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

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Run Oracle Connector using Airflow SDK	/connectors/database/oracle/airflow

Run Oracle using the Airflow SDK

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

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

• Requirements
• Metadata Ingestion
• Data Profiler
• 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.

Note: To fetch metadata from oracle db we use python-oracledb and this support 12c, 18c, 19c and 21c versions!

Python Requirements

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

pip3 install "openmetadata-ingestion[oracle]"

Metadata Ingestion

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

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 Oracle:

source:
  type: oracle
  serviceName: local_oracle
  serviceConnection:
    config:
      type: Oracle
      hostPort: hostPort
      username: username
      password: password
      # The type can either be oracleServiceName or databaseSchema
      oracleConnectionType:
        oracleServiceName: serviceName
        # databaseSchema: schema
  sourceConfig:
    config:
      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
      # For dbt, choose one of Cloud, Local, HTTP, S3 or GCS configurations
      # dbtConfigSource:
      # # For cloud
      #   dbtCloudAuthToken: token
      #   dbtCloudAccountId: ID
      # # For Local
      #   dbtCatalogFilePath: path-to-catalog.json
      #   dbtManifestFilePath: path-to-manifest.json
      # # For HTTP
      #   dbtCatalogHttpPath: http://path-to-catalog.json
      #   dbtManifestHttpPath: http://path-to-manifest.json
      # # For S3
      #   dbtSecurityConfig:  # These are modeled after all AWS credentials
      #     awsAccessKeyId: KEY
      #     awsSecretAccessKey: SECRET
      #     awsRegion: us-east-2
      #   dbtPrefixConfig:
      #     dbtBucketName: bucket
      #     dbtObjectPrefix: "dbt/"
      # # For GCS
      #   dbtSecurityConfig:  # These are modeled after all GCS credentials
      #     type: My Type
      #     projectId: project ID
      #     privateKeyId: us-east-2
      #     privateKey: |
      #      -----BEGIN PRIVATE KEY-----
      #      Super secret key
      #      -----END PRIVATE KEY-----
      #     clientEmail: client@mail.com
      #     clientId: 1234
      #     authUri: https://accounts.google.com/o/oauth2/auth (default)
      #     tokenUri: https://oauth2.googleapis.com/token (default)
      #     authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs (default)
      #     clientX509CertUrl: https://cert.url (URI)
      #   dbtPrefixConfig:
      #     dbtBucketName: bucket
      #     dbtObjectPrefix: "dbt/"
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

• username: Specify the User to connect to Oracle. It should have enough privileges to read all the metadata.
• password: Password to connect to Oracle.
• hostPort: Enter the fully qualified hostname and port number for your Oracle deployment in the Host and Port field.
• oracleConnectionType :
  • oracleServiceName: The Oracle Service name is the TNS alias that you give when you remotely connect to your database and this Service name is recorded in tnsnames.
  • databaseSchema: The name of the database schema available in Oracle that you want to connect with.
• Oracle instant client directory: The directory pointing to where the instantclient binaries for Oracle are located. In the ingestion Docker image we provide them by default at /instantclient. If this parameter is informed (it is by default), we will run the thick oracle client. We are shipping the binaries for ARM and AMD architectures from here and here for the instant client version 19.
• Connection Options (Optional): Enter the details for any additional connection options that can be sent to Oracle 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 Oracle 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.

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: oracle
  serviceName: local_oracle
  serviceConnection:
    config:
      type: Oracle
      hostPort: hostPort
      username: username
      password: password
      # The type can either be oracleServiceName or databaseSchema
      oracleConnectionType:
        oracleServiceName: serviceName
        # databaseSchema: schema
  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.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,
   )

dbt Integration

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