Skip to main content

The presto adpter plugin for dbt (data build tool)

Project description

dbt-presto

Documentation

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

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 is none) none or kerberos
user Username for authentication Required drew
password Password for authentication Optional (required if method is ldap or kerberos) none or abc123
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 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 is 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

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.

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dbt-presto-0.20.2.tar.gz (11.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dbt_presto-0.20.2-py3-none-any.whl (12.0 kB view details)

Uploaded Python 3

File details

Details for the file dbt-presto-0.20.2.tar.gz.

File metadata

  • Download URL: dbt-presto-0.20.2.tar.gz
  • Upload date:
  • Size: 11.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.8.11

File hashes

Hashes for dbt-presto-0.20.2.tar.gz
Algorithm Hash digest
SHA256 b84a44fd4c396c93bc836c56b958661a59e1135fbc137d36d9891ac73a53a171
MD5 e662a13382ccb2ca0415c6ca813e901f
BLAKE2b-256 92f02bb18306b458d7d28be305c291526db8fea977f829e27342b4682b7581f2

See more details on using hashes here.

File details

Details for the file dbt_presto-0.20.2-py3-none-any.whl.

File metadata

  • Download URL: dbt_presto-0.20.2-py3-none-any.whl
  • Upload date:
  • Size: 12.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.8.11

File hashes

Hashes for dbt_presto-0.20.2-py3-none-any.whl
Algorithm Hash digest
SHA256 1acaaa2236a037e50edc177f99ded0591cde3e1746838575825f730d53d705e4
MD5 45173635b6f5fce101185db9fc3c7cf7
BLAKE2b-256 be31bae194c14065832f7bef251d2fab464fd79166ef72c733bdec5e0e720933

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page