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172 lines
5.2 KiB
Markdown
172 lines
5.2 KiB
Markdown
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---
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title: Run the Airflow Connector Externally
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slug: /connectors/pipeline/airflow/yaml
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---
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{% connectorDetailsHeader
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name="Airflow"
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stage="PROD"
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platform="OpenMetadata"
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availableFeatures=["Pipelines", "Pipeline Status", "Lineage", "Owners"]
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unavailableFeatures=["Tags"]
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/ %}
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In this section, we provide guides and references to use the Airbyte connector.
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Configure and schedule Airbyte metadata and profiler workflows from the OpenMetadata UI:
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- [Requirements](#requirements)
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- [Metadata Ingestion](#metadata-ingestion)
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{% partial file="/v1.5/connectors/external-ingestion-deployment.md" /%}
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## Requirements
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### Python Requirements
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{% partial file="/v1.5/connectors/python-requirements.md" /%}
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To run the Airflow ingestion, you will need to install:
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```bash
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pip3 install "openmetadata-ingestion[airflow]"
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```
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{% note %}
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Note that this installs the same Airflow version that we ship in the Ingestion Container. If you are running
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the ingestion from Airflow already, you **DON'T NEED** to install the `airflow` plugin.
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Instead, just run `pip3 install "openmetadata-ingestion"`.
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{% /note %}
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**Note:** we only support officially supported Airflow versions. You can check the version list [here](https://airflow.apache.org/docs/apache-airflow/stable/installation/supported-versions.html).
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## Metadata Ingestion
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All connectors are defined as JSON Schemas.
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[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/pipeline/airbyteConnection.json)
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you can find the structure to create a connection to Airbyte.
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In order to create and run a Metadata Ingestion workflow, we will follow
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the steps to create a YAML configuration able to connect to the source,
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process the Entities if needed, and reach the OpenMetadata server.
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The workflow is modeled around the following
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[JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)
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### 1. Define the YAML Config
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This is a sample config for Airbyte:
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{% codePreview %}
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{% codeInfoContainer %}
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#### Source Configuration - Service Connection
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{% codeInfo srNumber=1 %}
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**connection**: Airflow metadata database connection. See
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these [docs](https://airflow.apache.org/docs/apache-airflow/stable/howto/set-up-database.html)
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for supported backends.
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In terms of `connection` we support the following selections:
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- `backend`: Should not be used from the UI. This is only applicable when ingesting Airflow metadata locally by running
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the ingestion from a DAG. It will use the current Airflow SQLAlchemy connection to extract the data.
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- `MySQL`, `Postgres`, and `SQLite`: Pass the required credentials to reach out each of these services. We will
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create a connection to the pointed database and read Airflow data from there.
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**hostPort**: URL to the Airflow instance.
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{% /codeInfo %}
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{% codeInfo srNumber=1 %}
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**numberOfStatus**: Number of status we want to look back to in every ingestion (e.g., Past executions from a DAG).
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{% /codeInfo %}
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{% codeInfo srNumber=1 %}
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**connection**: Airflow metadata database connection. See
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these [docs](https://airflow.apache.org/docs/apache-airflow/stable/howto/set-up-database.html)
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for supported backends.
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In terms of `connection` we support the following selections:
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- `backend`: Should not be used from the UI. This is only applicable when ingesting Airflow metadata locally by running
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the ingestion from a DAG. It will use the current Airflow SQLAlchemy connection to extract the data.
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- `MySQL`, `Postgres`, and `SQLite`: Pass the required credentials to reach out each of these services. We will
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create a connection to the pointed database and read Airflow data from there.
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{% /codeInfo %}
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{% partial file="/v1.5/connectors/yaml/pipeline/source-config-def.md" /%}
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{% partial file="/v1.5/connectors/yaml/ingestion-sink-def.md" /%}
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{% partial file="/v1.5/connectors/yaml/workflow-config-def.md" /%}
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{% /codeInfoContainer %}
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{% codeBlock fileName="filename.yaml" %}
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```yaml {% isCodeBlock=true %}
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source:
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type: airflow
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serviceName: airflow_source
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serviceConnection:
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config:
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type: Airflow
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```
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```yaml {% srNumber=6 %}
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hostPort: http://localhost:8080
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```
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```yaml {% srNumber=6 %}
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numberOfStatus: 10
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```
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```yaml {% srNumber=6 %}
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# Connection needs to be one of Mysql, Postgres or Sqlite
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connection:
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type: Mysql
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username: airflow_user
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authType:
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password: airflow_pass
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databaseSchema: airflow_db
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hostPort: localhost:3306
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# #
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# type: Postgres
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# username: airflow_user
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# authType:
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# password: airflow_pass
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# database: airflow_db
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# hostPort: localhost:3306
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# #
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# type: Sqlite
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# username: airflow_user
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# password: airflow_pass
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# database: airflow_db
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# hostPort: localhost:3306
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# databaseMode: ":memory:" (optional)
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```
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{% partial file="/v1.5/connectors/yaml/pipeline/source-config.md" /%}
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{% partial file="/v1.5/connectors/yaml/ingestion-sink.md" /%}
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{% partial file="/v1.5/connectors/yaml/workflow-config.md" /%}
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{% /codeBlock %}
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{% /codePreview %}
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{% partial file="/v1.5/connectors/yaml/ingestion-cli.md" /%}
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