are static, there's no need to use additional compute cycles running them in production. Use them when doing development for a test-driven approach and CI to ensure changes don't break them.

Use the --resource-type flag --exclude-resource-type or the DBT_EXCLUDE_RESOURCE_TYPES environment variable to exclude unit tests from your production builds and save compute.

```yaml

unit_tests:

- name: test_order_items_count_drink_items_with_zero_drinks

description: >

Scenario: Order without any drinks

When the order_items_summary table is built

Given an order with nothing but 1 food item

Then the count of drink items is 0

# Model

model: order_items_summary

# Inputs

given:

- input: ref('order_items')

rows:

- {

order_id: 76,

order_item_id: 3,

is_drink_item: false,

}

- input: ref('stg_orders')

rows:

- { order_id: 76 }

# Output

expect:

rows:

- {

order_id: 76,

count_drink_items: 0,

}

```

For more examples of unit tests, see [references/examples.md](references/examples.md)

• dbt only supports unit testing SQL models.

- Unit testing Python models is not supported.

- Unit testing non-model nodes like snapshots, seeds, sources, analyses, etc. is not supported.

• dbt only supports adding unit tests to models in your _current_ project.

- Unit testing cross-project models or models imported from a package is not supported.

• dbt _does not_ support unit testing models that use the materialized view materialization.
• dbt _does not_ support unit testing models that use recursive SQL.
• dbt _does not_ support unit testing models that use introspective queries.
• dbt _does not_ support an expect output for final state of the database table after inserting/merging for incremental models.
• dbt _does_ support an expect output for what will be merged/inserted for incremental models.

• Unit tests must be defined in a YAML file in your model-paths directory (models/ by default)
• Fixture files for unit tests must be defined in a SQL or CSV file in your test-paths directory (tests/fixtures by default)
• Include all ref or source model references in the unit test configuration as inputs to avoid "node not found" errors during compilation.
• If your model has multiple versions, by default the unit test will run on all versions of your model.
• If you want to unit test a model that depends on an ephemeral model, you must use format: sql for the ephemeral model input.
• Table names within the model must be aliased in order to unit test join logic

• For all the required and optional keys in the YAML definition of unit tests, see [references/spec.md](references/spec.md)

# Inputs for unit tests

Use inputs in your unit tests to reference a specific model or source for the test:

• For input:, use a string that represents a ref or source call:

- ref('my_model') or ref('my_model', v='2') or ref('dougs_project', 'users')

- source('source_schema', 'source_name')

• For seed inputs:

- If you do not supply an input for a seed, we will use the seed's CSV file _as_ the input.

- If you do supply an input for a seed, we will use that input instead.

• Use “empty” inputs by setting rows to an empty list rows: []

- This is useful if the model has a ref or source dependency, but its values are irrelevant to this particular unit test. Just beware if the model has a join on that input that would cause rows to drop out!

models/schema.yml

```yaml

unit_tests:

- name: test_is_valid_email_address # this is the unique name of the test

model: dim_customers # name of the model I'm unit testing

given: # the mock data for your inputs

- input: ref('stg_customers')

rows:

- {email: cool@example.com, email_top_level_domain: example.com}

- {email: cool@unknown.com, email_top_level_domain: unknown.com}

- {email: badgmail.com, email_top_level_domain: gmail.com}

- {email: missingdot@gmailcom, email_top_level_domain: gmail.com}

- input: ref('top_level_email_domains')

rows:

- {tld: example.com}

- {tld: gmail.com}

- input: ref('irrelevant_dependency') # dependency that we need to acknowlege, but does not need any data

rows: []

...

```

# Data formats for unit tests

dbt supports three formats for mock data within unit tests:

1. dict (default): Inline YAML dictionary values.
2. csv: Inline CSV values or a CSV file.
3. sql: Inline SQL query or a SQL file.

To see examples of each of the formats, see [references/examples.md](references/examples.md)

• Use the dict format by default, but fall back to another format as-needed.
• Use the sql format when testing a model that depends on an ephemeral model
• Use the sql format when unit testing a column whose data type is not supported by the dict or csv formats.
• Use the csv or sql formats when using a fixture file. Default to csv, but fallback to sql if any of the column data types are not supported by the csv format.
• The sql format is the least readable and requires suppling mock data for _all_ columns, so prefer other formats when possible. But it is also the most flexible, and should be used as the fallback in scenarios where dict or csv won't work.

Notes:

• For the sql format you must supply mock data for _all columns_ whereas dict and csv may supply only a subset.
• Only the sql format allows you to unit test a model that depends on an ephemeral model -- dict and csv can't be used in that case.
• There are no formats that support Jinja.

Fixture files

The dict format only supports inline YAML mock data, but you can also use csv or sql either inline or in a separate fixture file. Store your fixture files in a fixtures subdirectory in any of your test-paths. For example, tests/fixtures/my_unit_test_fixture.sql.

When using the dict or csv format, you only have to define the mock data for the columns relevant to you. This enables you to write succinct and _specific_ unit tests. For the sql format _all_ columns need to be defined.

• Unit testing incremental models. See [references/special-cases-incremental-model.md](references/special-cases-incremental-model.md).
• Unit testing a model that depends on ephemeral model(s). See [references/special-cases-ephemeral-dependency.md](references/special-cases-ephemeral-dependency.md).
• Unit test a model that depends on any introspective macros, project variables, or environment variables. See [references/special-cases-special-case-overrides.md](references/special-cases-special-case-overrides.md).
• Unit testing versioned SQL models. See [references/special-cases-versioned-model.md](references/special-cases-versioned-model.md).

Platform/adapter-specific caveats

There are platform-specific details required if implementing on (Redshift, BigQuery, etc). Read the caveats file for your database (if it exists):

• [references/warehouse-bigquery-caveats.md](references/warehouse-bigquery-caveats.md)
• [references/warehouse-redshift-caveats.md](references/warehouse-redshift-caveats.md)

# Platform/adapter-specific data types

Unit tests are designed to test for the expected _values_, not for the data types themselves. dbt takes the value you provide and attempts to cast it to the data type as inferred from the input and output models.

How you specify input and expected values in your unit test YAML definitions are largely consistent across data warehouses, with some variation for more complex data types.

Read the data types file for your database:

• [references/warehouse-bigquery-data-types.md](references/warehouse-bigquery-data-types.md)
• [references/warehouse-postgres-data-types.md](references/warehouse-postgres-data-types.md)
• [references/warehouse-redshift-data-types.md](references/warehouse-redshift-data-types.md)
• [references/warehouse-snowflake-data-types.md](references/warehouse-snowflake-data-types.md)
• [references/warehouse-spark-data-types.md](references/warehouse-spark-data-types.md)

# Disabling a unit test

By default, all specified unit tests are enabled and will be included according to the --select flag.

To disable a unit test from being executed, set:

```yaml

config:

enabled: false

```

This is helpful if a unit test is incorrectly failing and it needs to be disabled until it is fixed.

When a unit test fails

When a unit test fails, there will be a log message of "actual differs from expected", and it will show a "data diff" between the two:

```

actual differs from expected:

@@ ,email ,is_valid_email_address

→ ,cool@example.com,True→False

,cool@unknown.com,False

```

There are two main possibilities when a unit test fails:

1. There was an error in the way the unit test was constructed (false positive)
2. There is an bug is the model (true positive)

It takes expert judgement to determine one from the other.

The `--empty` flag

The direct parents of the model that you’re unit testing need to exist in the warehouse before you can execute the unit test. The run and build commands supports the --empty flag for building schema-only dry runs. The --empty flag limits the refs and sources to zero rows. dbt will still execute the model SQL against the target data warehouse but will avoid expensive reads of input data. This validates dependencies and ensures your models will build properly.

Use the --empty flag to build an empty version of the models to save warehouse spend.

```bash

dbt run --select "stg_customers top_level_email_domains" --empty

```

| Mistake | Fix |

|---------|-----|

| Testing simple SQL using built-in functions | Only unit test complex logic: regex, date math, window functions, multi-condition case statements |

| Mocking all columns in input data | Only include columns relevant to the test case |

| Using sql format when dict works | Prefer dict (most readable), fall back to csv or sql only when needed |

| Missing input for a ref or source | Include all model dependencies to avoid "node not found" errors |

| Testing Python models or snapshots | Unit tests only support SQL models |

Similar testing concepts

There are similar concepts that dbt's model, given, expect lines up with (Hoare triple, Arrange-Act-Assert, Gherkin, What's in a Story?, etc):

| dbt unit test | Description | Hoare triple | Arrange-Act-Assert | Gherkin | What's in a Story? |

|---------------|--------------------------------------------|------------------------------------------------------------------------|---------------------------------------------------------------------------------|--------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------|

| model | when running the command for this model | Command | Act | When | Event |

| given | given these test inputs as preconditions | Precondition | Arrange | Given | Givens |

| expect | then expect this output as a postcondition | Postcondition | Assert | Then | Outcome |