OpenMetadata/openmetadata-docs/content/connectors/database/mssql/airflow.md

title	slug
Run MSSQL Connector using Airflow SDK	/connectors/database/mssql/airflow

Run MSSQL using the Airflow SDK

Stage	Metadata	Query Usage	Data Profiler	Data Quality	Lineage	DBT	Supported Versions
PROD	✅	✅	✅	✅	✅	✅	--

Lineage	Table-level	Column-level
✅	✅	✅

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

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

• Requirements
• Metadata Ingestion
• Query Usage
• Data Profiler
• Lineage
• dbt Integration

Requirements

To deploy OpenMetadata, check the Deployment guides.

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.

Python Requirements

To run the MSSQL ingestion, you will need to install:

pip3 install "openmetadata-ingestion[mssql]"

If you want to run the Usage Connector, you'll also need to install:

pip3 install "openmetadata-ingestion[mssql-usage]"

Metadata Ingestion

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

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

1. Define the YAML Config

This is a sample config for MSSQL:

source:
  type: mssql
  serviceName: "<service name>"
  serviceConnection:
    config:
      type: Mssql
      username: <username>
      password: <password>
      hostPort: <hostPort>
      # database: <database>
  sourceConfig:
    config:
      type: DatabaseMetadata
      markDeletedTables: true
      includeTables: true
      includeViews: true
      # includeTags: true
      # databaseFilterPattern:
      #   includes:
      #     - database1
      #     - database2
      #   excludes:
      #     - database3
      #     - database4
      # schemaFilterPattern:
      #   includes:
      #     - schema1
      #     - schema2
      #   excludes:
      #     - schema3
      #     - schema4
      # tableFilterPattern:
      #   includes:
      #     - table1
      #     - table2
      #   excludes:
      #     - table3
      #     - table4
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: "<OpenMetadata host and port>"
    authProvider: "<OpenMetadata auth provider>"

Source Configuration - Service Connection

• scheme: Defines how to connect to MSSQL. We support mssql+pytds, mssql+pyodbc, and mssql+pymssql.
• username: Specify the User to connect to MSSQL. It should have enough privileges to read all the metadata.
• password: Password to connect to MSSQL.
• hostPort: Enter the fully qualified hostname and port number for your MSSQL deployment in the Host and Port field.
• uriString: In case of a pyodbc connection.
• Database: The database of the data source is an optional parameter, if you would like to restrict the metadata reading to a single database. If left blank, OpenMetadata ingestion attempts to scan all the databases.
• Connection Options (Optional): Enter the details for any additional connection options that can be sent to MSSQL 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 MSSQL 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"

Source Configuration - Source Config

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.
• databaseFilterPattern, schemaFilterPattern, tableFilternPattern: Note that the they support regex as include or exclude. E.g.,

tableFilterPattern:
  includes:
    - users
    - type_test

Sink Configuration

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

Workflow Configuration

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: openmetadata
    securityConfig:
      jwtToken: '{bot_jwt_token}'

We support different security providers. You can find their definitions here. You can find the different implementation of the ingestion below.

Openmetadata JWT Auth

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: openmetadata
    securityConfig:
      jwtToken: '{bot_jwt_token}'

Auth0 SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: auth0
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

Azure SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: azure
    securityConfig:
      clientSecret: '{your_client_secret}'
      authority: '{your_authority_url}'
      clientId: '{your_client_id}'
      scopes:
        - your_scopes

Custom OIDC SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: custom-oidc
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

Google SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: google
    securityConfig:
      secretKey: '{path-to-json-creds}'

Okta SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: http://localhost:8585/api
    authProvider: okta
    securityConfig:
      clientId: "{CLIENT_ID - SPA APP}"
      orgURL: "{ISSUER_URL}/v1/token"
      privateKey: "{public/private keypair}"
      email: "{email}"
      scopes:
        - token

Amazon Cognito SSO

The ingestion can be configured by Enabling JWT Tokens

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: auth0
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

OneLogin SSO

Which uses Custom OIDC for the ingestion

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: custom-oidc
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

KeyCloak SSO

Which uses Custom OIDC for the ingestion

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: custom-oidc
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

2. Prepare the Ingestion DAG

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.

Query Usage

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

1. Define the YAML Config

This is a sample config for MSSQL Usage:

source:
  type: mssql-usage
  serviceName: "<service name>"
  serviceConnection:
    config:
      type: Mssql
      username: <username>
      password: <password>
      hostPort: <hostPort>
      # database: <database>
  sourceConfig:
    config:
      # Number of days to look back
      queryLogDuration: 7
      # This is a directory that will be DELETED after the usage runs
      stageFileLocation: <path to store the stage file>
      # resultLimit: 1000
      # If instead of getting the query logs from the database we want to pass a file with the queries
      # queryLogFilePath: path-to-file
processor:
  type: query-parser
  config: {}
stage:
  type: table-usage
  config:
    filename: "/tmp/mssql_usage"
bulkSink:
  type: metadata-usage
  config:
    filename: "/tmp/mssql_usage"
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: "<OpenMetadata host and port>"
    authProvider: "<OpenMetadata auth provider>"

Source Configuration - Service Connection

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

Source Configuration - Source Config

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

Processor, Stage and Bulk Sink

To specify where the staging files will be located.

Note that the location is a directory that will be cleaned at the end of the ingestion.

Workflow Configuration

The same as the metadata ingestion.

2. Run with the CLI

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

pip3 install --upgrade 'openmetadata-ingestion[mssql-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.

Data Profiler

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.

1. Define the YAML Config

This is a sample config for the profiler:

source:
  type: mssql
  serviceName: "<service name>"
  serviceConnection:
    config:
      type: Mssql
      username: <username>
      password: <password>
      hostPort: <hostPort>
      # database: <database>
  sourceConfig:
    config:
      type: Profiler
      # generateSampleData: true
      # profileSample: 85
      # threadCount: 5 (default)
      # databaseFilterPattern:
      #   includes:
      #     - database1
      #     - database2
      #   excludes:
      #     - database3
      #     - database4
      # schemaFilterPattern:
      #   includes:
      #     - schema1
      #     - schema2
      #   excludes:
      #     - schema3
      #     - schema4
      # tableFilterPattern:
      #   includes:
      #     - table1
      #     - table2
      #   excludes:
      #     - table3
      #     - table4
processor:
  type: orm-profiler
  config: {}  # Remove braces if adding properties
  # tableConfig:
  #   - fullyQualifiedName: <table fqn>
  #     profileSample: <number between 0 and 99> # default will be 100 if omitted
  #     profileQuery: <query to use for sampling data for the profiler>
  #     columnConfig:
  #       excludeColumns:
  #         - <column name>
  #       includeColumns:
  #         - columnName: <column name>
  #         - metrics:
  #           - MEAN
  #           - MEDIAN
  #           - ...
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: "<OpenMetadata host and port>"
    authProvider: "<OpenMetadata auth provider>"

Source Configuration

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

Note that the filter patterns support regex as includes or excludes. E.g.,

tableFilterPattern:
  includes:
  - *users$

Processor

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:
    tableConfig:
      - fullyQualifiedName: <table fqn>
        profileSample: <number between 0 and 99>
        partitionConfig:
          partitionField: <field to use as a partition field>
          partitionQueryDuration: <for date/datetime partitioning based set the offset from today>
          partitionValues: <values to uses as a predicate for the query>
        profileQuery: <query to use for sampling data for the profiler>
        columnConfig:
          excludeColumns:
            - <column name>
          includeColumns:
            - columnName: <column name>
            - metrics:
                - MEAN
                - MEDIAN
                - ...

tableConfig allows you to set up some configuration at the table level. All the properties are optional. metrics should be one of the metrics listed here

Workflow Configuration

The same as the metadata ingestion.

2. Prepare the Profiler DAG

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.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,
   )

Lineage

You can learn more about how to ingest lineage here.

dbt Integration

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