dbt-flink-adapter 1.3.11

The Flink adapter plugin for dbt

dbt Flink Adapter

This is an MVP of dbt Flink Adapter. It allows materializing of dbt models as Flink cluster streaming pipelines and batch jobs.

Check out our blogpost about dbt-flink-adapter with tutorial

Prerequisites

• Flink 1.16+ with Flink SQL Gateway
• Python 3.8+ with pip
• (Optionally) venv

Setup

This adapter is connecting to Flink SQL Gateway which is not started in Flink by default. Please refer to flink-doc/starting-the-sql-gateway on how to start SQL gateway in your cluster.

For testing and developing purposes you can use envs/flink-1.16/docker-compose.yml to start one node Flink cluster with SQL Gateway.

$ cd envs/flink-1.16
$ docker compose up

Install dbt-flink-adapter

Create virtual environment and install dbt-flink-adapter from PyPI/dbt-flink-adapter with pip

$ python3 -m venv ~/.virtualenvs/dbt-example1
$ source ~/.virtualenvs/dbt-example1/bin/activate
$ pip3 install dbt-flink-adapter
$ dbt --version
...
Plugins:
  - flink: x.y.z

Create and initialize dbt project

Navigate to directory in which you want to create your project. If you are using Flink with SQL Gateway started from docker-compose.yml file in this repo you can leave all values as defaults.

$  dbt init
Enter a name for your project (letters, digits, underscore): example1
Which database would you like to use?
[1] flink

Enter a number: 1
host (Flink SQL Gateway host) [localhost]:
port [8083]:
session_name [test_session]:
database (Flink catalog) [default_catalog]:
schema (Flink database) [default_database]:
threads (1 or more) [1]:

$ cd example1

Creating and running dbt model

On how to create and run dbt model please refer to dbt-docs. This README will focus on things that are specific for this adapter.

dbt run

Source

In typical use-case dbt connects and runs its ETL processes on database engine that already has connection with underlying persistence layer. In case of Flink however it's only a processing engine, and we need to define connectivity with external persistence. To do so we have to define sources in our dbt model.

Connector properties

dbt-flink-adapter will read config/connector_properties key and use it as connector properties.

Type

Flink supports sources in batch and streaming mode, use type to select what execution environment will be used during source creation.

column type

current has these values, refer to flink-doc/create-table

• physical (default)
• metadata
• computed

Watermark

To provide watermark pass column and strategy reference under watermark key in config.

Please refer to Flink documentation about possible watermark strategies: flink-doc/watermark

Example

sources:
  - name: my_source
    tables:
      - name: clickstream
        config:
          type: streaming
          connector_properties:
            connector: 'kafka'
            properties.bootstrap.servers: 'kafka:29092'
            topic: 'clickstream'
          watermark:
            column: event_timestamp
            strategy: event_timestamp
        columns:
          - name: id
            data_type: BIGINT
          - name: id2
            column_type: computed
            expression: id + 1
          - name: event_timestamp
            data_type: TIMESTAMP(3)
          - name: ts2
            column_type: metadata
            data_type: TIMESTAMP(3)
            expression: timestamp

SQL passed to Flink will look like:

CREATE TABLE IF NOT EXISTS my_source (
  `id` BIGINT,
  `id2` AS id + 1,
  `event_timestamp` TIMESTAMP(3),
  `ts2` TIMESTAMP(3) METADATA  FROM 'timestamp',
  WATERMARK FOR event_timestamp AS event_timestamp
) WITH (
  'connector' = 'kafka',
  'properties.bootstrap.servers' = 'kafka:29092',
  'topic' = 'clickstream'
)

Model

This adapter currently supports two types of materialization table and view. Because in Flink table has to be associated with a connector type and connector_properties have to be provided similar like in case of defining sources.

Example

models.yml

models:
  - name: my_model
    config:
      type: streaming
      connector_properties:
        connector: 'kafka'
        properties.bootstrap.servers: 'kafka:29092'
        topic: 'some-output'

my_model.sql

select *
from {{ source('my_source', 'clickstream') }}
where event = 'some-event'

Seed

dbt-flink-adapter can use Flink to insert seed data in any Flink supported connector. Similar like in case of sources and models you have to provide connector configuration.

Example

seeds.yml

seeds:
  - name: clickstream
    config:
      connector_properties:
        connector: 'kafka'
        properties.bootstrap.servers: 'kafka:29092'
        topic: 'clickstream'

Tests

Dbt also allows executing assertions in a form of tests to validate input data or model output if it does not contain abnormal values. All generic tests are a select statement which is considered as passed when it did not found any rows.

The problem is how to define such thing in streaming pipeline? It is not possible to tell that in entire stream there are no such entries as stream by definition is infinite. What we can do however is to have run the test for some specific time and if in that time there are no abnormal values, test will be considered as passed.

To facilitate it dbt-flink-adapter when writing a sql query supports fetch_timeout_ms and mode directive.

select /** fetch_timeout_ms(5000) mode('streaming') */
  *
from {{ ref('my_model')}}
where
  event <> 'some-event'

In this example we are telling dbt-flink-adapter to fetch for 5 seconds in streaming mode.

dbt_project.yml

You can extract common configurations of your model and sources into dbt_project.yml dbt-docs/general-configuration. If you define the same kay in dbt_project.yml and in your model or source dbt will always override entire key value. In case you wish to extract some keys from under connector_properties you can specify configuration under default_connector_properties which will get merged with connection_properies.

Example

dbt_project.yml

models:
  example1:
    +materialized: table
    +type: streaming
    +default_connector_properties:
      connector: 'kafka'
      properties.bootstrap.servers: 'kafka:29092'

sources:
  example1:
    +type: streaming
    +default_connector_properties:
      connector: 'kafka'
      properties.bootstrap.servers: 'kafka:29092'

seeds:
  example1:
    +default_connector_properties:
      connector: 'kafka'
      properties.bootstrap.servers: 'kafka:29092'

models.yml

models:
  - name: my_model
    config:
      connector_properties:
        topic: 'some-output'

sources.yml

sources:
  - name: my_source
    tables:
      - name: clickstream
        config:
          connector_properties:
            topic: 'clickstream'
          watermark:
            column: event_timestamp
            strategy: event_timestamp
        columns:
          - name: event_timestamp
            data_type: TIMESTAMP(3)

seeds.yml

seeds:
  - name: clickstream
    config:
      connector_properties:
        topic: 'clickstream'

Sessions

Our interaction with Flink cluster is done in sessions any table and view created in one session will not be visible in another session. Session by default is only valid for 10 minutes. Because of that if you will run dbt test after more than 10 minutes from dbt run it will fail and in Flink logs you will find that it cannot find your tables. Currently, the only way to run this would be to rerun entire model.

Session handler is stored in ~/.dbt/flink-session.yml file, if you want to force new session you can simply delete that file.

Releasing

To release new version first execute prepare-release action. Please keep in mind that major and minor version have to be exactly the same as major and minor version of dbt-core.

This action will create a release branch with bumped version and changelog prepared for release. It will also open a Pull Request to main branch if everything is ok with it - merge it.

Next execute publish on branch that was just created by prepare-release action.