Adding Dbt Unit Test

---
name: adding-dbt-unit-test
description: Creates unit test YAML definitions that mock upstream model inputs and validate expected outputs. Use when adding unit tests for a dbt model or practicing test-driven development (TDD) in dbt.
user-invocable: false
metadata:
  author: dbt-labs
---

# Add unit test for a dbt model

## Additional Resources

- [Spec Reference](references/spec.md) - All required and optional YAML keys for unit tests
- [Examples](references/examples.md) - Unit test examples across formats (dict, csv, sql)
- [Incremental Models](references/special-cases-incremental-model.md) - Unit testing incremental models
- [Ephemeral Dependencies](references/special-cases-ephemeral-dependency.md) - Unit testing models depending on ephemeral models
- [Special Case Overrides](references/special-cases-special-case-overrides.md) - Introspective macros, project variables, environment variables
- [Versioned Models](references/special-cases-versioned-model.md) - Unit testing versioned SQL models
- [BigQuery Caveats](references/warehouse-bigquery-caveats.md) - BigQuery-specific caveats
- [BigQuery Data Types](references/warehouse-bigquery-data-types.md) - BigQuery data type handling
- [Postgres Data Types](references/warehouse-postgres-data-types.md) - Postgres data type handling
- [Redshift Caveats](references/warehouse-redshift-caveats.md) - Redshift-specific caveats
- [Redshift Data Types](references/warehouse-redshift-data-types.md) - Redshift data type handling
- [Snowflake Data Types](references/warehouse-snowflake-data-types.md) - Snowflake data type handling
- [Spark Data Types](references/warehouse-spark-data-types.md) - Spark data type handling

## What are unit tests in dbt

dbt unit tests validate SQL modeling logic on static inputs before materializing in production. If any unit test for a model fails, dbt will not materialize that model.

## When to use

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 `when`s
    - 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).

## When not to use

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.

## General format

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 `format`s 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 `format`s 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.

## Minimal unit test

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}
```

## Executing unit tests

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. 

## More realistic example

```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)

## Supported and unsupported scenarios

- 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.

## Handy to know

- 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 `input`s 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

## YAML for specifying unit tests

- 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 `input`s 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 `format`s 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)

## How to choose the `format`

- 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.

## Special cases

- 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 `ref`s 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

```

## Common Mistakes

| 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 |