Snowflake Utils

This dbt package contains Snowflake-specific macros that can be (re)used across dbt projects. It is maintained by Montreal Analytics, a Datatonic Company.

Installation Instructions

Check dbt Hub for the latest installation instructions, or read the docs for more information on installing packages.

Prerequisites

Snowflake Utils is compatible with dbt 1.1.0 and later.

Macros

snowflake_utils.warehouse_size() (source)

This macro returns an alternative warehouse if conditions are met. It will, in order, check the following conditions for incremental models:

Full refresh run and a warehouse has been configured
Incremental run and a warehouse has been configured
The relation doesn't exist (initial run) and a warehouse has been configured

Otherwise, it returns the target warehouse configured in the profile.

Usage

Call the macro from the snowflake_warehouse model configuration:

{{ 
    config(
      snowflake_warehouse=snowflake_utils.warehouse_size()
    )
}}

Macro Configuration

Out-of-the-box, the macro will return the target.warehouse for each condition, unless exceptions are configured using one or more of the following variables:

variable	information	required
snowflake_utils:initial_run_warehouse	Alternative warehouse when the relation doesn't exist	No
snowflake_utils:full_refresh_run_warehouse	Alternative warehouse when doing a `--full-refresh`	No
snowflake_utils:incremental_run_warehouse	Default warehouse for incremental runs	No

An example dbt_project.yml configuration:

# dbt_project.yml

...
vars:
    'snowflake_utils:initial_run_warehouse': "transforming_xl_wh"
    'snowflake_utils:full_refresh_run_warehouse': "transforming_xl_wh"
    'snowflake_utils:incremental_run_warehouse': "transforming_m_wh"

Console Output

When a variable is configured for a conditon and that condition is matched when executing a run, a log message will confirm which condition was matched and which warehouse is being used.

12:00:00 | Concurrency: 16 threads (target='dev')
12:00:00 | 
12:00:00 | 1 of 1 START incremental model DBT_MGUINDON.fct_orders... [RUN]
12:00:00 + Initial Run - Using warehouse TRANSFORMING_XL_WH

Known Issues

When compiling or generating docs, the console reports that dbt is using the incremental run warehouse. It isn't actually so. During these operations, only the target warehouse is activated.

snowflake_utils.clone_schema (source)

This macro is a part of the recommended 2-step Cloning Pattern for dbt development, explained in detail here.

This macro clones the source schema into the destination schema and optionally grants ownership over its tables and views to a new owner.

Note: the owner of the schema is the role that executed the command, but if configured, the owner of its sub-objects would be the new_owner_role. This is important for maintaining and replacing clones and is explained in more detail here.

Arguments

source_schema (required): The source schema name
destination_schema (required): The destination schema name
source_database (optional): The source database name; default value is your profile's target database.
destination_database (optional): The destination database name; default value is your profile's target database.
new_owner_role (optional): The new ownership role name. If no value is passed, the ownership will remain unchanged.

Usage

Call the macro as an operation:

dbt run-operation clone_schema \
  --args "{'source_schema': 'analytics', 'destination_schema': 'ci_schema'}"

# set the databases and new_owner_role
dbt run-operation clone_schema \
  --args "{'source_schema': 'analytics', 'destination_schema': 'ci_schema', 'source_database': 'production', 'destination_database': 'temp_database', 'new_owner_role': 'developer_role'}"

snowflake_utils.clone_database (source)

This macro is a part of the recommended 2-step Cloning Pattern for dbt development, explained in detail here.

This macro clones the source database into the destination database and optionally grants ownership over its schemata and its schemata's tables and views to a new owner.

Note: the owner of the database is the role that executed the command, but if configured, the owner of its sub-objects would be the new_owner_role. This is important for maintaining and replacing clones and is explained in more detail here.

Arguments

source_database (required): The source database name
destination_database (required): The destination database name
new_owner_role (optional): The new ownership role name. If no value is passed, the ownership will remain unchanged.

Usage

Call the macro as an operation:

dbt run-operation clone_database \
  --args "{'source_database': 'production_clone', 'destination_database': 'developer_clone'}"

# set the new_owner_role
dbt run-operation clone_database \
  --args "{'source_database': 'production_clone', 'destination_database': 'developer_clone', 'new_owner_role': 'developer_role'}"

snowflake_utils.drop_schema (source)

This macro drops a schema in the selected database (defaults to target database if no database is selected). A schema can only be dropped by the role that owns it.

Arguments

schema_name (required): The schema to drop
database (optional): The database name

Usage

Call the macro as an operation:

dbt run-operation drop_schema \
  --args "{'schema_name': 'customers_temp', 'database': 'production'}"

snowflake_utils.drop_database (source)

This macro drops a database. A database can only be dropped by the role that owns it.

Arguments

database_name (required): The database name

Usage

Call the macro as an operation:

dbt run-operation drop_database \
  --args "{'database_name': 'production_clone'}"

snowflake_utils.apply_meta_as_tags (source)

This macro applies specific model meta properties as Snowflake tags during on-run-end. This allows you to author Snowflake tags as part of your dbt project.

Arguments

results (required): The on-run-end context object.

Usage

First, configure your dbt model to have the 'database_tags' meta property as shown (tag examples borrowed from here):

schema.yml

models:
  - name: ACCOUNT
    +schema: FINANCE
    config:
      meta:
        database_tags:
          accounting_row_string: a

    columns:
      - name: ACCOUNT_NAME
        meta:
          database_tags:
            accounting_col_string: b

The above means: The Snowflake table ACCOUNT will have the tag 'accounting_row_string' set to the value 'visible'. Its columns ACCOUNT_NAME and ACCOUNT_NUMBER will both have the tag 'accounting_col_string' set to the value 'visible'

All tags are created in the schema of the model where they are added. In the above example the tags will end up in the FINANCE schema (name depends on how DBT has been configured).

The macro must be called as part of on-run-end, so add the following to dbt_project.yml:

on-run-end: "{{ snowflake_utils.apply_meta_as_tags(results) }}"

Tag removal

This macro only seeks to add or update the tags which are specified in dbt. It won't delete tags which are not defined. If you need this behaviour, it usually comes naturally as dbt drops and recreates tables/views for most materializations. If you are using the incremental materialization, be aware of this limitation.

Contributions

Contributions to this package are very welcome! Please create issues for bugs or feature requests, or open PRs against master.

tdeenes/dbt-snowflake-utils

Snowflake Utils

Installation Instructions

Prerequisites

Macros

snowflake_utils.warehouse_size() (source)

Usage

Macro Configuration

Console Output

Known Issues

snowflake_utils.clone_schema (source)

Arguments

Usage

snowflake_utils.clone_database (source)

Arguments

Usage

snowflake_utils.drop_schema (source)

Arguments

Usage

snowflake_utils.drop_database (source)

Arguments

Usage

snowflake_utils.apply_meta_as_tags (source)

Arguments

Usage

Tag removal

Contributions