dbt

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

!!! tip

If you've never used SQLMesh before, learn the basics of how it works in the [SQLMesh Quickstart](../quick_start.md)!

Getting started

Installing SQLMesh

SQLMesh is a Python library you install with the pip command. We recommend running your SQLMesh projects in a Python virtual environment, which must be created and activated before running any pip commands.

Most people do not use all of SQLMesh's functionality. For example, most projects only run on one SQL execution engine.

Therefore, SQLMesh is packaged with multiple "extras," which you may optionally install based on the functionality your project needs. You may specify all your project's extras in a single pip call.

At minimum, using the SQLMesh dbt adapter requires installing the dbt extra:

> pip install "sqlmesh[dbt]"

If your project uses any SQL execution engine other than DuckDB, you must install the extra for that engine. For example, if your project runs on the Postgres SQL engine:

> pip install "sqlmesh[dbt,postgres]"

If you would like to use the SQLMesh Browser UI to view column-level lineage, include the web extra:

> pip install "sqlmesh[dbt,web]"

Learn more about SQLMesh installation and extras here.

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

This will create a file called sqlmesh.yaml containing the default model start date. This configuration file is a minimum starting point for enabling SQLMesh to work with your DBT project.

As you become more comfortable with running your project under SQLMesh, you may specify additional SQLMesh configuration as required to unlock more features.

!!! note "profiles.yml"

SQLMesh will use the existing data warehouse connection target from your dbt project's `profiles.yml` file so the connection configuration does not need to be duplicated in `sqlmesh.yaml`. You may change the target at any time in the dbt config and SQLMesh will pick up the new target.

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 sqlmesh.yaml file as follows:

=== "sqlmesh.yaml"

```yaml
model_defaults:
  start: '2000-01-01'
```

=== "dbt Model"

```jinja
{{
  config(
    materialized='incremental',
    start='2000-01-01',
    ...
  )
}}
```

Configuration

SQLMesh derives a project's configuration from its dbt configuration files. This section outlines additional settings specific to SQLMesh that can be defined.

Selecting a different state connection

Certain engines, like Trino, cannot be used to store SQLMesh's state.

In addition, even if your warehouse is supported for state, you may find that you get better performance by using a traditional database to store state as these are a better fit for the state workload than a warehouse optimized for analytics workloads.

In these cases, we recommend specifying a supported production state engine using the state_connection configuration.

This involves updating sqlmesh.yaml to add a gateway configuration for the state connection:

gateways:
  "": # "" (empty string) is the default gateway
    state_connection:
      type: postgres
      ...

model_defaults:
  start: '2000-01-01'

Or, for a specific dbt profile defined in profiles.yml, eg dev:

gateways:
  dev: # must match the target dbt profile name
    state_connection:
      type: postgres
      ...

model_defaults:
  start: '2000-01-01'

Learn more about how to configure state connections here.

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 sqlmesh.yaml is created in your project directory.

You may define global variables in the same way as a native project by adding a variables section to the config.

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

variables:
  is_marketing: no

model_defaults:
  start: '2000-01-01'

Variables can also be set at the gateway/profile level which override variables set at the project level. See the variables documentation to learn more about how to specify them at different levels.

Combinations

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.

!!! info "Python config"

Switching between different config objects requires the use of [Python config](../guides/configuration.md#python) instead of the default YAML config.

You will need to create a file called `config.py` in the root of your project with the following contents:

```py
from pathlib import Path
from sqlmesh.dbt.loader import sqlmesh_config

config = sqlmesh_config(Path(__file__).parent)
```

Note that any config from `sqlmesh.yaml` will be overlayed on top of the active Python config so you dont need to remove the `sqlmesh.yaml` file

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 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 must contain:

The materialized key with value 'incremental'
The incremental_strategy key with the value incremental_by_time_range
The time_column key with the model's time column field name as the value (see time column for details)

Incremental logic

Unlike dbt incremental strategies, SQLMesh does not require the use of is_incremental jinja blocks to implement incremental logic. Instead, SQLMesh provides predefined time macro variables that can be used in the model's SQL to filter data based on the time column.

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:

>   WHERE
>     ds BETWEEN '{{ start_ds }}' AND '{{ end_ds }}'

{{ 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.

See Incremental Model Properties for the full list of incremental model configuration parameters.

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.

Similarly, the allow_partials parameter is set to true by default unless the allow_partials parameter is explicitly set to false in the model configuration.

on_schema_change

SQLMesh automatically detects both destructive and additive schema changes to forward-only incremental models and to all incremental models in forward-only plans.

A model's on_destructive_change and on_additive_change settings determine whether it errors, warns, silently allows, or ignores the changes. SQLMesh provides fine-grained control over both destructive changes (like dropping columns) and additive changes (like adding new columns).

on_schema_change configuration values are mapped to these SQLMesh settings:

`on_schema_change`	SQLMesh `on_destructive_change`	SQLMesh `on_additive_change`
ignore	ignore	ignore
fail	error	error
append_new_columns	ignore	allow
sync_all_columns	allow	allow

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 a column_types dictionary in your dbt_project.yml file, where the keys define the column names and the values the data types.

seeds:
  <seed name>
    +column_types:
      <column name>: <SQL data type>

Alternatively, you can define this dictionary in the seed seed properties configuration file.

seeds:
  - name: <seed name>
    config:
      column_types:
        <column name>: <SQL data type>

You may also 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.

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

Unsupported dbt jinja methods

The dbt jinja methods that are not currently supported are:

debug
selected_sources
graph.nodes.values
graph.metrics.values

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dbt

Getting started

Installing SQLMesh

Reading a dbt project

Setting model backfill start dates

Configuration

Selecting a different state connection

Runtime vars

Combinations

Registering comments

Running SQLMesh

Workflow differences between SQLMesh and dbt

How to use SQLMesh incremental models with dbt projects

Incremental types

Incremental by unique key

Incremental by time range

Incremental logic

Incremental model configuration

on_schema_change

Snapshot support

Tests

Seed column types

Package Management

Documentation

Supported dbt jinja methods

Unsupported dbt jinja methods

Missing something you need?

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dbt

Getting started

Installing SQLMesh

Reading a dbt project

Setting model backfill start dates

Configuration

Selecting a different state connection

Runtime vars

Combinations

Registering comments

Running SQLMesh

Workflow differences between SQLMesh and dbt

How to use SQLMesh incremental models with dbt projects

Incremental types

Incremental by unique key

Incremental by time range

Incremental logic

Incremental model configuration

on_schema_change

Snapshot support

Tests

Seed column types

Package Management

Documentation

Supported dbt jinja methods

Unsupported dbt jinja methods

Missing something you need?