dbt

SQLMesh has native support for running dbt projects with its dbt adapter.

Getting started

Reading a dbt project

Prepare an existing dbt project to be run by SQLMesh by executing the sqlmesh init command within the dbt project root directory and with the dbt template option:

$ sqlmesh init -t dbt

SQLMesh will use the data warehouse connection target in your dbt project profiles.yml file. The target can be changed at any time.

Setting model backfill start dates

Models require a start date for backfilling data through use of the start configuration parameter. start can be defined individually for each model in its config block or globally in the dbt_project.yml file as follows:

> models:
>   +start: Jan 1 2000

Configuration

SQLMesh determines a project's configuration settings from its dbt configuration files.

This section describes using runtime variables to create multiple configurations and how to disable SQLMesh's automatic model description and comment registration.

Runtime vars

dbt supports passing variable values at runtime with its CLI vars option.

In SQLMesh, these variables are passed via configurations. When you initialize a dbt project with sqlmesh init, a file config.py is created in your project directory.

The file creates a SQLMesh config object pointing to the project directory:

config = sqlmesh_config(Path(__file__).parent)

Specify runtime variables by adding a Python dictionary to the sqlmesh_config() variables argument.

For example, we could specify the runtime variable is_marketing and its value no as:

config = sqlmesh_config(
    Path(__file__).parent,
    variables={"is_marketing": "no"}
    )

Some projects use combinations of runtime variables to control project behavior. Different combinations can be specified in different sqlmesh_config objects, with the relevant configuration passed to the SQLMesh CLI command.

For example, consider a project with a special configuration for the marketing department. We could create separate configurations to pass at runtime like this:

config = sqlmesh_config(
    Path(__file__).parent,
    variables={"is_marketing": "no", "include_pii": "no"}
    )

marketing_config = sqlmesh_config(
    Path(__file__).parent,
    variables={"is_marketing": "yes", "include_pii": "yes"}
    )

By default, SQLMesh will use the configuration object named config. Use a different configuration by passing the object name to SQLMesh CLI commands with the --config option. For example, we could run a plan with the marketing configuration like this:

sqlmesh --config marketing_config plan

Note that the --config option is specified between the word sqlmesh and the command being executed (e.g., plan, run).

Registering comments

SQLMesh automatically registers model descriptions and column comments with the target SQL engine, as described in the Models Overview documentation. Comment registration is on by default for all engines that support it.

dbt offers similar comment registration functionality via its persist_docs model configuration parameter, specified by model. SQLMesh comment registration is configured at the project level, so it does not use dbt's model-specific persist_docs configuration.

SQLMesh's project-level comment registration defaults are overridden with the sqlmesh_config() register_comments argument. For example, this configuration turns comment registration off:

config = sqlmesh_config(
    Path(__file__).parent,
    register_comments=False,
    )

Running SQLMesh

Run SQLMesh as with a SQLMesh project, generating and applying plans, running tests or audits, and executing models with a scheduler if desired.

You continue to use your dbt file and project format.

Workflow differences between SQLMesh and dbt

Consider the following when using a dbt project:

• SQLMesh will detect and deploy new or modified seeds as part of running the plan command and applying changes - there is no separate seed command. Refer to seed models for more information.
• The plan command dynamically creates environments, so environments do not need to be hardcoded into your profiles.yml file as targets. To get the most out of SQLMesh, point your dbt profile target at the production target and let SQLMesh handle the rest for you.
• The term "test" has a different meaning in dbt than in SQLMesh:
  • dbt "tests" are audits in SQLMesh.
  • SQLMesh "tests" are unit tests, which test query logic before applying a SQLMesh plan.
• dbt's' recommended incremental logic is not compatible with SQLMesh, so small tweaks to the models are required (don't worry - dbt can still use the models!).

How to use SQLMesh incremental models with dbt projects

Incremental loading is a powerful technique when datasets are large and recomputing tables is expensive. SQLMesh offers first-class support for incremental models, and its approach differs from dbt's.

This section describes how to adapt dbt's incremental models to run on sqlmesh and maintain backwards compatibility with dbt.

Incremental types

SQLMesh supports two approaches to implement idempotent incremental loads:

• Using merge (with the sqlmesh INCREMENTAL_BY_UNIQUE_KEY model kind)
• Using insert-overwrite/delete+insert (with the sqlmesh INCREMENTAL_BY_TIME_RANGE model kind)

Incremental by unique key

To enable incremental_by_unique_key incrementality, the model configuration should contain:

• The unique_key key with the model's unique key field name or names as the value
• The materialized key with value 'incremental'
• Either:
  • No incremental_strategy key or
  • The incremental_strategy key with value 'merge'

Incremental by time range

To enable incremental_by_time_range incrementality, the model configuration should contain:

• The time_column key with the model's time column field name as the value (see time column for details)
• The materialized key with value 'incremental'
• Either:
  • The incremental_strategy key with value 'insert_overwrite' or
  • The incremental_strategy key with value 'delete+insert'
  • Note: in this context, these two strategies are synonyms. Regardless of which one is specified SQLMesh will use the best incremental strategy for the target engine.

Incremental logic

SQLMesh requires a new jinja block gated by {% if sqlmesh_incremental is defined %}. The new block should supersede the existing {% if is_incremental() %} block and contain the WHERE clause selecting the time interval.

For example, the SQL WHERE clause with the "ds" column goes in a new jinja block gated by {% if sqlmesh_incremental is defined %} as follows:

> {% if sqlmesh_incremental is defined %}
>   WHERE
>     ds BETWEEN '{{ start_ds }}' AND '{{ end_ds }}'
> {% elif is_incremental() %}
>   ; < your existing is_incremental block >
> {% endif %}

{{ start_ds }} and {{ end_ds }} are the jinja equivalents of SQLMesh's @start_ds and @end_ds predefined time macro variables. See all predefined time variables available in jinja.

Incremental model configuration

SQLMesh provides configuration parameters that enable control over how incremental computations occur. These parameters are set in the model's config block.

The batch_size parameter determines the maximum number of time intervals to run in a single job.

The lookback parameter is used to capture late arriving data. It sets the number of units of late arriving data the model should expect and must be a positive integer.

Note: By default, all incremental dbt models are configured to be forward-only. However, you can change this behavior by setting the forward_only: false setting either in the configuration of an individual model or globally for all models in the dbt_project.yaml file. The forward-only mode aligns more closely with the typical operation of dbt and therefore better meets user's expectations.

on_schema_change

It's important to note, that the on_schema_change setting is ignored by SQLMesh. Schema changes are only applied during the plan application (i.e. sqlmesh plan) and never during runtime (i.e. sqlmesh run). The target table's schema is always updated to match the model's query, as if the on_schema_change setting was set to sync_all_columns.

Snapshot support

SQLMesh supports both dbt snapshot strategies of either timestamp or check. Only unsupported snapshot functionality is invalidate_hard_deletes which must be set to True. If set to False, then the snapshot will be skipped and a warning will be logged indicating this happened. Support for this will be added soon.

Tests

SQLMesh uses dbt tests to perform SQLMesh audits (coming soon).

Add SQLMesh unit tests to a dbt project by placing them in the "tests" directory.

Seed column types

SQLMesh parses seed CSV files using Panda's read_csv utility and its default column type inference.

dbt parses seed CSV files using agate's csv reader and customizes agate's default type inference.

If SQLMesh and dbt infer different column types for a seed CSV file, you may specify your desired data types in a seed properties configuration file.

Specify a column's SQL data type in its data_type key, as shown below. The file must list all columns present in the CSV file; SQLMesh's default type inference will be used for columns that do not specify the data_type key.

seeds:
  - name: <seed name>
    columns:
      - name: <column name>
        data_type: <SQL data type>

Package Management

SQLMesh does not have its own package manager; however, SQLMesh's dbt adapter is compatible with dbt's package manager. Continue to use dbt deps and dbt clean to update, add, or remove packages.

Documentation

Model documentation is available in the SQLMesh UI.

Using Airflow

To use SQLMesh and dbt projects with Airflow, first configure SQLMesh to use Airflow as described in the Airflow integrations documentation.

Then, install dbt-core within airflow.

Finally, replace the contents of config.py with:

> from pathlib import Path
>
> from sqlmesh.core.config import AirflowSchedulerConfig
> from sqlmesh.dbt.loader import sqlmesh_config
>
> config = sqlmesh_config(
>     Path(__file__).parent,
>     default_scheduler=AirflowSchedulerConfig(
>         airflow_url="https://<Airflow Webserver Host>:<Airflow Webserver Port>/",
>         username="<Airflow Username>",
>         password="<Airflow Password>",
>     )
> )

See the Airflow configuration documentation for a list of all AirflowSchedulerConfig configuration options. Note: only the python config file format is supported for dbt at this time.

The project is now configured to use airflow. Going forward, this also means that the engine configured in airflow will be used instead of the target engine specified in profiles.yml.

Supported dbt jinja methods

SQLMesh supports running dbt projects using the majority of dbt jinja methods, including:

Method	Method	Method	Method
adapter (*)	env_var	project_name	target
as_bool	exceptions	ref	this
as_native	from_yaml	return	to_yaml
as_number	is_incremental	run_query	var
as_text	load_result	schema	zip
api	log	set
builtins	modules	source
config	print	statement

* adapter.rename_relation and adapter.expand_target_column_types are not currently supported.

Unsupported dbt jinja methods

The dbt jinja methods that are not currently supported are:

• debug
• selected_sources
• adapter.expand_target_column_types
• adapter.rename_relation
• schemas
• graph.nodes.values
• graph.metrics.values
• version - learn more about why SQLMesh doesn't support model versions at the Tobiko Data blog

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