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The trino adapter plugin for dbt (data build tool)

Project description



One frequently asked question in the context of using dbt tool is:

Can I connect my dbt project to two databases?

(see the answered question on the dbt website).

tldr; dbt stands for transformation as in T within ELT pipelines, it doesn't move data from source to a warehouse.

The creators of the dbt tool have added however support for handling such scenarios via dbt-presto plugin.

This repository represents a fork of the dbt-presto with slight adaptations to make it work with Trino SQL compute engine.


This dbt plugin has been tested against trino version 362.


This dbt adapter can be installed via pip:

$ pip install dbt-trino

Configuring your profile

A dbt profile can be configured to run against Trino using the following configuration:

Option Description Required? Example
method The Trino authentication method to use Optional (default is none) none or kerberos
user Username for authentication Required commander
password Password for authentication Optional (required if method is ldap or kerberos) none or abc123
http_headers HTTP Headers to send alongside requests to Trino, specified as a yaml dictionary of (header, value) pairs. Optional X-Trino-Client-Info: dbt-trino
http_scheme The HTTP scheme to use for requests to Trino Optional (default is http, or https for method: kerberos and method: ldap) https or http
session_properties Sets Trino session properties used in the connection Optional query_max_run_time: 5d
database Specify the database to build models into Required analytics
schema Specify the schema to build models into. Note: it is not recommended to use upper or mixed case schema names Required public
host The hostname to connect to Required
port The port to connect to the host on Required 8080
threads How many threads dbt should use Optional (default is 1) 8

Example profiles.yml entry:

  target: dev
      type: trino
      user: commander
      port: 8080
      database: analytics
      schema: public
      threads: 8
      http_scheme: http
        query_max_run_time: 5d
        exchange_compression: True

For reference on which session properties can be set on the the dbt profile do execute


on your Trino instance.

Usage Notes

Supported Functionality

Due to the nature of Trino, not all core dbt functionality is supported. The following features of dbt are not implemented on Trino:

  • Archival
  • Incremental models

Also, note that upper or mixed case schema names will cause catalog queries to fail. Please only use lower case schema names with this adapter.

Required configuration

dbt fundamentally works by dropping and creating tables and views in databases. As such, the following Trino configs must be set for dbt to work properly on Trino:

hive.metastore-refresh-interval = 5s

Use table properties to configure connector specifics

Trino connectors use table properties to configure connector specifics.

Check the Trino connector documentation for more information.

      "format": "'PARQUET'",
      "partitioning": "ARRAY['bucket(id, 2)']",

Running tests

Build dbt container locally:


Run a Trino server locally:


Run tests against Trino:


Run the locally-built docker image (from docker/dbt/

export DBT_PROJECT_DIR=$HOME/... # wherever the dbt project you want to run is
docker run -it --mount "type=bind,source=$HOME/.dbt/,target=/root/.dbt" --mount="type=bind,source=$DBT_PROJECT_DIR,target=/usr/app" --network dbt-net dbt-trino /bin/bash

Running integration tests

Install the libraries required for development in order to be able to run the dbt tests:

pip install -r dev_requirements.txt

Run from the base directory of the project the command:



pytest test/integration/trino.dbtspec

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.

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dbt-trino-0.20.2.tar.gz (16.4 kB view hashes)

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