dbt

dbt-starrocks enables the use of dbt to transform data in StarRocks using dbt's modeling patterns and best practices.

dbt-starrocks GitHub repo.

Experimental feature

Advice on use of experimental features

Supported features

StarRocks >= 3.1	StarRocks >= 3.4	Feature
✅	✅	Table materialization
✅	✅	View materialization
✅	✅	Materialized View materialization
✅	✅	Incremental materialization
✅	✅	Primary Key Model
✅	✅	Sources
✅	✅	Data tests (generic and singular)
✅	✅	Unit tests
✅	✅	Storing test failures
✅	✅	Source freshness (loaded_at_field)
❌	❌	Metadata-based source freshness
✅	✅	Docs generate
❌	❌	persist_docs
✅	✅	Expression Partition
❌	❌	Kafka
❌	✅	Dynamic Overwrite
*	✅	Submit task
✅	✅	Microbatch (Insert Overwrite)
❌	✅	Microbatch (Dynamic Overwrite)

* Verify the specific submit task support for your version, see SUBMIT TASK

Installation

Install the StarRocks DBT adapter using pip:

pip install dbt-starrocks

Verify Installation

Verify the installation by checking the version:

dbt --version

This should list starrocks under plugins.

Configuration

Profiles

Create or update profiles.yml with StarRocks-specific settings.

starrocks_project:
  target: dev
  outputs:
    dev:
      type: starrocks
      host: your-starrocks-host.com
      port: 9030
      schema: your_database
      username: your_username
      password: your_password
      catalog: test_catalog

Parameters

type

Description: The specific adapter to use, this must be set to starrocks
Required?: Required
Example: starrocks

host

Description: The hostname to connect to
Required?: Required
Example: 192.168.100.28

port

Description: The port to use
Required?: Required
Example: 9030

catalog

Description: Specify the catalog to build models into
Required?: Optional
Example: default_catalog

schema

Description: Specify the schema (database in StarRocks) to build models into
Required?: Required
Example: analytics

username

Description: The username to use to connect to the server
Required?: Required
Example: dbt_admin

password

Description: The password to use for authenticating to the server
Required?: Required
Example: correct-horse-battery-staple

version

Description: Let Plugin try to go to a compatible starrocks version
Required?: Optional
Example: 3.1.0

use_pure

Description: set to "true" to use C extensions
Required?: Optional
Example: true

is_async

Description: "true" to submit suitable tasks as etl tasks.
Required?: Optional
Example: true

async_query_timeout

Description: Sets the query_timeout value when submitting a task to StarRocks
Required?: Optional
Example: 300

Sources

Create or update sources.yml

sources:
  - name: your_source
    database: your_sr_catalog
    schema: your_sr_database
    tables:
      - name: your_table

If the catalog is not specified in the schema, it will default to the catalog defined in the profile. Using the profile from earlier, if catalog is not defined, the model will assume the source is located at test_catalog.your_sr_database.

Materializations

Table

Basic Table Configuration

{{ config(
    materialized='table',
    engine='OLAP',
    keys=['id', 'name', 'created_date'],
    table_type='PRIMARY',
    distributed_by=['id'],
    buckets=3,
    partition_by=['created_date'],
    properties=[
        {"replication_num": "1"}
    ]
) }}


SELECT 
    id,
    name,
    email,
    created_date,
    last_modified_date
FROM {{ source('your_source', 'users') }}

Configuration Options

• engine: Storage engine (default: OLAP)
• keys: Columns that define the sort key
• table_type: Table model type
  • PRIMARY: Primary key model (supports upserts and deletes)
  • DUPLICATE: Duplicate key model (allows duplicate rows)
  • UNIQUE: Unique key model (enforces uniqueness)
• distributed_by: Columns for hash distribution
• buckets: Number of buckets for data distribution (leave empty for auto bucketing)
• partition_by: Columns for table partitioning
• partition_by_init: Initial partition definitions
• properties: Additional StarRocks table properties

Tables in External Catalogs

Read from External into StarRocks

This example creates a materialized table in StarRocks containing aggregated data from an external Hive catalog.

tip

Configure the external catalog if it does not already exist:

CREATE EXTERNAL CATALOG `hive_external`
PROPERTIES (
    "hive.metastore.uris"  =  "thrift://127.0.0.1:8087",
    "type"="hive"
);

{{ config(
    materialized='table',
    keys=['product_id', 'order_date'],
    distributed_by=['product_id'],
    partition_by=['order_date']
) }}

-- Aggregate data from Hive external catalog into StarRocks table
SELECT 
    h.product_id,
    h.order_date,
    COUNT(*) as order_count,
    SUM(h.amount) as total_amount,
    MAX(h.last_updated) as last_updated
FROM {{ source('hive_external', 'orders') }} h
GROUP BY 
    h.product_id,
    h.order_date

Write to External

{{
  config(
    materialized='table',
    on_table_exists = 'replace',
    partition_by=['order_date'],
    properties={},
    catalog='external_catalog',
    database='test_db'
  )
}}

SELECT * FROM {{ source('iceberg_external', 'orders') }}

The configuration for materialization to external catalogs supports fewer options. on_table_exists, partition_by, and properties are supported. If catalog and database are not set, the defaults from the profile will be used.

Incremental

Incremental materializations are supported in StarRocks as well:

{{ config(
    materialized='incremental',
    unique_key='id',
    table_type='PRIMARY',
    keys=['id'],
    distributed_by=['id'],
    incremental_strategy='default'
) }}

SELECT
    id,
    user_id,
    event_name,
    event_timestamp,
    properties
FROM {{ source('raw', 'events') }}

{% if is_incremental() %}
    WHERE event_timestamp > (SELECT MAX(event_timestamp) FROM {{ this }})
{% endif %}

Incremental Strategies

dbt-starrocks supports multiple incremental strategies:

1. append (default): Simply appends new records without deduplication
2. insert_overwrite: Overwrites table partitions with insertion
3. dynamic_overwrite: Overwrites, creates, and writes table partitions

For more information about which overwrite strategy to use, see the INSERT documentation.

note

Currently, incremental merge is not supported.

Testing

dbt-starrocks supports every dbt test type. No adapter-specific configuration is required — the tests compile to standard SQL that StarRocks executes directly.

Data tests

The four built-in generic tests (not_null, unique, accepted_values, and relationships) are supported, as are custom generic tests defined in macros/ and singular tests defined as .sql files under test-paths.

models:
  - name: stg_customers
    columns:
      - name: id
        data_tests: [not_null, unique]
      - name: region
        data_tests:
          - accepted_values:
              values: ['us', 'eu']
          - relationships:
              to: ref('dim_customers')
              field: region

Test severity settings (severity, error_if, warn_if, fail_calc, and limit) are handled by dbt before any SQL reaches StarRocks and behave as they do on other adapters.

Unit tests

Unit tests are supported. dbt builds the fixture rows into the compiled query, so no data is written to StarRocks:

unit_tests:
  - name: test_dim_customers_counts
    model: dim_customers
    given:
      - input: ref('stg_customers')
        rows:
          - {id: 1, name: 'a', region: 'us'}
          - {id: 2, name: 'b', region: 'us'}
    expect:
      rows:
        - {region: 'us', n: 2}

Run them with:

dbt test --select test_type:unit

Storing test failures

dbt test --store-failures, the store_failures config, and store_failures_as (table or view) are all supported. Failing rows are written to a separate schema named <schema>_dbt_test__audit, which the adapter creates if it does not exist:

SELECT * FROM `analytics_dbt_test__audit`.`not_null_stg_customers_name`;

Failure tables are created with CREATE TABLE AS, so they accept the same configuration options as table models. Use this to control how the failure table is stored:

      - name: name
        data_tests:
          - not_null:
              config:
                store_failures: true
                alias: nn_name_failures
                distributed_by: ['id']
                buckets: 3

Without a distributed_by value, the failure table is created with random distribution.

tip

store_failures_as: view stores the failing rows as a view instead of a table, which avoids creating and reloading a table on every test run.

Source freshness

dbt source freshness requires each source table to declare a loaded_at_field:

sources:
  - name: raw
    tables:
      - name: events
        loaded_at_field: updated_at
        freshness:
          warn_after: {count: 1, period: hour}
          error_after: {count: 24, period: hour}

warning

Metadata-based freshness is not supported. Every source that needs a freshness check must expose a timestamp column.

Generating documentation

dbt docs generate builds the project catalog by querying information_schema.tables and information_schema.columns. Models, seeds, views, materialized views, and sources are all included, each with its columns, ordinal positions, and data types. dbt docs generate --static and dbt docs serve work as well.

The descriptions you write in your .yml files are read from the dbt manifest, so they appear in the documentation site as usual. However, the following metadata is not collected from StarRocks:

• Table and column comments stored in the database.
• Table owner.
• Table statistics, such as row counts and sizes.

Two further details affect how relations are described:

• Column types are reported without precision. A VARCHAR(64) column appears as varchar, and a DECIMAL(18,4) column appears as decimal. Use SHOW CREATE TABLE when the exact type matters.
• Materialized views are documented as views. This affects the documentation site only, not dbt run.

warning

persist_docs is not supported. Do not set persist_docs in dbt_project.yml, where it would apply to every model in the project.

Troubleshooting

• Before using external catalogs in dbt, you must create them in StarRocks. See the catalog overview.
• External sources should be accessed using the {{ source('external_source_name', 'table_name' }} macro.
• dbt seed was not tested for external catalogs and is not currently supported.
• In order for dbt to create models in external databases that do not currently exist, the location of the models must be set through properties.
• External models need to define the location they are stored at. This location will be defined if the destination database exists and sets the location property. Otherwise, the location needs to be set.
  • We will currently only support creating external models in databases that already exist.