Run Clickhouse using Airflow SDK

In this section, we provide guides and references to use the Clickhouse connector.


Configure and schedule Clickhouse metadata and profiler workflows from the OpenMetadata UI:

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment.

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Clickhouse.

In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server.

The workflow is modeled around the following JSON Schema.

This is a sample config for Clickhouse:

Source Configuration - Service Connection

  • username: Specify the User to connect to Clickhouse. It should have enough privileges to read all the metadata.
  • password: Password to connect to Clickhouse.
  • hostPort: Enter the fully qualified hostname and port number for your Clickhouse deployment in the Host and Port field.
  • Connection Options (Optional): Enter the details for any additional connection options that can be sent to Clickhouse during the connection. These details must be added as Key-Value pairs.
  • Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Clickhouse during the connection. These details must be added as Key-Value pairs.
    • In case you are using Single-Sign-On (SSO) for authentication, add the authenticator details in the Connection Arguments as a Key-Value pair as follows: "authenticator" : "sso_login_url"
    • In case you authenticate with SSO using an external browser popup, then add the authenticator details in the Connection Arguments as a Key-Value pair as follows: "authenticator" : "externalbrowser"

The sourceConfig is defined here:

  • markDeletedTables: To flag tables as soft-deleted if they are not present anymore in the source system.
  • includeTables: true or false, to ingest table data. Default is true.
  • includeViews: true or false, to ingest views definitions.
  • schemaFilterPattern and tableFilternPattern: Note that the schemaFilterPattern and tableFilterPattern both support regex as include or exclude. E.g.,
    tableFilterPattern:
      includes:
        - users
        - type_test
    

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

For a simple, local installation using our docker containers, this looks like:

workflowConfig:
  openMetadataServerConfig:
    hostPort: http://localhost:8585/api
    authProvider: no-auth

We support different security providers. You can find their definitions here. An example of an Auth0 configuration would be the following:

workflowConfig:
  openMetadataServerConfig:
    hostPort: http://localhost:8585/api
    authProvider: auth0
    securityConfig:
      clientId: <client ID>
      secretKey: <secret key>
      domain: <domain>

Create a Python file in your Airflow DAGs directory with the following contents:

import pathlib
import yaml
from datetime import timedelta
from airflow import DAG

try:
    from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
    from airflow.operators.python_operator import PythonOperator

from metadata.config.common import load_config_file
from metadata.ingestion.api.workflow import Workflow
from airflow.utils.dates import days_ago

default_args = {
    "owner": "user_name",
    "email": ["username@org.com"],
    "email_on_failure": False,
    "retries": 3,
    "retry_delay": timedelta(minutes=5),
    "execution_timeout": timedelta(minutes=60)
}

config = """
<your YAML configuration>
"""

def metadata_ingestion_workflow():
    workflow_config = yaml.safe_load(config)
    workflow = Workflow.create(workflow_config)
    workflow.execute()
    workflow.raise_from_status()
    workflow.print_status()
    workflow.stop()

with DAG(
    "sample_data",
    default_args=default_args,
    description="An example DAG which runs a OpenMetadata ingestion workflow",
    start_date=days_ago(1),
    is_paused_upon_creation=False,
    schedule_interval='*/5 * * * *',
    catchup=False,
) as dag:
    ingest_task = PythonOperator(
        task_id="ingest_using_recipe",
        python_callable=metadata_ingestion_workflow,
    )

Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.

To ingest the Query Usage and Lineage information, the serviceConnection configuration will remain the same. However, the sourceConfig is now modeled after this JSON Schema.

This is a sample config for Clickhouse Usage:

You can find all the definitions and types for the serviceConnection here. They are the same as metadata ingestion.

The sourceConfig is defined here.

  • queryLogDuration: Configuration to tune how far we want to look back in query logs to process usage data.
  • resultLimit: Configuration to set the limit for query logs

To specify where the staging files will be located.

The same as the metadata ingestion.

There is an extra requirement to run the Usage pipelines. You will need to install:

pip3 install --upgrade 'openmetadata-ingestion[clickhouse-usage]'

For the usage workflow creation, the Airflow file will look the same as for the metadata ingestion. Updating the YAML configuration will be enough.

The Data Profiler workflow will be using the orm-profiler processor. While the serviceConnection will still be the same to reach the source system, the sourceConfig will be updated from previous configurations.

This is a sample config for the profiler:

  • You can find all the definitions and types for the serviceConnection here.
  • The sourceConfig is defined here.

Note that the fqnFilterPattern supports regex as includes or excludes. E.g.,

fqnFilterPattern:
    includes:
    - service.database.schema.*

Choose the orm-profiler. Its config can also be updated to define tests from the YAML itself instead of the UI:

processor:
    type: orm-profiler
    config:
    test_suite:
        name: <Test Suite name>
        tests:
        - table: <Table FQN>
            table_tests:
            - testCase:
                config:
                    value: 100
                tableTestType: tableRowCountToEqual
            column_tests:
            - columnName: <Column Name>
                testCase:
                config:
                    minValue: 0
                    maxValue: 99
                columnTestType: columnValuesToBeBetween
tests is a list of test definitions that will be applied to table, informed by its FQN. For each table, one can then define a list of table_tests and column_tests. Review the supported tests and their definitions to learn how to configure the different cases here. // TODO: Link to tests

The same as the metadata ingestion.

Here, we follow a similar approach as with the metadata and usage pipelines, although we will use a different Workflow class:

import yaml
from datetime import timedelta

from airflow import DAG

try:
   from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
   from airflow.operators.python_operator import PythonOperator

from airflow.utils.dates import days_ago

from metadata.orm_profiler.api.workflow import ProfilerWorkflow


default_args = {
   "owner": "user_name",
   "email_on_failure": False,
   "retries": 3,
   "retry_delay": timedelta(seconds=10),
   "execution_timeout": timedelta(minutes=60),
}

config = """
<your YAML configuration>
"""

def metadata_ingestion_workflow():
   workflow_config = yaml.safe_load(config)
   workflow = ProfilerWorkflow.create(workflow_config)
   workflow.execute()
   workflow.raise_from_status()
   workflow.print_status()
   workflow.stop()

with DAG(
   "profiler_example",
   default_args=default_args,
   description="An example DAG which runs a OpenMetadata ingestion workflow",
   start_date=days_ago(1),
   is_paused_upon_creation=False,
   catchup=False,
) as dag:
   ingest_task = PythonOperator(
       task_id="profile_and_test_using_recipe",
       python_callable=metadata_ingestion_workflow,
   )
                

You can learn more about how to ingest DBT models' definitions and their lineage here.

Still have questions?

You can take a look at our Q&A or reach out to us in Slack

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