Use dbt for data transformations

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Install the plugin

To use dbt to manage data in RisingWave, you need to install the dbt-risingwave plugin.

As a prerequisite, you need to have Python3 installed in your environment.

Run the following command to install the plugin:

python3 -m pip install dbt-risingwave

After the plugin is installed, run the following command to ensure risingwave shows up under plugins.

dbt --version

If you see something like below, the plugin is successfully installed:

Plugins:
...
  - risingwave: 1.6.0 - Up to date!
...

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Initiate a dbt project

Before you initial a dbt project, you need to ensure that RisingWave is installed and running. To learn about how to install and run RisingWave, see the topics under Get started.

You can initiate a dbt project by running the following command.

dbt init

It will ask you to enter a project name, choose the database you like to use (risingwave), and specify other database configurations such as hostname, port, user name, etc.

The default database configurations are:

• host: localhost
• port: 4566
• user: root
• database: dev

If you use different RisingWave configurations, please ensure that the configurations here match those in RisingWave.

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Define dbt models

The dbt models for managing data transformations in RisingWave are similar to typical dbt SQL models. The main differences are the materializations. We customized the materializations to fit the data processing model of RisingWave.

RisingWave accepts these materializations.

To learn about how to define SQL models in general, see SQL models.

To learn about how dbt works with RisingWave in practice, check out our demo project, which uses dbt to manage Nexmark queries in RisingWave.

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Run dbt models

This step assumes that you have defined your dbt models.

Navigate to your project directory.

cd <your project name>

First, you can run dbt debug to check your connection to RisingWave.

dbt debug

If the connection is valid, you will see an “OK connection ok” message. In this case, you can run your models. Otherwise, please check ~/.dbt/profiles.yml to ensure your connection configurations are valid.

Run the following command to run your models.

dbt run

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Example model files

{{ config(materialized='source') }}
CREATE SOURCE {{ this }} (v1 int, v2 varchar) WITH (
  connector = 'kafka',
  topic = 'kafka_1_partition_topic',
  properties.bootstrap.server = 'message_queue:29092',
  scan.startup.mode = 'earliest'
) FORMAT PLAIN ENCODE JSON

{{ config(materialized='table_with_connector') }}
CREATE TABLE {{ this }} (v1 int, v2 varchar) WITH (
  connector = 'kafka',
  topic = 'kafka_1_partition_topic',
  properties.bootstrap.server = 'message_queue:29092',
  scan.startup.mode = 'earliest'
) FORMAT PLAIN ENCODE JSON

{{ config(materialized='sink') }}
CREATE SINK {{ this }} AS
SELECT
   avg(distance) as avg_distance,
   avg(duration) as avg_duration
FROM taxi_trips
WITH (
   connector='kafka',
   properties.bootstrap.server='localhost:9092',
   topic='test'
)
FORMAT PLAIN ENCODE JSON;

{{ config(materialized='materialized_view') }}
SELECT
    auction,
    to_char(date_time, 'YYYY-MM-DD') AS day,
    count(*) AS total_bids,
    count(*) filter (where price < 10000) AS rank1_bids,
    count(*) filter (where price >= 10000 and price < 1000000) AS rank2_bids,
    count(*) filter (where price >= 1000000) AS rank3_bids,
    min(price) AS min_price,
    max(price) AS max_price,
    avg(price) AS avg_price,
    sum(price) AS sum_price
FROM {{ ref('bid') }}
GROUP BY auction, to_char(date_time, 'YYYY-MM-DD')

{{ config(
    materialized = 'table',
    indexes=[
      {'columns': ['c1']},
      {'columns': ['c2', 'c1']},
    ]
)}}
select 1 as c1, 1 c2 union select 2 as c1, 2 as c2

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Additional resources

For details about building and deploying dbt models in general, please refer to the dbt documentation.