dbt-trino 0.21.0

The trino adapter plugin for dbt (data build tool)

dbt-trino

Introduction

dbt is a data transformation workflow tool that lets teams quickly and collaboratively deploy analytics code, following software engineering best practices like modularity, CI/CD, testing, and documentation. It enables anyone who knows SQL to build production-grade data pipelines.

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.

dbt-trino adapter uses Trino as a underlying query engine to perform query federation across disperse data sources. Trino connects to multiple and diverse data sources (available connectors) via one dbt connection and process SQL queries at scale. Transformations defined in dbt are passed to Trino which handles these SQL transformation queries and translates them to queries specific to the systems it connects to create tables or views and manipulate data.

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

Compatibility

This dbt plugin has been tested against trino version 363.

Installation

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	127.0.0.1
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:

my-trino-db:
  target: dev
  outputs:
    dev:
      type: trino
      user: commander
      host: 127.0.0.1
      port: 8080
      database: analytics
      schema: public
      threads: 8
      http_scheme: http
      session_properties:
        query_max_run_time: 5d
        exchange_compression: True

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

SHOW SESSION;

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 in dbt-trino:

• Snapshot

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-cache-ttl=0s
hive.metastore-refresh-interval = 5s
hive.allow-drop-table=true
hive.allow-rename-table=true

Incremental models

The incremental strategy currently supported by this adapter is to append new records without updating/overwriting any existing data from the target model.

{{
    config(materialized = 'incremental')
}}

Use table properties to configure connector specifics

Trino connectors use table properties to configure connector specifics.

Check the Trino connector documentation for more information.

{{
  config(
    materialized='table',
    properties={
      "format": "'PARQUET'",
      "partitioning": "ARRAY['bucket(id, 2)']",
    }
  )
}}

Generating lineage flow in docs

In order to generate lineage flow in docs use ref function in the place of table names in the query. It builts dependencies between models and allows to create DAG with data flow. Refer to examples here.

dbt docs generate          # generate docs 
dbt docs serve --port 8081 # starts local server (by default docs server runs on 8080 port, it may cause conflict with Trino in case of local development)

Running tests

Build dbt container locally:

./docker/dbt/build.sh

Run a Trino server locally:

./docker/init.bash

Run tests against Trino:

./docker/run_tests.bash

Run the locally-built docker image (from docker/dbt/build.sh):

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:

tox

or

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.