dbt-spark

Documentation

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

Installation

This plugin can be installed via pip:

# Install dbt-spark from PyPi:
$ 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
organization	The id of the Azure Databricks workspace being used; only for Azure Databricks	See Databricks Note	1234567891234567
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

Databricks Note

AWS and Azure Databricks have differences in their connections, likely due to differences in how their URLs are generated between the two services.

To connect to an Azure Databricks cluster, you will need to obtain your organization ID, which is a unique ID Azure Databricks generates for each customer workspace. To find the organization ID, see https://docs.microsoft.com/en-us/azure/databricks/dev-tools/databricks-connect#step-2-configure-connection-properties. When connecting to Azure Databricks, the organization tag is required to be set in the profiles.yml connection file, as it will be defaulted to 0 otherwise, and will not connect to Azure. This connection method follows the databricks-connect package's semantics for connecting to Databricks.

Of special note is the fact that organization ID is treated as a string by dbt-spark, as opposed to a large number. While all examples to date have contained numeric digits, it is unknown how long that may continue, and what the upper limit of this number is. If you do have a leading zero, please include it in the organization tag and dbt-spark will pass that along.

dbt-spark has also been tested against AWS Databricks, and it has some differences in the URLs used. It appears to default the positional value where organization lives in AWS connection URLs to 0, so dbt-spark does the same for AWS connections (i.e. simply leave organization-id out when connecting to the AWS version and dbt-spark will construct the correct AWS URL for you). Note the missing reference to organization here: https://docs.databricks.com/dev-tools/databricks-connect.html#step-2-configure-connection-properties.

Please ignore all references to port 15001 in the databricks-connect docs as that is specific to that tool; port 443 is used for dbt-spark's https connection.

Lastly, the host field for Databricks can be found at the start of your workspace or cluster url (but don't include https://): region.azuredatabricks.net for Azure, or account.cloud.databricks.com for AWS.

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:

http, e.g. AWS Databricks

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

Azure Databricks, via http

your_profile_name:
  target: dev
  outputs:
    dev:
      method: http
      type: spark
      schema: analytics
      host: yourorg.sparkhost.com
      port: 443
      token: abc123
      organization: 1234567891234567
      cluster: 01234-23423-coffeetime
      connect_retries: 5
      connect_timeout: 60

Thrift connection

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
location_root	The created table uses the specified directory to store its data. The table alias is appended to it.	Optional	/mnt/root
partition_by	Partition the created table by the specified columns. A directory is created for each partition.	Optional	partition_1
clustered_by	Each partition in the created table will be split into a fixed number of buckets by the specified columns.	Optional	cluster_1
buckets	The number of buckets to create while clustering	Required if clustered_by is specified	8

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.