dbt-spark 0.13.0

The SparkSQL plugin for dbt (data build tool)

dbt-spark

Documentation

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

Installation

This plugin can be installed via pip:

$ pip install dbt-spark

Configuring your profile

Connection Method

Connections can be made to Spark in two different modes. The http mode is used when connecting to a managed service such as Databricks, which provides an HTTP endpoint; the thrift mode is used to connect directly to the master node of a cluster (either on-premise or in the cloud).

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

Option	Description	Required?	Example
method	Specify the connection method (thrift or http)	Required	http
schema	Specify the schema (database) to build models into	Required	analytics
host	The hostname to connect to	Required	yourorg.sparkhost.com
port	The port to connect to the host on	Optional (default: 443 for http, 10001 for thrift)	443
token	The token to use for authenticating to the cluster	Required for http	abc123
cluster	The name of the cluster to connect to	Required for http	01234-23423-coffeetime
user	The username to use to connect to the cluster	Optional	hadoop
connect_timeout	The number of seconds to wait before retrying to connect to a Pending Spark cluster	Optional (default: 10)	60
connect_retries	The number of times to try connecting to a Pending Spark cluster before giving up	Optional (default: 0)	5

Usage with Amazon EMR

To connect to Spark running on an Amazon EMR cluster, you will need to run sudo /usr/lib/spark/sbin/start-thriftserver.sh on the master node of the cluster to start the Thrift server (see https://aws.amazon.com/premiumsupport/knowledge-center/jdbc-connection-emr/ for further context). You will also need to connect to port 10001, which will connect to the Spark backend Thrift server; port 10000 will instead connect to a Hive backend, which will not work correctly with dbt.

Example profiles.yml entries:

your_profile_name:
  target: dev
  outputs:
    dev:
      method: http
      type: spark
      schema: analytics
      host: yourorg.sparkhost.com
      port: 443
      token: abc123
      cluster: 01234-23423-coffeetime
      connect_retries: 5
      connect_timeout: 60

your_profile_name:
  target: dev
  outputs:
    dev:
      method: thrift
      type: spark
      schema: analytics
      host: 127.0.0.1
      port: 10001
      user: hadoop
      connect_retries: 5
      connect_timeout: 60

Usage Notes

Model Configuration

The following configurations can be supplied to models run with the dbt-spark plugin:

Option	Description	Required?	Example
file_format	The file format to use when creating tables	Optional	parquet

Incremental Models

Spark does not natively support delete, update, or merge statements. As such, incremental models are implemented differently than usual in this plugin. To use incremental models, specify a partition_by clause in your model config. dbt will use an insert overwrite query to overwrite the partitions included in your query. Be sure to re-select all of the relevant data for a partition when using incremental models.

{{ config(
    materialized='incremental',
    partition_by=['date_day'],
    file_format='parquet'
) }}

/*
  Every partition returned by this query will be overwritten
  when this model runs
*/

select
    date_day,
    count(*) as users

from {{ ref('events') }}
where date_day::date >= '2019-01-01'
group by 1

Reporting bugs and contributing code

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

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.