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Configuration guide

SQLMesh's behavior is determined by three things: a project's files (e.g., models), user actions (e.g., creating a plan), and how SQLMesh is configured.

This page describes how SQLMesh configuration works and discusses the aspects of SQLMesh behavior that can be modified via configuration.

The configuration reference page contains concise lists of all configuration parameters and their default values.

Configuration files

NOTE: SQLMesh project configurations have the following two requirements:

  1. A config.yaml or config.py file must be present in the project's folder.
  2. That configuration file must contain a default SQL dialect for the project's models in the model_defaults dialect key.

SQLMesh configuration parameters can be set as environment variables, in a configuration file in the ~/.sqlmesh folder, and in the configuration file within a project folder.

The sources have the following order of precedence:

  1. Environment variable (e.g., SQLMESH__MODEL_DEFAULTS__DIALECT). [HIGHEST PRECEDENCE]
  2. config.yaml or config.py in the ~/.sqlmesh folder.
  3. config.yaml or config.py in a project folder. [LOWEST PRECEDENCE]

File type

You can specify a SQLMesh configuration in either YAML or Python.

YAML configuration is simpler, and we recommend it for most projects. Python configuration is more complex, but it enables functionality that YAML does not support.

Because Python configuration files are evaluated by Python when SQLMesh reads them, they support dynamic parameters based on the computational environment in which SQLMesh is running.

For example, Python configuration files enable use of third-party secrets managers for storing passwords and other sensitive information. They also support user-specific parameters such as automatically setting project defaults based on which user account is running SQLMesh.

YAML

YAML configuration files consist of configuration keys and values. Strings are not quoted, and some keys are "dictionaries" that contain one or more sub-keys.

For example, the default_gateway key specifies the default gateway SQLMesh should use when executing commands. It takes a single, unquoted gateway name as its value:

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default_gateway: local

In contrast, the gateways key takes dictionaries as values, and each gateway dictionary contains one or more connection dictionaries. This example specifies the my_gateway gateway with a Snowflake connection:

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gateways:
    my_gateway:
        connection:
            type: snowflake
            user: <username>
            password: <password>
            account: <account>

Gateway dictionaries can contain multiple connection dictionaries if different SQLMesh components should use different connections (e.g., SQLMesh tests should run in a different database than SQLMesh plans). See the gateways section for more information on gateway configuration.

Python

Python configuration files consist of statements that import SQLMesh configuration classes and a configuration specification using those classes.

At minimum, a Python configuration file must:

  1. Create an object of the SQLMesh Config class named config
  2. Specify that object's model_defaults argument with a ModelDefaultsConfig() object specifying the default SQL dialect for the project's models

For example, this minimal configuration specifies a default SQL dialect of duckdb and uses the default values for all other configuration parameters:

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from sqlmesh.core.config import Config, ModelDefaultsConfig

config = Config(
    model_defaults=ModelDefaultsConfig(dialect="duckdb"),
)

Python configuration files may optionally define additional configuration objects and switch between the configurations when issuing sqlmesh commands. For example, if a configuration file contained a second configuration object my_second_config, you could create a plan using that config with sqlmesh --config my_second_config plan.

Different Config arguments accept different object types. Some, such as model_defaults, take SQLMesh configuration objects. Others, such as default_gateway, take strings or other Python object types like dictionaries.

SQLMesh's Python configuration components are documented in the sqlmesh.core.config module's API documentation.

The config sub-module API documentation describes the individual classes used for the relevant Config arguments:

See the notifications guide for more information about user and notification specification.

Environment variables

All software runs within a system environment that stores information as "environment variables."

SQLMesh can access environment variables during configuration, which enables approaches like storing passwords/secrets outside the configuration file and changing configuration parameters dynamically based on which user is running SQLMesh.

You can use environment variables in two ways: specifying them in the configuration file or creating properly named variables to override configuration file values.

Configuration file

This section demonstrates using environment variables in YAML and Python configuration files.

The examples specify a Snowflake connection whose password is stored in an environment variable SNOWFLAKE_PW.

Specify environment variables in a YAML configuration with the syntax {{ env_var('<ENVIRONMENT VARIABLE NAME>') }}. Note that the environment variable name is contained in single quotes.

Access the SNOWFLAKE_PW environment variable in a Snowflake connection configuration like this:

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gateways:
    my_gateway:
        connection:
            type: snowflake
            user: <username>
            password: {{ env_var('SNOWFLAKE_PW') }}
            account: <account>

Python accesses environment variables via the os library's environ dictionary.

Access the SNOWFLAKE_PW environment variable in a Snowflake connection configuration like this:

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import os
from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    GatewayConfig,
    SnowflakeConnectionConfig
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    gateways={
        "my_gateway": GatewayConfig(
            connection=SnowflakeConnectionConfig(
                user=<username>,
                password=os.environ['SNOWFLAKE_PW'],
                account=<account>,
            ),
        ),
    }
)

Overrides

Environment variables have the highest precedence among configuration methods, as noted above. They will automatically override configuration file specifications if they follow a specific naming structure.

The structure is based on the names of the configuration fields, with double underscores __ between the field names. The environment variable name must begin with SQLMESH__, followed by the YAML field names starting at the root and moving downward in the hierarchy.

For example, we can override the password specified in a Snowflake connection. This is the YAML specification contained in our configuration file, which specifies a password dummy_pw:

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gateways:
    my_gateway:
        connection:
            type: snowflake
            user: <username>
            password: dummy_pw
            account: <account>

We can override the dummy_pw value with the true password real_pw by creating the environment variable. This example demonstrates creating the variable with the bash export function:

$ export SQLMESH__GATEWAYS__MY_GATEWAY__CONNECTION__PASSWORD="real_pw"

After the initial string SQLMESH__, the environment variable name components move down the key hierarchy in the YAML specification: GATEWAYS --> MY_GATEWAY --> CONNECTION --> PASSWORD.

Configuration types

A SQLMesh project configuration is hierarchical and consists of root level parameters within which other parameters are defined.

Conceptually, we can group the root level parameters into the following types. Each type links to its table of parameters in the SQLMesh configuration reference page:

  1. Project - configuration options for SQLMesh project directories.
  2. Environment - configuration options for SQLMesh environment creation/promotion, physical table schemas, and view schemas.
  3. Gateways - configuration options for how SQLMesh should connect to the data warehouse, state backend, and scheduler.
  4. Gateway/connection defaults - configuration options for what should happen when gateways or connections are not all explicitly specified.
  5. Model defaults - configuration options for what should happen when model-specific configurations are not explicitly specified in a model's file.
  6. Debug mode - configuration option for SQLMesh to print and log actions and full backtraces.

Configuration details

The rest of this page provides additional detail for some of the configuration options and provides brief examples. Comprehensive lists of configuration options are at the configuration reference page.

Environment schemas

SQLMesh creates both physical tables and views in the data warehouse/engine.

The physical_schema_override and environment_suffix_target configuration options control where SQLMesh creates the tables and views. These options are in the environments section of the configuration reference page.

Physical table schemas

By default, SQLMesh creates physical tables for a model with a naming convention of sqlmesh__[model schema].

This can be overridden on a per-schema basis using the physical_schema_override option, which removes the sqlmesh__ prefix and uses the name you provide.

This example configuration overrides the default physical schemas for the my_schema model schema:

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physical_schema_override:
    my_schema: my_new_schema

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from sqlmesh.core.config import Config, ModelDefaultsConfig

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    physical_schema_override={"my_schema":"my_new_schema"},
)

If you had a model name of my_schema.table, the physical table would be created as my_new_schema.table_<fingerprint> instead of the default behavior of sqlmesh__my_schema.table_<fingerprint>.

This key only applies to the physical tables that SQLMesh creates - the views are still created in my_schema (prod) or my_schema__<env>.

View schemas

By default, SQLMesh appends the environment name to the schema name when creating new environments. This can be changed to append a suffix at the end of table/view name instead.

Appending the suffix to a table/view means that that new environment views will be created in the same schema as production but be differentiated by having their names end with __<env>.

Config example:

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environment_suffix_target: table

The Python environment_suffix_target argument takes an EnvironmentSuffixTarget enumeration with a value of EnvironmentSuffixTarget.TABLE or EnvironmentSuffixTarget.SCHEMA (default).

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from sqlmesh.core.config import Config, ModelDefaultsConfig, EnvironmentSuffixTarget

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    environment_suffix_target=EnvironmentSuffixTarget.TABLE,
)

If you created a dev environment for a project containing a model named my_schema.users, the model view would be created as my_schema.users__dev instead of the default behavior of my_schema__dev.users.

The default behavior of appending the suffix to schemas is recommended because it leaves production with a single clean interface for accessing the views. However, if you are deploying SQLMesh in an environment with tight restrictions on schema creation then this can be a useful way of reducing the number of schemas SQLMesh uses.

Auto-categorize model changes

SQLMesh compares the current state of project files to an environment when sqlmesh plan is run. It detects changes to models, which can be classified as breaking or non-breaking.

SQLMesh can attempt to automatically categorize the changes it detects. The auto_categorize_changes option determines whether SQLMesh should attempt automatic change categorization. This option is in the environments section of the configuration reference page.

Supported values:

  • full: Never prompt the user for input, instead fall back to the most conservative category (breaking) if the category can't be determined automatically.
  • semi: Prompt the user for input only if the change category can't be determined automatically.
  • off: Always prompt the user for input; automatic categorization will not be attempted.

Example showing default values:

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auto_categorize_changes:
    external: full
    python: off
    sql: full
    seed: full

The Python auto_categorize_changes argument takes CategorizerConfig object. That object's arguments take an AutoCategorizationMode enumeration with values of AutoCategorizationMode.FULL, AutoCategorizationMode.SEMI, or AutoCategorizationMode.OFF.

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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    AutoCategorizationMode,
    CategorizerConfig,
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    auto_categorize_changes=CategorizerConfig(
        external=AutoCategorizationMode.FULL,
        python=AutoCategorizationMode.OFF,
        sql=AutoCategorizationMode.FULL,
        seed=AutoCategorizationMode.FULL,
    ),
)

Gateways

The gateways configuration defines how SQLMesh should connect to the data warehouse, state backend, and scheduler. These options are in the gateway section of the configuration reference page.

Each gateway key represents a unique gateway name and configures its connections. For example, this configures the my_gateway gateway:

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gateways:
    my_gateway:
        connection:
            ...
        state_connection:
            ...
        test_connection:
            ...
        scheduler:
            ...

The Python gateways argument takes a dictionary of gateway names and GatewayConfig objects. A GatewayConfig's connection-related arguments take an engine-specific connection config object, and the scheduler argument takes a scheduler config object.

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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    GatewayConfig,
    ...
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    gateways={
        "my_gateway": GatewayConfig(
            connection=...,
            state_connection=...,
            test_connection=...,
            scheduler=...,
        ),
    }
)

Gateways do not need to specify all four components in the example above. The gateway defaults options control what happens if they are not all specified - find more information on gateway defaults below.

Connections

The connection configuration controls the data warehouse connection. These options are in the connection section of the configuration reference page.

The allowed keys include:

  • The optional concurrent_tasks key specifies the maximum number of concurrent tasks SQLMesh will run. Default value is 4 for engines that support concurrent tasks.
  • Most keys are specific to the connection engine type - see below. The default data warehouse connection type is an in-memory DuckDB database.

Example snowflake connection configuration:

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gateways:
    my_gateway:
        connection:
            type: snowflake
            user: <username>
            password: <password>
            account: <account>

A Snowflake connection is specified with a SnowflakeConnectionConfig object.

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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    GatewayConfig,
    SnowflakeConnectionConfig
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    gateways={
        "my_gateway": GatewayConfig(
            connection=SnowflakeConnectionConfig(
                user=<username>,
                password=<password>,
                account=<account>,
            ),
        ),
    }
)

Engine connection configuration

These pages describe the connection configuration options for each execution engine.

State connection

Configuration for the state backend connection if different from the data warehouse connection. The data warehouse connection is used if the state_connection key is not specified.

Example postgres state connection configuration:

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gateways:
    my_gateway:
        state_connection:
            type: postgres
            host: <host>
            port: <port>
            user: <username>
            password: <password>
            database: <database>

A Postgres connection is specified with a PostgresConnectionConfig object.

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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    GatewayConfig,
    PostgresConnectionConfig
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    gateways={
        "my_gateway": GatewayConfig(
            state_connection=PostgresConnectionConfig(
                host=<host>,
                port=<port>,
                user=<username>,
                password=<password>,
                database=<database>,
            ),
        ),
    }
)

State schema name

By default, the schema name used to store state tables is sqlmesh. This can be changed by providing the state_schema config key in the gateway configuration.

Example configuration to store state information in a postgres database's custom_name schema:

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gateways:
    my_gateway:
        state_connection:
            type: postgres
            host: <host>
            port: <port>
            user: <username>
            password: <password>
            database: <database>
        state_schema: custom_name

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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    GatewayConfig,
    PostgresConnectionConfig
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    gateways={
        "my_gateway": GatewayConfig(
            state_connection=PostgresConnectionConfig(
                host=<host>,
                port=<port>,
                user=<username>,
                password=<password>,
                database=<database>,
            ),
            state_schema="custom_name",
        ),
    }
)

This would create all state tables in the schema custom_name.

Test connection

Configuration for a connection used to run unit tests. An in-memory DuckDB database is used if the test_connection key is not specified.

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gateways:
    my_gateway:
        test_connection:
            type: duckdb

A DuckDB connection is specified with a DuckDBConnectionConfig object. A DuckDBConnectionConfig with no arguments specified uses an in-memory DuckDB database.

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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    GatewayConfig,
    DuckDBConnectionConfig
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    gateways={
        "my_gateway": GatewayConfig(
            test_connection=DuckDBConnectionConfig(),
        ),
    }
)

Scheduler

Identifies which scheduler backend to use. The scheduler backend is used both for storing metadata and for executing plans. By default, the scheduler type is set to builtin, which uses the existing SQL engine to store metadata. Use the airflow type integrate with Airflow.

These options are in the scheduler section of the configuration reference page.

Builtin

Example configuration:

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gateways:
    my_gateway:
        scheduler:
            type: builtin

A built-in scheduler is specified with a BuiltInSchedulerConfig object.

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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    GatewayConfig,
    BuiltInSchedulerConfig,
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    gateways={
        "my_gateway": GatewayConfig(
            scheduler=BuiltInSchedulerConfig(),
        ),
    }
)

No additional configuration options are supported by this scheduler type.

Airflow

Example configuration:

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gateways:
    my_gateway:
        scheduler:
            type: airflow
            airflow_url: <airflow_url>
            username: <username>
            password: <password>

An Airflow scheduler is specified with an AirflowSchedulerConfig object.

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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    GatewayConfig,
    AirflowSchedulerConfig,
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    gateways={
        "my_gateway": GatewayConfig(
            scheduler=AirflowSchedulerConfig(
                airflow_url=<airflow_url>,
                username=<username>,
                password=<password>,
            ),
        ),
    }
)

See Airflow Integration Guide for information about how to integrate Airflow with SQLMesh. See the configuration reference page for a list of all parameters.

Cloud Composer

The Google Cloud Composer scheduler type shares the same configuration options as the airflow type, except for username and password. Cloud Composer relies on gcloud authentication, so the username and password options are not required.

Example configuration:

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gateways:
    my_gateway:
        scheduler:
            type: cloud_composer
            airflow_url: <airflow_url>

An Google Cloud Composer scheduler is specified with an CloudComposerSchedulerConfig object.

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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    GatewayConfig,
    CloudComposerSchedulerConfig,
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    gateways={
        "my_gateway": GatewayConfig(
            scheduler=CloudComposerSchedulerConfig(
                airflow_url=<airflow_url>,
            ),
        ),
    }
)

Gateway/connection defaults

The default gateway and connection keys specify what should happen when gateways or connections are not explicitly specified. These options are in the gateway/connection defaults section of the configuration reference page.

The gateway specified in default_gateway is used when a sqlmesh command does not explicitly specify a gateway. All SQLMesh CLI commands accept a gateway option after sqlmesh and before the command name; for example, sqlmesh --gateway my_gateway plan. If the option is not specified in a command call, the default_gateway is used.

The three default connection types are used when some gateways in the gateways configuration dictionaries do not specify every connection type.

Default gateway

If a configuration contains multiple gateways, SQLMesh will use the first one in the gateways dictionary by default. The default_gateway key is used to specify a different gateway name as the SQLMesh default.

Example configuration:

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gateways:
    my_gateway:
        <gateway specification>
default_gateway: my_gateway

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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    GatewayConfig,
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    gateways={
        "my_gateway": GatewayConfig(
            <gateway specification>
        ),
    },
    default_gateway="my_gateway",
)

Default connections/scheduler

The default_connection, default_test_connection, and default_scheduler keys are used to specify shared defaults across multiple gateways.

For example, you might have a specific connection where your tests should run regardless of which gateway is being used. Instead of duplicating the test connection information in each gateway specification, specify it once in the default_test_connection key.

Example configuration specifying a Postgres default connection, in-memory DuckDB default test connection, and builtin default scheduler:

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default_connection:
    type: postgres
    host: <host>
    port: <port>
    user: <username>
    password: <password>
    database: <database>
default_test_connection:
    type: duckdb
default_scheduler:
    type: builtin

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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    PostgresConnectionConfig,
    DuckDBConnectionConfig,
    BuiltInSchedulerConfig
)

config = Config(
    model_defaults=ModelDefaultsConfig(dialect=<dialect>),
    default_connection=PostgresConnectionConfig(
        host=<host>,
        port=<port>,
        user=<username>,
        password=<password>,
        database=<database>,
    ),
    default_test_connection=DuckDBConnectionConfig(),
    default_scheduler=BuiltInSchedulerConfig(),
)

Models

The model_defaults key is required and must contain a value for the dialect key. All SQL dialects supported by the SQLGlot library are allowed. Other values are set automatically unless explicitly overridden in the model definition.

All supported model_defaults keys are listed in the models configuration reference page.

Example configuration:

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model_defaults:
    dialect: snowflake
    owner: jen
    start: 2022-01-01

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from sqlmesh.core.config import Config, ModelDefaultsConfig

config = Config(
    model_defaults=ModelDefaultsConfig(
        dialect="snowflake",
        owner="jen",
        start="2022-01-01",
    ),
)

Model Kind

The default model kind is 'view' unless overridden with the kind key. For more information, refer to model kinds.

Example:

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model_defaults:
    dialect: snowflake
    kind: full

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from sqlmesh.core.config import Config, ModelDefaultsConfig

config = Config(
    model_defaults=ModelDefaultsConfig(
        dialect="snowflake",
        kind="full",
    ),
)

If a kind requires additional parameters it can be provided as an object:

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model_defaults:
    dialect: snowflake,
    kind:
        name: incremental_by_time_range
        time_column: ds

The Python model_defaults kind argument takes a model kind object with a value of:

  • EmbeddedKind
  • ExternalKind
  • FullKind
  • IncrementalByTimeRangeKind
  • IncrementalByUniqueKeyKind
  • IncrementalUnmanagedKind
  • SeedKind
  • ViewKind
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from sqlmesh.core.config import (
    Config,
    ModelDefaultsConfig,
    IncrementalByTimeRangeKind
)

config = Config(
    model_defaults=ModelDefaultsConfig(
        dialect="snowflake",
        kind=IncrementalByTimeRangeKind(
            time_column="ds",
        ),
    ),
)

Debug mode

To enable debug mode set the SQLMESH_DEBUG environment variable to one of the following values: 1, true, t, yes or y.

Enabling this mode ensures that full backtraces are printed when using CLI. Additionally, the default log level is set to DEBUG when this mode is enabled.

Example enabling debug mode for the CLI command sqlmesh plan:

$ SQLMESH_DEBUG=1 sqlmesh plan