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description
This guide will help you configure metadata ingestion workflows using the MySQL connector.

MySQL Metadata Extraction

There are three options for configuring metadata ingestion for this connector. They are as follows:

  1. Schedule metadata ingestion workflows via the Airflow SDK. Use this option if you already have an Airflow instance running that you plan to use for workflow scheduling with OpenMetadata.
  2. Schedule metadata ingestion workflows via the OpenMetadata UI. Use this option if you prefer to manage ingestion through the UI and are prepared to either install the OpenMetadata Airflow REST API plugin in your Airflow deployment or will use the Airflow container that ships with OpenMetadata.
  3. Use the OpenMetadata ingestion Python module to perform a One-time Ingestion. Use this option if you want to perform a trial of OpenMetadata in a test environment

Please select the approach you would prefer to use for metadata ingestion from the tabs below.

{% tabs %} {% tab title="Airflow SDK" %}

Schedule Ingestion via the Airflow SDK

Requirements

Using the OpenMetadata MySQL connector requires supporting services and software. Please ensure that your host system meets the requirements listed below. Then continue to follow the procedure for installing and configuring this connector.

OpenMetadata (version 0.8.0 or later)

You must have a running deployment of OpenMetadata to use this guide. OpenMetadata includes the following services:

  • OpenMetadata server supporting the metadata APIs and user interface
  • Elasticsearch for metadata search and discovery
  • MySQL as the backing store for all metadata
  • Airflow for metadata ingestion workflows

Python (version 3.8.0 or later)

Please use the following command to check the version of Python you have.

python3 --version

Procedure

Here’s an overview of the steps in this procedure. Please follow the steps relevant to your use case.

  1. Prepare a Python virtual environment
  2. Install the Python module for this connector
  3. Create a configuration file using template JSON
  4. Configure service settings
  5. Configure data filters (optional)
  6. Configure sample data (optional)
  7. Configure DBT (optional)
  8. Confirm sink settings
  9. Confirm metadata_server settings
  10. Edit a Python script to define your ingestion DAG
  11. Copy your configuration JSON into the ingestion script
  12. Run the script to create your ingestion DAG

1. Prepare a Python virtual environment

In this step, we’ll create a Python virtual environment. Using a virtual environment enables us to avoid conflicts with other Python installations and packages on your host system.

In a later step, you will install the Python module for this connector and its dependencies in this virtual environment.

1.1 Create a directory for openmetadata

Throughout the docs, we use a consistent directory structure for OpenMetadata services and connector installation. If you have not already done so by following another guide, please create an openmetadata directory now and change into that directory in your command line environment.

mkdir openmetadata; cd openmetadata

1.2 Create a virtual environment

Run the following command to create a Python virtual environment called, env. You can try multiple connectors in the same virtual environment.

python3 -m venv env

1.3 Activate the virtual environment

Run the following command to activate the virtual environment.

source env/bin/activate

Once activated, you should see your command prompt change to indicate that your commands will now be executed in the environment named env.

1.4 Upgrade pip and setuptools to the latest versions

Ensure that you have the latest version of pip by running the following command. If you have followed the steps above, this will upgrade pip in your virtual environment.

pip3 install --upgrade pip setuptools

2. Install the Python module for this connector

Once the virtual environment is set up and activated as described in Step 1, run the following command to install the Python module for this connector.

pip3 install 'openmetadata-ingestion[mysql]'

3. Create a configuration file using template JSON

Create a new file called mysql.json in the current directory. Note that the current directory should be the openmetadata directory.

Copy and paste the configuration template below into the mysql.json file you created.

{% hint style="info" %} Note: The source.config field in the configuration JSON will include the majority of the settings for your connector. In the steps below we describe how to customize the key-value pairs in the source.config field to meet your needs. {% endhint %}

{% code title="mysql.json" %}

{
  "source": {
    "type": "mysql",
    "config": {
      "host_port": "hostname.domain.com:5439",
      "username": "username",
      "password": "strong_password",
      "database": "mysql_db",
      "service_name": "local_mysql",
      "data_profiler_enabled": "false",
      "table_filter_pattern": {
        "excludes": ["[\\w]*event_vw.*"]
      },
      "schema_filter_pattern": {
        "excludes": ["mysql.*", "information_schema.*", "performance_schema.*", "sys.*"]
      }
    }
  },
  "sink": {
    "type": "metadata-rest",
    "config": {}
  },
  "metadata_server": {
    "type": "metadata-server",
    "config": {
      "api_endpoint": "http://localhost:8585/api",
      "auth_provider_type": "no-auth"
    }
  }
}

{% endcode %}

4. Configure service settings

In this step, we will configure the MySQL service settings required for this connector. Please follow the instructions below to ensure that you've configured the connector to read from your MySQL service as desired.

host_port

Edit the value for source.config.host_port in mysql.json for your MySQL deployment. Use the host:port format illustrated in the example below.

"host_port": "hostname.domain.com:5439"

Please ensure that your MySQL deployment is reachable from the host you are using to run metadata ingestion.

username

Edit the value for source.config.username to identify your MySQL user.

"username": "username"

{% hint style="danger" %} Note: The user specified should be authorized to read all databases you want to include in the metadata ingestion workflow. {% endhint %}

password

Edit the value for source.config.password with the password for your MySQL user.

"password": "strong_password"

service_name

OpenMetadata uniquely identifies services by their service_name. Edit the value for source.config.service_name with a name that distinguishes this deployment from other services, including other MySQL services that you might be ingesting metadata from.

"service_name": "local_mysql"

database (optional)

If you want to limit metadata ingestion to a single database, include the source.config.database field in your configuration file. If this field is not included, the connector will ingest metadata from all databases that the specified user is authorized to read.

To specify a single database to ingest metadata from, provide the name of the database as the value for the source.config.database key as illustrated in the example below.

"database": "mysql_db"

5. Configure data filters (optional)

include_views (optional)

Use source.config.include_views to control whether or not to include views as part of metadata ingestion and data profiling.

Explicitly include views by adding the following key-value pair in the source.config field of your configuration file.

"include_views": "true"

Exclude views as follows.

"include_views": "false"

{% hint style="info" %} Note: source.config.include_views is set to true by default. {% endhint %}

include_tables (optional)

Use source.config.include_tables to control whether or not to include tables as part of metadata ingestion and data profiling.

Explicitly include tables by adding the following key-value pair in the source.config field of your configuration file.

"include_tables": "true"

Exclude tables as follows.

"include_tables": "false"

{% hint style="info" %} Note: source.config.include_tables is set to true by default. {% endhint %}

table_filter_pattern (optional)

Use source.config.table_filter_pattern to select tables for metadata ingestion by name.

Use source.config.table_filter_pattern.excludes to exclude all tables with names matching one or more of the supplied regular expressions. All other tables will be included. See below for an example. This example is also included in the configuration template provided.

"table_filter_pattern": {
    "excludes": ["information_schema.*", "[\\w]*event_vw.*"]
}

Use source.config.table_filter_pattern.includes to include all tables with names matching one or more of the supplied regular expressions. All other tables will be excluded. See below for an example.

"table_filter_pattern": {
    "includes": ["corp.*", "dept.*"]
}

See the documentation for the Python re module for information on how to construct regular expressions.

{% hint style="info" %} You may use either excludes or includes but not both in table_filter_pattern. {% endhint %}

schema_filter_pattern (optional)

Use source.config.schema_filter_pattern.excludes and source.config.schema_filter_pattern.includes field to select the schemas for metadata ingestion by name. The configuration template provides an example.

The syntax and semantics for schema_filter_pattern are the same as for table_filter_pattern. Please check that section for details.

6. Configure sample data (optional)

generate_sample_data (optional)

Use the source.config.generate_sample_data field to control whether or not to generate sample data to include in table views in the OpenMetadata user interface. The image below provides an example.

Explicitly include sample data by adding the following key-value pair in the source.config field of your configuration file.

"generate_sample_data": "true"

If set to true, the connector will collect the first 50 rows of data from each table included in ingestion, and catalog that data as sample data, which users can refer to in the OpenMetadata user interface.

You can exclude the collection of sample data by adding the following key-value pair in the source.config field of your configuration file.

"generate_sample_data": "false"

{% hint style="info" %} Note: generate_sample_data is set to true by default. {% endhint %}

7. Configure DBT (optional)

DBT provides transformation logic that creates tables and views from raw data. OpenMetadata includes an integration for DBT that enables you to see the models used to generate a table from that table's details page in the OpenMetadata user interface. The image below provides an example.

To include DBT models and metadata in your ingestion workflows, specify the location of the DBT manifest and catalog files as fields in your configuration file.

dbt_manifest_file (optional)

Use the field source.config.dbt_manifest_file to specify the location of your DBT manifest file. See below for an example.

"dbt_manifest_file": "./dbt/manifest.json"

dbt_catalog_file (optional)

Use the field source.config.dbt_catalog_file to specify the location of your DBT catalog file. See below for an example.

"dbt_catalog_file": "./dbt/catalog.json"

8. Confirm sink settings

You need not make any changes to the fields defined for sink in the template code you copied into mysql.json in Step 3. This part of your configuration file should be as follows.

"sink": {
    "type": "metadata-rest",
    "config": {}
},

9. Confirm metadata_server settings

You need not make any changes to the fields defined for metadata_server in the template code you copied into mysql.json in Step 3. This part of your configuration file should be as follows.

"metadata_server": {
    "type": "metadata-server",
    "config": {
        "api_endpoint": "http://localhost:8585/api",
        "auth_provider_type": "no-auth"
    }
}

10. Edit a Python script to define your ingestion DAG

Copy and paste the code below into a file called openmetadata-airflow.py. 

import json
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.ingestion.api.workflow import Workflow

default_args = {
    "owner": "user_name",
    "email": ["username@org.com"],
    "email_on_failure": False,
    "retries": 3,
    "retry_delay": timedelta(seconds=10),
    "execution_timeout": timedelta(minutes=60),
}

config = """
  ## REPLACE THIS LINE WITH YOUR CONFIGURATION JSON
"""

def metadata_ingestion_workflow():
    workflow_config = json.loads(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,
    catchup=False,
) as dag:
    ingest_task = PythonOperator(
        task_id="ingest_using_recipe",
        python_callable=metadata_ingestion_workflow,
    )

11. Copy your configuration JSON into the ingestion script

In steps 3 - 9 above you created a JSON file with the configuration for your ingestion connector. Copy that JSON into the openmetadata-airflow.py file that you created in step 10 as directed by the comment below.

config = """
  ## REPLACE THIS LINE WITH YOUR CONFIGURATION JSON
"""

12. Run the script to create your ingestion DAG

Run the following command to create your ingestion DAG in Airflow.

python openmetadata-airflow.py

{% endtab %}

{% tab title="OpenMetadata UI" %}

Schedule Ingestion via the OpenMetadata UI

The OpenMetadata UI provides an integrated workflow for adding a new data service and configuring ingestion workflows.

Requirements

Using the OpenMetadata MySQL connector requires supporting services and software. Please ensure that your host system meets the requirements listed below. Then continue to follow the procedure for setting up a MySQL service and ingestion workflow using the OpenMetadata UI.

OpenMetadata (version 0.8.0 or later)

You must have a running deployment of OpenMetadata to use this guide. By default, OpenMetadata includes the following services:

  • OpenMetadata server supporting the metadata APIs and user interface
  • Elasticsearch for metadata search and discovery
  • MySQL as the backing store for all metadata
  • Apache Airflow for metadata ingestion workflows

Apache Airflow (version 2.2 or later)

By default, OpenMetadata ships with Apache Airflow and is configured to use the distributed Airflow container. However, you may also use your own Airflow instance. To use your own Airflow instance, you will need to install the OpenMetadata Airflow REST API plugin.

Procedure (in Beta)

1. Visit the Services page

You may configure scheduled ingestion workflows from the Services page in the OpenMetadata UI. To visit the Services page, select Services from the Settings menu.

2. Initiate a new service creation

From the Database Service UI, click the Add New Service button to add your MySQL service to OpenMetadata for metadata ingestion.

3. Select service type

Select MySQL as the service type.

4. Name and describe your service

Provide a name and description for your service as illustrated below.

Name

OpenMetadata uniquely identifies services by their Name. Provide a name that distinguishes your deployment from other services, including other MySQL services that you might be ingesting metadata from.

Description

Provide a description for your MySQL service that enables other users to determine whether it might provide data of interest to them.

5. Configure service connection

In this step, we will configure the connection settings required for this connector. Please follow the instructions below to ensure that you've configured the connector to read from your MySQL service as desired.

Host

Enter fully qualified hostname for your MySQL deployment in the Host field.

Port

Enter the port number on which your MySQL deployment listens for client connections in the Port field.

Username

Enter username of your MySQL user in the Username field. The user specified should be authorized to read all databases you want to include in the metadata ingestion workflow.

Password

Enter the password for your MySQL user in the Password field.

Database (optional)

If you want to limit metadata ingestion to a single database, enter the name of this database in the Database field. If no value is entered for this field, the connector will ingest metadata from all databases that the specified user is authorized to read.

6. Configure metadata ingestion

In this step we will configure the metadata ingestion settings for your MySQL deployment. Please follow the instructions below to ensure that you've configured the connector to read from your MySQL service as desired.

Ingestion name

OpenMetadata will pre-populate the Ingestion name field. You may modify the Ingestion name, but if you do, please ensure it is unique for this service.

Include (Table Filter Pattern)

Use to table filter patterns to control whether or not to include tables as part of metadata ingestion and data profiling.

Explicitly include tables by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all tables with names matching one or more of the supplied regular expressions. All other tables will be excluded. See the figure above for an example.

Exclude (Table Filter Pattern)

Explicitly exclude tables by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all tables with names matching one or more of the supplied regular expressions. All other tables will be included. See the figure above for an example.

Include (Schema Filter Pattern)

Use to schema filter patterns to control whether or not to include schemas as part of metadata ingestion and data profiling.

Explicitly include schemas by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all schemas with names matching one or more of the supplied regular expressions. All other schemas will be excluded.

Exclude (Schema Filter Pattern)

Explicitly exclude schemas by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all schemas with names matching one or more of the supplied regular expressions. All other schemas will be included.

Include views (toggle)

Set the Include views toggle to the on position to control whether or not to include views as part of metadata ingestion and data profiling.

Explicitly include views by adding the following key-value pair in the source.config field of your configuration file.

Enable data profiler (toggle)

The data profiler ingests usage information for tables. This enables you to assess the frequency of use, reliability, and other details.

When enabled, the data profiler will run as part of metadata ingestion. Running the data profiler increases the amount of time it takes for metadata ingestion, but provides the benefits mentioned above.

Set the Enable data profiler toggle to the on position to enable the data profiler.

Ingest sample data (toggle)

Set the Ingest sample data toggle to the on position to control whether or not to generate sample data to include in table views in the OpenMetadata user interface.

Every

Use the Every drop down menu to select the interval at which you want to ingest metadata. Your options are as follows:

  • Hour: Ingest metadata once per hour
  • Day: Ingest metadata once per day
  • Week: Ingest metadata once per week

Day

The Day selector is only active when ingesting metadata once per week. Use the Day selector to set the day of the week on which to ingest metadata.

Minute

The Minute dropdown is only active when ingesting metadata once per hour. Use the Minute drop down menu to select the minute of the hour at which to begin ingesting metadata.

Time

The Time drop down menus are active when ingesting metadata either once per day or once per week. Use the time drop downs to select the time of day at which to begin ingesting metadata.

Start date (UTC)

Use the Start date selector to choose the date at which to begin ingesting metadata according to the defined schedule.

End date (UTC)

Use the End date selector to choose the date at which to stop ingesting metadata according to the defined schedule. If no end date is set, metadata ingestion will continue according to the defined schedule indefinitely.

7. Review configuration and save

Review your configuration settings. If they match what you intended, click Save to create the service and schedule metadata ingestion.

If something doesn't look right, click the Previous button to return to the appropriate step and change the settings as needed.

{% endtab %}

{% tab title="One-time Ingestion" %}

One-time Ingestion

Requirements

Using the OpenMetadata MySQL connector requires supporting services and software. Please ensure your host system meets the requirements listed below. Then continue to follow the procedure for installing and configuring this connector.

OpenMetadata (version 0.8.0 or later)

You must have a running deployment of OpenMetadata to use this guide. OpenMetadata includes the following services:

  • OpenMetadata server supporting the metadata APIs and user interface
  • Elasticsearch for metadata search and discovery
  • MySQL as the backing store for all metadata
  • Airflow for metadata ingestion workflows

Python (version 3.8.0 or later)

Please use the following command to check the version of Python you have.

python3 --version

Procedure

Here’s an overview of the steps in this procedure. Please follow the steps relevant to your use case.

  1. Prepare a Python virtual environment
  2. Install the Python module for this connector
  3. Create a configuration file using template JSON
  4. Configure service settings
  5. Configure data filters (optional)
  6. Configure sample data (optional)
  7. Configure DBT (optional)
  8. Confirm sink settings
  9. Confirm metadata_server settings
  10. Run ingestion workflow

1. Prepare a Python virtual environment

In this step, we’ll create a Python virtual environment. Using a virtual environment enables us to avoid conflicts with other Python installations and packages on your host system.

In a later step, you will install the Python module for this connector and its dependencies in this virtual environment.

1.1 Create a directory for openmetadata

Throughout the docs, we use a consistent directory structure for OpenMetadata services and connector installation. If you have not already done so by following another guide, please create an openmetadata directory now and change into that directory in your command line environment.

mkdir openmetadata; cd openmetadata

1.2 Create a virtual environment

Run the following command to create a Python virtual environment called, env. You can try multiple connectors in the same virtual environment.

python3 -m venv env

1.3 Activate the virtual environment

Run the following command to activate the virtual environment.

source env/bin/activate

Once activated, you should see your command prompt change to indicate that your commands will now be executed in the environment named env.

1.4 Upgrade pip and setuptools to the latest versions

Ensure that you have the latest version of pip by running the following command. If you have followed the steps above, this will upgrade pip in your virtual environment.

pip3 install --upgrade pip setuptools

2. Install the Python module for this connector

Once the virtual environment is set up and activated as described in Step 1, run the following command to install the Python module for this connector.

pip3 install 'openmetadata-ingestion[mysql]'

3. Create a configuration file using template JSON

Create a new file called mysql.json. Copy and paste the configuration template below into the mysql.json file you created.

{% hint style="info" %} Note: The source.config field in the configuration JSON will include the majority of the settings for your connector. In the steps below we describe how to customize the key-value pairs in the source.config field to meet your needs. {% endhint %}

{% code title="mysql.json" %}

{
  "source": {
    "type": "mysql",
    "config": {
      "host_port": "hostname.domain.com:5439",
      "username": "username",
      "password": "strong_password",
      "database": "mysql_db",
      "service_name": "local_mysql",
      "data_profiler_enabled": "false",
      "table_filter_pattern": {
        "excludes": ["[\\w]*event_vw.*"]
      },
      "schema_filter_pattern": {
        "excludes": ["mysql.*", "information_schema.*", "performance_schema.*", "sys.*"]
      }
    }
  },
  "sink": {
    "type": "metadata-rest",
    "config": {}
  },
  "metadata_server": {
    "type": "metadata-server",
    "config": {
      "api_endpoint": "http://localhost:8585/api",
      "auth_provider_type": "no-auth"
    }
  }
}

{% endcode %}

4. Configure service settings

In this step we will configure the MySQL service settings required for this connector. Please follow the instructions below to ensure that you've configured the connector to read from your MySQL service as desired.

host_port

Edit the value for source.config.host_port in mysql.json for your MySQL deployment. Use the host:port format illustrated in the example below.

"host_port": "hostname.domain.com:5439"

Please ensure that your MySQL deployment is reachable from the host you are using to run metadata ingestion.

username

Edit the value for source.config.username to identify your MySQL user.

"username": "username"

{% hint style="danger" %} Note: The user specified should be authorized to read all databases you want to include in the metadata ingestion workflow. {% endhint %}

password

Edit the value for source.config.password with the password for your MySQL user.

"password": "strong_password"

service_name

OpenMetadata uniquely identifies services by their service_name. Edit the value for source.config.service_name with a name that distinguishes this deployment from other services, including other MySQL services that you might be ingesting metadata from.

"service_name": "local_mysql"

database (optional)

If you want to limit metadata ingestion to a single database, include the source.config.database field in your configuration file. If this field is not included, the connector will ingest metadata from all databases that the specified user is authorized to read.

To specify a single database to ingest metadata from, provide the name of the database as the value for the source.config.database key as illustrated in the example below.

"database": "mysql_db"

5. Configure data filters (optional)

include_views (optional)

Use source.config.include_views to control whether or not to include views as part of metadata ingestion and data profiling.

Explicitly include views by adding the following key-value pair in the source.config field of your configuration file.

"include_views": "true"

Exclude views as follows.

"include_views": "false"

{% hint style="info" %} Note: source.config.include_views is set to true by default. {% endhint %}

include_tables (optional)

Use source.config.include_tables to control whether or not to include tables as part of metadata ingestion and data profiling.

Explicitly include tables by adding the following key-value pair in the source.config field of your configuration file.

"include_tables": "true"

Exclude tables as follows.

"include_tables": "false"

{% hint style="info" %} Note: source.config.include_tables is set to true by default. {% endhint %}

table_filter_pattern (optional)

Use source.config.table_filter_pattern to select tables for metadata ingestion by name.

Use source.config.table_filter_pattern.excludes to exclude all tables with names matching one or more of the supplied regular expressions. All other tables will be included. See below for an example. This example is also included in the configuration template provided.

"table_filter_pattern": {
    "excludes": ["information_schema.*", "[\\w]*event_vw.*"]
}

Use source.config.table_filter_pattern.includes to include all tables with names matching one or more of the supplied regular expressions. All other tables will be excluded. See below for an example.

"table_filter_pattern": {
    "includes": ["corp.*", "dept.*"]
}

See the documentation for the Python re module for information on how to construct regular expressions.

{% hint style="info" %} You may use either excludes or includes but not both in table_filter_pattern. {% endhint %}

schema_filter_pattern (optional)

Use source.config.schema_filter_pattern.excludes and source.config.schema_filter_pattern.includes field to select the schemas for metadata ingestion by name. The configuration template provides an example.

The syntax and semantics for schema_filter_pattern are the same as for table_filter_pattern. Please check that section for details.

6. Configure sample data (optional)

generate_sample_data (optional)

Use the source.config.generate_sample_data field to control whether or not to generate sample data to include in table views in the OpenMetadata user interface. The image below provides an example.

Explicitly include sample data by adding the following key-value pair in the source.config field of your configuration file.

"generate_sample_data": "true"

If set to true, the connector will collect the first 50 rows of data from each table included in ingestion, and catalog that data as sample data, which users can refer to in the OpenMetadata user interface.

You can exclude the collection of sample data by adding the following key-value pair in the source.config field of your configuration file.

"generate_sample_data": "false"

{% hint style="info" %} Note: generate_sample_data is set to true by default. {% endhint %}

7. Configure DBT (optional)

DBT provides transformation logic that creates tables and views from raw data. OpenMetadata includes an integration for DBT that enables you to see the models used to generate a table from that table's details page in the OpenMetadata user interface. The image below provides an example.

To include DBT models and metadata in your ingestion workflows, specify the location of the DBT manifest and catalog files as fields in your configuration file.

dbt_manifest_file (optional)

Use the field source.config.dbt_manifest_file to specify the location of your DBT manifest file. See below for an example.

"dbt_manifest_file": "./dbt/manifest.json"

dbt_catalog_file (optional)

Use the field source.config.dbt_catalog_file to specify the location of your DBT catalog file. See below for an example.

"dbt_catalog_file": "./dbt/catalog.json"

8. Confirm sink settings

You need not make any changes to the fields defined for sink in the template code you copied into mysql.json in Step 3. This part of your configuration file should be as follows.

"sink": {
    "type": "metadata-rest",
    "config": {}
},

9. Confirm metadata_server settings

You need not make any changes to the fields defined for metadata_server in the template code you copied into mysql.json in Step 3. This part of your configuration file should be as follows.

"metadata_server": {
    "type": "metadata-server",
    "config": {
        "api_endpoint": "http://localhost:8585/api",
        "auth_provider_type": "no-auth"
    }
}

10. Run ingestion workflow

Your mysql.json configuration file should now be fully configured and ready to use in an ingestion workflow.

To run an ingestion workflow, execute the following command from the openmetadata directory.

metadata ingest -c ./mysql.json

Next Steps

As the ingestion workflow runs, you may observe progress both from the command line and from the OpenMetadata user interface. To view the metadata ingested from MySQL, visit http://localhost:8585/explore/tables. Select the MySQL service to filter for the data you've ingested using the workflow you configured and ran following this guide. The image below provides an example.

Troubleshooting

ERROR: Failed building wheel for cryptography

When attempting to install the openmetadata-ingestion[mysql] Python package, you might encounter the following error. The error might include a mention of a Rust compiler.

Failed to build cryptography
ERROR: Could not build wheels for cryptography which use PEP 517 and cannot be installed directly

This error usually occurs due to an older version of pip. Try upgrading pip as follows.

pip3 install --upgrade pip setuptools

Then re-run the install command in Step 2.

requests.exceptions.ConnectionError

If you encounter the following error when attempting to run the ingestion workflow in Step 10, this is probably because there is no OpenMetadata server running at http://localhost:8585.

requests.exceptions.ConnectionError: HTTPConnectionPool(host='localhost', port=8585): 
Max retries exceeded with url: /api/v1/services/databaseServices/name/local_mysql 
(Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x1031fa310>: 
Failed to establish a new connection: [Errno 61] Connection refused'))

To correct this problem, please follow the steps in the Run OpenMetadata guide to deploy OpenMetadata in Docker on your local machine. Then re-run the metadata ingestion workflow in Step 10. {% endtab %} {% endtabs %}