Skip to main content

Apache Spark Profile

Community plugin

Some core functionality may be limited. If you're interested in contributing, check out the source code for each repository listed below.

Overview of dbt-spark#

Maintained by: core dbt maintainers
Author: dbt Labs
Source: Github
Core version: v0.13.0 and newer
dbt Cloud: Supported
dbt Slack channel Link to channel

dbt-spark stars

Connection Methods#

dbt-spark can connect to Spark clusters by three different methods:

  • odbc is the preferred method when connecting to Databricks. It supports connecting to a SQL Endpoint or an all-purpose interactive cluster.
  • http is a more generic method for connecting to a managed service that provides an HTTP endpoint. Currently, this includes connections to a Databricks interactive cluster.
  • thrift connects directly to the lead node of a cluster, either locally hosted / on premise or in the cloud (e.g. Amazon EMR).



Use the odbc connection method if you are connecting to a Databricks SQL endpoint or interactive cluster via ODBC driver. (Download the latest version of the official driver here.)

your_profile_name:  target: dev  outputs:    dev:      type: spark      method: odbc      driver: [path/to/driver]      schema: [database/schema name]      host: []      organization: [org id]    # Azure Databricks only      token: [abc123]            # one of:      endpoint: [endpoint id]      cluster: [cluster id]            # optional      port: [port]              # default 443      user: [user]      


Use the thrift connection method if you are connecting to a Thrift server sitting in front of a Spark cluster, e.g. a cluster running locally or on Amazon EMR.

your_profile_name:  target: dev  outputs:    dev:      type: spark      method: thrift      schema: [database/schema name]      host: [hostname]            # optional      port: [port]              # default 10001      user: [user]      auth: [e.g. KERBEROS]      kerberos_service_name: [e.g. hive]


Use the http method if your Spark provider supports generic connections over HTTP (e.g. Databricks interactive cluster).

your_profile_name:  target: dev  outputs:    dev:      type: spark      method: http      schema: [database/schema name]      host: []      organization: [org id]    # Azure Databricks only      token: [abc123]      cluster: [cluster id]            # optional      port: [port]              # default: 443      user: [user]      connect_timeout: 60       # default 10      connect_retries: 5        # default 0

Databricks interactive clusters can take several minutes to start up. You may include the optional profile configs connect_timeout and connect_retries, and dbt will periodically retry the connection.

Installation and Distribution#

dbt's adapter for Apache Spark and Databricks is managed in its own repository, dbt-spark. To use it, you must install the dbt-spark plugin.

Using pip#

The following commands will install the latest version of dbt-spark as well as the requisite version of dbt-core.

If connecting to Databricks via ODBC driver, it requires pyodbc. Depending on your system, you can install it seperately or via pip. See the pyodbc wiki for OS-specific installation details.

If connecting to a Spark cluster via the generic thrift or http methods, it requires PyHive.

# odbc connections$ pip install "dbt-spark[ODBC]"
# thrift or http connections$ pip install "dbt-spark[PyHive]"


Usage with EMR#

To connect to Apache Spark running on an Amazon EMR cluster, you will need to run sudo /usr/lib/spark/sbin/ on the master node of the cluster to start the Thrift server (see the docs for more information). 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.

Supported Functionality#

Most dbt Core functionality is supported, but some features are only available on Delta Lake (Databricks).

Delta-only features:

  1. Incremental model updates by unique_key instead of partition_by (see merge strategy)
  2. Snapshots

Some dbt features, available on the core adapters, are not yet supported on Spark:

  1. Persisting column-level descriptions as database comments