dbt Event Logging

⚠️ ADDING THIS PACKAGE TO YOUR DBT PROJECT CAN SIGNIFICANTLY SLOW DOWN YOUR DBT RUNS. This is due to the number of insert statements executed by this package, especially as a post-hook. Please consider if this package is appropriate for your use case before using it.

Requires dbt >= 0.18.0

This package provides out-of-the-box functionality to log events for all dbt invocations, including run start, run end, model start, and model end. It outputs all data and models to schema [target.schema]_meta. There are three convenience models to make it easier to parse the event log data.

Setup

1. Include this package in your packages.yml -- check here for installation instructions.
2. Include the following in your dbt_project.yml directly within your models: block (making sure to handle indenting appropriately):

# dbt_project.yml
...

models:
  ...
  pre-hook: "{{ logging.log_model_start_event() }}"
  post-hook: "{{ logging.log_model_end_event() }}"

That's it! You'll now have a stream of events for all dbt invocations in your warehouse.

Customising audit schema

It's possible to customise the audit schema for any project by adding a macro named: get_audit_schema into your DBT project.

For example to always log into a specific schema, say analytics_meta, regardless of DBT schema, you can include the following in your project:

-- your_dbt_project/macros/get_audit_schema.sql
{% macro get_audit_schema() %}

   {{ return('analytics_meta') }}

{% endmacro %}

Adapter support

This package is currently compatible with dbt's BigQuery¹, Snowflake, Redshift, and Postgres integrations.

¹ BigQuery support may only work when 1 thread is set in your profiles.yml file. Anything larger may result in "quota exceeded" errors.

Migration guide

v0.1.17 -> v0.2.0

New columns were added in v0.2.0:

• event_user as user - varchar(512)the user who ran the model
• event_target as target - varchar(512) the target used when running DBT
• event_is_full_refresh as is_full_refresh - boolean whether the DBT run was a full refresh

These will be added to your existing audit table automatically in the on-run-start DBT hook, and added to the staging tables deployed by this table when they are ran. The existing event_schema column will also be propagated into to stg_dbt_model_deployments as schema.

Contributing

Additional contributions to this repo are very welcome! Check out this post on the best workflow for contributing to a package. All PRs should only include functionality that is contained within all Segment deployments; no implementation-specific details should be included.