dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

dbt is the T in ELT. Organize, cleanse, denormalize, filter, rename, and pre-aggregate the raw data in your warehouse so that it's ready for analysis.

Citus

Postgres-inherited dbt adapter for Citus database. Many parts copied from dbt-greenplum

Materializing a Distributed Table

via create_distributed_table

{{
  config(materialized='table', distribution_column='name')
}}

arg name	required	default value
distribution_column	no	None

Testing

Citus provides an easy way to spin up a local cluster via docker:

git clone https://github.com/citusdata/docker
cd docker
docker compose up --scale worker=1

Now, you can run the integration tests in this repo:

pytest .\tests\functional\adapter\test_distributed.py

Notes

To understand the inner workings, check out the citus__create_table_as macros definite in adapters.sql

{% macro citus__create_table_as(temporary, relation, sql) %}
...
{% endmacro %}

Join the dbt Community

• Be part of the conversation in the dbt Community Slack
• If one doesn't exist feel free to request a #db-Citus channel be made in the #channel-requests on dbt community slack channel.
• Read more on the dbt Community Discourse

Reporting bugs and contributing code

• Want to report a bug or request a feature? Let us know on Slack, or open an issue
• Want to help us build dbt? Check out the Contributing Guide

Code of Conduct

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