dbt-invoke

dbt-invoke is a CLI (built with Invoke) for creating, updating, and deleting dbt property files.

• Supported dbt resource types:

  • models
  • seeds
  • snapshots
  • analyses

• Under the hood, this tool works by combining the power of the dbt ls and dbt run-operation commands with dbt's built-in get_columns_in_query macro.

  • This methodology allows the tool to work on ephemeral models and analyses, which other approaches, such as those based on listing data warehouse tables/views, can miss.

Installation

pip install dbt-invoke

Usage

• You must have previously executed dbt run/dbt seed/dbt snapshot on the resources for which you wish to create/update property files.

  • If you have made updates to your resources, execute the appropriate command (dbt run/dbt seed/dbt snapshot) before using this tool to create/update property files.

• Property files will be created, updated, or deleted on a one-to-one basis in the same paths as the resource files they represent (the only change being a .yml file extension).

  • For example, given a resource file in the location models/marts/core/users.sql, this tool will create, update, or delete a property file in the location models/marts/core/users.yml.

• Any newly generated property files are created with the correct resource type, resource name, and columns. A blank description field will be included for each column and for the resource itself.

  • For example, when generating a new property file for a model users with column names user_id and created_at, the following yaml will be generated:

    • version: 2
      models:
      - name: users
        description: ''
        columns:
        - name: user_id
          description: ''
        - name: created_at
          description: ''

• When updating an already existing property file, new columns in the resource will be added, and columns that no longer exist will be removed.

• You may fill in the blank description properties and add other properties (e.g. tests). They will remain intact when updating existing property files as long as the column/resource name to which they belong still exists.

Creating/Updating Property Files

dbt-invoke properties.update <options>

# OR, because 'update' is set as the default command:
dbt-invoke properties <options>

• The first time you run this command, you should be prompted to add a short macro called _log_columns_list to your dbt project.

  • You may accept the prompt to add it automatically.
  • Otherwise, copy/paste it into one your dbt project's macro-paths yourself.
  • To print the macro, at any time, run dbt-invoke properties.echo-macro.

• <options> primarily uses the same arguments as the dbt ls command to allow flexibility in selecting the dbt resources for which you wish to create/update property files (run dbt ls --help for details).

  • --resource-type
  • --models
  • --select
  • --selector
  • --exclude
  • --project-dir
  • --profiles-dir
  • --profile
  • --target
  • --vars
  • --bypass-cache
  • --state

• Notes:

  • This tool supports only the long flags of dbt ls options (for example: --models, and not short flags like -m).
  • Multiple values for the same argument can be passed as a comma separated string (Example: --models modelA,modelB)
    • Keep in mind that dbt may not support multiple values for certain options.

• Two additional flags are made available.

  • --log-level to alter the verbosity of logs.
    • It accepts one of Python's standard logging levels (debug, info, warning, error, critical).
  • --threads to set a maximum number of concurrent threads to use in collecting resources' column information from the data warehouse and in creating/updating the corresponding property files. Each thread will run dbt's get_columns_in_query macro against the data warehouse.

• Some examples:

  # Create/update property files for all supported resource types
  dbt-invoke properties
  
  # Create/update property files for all supported resource types, using 4 concurrent threads
  dbt-invoke properties --threads 4
  
  # Create/update property files for all models in a models/marts directory
  dbt-invoke properties --models marts
  
  # Create/update property files for a 'users' model and an 'orders' models
  dbt-invoke properties --models users,orders
  
  # Create/update property files for a 'users' model and all downstream models
  dbt-invoke properties --models users+
  
  # Create/update property files for all seeds
  dbt-invoke properties --resource-type seed
  
  # Create/update a property file for a snapshot called 'users_snapshot'
  dbt-invoke properties --resource-type snapshot --select users_snapshot
  
  # Create/update property files when your working directory is above your dbt project directory
  dbt-invoke properties --project-dir path/to/dbt/project/directory

Deleting Property Files

dbt-invoke properties.delete <options>

• <options> uses the same arguments as for creating/updating property files, except for --threads.

Help

• To view the list of available commands and their short descriptions, run:

  dbt-invoke --list

• To view in depth command descriptions and available options/flags, run:

  dbt-invoke <command_name> --help

Limitations

• dbt-invoke will try to preserve formatting and comments when updating existing files. If you want to preserve line-breaks, use > or | on your multiline strings, as recommended here.
• In order to collect or update the list of columns that should appear in each property file, dbt's get_columns_in_query macro is run for each matching resource. As of the time of writing, get_columns_in_query uses a SELECT statement limited to zero rows. While this is not typically a performance issue for table or incremental materializations, execution may be slow for complex analyses, views, or ephemeral materializations.
  • This may be partially remedied by increasing the value of the --threads option in dbt-invoke properties.update.
• dbt-invoke is tested against:
  • Python 3.7 and 3.9
  • dbt 0.18, 0.19, and 1.0
  • macos-latest, windows-latest, ubuntu-latest
• dbt-invoke has not been tested across different types of data warehouses.