dbt-iceberg 1.0.5

The Apache Iceberg adapter plugin for dbt with spark-submit and spark-sql CLI support

dbt

dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

dbt is the T in ELT. Organize, cleanse, denormalize, filter, rename, and pre-aggregate the raw data in your warehouse so that it's ready for analysis.

dbt-spark

dbt-spark enables dbt to work with Apache Spark. For more information on using dbt with Spark, consult the docs.

Getting started

Review the repository README.md as most of that information pertains to dbt-spark.

Running locally

A docker-compose environment starts three services:

Service	Description	Port
dbt-spark3-thrift	Spark Thrift Server (HiveThriftServer2) — long-running JDBC endpoint	10000
dbt-spark-history	Spark History Server — UI for completed queries and jobs	18080
dbt-hive-metastore	Postgres-backed Hive Metastore	internal

Note: dbt-spark now supports Spark 4.1.0.

The following command starts all containers:

docker-compose up -d

It will take a bit of time for the instance to start; you can check the logs of the containers. If the instance doesn't start correctly, try the complete reset command listed below and then try again.

Create a profile using the spark_sql method in thrift server mode:

spark_testing:
  target: local
  outputs:
    local:
      type: spark
      method: spark_sql
      host: 127.0.0.1
      port: 10000
      user: dbt
      schema: analytics
      connect_retries: 5
      connect_timeout: 60
      retry_all: true
      spark_history_server: http://localhost:18080

Or using the thrift method directly:

spark_testing:
  target: local
  outputs:
    local:
      type: spark
      method: thrift
      host: 127.0.0.1
      port: 10000
      user: dbt
      schema: analytics
      connect_retries: 5
      connect_timeout: 60
      retry_all: true

Connecting to the local Spark instance:

• Spark UI (active queries): http://localhost:4040/sqlserver/
• Spark History Server (completed queries): http://localhost:18080
• Thrift endpoint: jdbc:hive2://localhost:10000 — credentials dbt:dbt

Note that the Hive metastore data is persisted under ./.hive-metastore/, Spark warehouse data under ./.spark-warehouse/, and event logs under ./.spark-events/. To completely reset your environment run:

docker-compose down
rm -rf ./.hive-metastore/ ./.spark-warehouse/ ./.spark-events/

Additional Configuration for MacOS

If installing on MacOS, use homebrew to install required dependencies.

brew install unixodbc

Configuring spark-submit and spark-sql methods

The spark_submit and spark_sql connection methods run SQL and Python models using either a long-running Spark Thrift Server or the Spark CLI tools directly.

Requirements

• A working Spark installation accessible via SPARK_HOME or on PATH (CLI mode), or a running HiveThriftServer2 (thrift server mode).
• For spark_submit: spark-submit must be executable.
• For spark_sql CLI mode: spark-sql must be executable.
• For spark_sql thrift server mode: pyhive must be installed (pip install dbt-spark[PyHive]).

spark-sql — thrift server mode (recommended)

When host is set, spark_sql connects to a long-running Spark Thrift Server (HiveThriftServer2) via the Thrift protocol. This avoids the JVM startup cost of spawning a new Spark application for every dbt invocation, and lets you share a single Spark context across many dbt runs. The Spark UI and History Server remain accessible throughout.

my_spark_project:
  target: dev
  outputs:
    dev:
      type: spark
      method: spark_sql
      host: 127.0.0.1
      port: 10000
      schema: analytics
      user: dbt
      connect_retries: 5
      connect_timeout: 60
      retry_all: true
      # Optional: URL of the Spark History Server UI (informational — logged at startup)
      spark_history_server: http://127.0.0.1:18080
      # Optional: server-side Spark configuration applied to each session
      server_side_parameters:
        spark.executor.memory: "4g"
        spark.eventLog.enabled: "true"
        spark.eventLog.dir: /tmp/spark-events

Start a thrift server locally using the bundled docker-compose:

docker-compose up -d

Or start one manually:

$SPARK_HOME/sbin/start-thriftserver.sh \
  --master local[*] \
  --conf spark.sql.ansi.enabled=false \
  --conf spark.eventLog.enabled=true \
  --conf spark.eventLog.dir=/tmp/spark-events

spark-sql — CLI mode

When host is not set, dbt executes each statement with spark-sql -e '...' (or -f for batches). A new Spark application is launched per dbt invocation.

my_spark_project:
  target: dev
  outputs:
    dev:
      type: spark
      method: spark_sql
      schema: analytics
      # Optional: explicit path to SPARK_HOME if not set in environment
      spark_home: /opt/spark
      # Optional: extra CLI flags passed to spark-sql
      spark_sql_args:
        - "--master"
        - "local[*]"
        - "--conf"
        - "spark.executor.memory=4g"

spark-submit

Use spark_submit when your project includes Python models. dbt executes Python models via spark-submit. SQL statements (e.g. catalog introspection) fall back to thrift when host is set, or to the spark-sql CLI otherwise.

With a thrift server (recommended):

my_spark_project:
  target: dev
  outputs:
    dev:
      type: spark
      method: spark_submit
      host: 127.0.0.1
      port: 10000
      schema: analytics
      user: dbt
      spark_history_server: http://127.0.0.1:18080
      # Extra CLI flags for spark-submit (Python models only)
      spark_submit_args:
        - "--master"
        - "local[*]"
        - "--conf"
        - "spark.executor.memory=4g"
      spark_submit_timeout: 3600

CLI only (no thrift server):

my_spark_project:
  target: dev
  outputs:
    dev:
      type: spark
      method: spark_submit
      schema: analytics
      spark_home: /opt/spark
      spark_submit_args:
        - "--master"
        - "local[*]"
        - "--conf"
        - "spark.executor.memory=4g"
      spark_sql_args:
        - "--master"
        - "local[*]"
      spark_submit_timeout: 3600

Profile fields

Field	Required	Default	Description
method	Yes	—	spark_sql or spark_submit
schema	Yes	—	The default schema (database) to use
host	No	—	Thrift server hostname. When set, spark_sql and spark_submit connect via Thrift instead of CLI
port	No	443	Thrift server port (typically 10000)
user	No	—	Username for thrift server authentication
auth	No	—	Auth mechanism for thrift (e.g. NONE, LDAP, KERBEROS)
use_ssl	No	false	Use SSL/TLS for the thrift connection
spark_history_server	No	—	URL of the Spark History Server UI (informational)
spark_home	No	$SPARK_HOME env var	Path to your Spark installation (CLI mode)
spark_sql_args	No	[]	Extra CLI flags for spark-sql (CLI mode only)
spark_submit_args	No	[]	Extra CLI flags for spark-submit (Python models only)
spark_submit_timeout	No	null (no timeout)	Max seconds to wait for a spark-submit job
poll_interval	No	5	Seconds between status polls for thrift queries
query_timeout	No	null (no timeout)	Max seconds for a single thrift query
query_retries	No	1	Times to retry on connection loss during query polling
server_side_parameters	No	{}	Spark configuration sent to the thrift server per session

Notes

• Thrift server mode (host set) requires pip install dbt-spark[PyHive] for both spark_sql and spark_submit.
• spark_submit only invokes spark-submit for Python models. SQL statements use thrift when host is set, otherwise spark-sql CLI.
• spark_sql_args is only used in CLI mode; it is ignored when host is set.
• If spark_home is not set in the profile, dbt falls back to the SPARK_HOME environment variable, then to spark-sql/spark-submit on PATH.
• The Spark History Server requires event logging on the thrift server (spark.eventLog.enabled=true). The bundled docker-compose configures this automatically.

Contribute

• Want to help us build dbt-spark? Check out the Contributing Guide.