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contract

Supported in dbt v1.5 and higher.

When the contract configuration is enforced, dbt will ensure that your model's returned dataset exactly matches the attributes you have defined in yaml:

  • name and data_type for every column
  • Additional constraints, as supported for this materialization and data platform

This is to ensure that the people querying your model downstream—both inside and outside dbt—have a predictable and consistent set of columns to use in their analyses. Even a subtle change in data type, such as from boolean (true/false) to integer (0/1), could cause queries to fail in surprising ways.

When dbt compares data types, it will not compare granular details such as size, precision, or scale. We don't think you should sweat the difference between varchar(256) and varchar(257), because it doesn't really affect the experience of downstream queriers. You can accomplish a more-precise assertion by writing or using a custom test.

Note that you need to specify a varchar size or numeric scale, otherwise dbt relies on default values. For example, if a numeric type defaults to a precision of 38 and a scale of 0, then the numeric column stores 0 digits to the right of the decimal (it only stores whole numbers), which might cause it to fail contract enforcement. To avoid this implicit coercion, specify your data_type with a nonzero scale, like numeric(38, 6). dbt Core 1.7 and higher provides a warning if you don't specify precision and scale when providing a numeric data type.

Example​

models/dim_customers.yml
models:
  - name: dim_customers
    config:
      materialized: table
      contract:
        enforced: true
    columns:
      - name: customer_id
        data_type: int
        constraints:
          - type: not_null
      - name: customer_name
        data_type: string
      - name: non_integer
        data_type: numeric(38,3)

Let's say your model is defined as:

models/dim_customers.sql
select
  'abc123' as customer_id,
  'My Best Customer' as customer_name

When you dbt run your model, before dbt has materialized it as a table in the database, you will see this error:

20:53:45  Compilation Error in model dim_customers (models/dim_customers.sql)
20:53:45    This model has an enforced contract that failed.
20:53:45    Please ensure the name, data_type, and number of columns in your contract match the columns in your model's definition.
20:53:45
20:53:45    | column_name | definition_type | contract_type | mismatch_reason    |
20:53:45    | ----------- | --------------- | ------------- | ------------------ |
20:53:45    | customer_id | TEXT            | INT           | data type mismatch |
20:53:45
20:53:45
20:53:45    > in macro assert_columns_equivalent (macros/materializations/models/table/columns_spec_ddl.sql)

Support​

At present, model contracts are supported for:

  • SQL models (not yet Python)
  • Models materialized as table, view, and incremental (with on_schema_change: append_new_columns)
  • The most popular data platforms — though support and enforcement of different constraint types vary by platform

Incremental models and on_schema_change​

Why require that incremental models also set on_schema_change, and why to append_new_columns?

Imagine:

  • You add a new column to both the SQL and the YAML spec
  • You don't set on_schema_change, or you set on_schema_change: 'ignore'
  • dbt doesn't actually add that new column to the existing table — and the upsert/merge still succeeds, because it does that upsert/merge on the basis of the already-existing "destination" columns only (this is long-established behavior)
  • The result is a delta between the yaml-defined contract, and the actual table in the database - which means the contract is now incorrect!

Why append_new_columns, rather than sync_all_columns? Because removing existing columns is a breaking change for contracted models!

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