dbt-presto

Documentation

For more information on using Spark with dbt, consult the dbt documentation:

• Presto profile

Installation

This plugin can be installed via pip:

$ pip install dbt-presto

Configuring your profile

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

Option	Description	Required?	Example
method	The Presto authentication method to use	Optional (default=none)	none
user	Username for authentication	Required	none
password	Password for authentication	Optional (required if method is `ldap	kerberos`)
database	Specify the database to build models into	Required	analytics
schema	Specify the schema to build models into	Required	dbt_drew
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=1)	8

Example profiles.yml entry:

my-presto-db:
  target: dev
  outputs:
    dev:
      type: presto
      user: drew
      host: 127.0.0.1
      port: 8080
      database: analytics
      schema: dbt_drew
      threads: 8

Usage Notes

Supported Functionality

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

• Archival
• Incremental models

If you are interested in helping to add support for this functionality in dbt on Presto, please open an issue!

Required configuration

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

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

Reporting bugs and contributing code

• Want to report a bug or request a feature? Let us know on Slack, or open an issue.

Running tests

Build dbt container locally:

./docker/dbt/build.sh

Run a Presto server locally:

./docker/init.bash

If you see errors while about "inconsistent state" while bringing up presto, you may need to drop and re-create the public schema in the hive metastore:

# Example error

Initialization script hive-schema-2.3.0.postgres.sql
Error: ERROR: relation "BUCKETING_COLS" already exists (state=42P07,code=0)
org.apache.hadoop.hive.metastore.HiveMetaException: Schema initialization FAILED! Metastore state would be inconsistent !!
Underlying cause: java.io.IOException : Schema script failed, errorcode 2
Use --verbose for detailed stacktrace.
*** schemaTool failed ***

Solution: Drop (or rename) the public schema to allow the init script to recreate the metastore from scratch. Only run this against a test Presto deployment. Do not run this in production!

-- run this against the hive metastore (port forwarded to 10005 by default)
-- DO NOT RUN THIS IN PRODUCTION!

drop schema public cascade;
create schema public;

You probably should be slightly less reckless than this.

Run tests against Presto:

./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=/home/dbt_user/.dbt" --mount="type=bind,source=$DBT_PROJECT_DIR,target=/usr/app" --network dbt-net dbt-presto /bin/bash

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.