This dbt package contains macros that:

• can be (re)used across dbt projects running on Spark
• define Spark-specific implementations of dispatched macros from other packages

Installation Instructions

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

Compatibility

This package provides "shims" for:

• dbt_utils, except for:
  • dbt_utils.get_relations_by_pattern
  • dbt_utils.groupby
  • dbt_utils.recency
  • dbt_utils.any_value
  • dbt_utils.listagg
  • dbt_utils.pivot with apostrophe(s) in the values
• snowplow (tested on Databricks only)

In order to use these "shims," you should set a dispatch config in your root project (on dbt v0.20.0 and newer). For example, with this project setting, dbt will first search for macro implementations inside the spark_utils package when resolving macros from the dbt_utils namespace:

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Note to maintainers of other packages

The spark-utils package may be able to provide compatibility for your package, especially if your package leverages dbt-utils macros for cross-database compatibility. This package does not need to be specified as a dependency of your package in packages.yml. Instead, you should encourage anyone using your package on Apache Spark / Databricks to:

• Install spark_utils alongside your package
• Add a dispatch config in their root project, like the one above

Useful macros: maintenance

Caveat: These are not tested in CI, or guaranteed to work on all platforms.

Each of these macros accepts a regex pattern, finds tables with names matching the pattern, and will loop over those tables to perform a maintenance operation:

• spark_optimize_delta_tables: Runs optimize for all matched Delta tables
• spark_vacuum_delta_tables: Runs vacuum for all matched Delta tables
• spark_analyze_tables: Compute statistics for all matched tables

Contributing

We welcome contributions to this repo! To contribute a new feature or a fix, please open a Pull Request with 1) your changes and 2) updated documentation for the README.md file.

Testing

The macros are tested with pytest and pytest-dbt-core. For example, the create_tables macro is tested by:

1. Create a test table (test setup):

   spark_session.sql(f"CREATE TABLE {table_name} (id int) USING parquet")

2. Call the macro generator:

   tables = macro_generator()

3. Assert test condition:

   assert simple_table in tables

4. Delete the test table (test cleanup):

   spark_session.sql(f"DROP TABLE IF EXISTS {table_name}")

A macro is fetched using the macro_generator fixture and providing the macro name trough indirect parameterization:

@pytest.mark.parametrize(
    "macro_generator", ["macro.spark_utils.get_tables"], indirect=True
)
def test_create_table(macro_generator: MacroGenerator) -> None:

Getting started with dbt + Spark

• What is dbt?
• Installation
• Join the #spark channel in dbt Slack

Code of Conduct

Everyone interacting in the dbt project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow the PyPA Code of Conduct.