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dbt-starrocks

PyPI PyPI - Python Version PyPI - Downloads

This project is under development.

The dbt-starrocks package contains all the code to enable dbt to work with StarRocks.

This is an experimental plugin:

  • We have not tested it extensively
  • Requires StarRocks version 2.5.0 or higher
    • version 3.1.x is recommended
    • StarRocks versions 2.4 and below are no longer supported

Installation

This plugin can be installed via pip:

$ pip install dbt-starrocks

Supported features

Starrocks <= 2.5 Starrocks 2.5 ~ 3.1 Starrocks >= 3.1 Feature
✅ ✅ ✅ Table materialization
✅ ✅ ✅ View materialization
❌ ❌ ✅ Materialized View materialization
❌ ✅ ✅ Incremental materialization
❌ ✅ ✅ Primary Key Model
✅ ✅ ✅ Sources
✅ ✅ ✅ Custom data tests
✅ ✅ ✅ Docs generate
❌ ❌ ✅ Expression Partition
❌ ❌ ❌ Kafka

Notice

  1. When StarRocks Version < 2.5, Create table as can only set engine='OLAP' and table_type='DUPLICATE'
  2. When StarRocks Version >= 2.5, Create table as supports table_type='PRIMARY'
  3. When StarRocks Version < 3.1 distributed_by is required

Profile Configuration

Example entry for profiles.yml:

starrocks:
  target: dev
  outputs:
    dev:
      type: starrocks
      host: localhost
      port: 9030
      schema: analytics
      username: your_starrocks_username
      password: your_starrocks_password
Option Description Required? Example
type The specific adapter to use Required starrocks
host The hostname to connect to Required 192.168.100.28
port The port to use Required 9030
schema Specify the schema (database) to build models into Required analytics
username The username to use to connect to the server Required dbt_admin
password The password to use for authenticating to the server Required correct-horse-battery-staple
version Let Plugin try to go to a compatible starrocks version Optional 3.1.0
use_pure set to "true" to use C extensions Optional true

More details about setting use_pure and other connection arguments here

Example

dbt seed properties(yml):

Complete configuration:

models:
  materialized: table                   // table or view or materialized_view
  engine: 'OLAP'
  keys: ['id', 'name', 'some_date']
  table_type: 'PRIMARY'                 // PRIMARY or DUPLICATE or UNIQUE
  distributed_by: ['id']
  buckets: 3                            // leave empty for auto bucketing
  indexs=[{ 'columns': 'idx_column' }]  
  partition_by: ['some_date']
  partition_by_init: ["PARTITION p1 VALUES [('1971-01-01 00:00:00'), ('1991-01-01 00:00:00')),PARTITION p1972 VALUES [('1991-01-01 00:00:00'), ('1999-01-01 00:00:00'))"]
  // RANGE, LIST, or Expr partition types should be used in conjunction with partition_by configuration
  // Expr partition type requires an expression (e.g., date_trunc) specified in partition_by
  order_by: ['some_column']             // only for PRIMARY table_type
  partition_type: 'RANGE'               // RANGE or LIST or Expr Need to be used in combination with partition_by configuration
  properties: [{"replication_num":"1", "in_memory": "true"}]
  refresh_method: 'async'               // only for materialized view default manual

dbt run config:

Example configuration:

{{ config(materialized='view') }}
{{ config(materialized='table', engine='OLAP', buckets=32, distributed_by=['id']) }}
{{ config(materialized='table', indexs=[{ 'columns': 'idx_column' }]) }}
{{ config(materialized='table', partition_by=['date_trunc("day", first_order)'], partition_type='Expr') }}
{{ config(materialized='table', table_type='PRIMARY', keys=['customer_id'], order_by=['first_name', 'last_name'] }}
{{ config(materialized='incremental', table_type='PRIMARY', engine='OLAP', buckets=32, distributed_by=['id']) }}
{{ config(materialized='materialized_view') }}
{{ config(materialized='materialized_view', properties={"storage_medium":"SSD"}) }}
{{ config(materialized='materialized_view', refresh_method="ASYNC START('2022-09-01 10:00:00') EVERY (interval 1 day)") }}

For materialized view only support partition_by、buckets、distributed_by、properties、refresh_method configuration.

Read From Catalog

First you need to add this catalog to starrocks. The following is an example of hive.

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

How to add other types of catalogs can be found in the documentation. https://docs.starrocks.io/en-us/latest/data_source/catalog/catalog_overview Then write the sources.yaml file.

sources:
  - name: external_example
    schema: hive_catalog.hive_db
    tables:
      - name: hive_table_name

Finally, you might use below marco quote

{{ source('external_example', 'hive_table_name') }}

Test Adapter

Run the following

python3 -m pytest tests/functional

consult the project

Contributing

We welcome you to contribute to dbt-starrocks. Please see the Contributing Guide for more information.