datamocktool

About

datamocktool (dmt) is a simple package for unit testing dbt projects.

Using dmt, you can create mock CSV seeds to stand in for the sources and refs that your models use and test that the model produces the desired output (using another CSV seed).

Requirements

• dbt version:
  • 1.3 or greater for datamocktool>=0.2.1
  • 1.0 or greater for datamocktool>=0.1.8
  • 0.19.2 or greater for datamocktool<0.1.8
• BigQuery, Redshift, Postgres, or SQL Server (likely works on Snowflake but has not been specifically tested)

Quickstart

1. Install this package by adding the following to your packages.yml file:

   • - package: mjirv/dbt_datamocktool
       version: [">=0.3.0"]

2. Create your mocks: sample inputs for your models and the expected outputs of those models given the inputs.

   • Save them to your seeds directory (usually data/; note that you can use any folder structure you would like within that directory)
   • See the integration_tests/data/ directory of this project for some examples

3. Define your tests: Add unit tests to your schema.yml files, using the following example:

   • models:
       - name: stg_customers
         tests:
           - dbt_datamocktool.unit_test:
               input_mapping:
                 source('jaffle_shop', 'raw_customers'): ref('dmt__raw_customers_1')
               expected_output: ref('dmt__expected_stg_customers_1')
               depends_on:
                 - ref('raw_customers')
         columns: ...
     
       - name: stg_orders
         tests:
           - dbt_datamocktool.unit_test:
               input_mapping:
                 ref('raw_orders'): ref('dmt__raw_orders_1')
               expected_output: ref('dmt__expected_stg_orders_1')
         columns: ...

4. Run your tests: dbt deps && dbt seed && dbt test

Advanced Usage

Using Other Materializations

Inputs can also be models, SQL statements, and/or macros instead of seeds. See integration_tests/macros/dmt_raw_customers.sql and the related test in integration_tests/models/staging/schema.yml where this is implemented (copied below).

Note that you must wrap your SQL in parentheses in order to create a valid subquery, as below.

Expected outputs must be seeds or models because the dbt_utils.equality test expects a relation. If you want to write SQL instead of a CSV for the expectation, you can use a model that is materialized as a view. See integration_tests/models/unit_test_helpers/dmt__expected_stg_customers_2.sql where this is implemented (copied below).

Test:

- dbt_datamocktool.unit_test:
    input_mapping:
      source('jaffle_shop', 'raw_customers'): "{{ dmt_raw_customers() }}" # this is a macro
    expected_output: ref('dmt__expected_stg_customers_2') # this is a model

Model (expected output):

  {{
      config(materialized='view')
  }}

  select 1 as customer_id, 'Michael' as first_name, 'P.' as last_name
  union all
  select 2 as customer_id, 'Shawn' as first_name, 'M.' as last_name

Macro (input):

  {% macro dmt_raw_customers() %}
    (

    {% set records = [
        [1,"Michael","P."],
        [2,"Shawn","M."]
    ] %}

    {% for record in records %}
        select {{ record[0] }} as id, '{{ record[1] }}' as first_name, '{{ record[2] }}' as last_name
        {% if not loop.last %}
            union all
        {% endif %}
    {% endfor %}
    ) raw_customers
{% endmacro %}

Test Names/Descriptions

You can add optional names and descriptions to your tests to make them easier to work with.

For example:

- dbt_datamocktool.unit_test:
    input_mapping:
      source('jaffle_shop', 'raw_customers'): "{{ dmt_raw_customers() }}" # this is a macro
    expected_output: ref('dmt__expected_stg_customers_2') # this is a model
    name: "Raw Customers 2"
    description: "This test is a unit test"

will show up in your test run as follows:

21:37:48 | 4 of 23 START test dbt_datamocktool_unit_test_stg_customers_This_test_is_a_unit_test__ref_dmt__expected_stg_customers_2____dmt_raw_customers___Raw_Customers_2 [RUN]
21:37:49 | 4 of 23 PASS dbt_datamocktool_unit_test_stg_customers_This_test_is_a_unit_test__ref_dmt__expected_stg_customers_2____dmt_raw_customers___Raw_Customers_2 [PASS in 0.27s]

Compare Columns

If you only want to mock a few columns, you can do so and use the compare_columns field to tell the test which columns to look at, like so:

models:
  - name: stg_customers
    tests:
      - dbt_datamocktool.unit_test:
          input_mapping:
            source('jaffle_shop', 'raw_customers'): ref('dmt__raw_customers_1')
          expected_output: ref('dmt__expected_stg_customers_1')
          compare_columns:
            - first_name
            - last_name
    columns: ...

Manual Dependencies

Sometimes dbt won't pick up all the needed dependencies. You can manually add dependencies using depends_on:

models:
  - name: stg_customers
    tests:
      - dbt_datamocktool.unit_test:
          input_mapping:
            source('jaffle_shop', 'raw_customers'): ref('dmt__raw_customers_1')
          expected_output: ref('dmt__expected_stg_customers_1')
          depends_on:
            - ref('raw_customers')
    columns: ...

Incremental testing

You can test incremental models with the unit_test_incremental macro.

Steps:

1. Create a mock input corresponding to the initial state of the table
2. Use it as this in the input mapping

NOTE: currently only the MERGE strategy is supported, so unit_test_incremental can only be used on databases that support it (BigQuery and Snowflake).

- name: stg_orders
  tests:
    - dbt_datamocktool.unit_test:
        input_mapping:
          ref('raw_orders'): ref('dmt__raw_orders_1')
        expected_output: ref('dmt__expected_stg_orders_1')
    - dbt_datamocktool.unit_test_incremental:
        input_mapping:
          ref('raw_orders'): ref('dmt__raw_orders_3')
          this: ref('dmt__current_state_orders_2')
        expected_output: ref('dmt__expected_stg_orders_2')