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Model contracts

Why define a contract?

Defining a dbt model is as easy as writing a SQL select statement. Your query naturally produces a dataset with columns of names and types based on the columns you select and the transformations you apply.

While this is ideal for quick and iterative development, for some models, constantly changing the shape of its returned dataset poses a risk when other people and processes are querying that model. It's better to define a set of upfront "guarantees" that define the shape of your model. We call this set of guarantees a "contract." While building your model, dbt will verify that your model's transformation will produce a dataset matching up with its contract, or it will fail to build.

Where are contracts supported?

At present, model contracts are supported for:

  • SQL models.
  • Models materialized as one of the following:
    • table
    • view Views offer limited support for column names and data types, but not constraints.
    • incremental with on_schema_change: append_new_columns or on_schema_change: fail.
  • Certain data platforms, but the supported and enforced constraints vary by platform.

Model contracts are not supported for:

  • Python models.
  • ephemeral-materialized SQL models.
  • Models with recursive CTE's in BigQuery.
  • Other resource types, such as sources, seeds, snapshots, and so on.

How to define a contract

Let's say you have a model with a query like:

-- lots of SQL

final as (

-- ... many more ...
from ...


select * from final

To enforce a model's contract, set enforced: true under the contract configuration.

When enforced, your contract must include every column's name and data_type (where data_type matches one that your data platform understands).

If your model is materialized as table or incremental, and depending on your data platform, you may optionally specify additional constraints, such as not_null (containing zero null values).

- name: dim_customers
enforced: true
- name: customer_id
data_type: int
- type: not_null
- name: customer_name
data_type: string

When building a model with a defined contract, dbt will do two things differently:

  1. dbt will run a "preflight" check to ensure that the model's query will return a set of columns with names and data types matching the ones you have defined. This check is agnostic to the order of columns specified in your model (SQL) or YAML spec.
  2. dbt will include the column names, data types, and constraints in the DDL statements it submits to the data platform, which will be enforced while building or updating the model's table.

Platform constraint support

Select the adapter-specific tab for more information on constraint support across platforms. Constraints fall into three categories based on definability and platform enforcement:

  • Definable and enforced The model won't build if it violates the constraint.
  • Definable and not enforced The platform supports specifying the type of constraint, but a model can still build even if building the model violates the constraint. This constraint exists for metadata purposes only. This approach is more typical in cloud data warehouses than in transactional databases, where strict rule enforcement is more common.
  • Not definable and not enforced You can't specify the type of constraint for the platform.
Constraint typeDefinableEnforced


Which models should have contracts?

Any model meeting the criteria described above can define a contract. We recommend defining contracts for "public" models that are being relied on downstream.

  • Inside of dbt: Shared with other groups, other teams, and (in the future) other dbt projects.
  • Outside of dbt: Reports, dashboards, or other systems & processes that expect this model to have a predictable structure. You might reflect these downstream uses with exposures.

How are contracts different from tests?

A model's contract defines the shape of the returned dataset. If the model's logic or input data doesn't conform to that shape, the model does not build.

Data Tests are a more flexible mechanism for validating the content of your model after it's built. So long as you can write the query, you can run the data test. Data tests are more configurable, such as with custom severity thresholds. They are easier to debug after finding failures, because you can query the already-built model, or store the failing records in the data warehouse.

In some cases, you can replace a data test with its equivalent constraint. This has the advantage of guaranteeing the validation at build time, and it probably requires less compute (cost) in your data platform. The prerequisites for replacing a data test with a constraint are:

  • Making sure that your data platform can support and enforce the constraint that you need. Most platforms only enforce not_null.
  • Materializing your model as table or incremental (not view or ephemeral).
  • Defining a full contract for this model by specifying the name and data_type of each column.

Why aren't tests part of the contract? In a parallel for software APIs, the structure of the API response is the contract. Quality and reliability ("uptime") are also very important attributes of an API's quality, but they are not part of the contract per se. When the contract changes in a backwards-incompatible way, it is a breaking change that requires a bump in major version.

Do I need to define every column for a contract?

Currently, dbt contracts apply to all columns defined in a model, and they require declaring explicit expectations about all of those columns. The explicit declaration of a contract is not an accident—it's very much the intent of this feature.

At the same time, for models with many columns, we understand that this can mean a lot of yaml. We are investigating the feasibility of supporting "inferred" contracts. This would enable you to define constraints and strict data typing for a subset of columns, while still detecting breaking changes on other columns by comparing against the same model in production. This isn't the same as a "partial" contract, because all columns in the model are still checked at runtime, and matched up with what's defined explicitly in your yaml contract or implicitly with the comparison state. If you're interested in "inferred" contract, please upvote or comment on dbt-core#7432.

How are breaking changes handled?

When comparing to a previous project state, dbt will look for breaking changes that could impact downstream consumers. If breaking changes are detected, dbt will present a contract error.

Breaking changes include:

  • Removing an existing column
  • Changing the data_type of an existing column
  • Removing or modifying one of the constraints on an existing column (dbt v1.6 or higher)

More details are available in the contract reference.