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OpenMetadata/openmetadata-docs/content/v1.5.x-SNAPSHOT/deployment/ingestion/external/index.md
IceS2 c522f14178
MINOR: Refactor output_handlers to a WorkflowOutputHandler class (#17149) 
* Refactor output_handlers to a WorkflowOutputHandler class

* Add old methods as deprecated to avoid breaking changes

* Extract WorkflowInitErrorHandler from workflow_output_handler

* Fix static checks

* Fix tests

* Fix tests

* Update code based on comments from PR

* Update comment
2024-07-29 09:20:34 +02:00

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---
title: Run the Ingestion Framework Externally
slug: /deployment/ingestion/external
---
# Ingestion Framework External Deployment
Any tool capable of running Python code can be used to configure the metadata extraction from your sources.
{% partial file="/v1.5/connectors/python-requirements.md" /%}
## 1. How does the Ingestion Framework work?
The Ingestion Framework contains all the logic about how to connect to the sources, extract their metadata
and send it to the OpenMetadata server. We have built it from scratch with the main idea of making it an independent
component that can be run from - **literally** - anywhere.
In order to install it, you just need to get it from [PyPI](https://pypi.org/project/openmetadata-ingestion/).
```shell
pip install openmetadata-ingestion
```
We will show further examples later, but a piece of code is the best showcase for its simplicity. In order to run
a full ingestion process, you just need to execute a single function. For example, if we wanted to run the metadata
ingestion from within a simple Python script:
```python
from metadata.workflow.metadata import MetadataWorkflow
# Specify your YAML configuration
CONFIG = """
source:
...
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: openmetadata
securityConfig:
jwtToken: ...
"""
def run():
workflow_config = yaml.safe_load(CONFIG)
workflow = MetadataWorkflow.create(workflow_config)
workflow.execute()
workflow.raise_from_status()
workflow.print_status()
workflow.stop()
if __name__ == "__main__":
run()
```
Where this function runs is completely up to you, and you can adapt it to what makes the most sense within your
organization and engineering context. Below you'll see some examples of different orchestrators you can leverage
to execute the ingestion process.
## 2. Ingestion Configuration
In the example above, the `Workflow` class got created from a YAML configuration. Any Workflow that you execute (ingestion,
profiler, lineage,...) will have its own YAML representation.
You can think about this configuration as the recipe you want to execute: where is your source, which pieces do you
extract, how are they processed and where are they sent.
An example YAML config for extracting MySQL metadata looks like this:
```yaml
source:
type: mysql
serviceName: mysql
serviceConnection:
config:
type: Mysql
username: openmetadata_user
authType:
password: openmetadata_password
hostPort: localhost:3306
databaseSchema: openmetadata_db
sourceConfig:
config:
type: DatabaseMetadata
sink:
type: metadata-rest
config: {}
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: openmetadata
securityConfig:
jwtToken: ...
```
{% note %}
You will find examples of all the workflow's YAML files at each Connector [page](/connectors).
{% /note %}
We will now show you examples on how to configure and run every workflow externally by using Snowflake as an example. But
first, let's digest some information that will be common everywhere, the `workflowConfig`.
### Workflow Config
Here you will define information such as where are you hosting the OpenMetadata server, and the JWT token to authenticate.
{% note noteType="Warning" %}
Review this section carefully to ensure you are properly managing service credentials and other security configurations.
{% /note %}
**Logger Level**
You can specify the `loggerLevel` depending on your needs. If you are trying to troubleshoot an ingestion, running
with `DEBUG` will give you far more traces for identifying issues.
**JWT Token**
JWT tokens will allow your clients to authenticate against the OpenMetadata server.
To enable JWT Tokens, you will get more details [here](/deployment/security/enable-jwt-tokens).
You can refer to the JWT Troubleshooting section [link](/deployment/security/jwt-troubleshooting) for any issues in
your JWT configuration.
**Store Service Connection**
If set to `true` (default), we will store the sensitive information either encrypted via the Fernet Key in the database
or externally, if you have configured any [Secrets Manager](/deployment/secrets-manager).
If set to `false`, the service will be created, but the service connection information will only be used by the Ingestion
Framework at runtime, and won't be sent to the OpenMetadata server.
**Secrets Manager Configuration**
If you have configured any [Secrets Manager](/deployment/secrets-manager), you need to let the Ingestion Framework know
how to retrieve the credentials securely.
Follow the [docs](/deployment/secrets-manager) to configure the secret retrieval based on your environment.
**SSL Configuration**
If you have added SSL to the [OpenMetadata server](/deployment/security/enable-ssl), then you will need to handle
the certificates when running the ingestion too. You can either set `verifySSL` to `ignore`, or have it as `validate`,
which will require you to set the `sslConfig.caCertificate` with a local path where your ingestion runs that points
to the server certificate file.
Find more information on how to troubleshoot SSL issues [here](/deployment/security/enable-ssl/ssl-troubleshooting).
```yaml
workflowConfig:
loggerLevel: INFO # DEBUG, INFO, WARNING or ERROR
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
## Store the service Connection information
# storeServiceConnection: true or false
## Secrets Manager Configuration
# secretsManagerProvider: aws, azure or noop
# secretsManagerLoader: airflow or env
## If SSL, fill the following
# verifySSL: validate # or ignore
# sslConfig:
# caCertificate: /local/path/to/certificate
```
#### JWT Token with Secrets Manager
If you are using the [Secrets Manager](/deployment/secrets-manager), you can let the Ingestion client to pick up
the JWT Token dynamically from the Secrets Manager at runtime. Let's show an example:
We have an OpenMetadata server running with the `managed-aws` Secrets Manager. Since we used the `OPENMETADATA_CLUSTER_NAME` env var
as `test`, our `ingestion-bot` JWT Token is safely stored under the secret ID `
/test/bot/ingestion-bot/config/jwttoken`.
Now, we can use the following workflow config to run the ingestion without having to pass the token, but just pointing to the secret itself:
```yaml
workflowConfig:
loggerLevel: INFO # DEBUG, INFO, WARNING or ERROR
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "secret:/test/bot/ingestion-bot/config/jwttoken"
secretsManagerProvider: aws
secretsManagerLoader: env
```
Notice how:
1. We specify the `secretsManagerProvider` pointing to `aws`, since that's the manager we are using.
2. We set `secretsManagerLoader` as `env`. Since we're running this from our local, we'll let the AWS credentials to be
loaded from the local env vars. (When running this using the UI, note that the generated workflows will have this
value set as `airflow`!)
3. We set the `jwtToken` value as `secret:/test/bot/ingestion-bot/config/jwttoken`, which tells the client that
this value is a `secret` located under `/test/bot/ingestion-bot/config/jwttoken`.
Those are our env vars:
```
export AWS_ACCESS_KEY_ID=...
export AWS_SECRET_ACCESS_KEY=...
export AWS_DEFAULT_REGION=...
```
And we can run this normally with `metadata ingest -c <path to yaml>`.
{% note %}
Note that **even if you are not using the Secrets Manager for the OpenMetadata Server**, you can still apply the same
approach by storing the JWT token manually to the secrets manager, and let the Ingestion client pick it up
from there automatically.
{% /note %}
## 3. (Optional) Ingestion Pipeline
Additionally, if you want to see your runs logged in the `Ingestions` tab of the connectors page in the UI as you would
when running the connectors natively with OpenMetadata, you can add the following configuration on your YAMLs:
```yaml
source:
type: mysql
serviceName: mysql
[...]
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: openmetadata
securityConfig:
jwtToken: ...
ingestionPipelineFQN: <serviceName>.<pipelineName> # E.g., mysql.marketing_metadata`
```
Adding the `ingestionPipelineFQN` - the Ingestion Pipeline Fully Qualified Name - will tell the Ingestion Framework
to log the executions and update the ingestion status, which will appear on the UI. Note that the action buttons
will be disabled, since OpenMetadata won't be able to interact with external systems.
## 4. (Optional) Disable the Pipeline Service Client
If you want to run your workflows **ONLY externally** without relying on OpenMetadata for any workflow management
or scheduling, you can update the following server configuration:
```yaml
pipelineServiceClientConfiguration:
enabled: ${PIPELINE_SERVICE_CLIENT_ENABLED:-true}
```
by setting `enabled: false` or setting the `PIPELINE_SERVICE_CLIENT_ENABLED=false` as an environment variable.
This will stop certain APIs and monitors related to the Pipeline Service Client (e.g., Airflow) from being operative.
## Examples
{% note %}
This is not an exhaustive list, and it will keep growing over time. Not because the orchestrators X or Y are not supported,
but just because we did not have the time yet to add it here. If you'd like to chip in and help us expand these guides and examples,
don't hesitate to reach to us in [Slack](https://slack.open-metadata.org/) or directly open a PR in
[GitHub](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-docs/content).
{% /note %}
{% inlineCalloutContainer %}
{% inlineCallout
color="violet-70"
icon="10k"
bold="Airflow"
href="/deployment/ingestion/external/airflow" %}
Run the ingestion process externally from Airflow
{% /inlineCallout %}
{% inlineCallout
color="violet-70"
icon="10k"
bold="MWAA"
href="/deployment/ingestion/external/mwaa" %}
Run the ingestion process externally using AWS MWAA
{% /inlineCallout %}
{% inlineCallout
color="violet-70"
icon="10k"
bold="GCS Composer"
href="/deployment/ingestion/external/gcs-composer" %}
Run the ingestion process externally from GCS Composer
{% /inlineCallout %}
{% inlineCallout
color="violet-70"
icon="10k"
bold="GitHub Actions"
href="/deployment/ingestion/external/github-actions" %}
Run the ingestion process externally from GitHub Actions
{% /inlineCallout %}
{% /inlineCalloutContainer %}
Let's jump now into some examples on how you could create the function the run the different workflows. Note that this code
can then be executed inside a DAG, a GitHub action, or a vanilla Python script. It will work for any environment.
### Testing
You can easily test every YAML configuration using the `metadata` CLI from the Ingestion Framework.
In order to install it, you just need to get it from [PyPI](https://pypi.org/project/openmetadata-ingestion/).
In each of the examples below, we'll showcase how to run the CLI, assuming you have a YAML file that contains
the workflow configuration.
### Metadata Workflow
This is the first workflow you have to configure and run. It will take care of fetching the metadata from your sources,
be it Database Services, Dashboard Services, Pipelines, etc.
The rest of the workflows (Lineage, Profiler,...) will be executed on top of the metadata already available in the platform.
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=1 %}
**Adding the imports**
The first step is to import the `MetadataWorkflow` class, which will take care of the full ingestion logic. We'll
add the import for printing the results at the end.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**Defining the YAML**
Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can
read from a file, parse secrets from your environment, or any other approach you'd need. In the end, it's just
Python code.
{% note %}
You can find complete YAMLs in each connector [docs](/connectors) and find more information about the available
configurations.
{% /note %}
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**Preparing the Workflow**
Finally, we'll prepare a function that we can execute anywhere.
It will take care of instantiating the workflow, executing it and giving us the results.
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="ingestion.py" %}
```python {% isCodeBlock=true %}
import yaml
```
```python {% srNumber=1 %}
from metadata.workflow.metadata import MetadataWorkflow
```
```python {% srNumber=2 %}
CONFIG = """
source:
type: snowflake
serviceName: <service name>
serviceConnection:
config:
type: Snowflake
...
sourceConfig:
config:
type: DatabaseMetadata
markDeletedTables: true
includeTables: true
...
sink:
type: metadata-rest
config: {}
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
"""
```
```python {% srNumber=3 %}
def run():
workflow = MetadataWorkflow.create(CONFIG)
workflow.execute()
workflow.raise_from_status()
workflow.print_status()
workflow.stop()
```
{% /codeBlock %}
{% /codePreview %}
{% note %}
You can test the workflow via `metadata ingest -c <path-to-yaml>`.
{% /note %}
### Lineage Workflow
This workflow will take care of scanning your query history and defining lineage relationships between your tables.
You can find more information about this workflow [here](/connectors/ingestion/lineage).
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=1 %}
**Adding the imports**
The first step is to import the `MetadataWorkflow` class, which will take care of the full ingestion logic. We'll
add the import for printing the results at the end.
Note that we are using the same class as in the Metadata Ingestion.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**Defining the YAML**
Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can
read from a file, parse secrets from your environment, or any other approach you'd need.
Note how we have not added here the `serviceConnection`. Since the service would have been created during the
metadata ingestion, we can let the Ingestion Framework dynamically fetch the Service Connection information.
If, however, you are configuring the workflow with `storeServiceConnection: false`, you'll need to explicitly
define the `serviceConnection`.
{% note %}
You can find complete YAMLs in each connector [docs](/connectors) and find more information about the available
configurations.
{% /note %}
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**Preparing the Workflow**
Finally, we'll prepare a function that we can execute anywhere.
It will take care of instantiating the workflow, executing it and giving us the results.
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="ingestion.py" %}
```python {% isCodeBlock=true %}
import yaml
```
```python {% srNumber=1 %}
from metadata.workflow.metadata import MetadataWorkflow
```
```python {% srNumber=2 %}
CONFIG = """
source:
type: snowflake-lineage
serviceName: <service name>
sourceConfig:
config:
type: DatabaseLineage
queryLogDuration: 1
parsingTimeoutLimit: 300
...
sink:
type: metadata-rest
config: {}
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
"""
```
```python {% srNumber=3 %}
def run():
workflow = MetadataWorkflow.create(CONFIG)
workflow.execute()
workflow.raise_from_status()
workflow.print_status()
workflow.stop()
```
{% /codeBlock %}
{% /codePreview %}
{% note %}
You can test the workflow via `metadata ingest -c <path-to-yaml>`.
{% /note %}
### Usage Workflow
As with the lineage workflow, we'll scan the query history for any DML statements. The goal is to ingest queries
into the platform, figure out the relevancy of your assets and frequently joined tables.
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=1 %}
**Adding the imports**
The first step is to import the `UsageWorkflow` class, which will take care of the full ingestion logic. We'll
add the import for printing the results at the end.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**Defining the YAML**
Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can
read from a file, parse secrets from your environment, or any other approach you'd need.
Note how we have not added here the `serviceConnection`. Since the service would have been created during the
metadata ingestion, we can let the Ingestion Framework dynamically fetch the Service Connection information.
If, however, you are configuring the workflow with `storeServiceConnection: false`, you'll need to explicitly
define the `serviceConnection`.
{% note %}
You can find complete YAMLs in each connector [docs](/connectors) and find more information about the available
configurations.
{% /note %}
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**Preparing the Workflow**
Finally, we'll prepare a function that we can execute anywhere.
It will take care of instantiating the workflow, executing it and giving us the results.
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="ingestion.py" %}
```python {% isCodeBlock=true %}
import yaml
```
```python {% srNumber=1 %}
from metadata.workflow.usage import UsageWorkflow
```
```python {% srNumber=2 %}
CONFIG = """
source:
type: snowflake-usage
serviceName: <service name>
sourceConfig:
config:
type: DatabaseUsage
queryLogDuration: 1
parsingTimeoutLimit: 300
...
processor:
type: query-parser
config: {}
stage:
type: table-usage
config:
filename: "/tmp/snowflake_usage"
bulkSink:
type: metadata-usage
config:
filename: "/tmp/snowflake_usage"
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
"""
```
```python {% srNumber=3 %}
def run():
workflow = UsageWorkflow.create(CONFIG)
workflow.execute()
workflow.raise_from_status()
workflow.print_status()
workflow.stop()
```
{% /codeBlock %}
{% /codePreview %}
{% note %}
You can test the workflow via `metadata usage -c <path-to-yaml>`.
{% /note %}
### Profiler Workflow
This workflow will execute queries against your database and send the results into OpenMetadata. The goal is to compute
metrics about your data and give you a high-level view of its shape, together with the sample data.
This is an interesting previous step before creating Data Quality Workflows.
You can find more information about this workflow [here](/how-to-guides/data-quality-observability/profiler/workflow).
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=1 %}
**Adding the imports**
The first step is to import the `ProfilerWorkflow` class, which will take care of the full ingestion logic. We'll
add the import for printing the results at the end.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**Defining the YAML**
Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can
read from a file, parse secrets from your environment, or any other approach you'd need.
Note how we have not added here the `serviceConnection`. Since the service would have been created during the
metadata ingestion, we can let the Ingestion Framework dynamically fetch the Service Connection information.
If, however, you are configuring the workflow with `storeServiceConnection: false`, you'll need to explicitly
define the `serviceConnection`.
{% note %}
You can find complete YAMLs in each connector [docs](/connectors) and find more information about the available
configurations.
{% /note %}
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**Preparing the Workflow**
Finally, we'll prepare a function that we can execute anywhere.
It will take care of instantiating the workflow, executing it and giving us the results.
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="ingestion.py" %}
```python {% isCodeBlock=true %}
import yaml
```
```python {% srNumber=1 %}
from metadata.workflow.profiler import ProfilerWorkflow
```
```python {% srNumber=2 %}
CONFIG = """
source:
type: snowflake
serviceName: <service name>
sourceConfig:
config:
type: Profiler
generateSampleData: true
...
processor:
type: orm-profiler
config: {}
sink:
type: metadata-rest
config: {}
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
"""
```
```python {% srNumber=3 %}
def run():
workflow = ProfilerWorkflow.create(CONFIG)
workflow.execute()
workflow.raise_from_status()
workflow.print_status()
workflow.stop()
```
{% /codeBlock %}
{% /codePreview %}
{% note %}
You can test the workflow via `metadata profile -c <path-to-yaml>`.
{% /note %}
### Data Quality Workflow
This workflow will execute queries against your database and send the results into OpenMetadata. The goal is to compute
metrics about your data and give you a high-level view of its shape, together with the sample data.
This is an interesting previous step before creating Data Quality Workflows.
You can find more information about this workflow [here](/how-to-guides/data-quality-observability/quality/configure).
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=1 %}
**Adding the imports**
The first step is to import the `TestSuiteWorkflow` class, which will take care of the full ingestion logic. We'll
add the import for printing the results at the end.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**Defining the YAML**
Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can
read from a file, parse secrets from your environment, or any other approach you'd need.
Note how we have not added here the `serviceConnection`. Since the service would have been created during the
metadata ingestion, we can let the Ingestion Framework dynamically fetch the Service Connection information.
If, however, you are configuring the workflow with `storeServiceConnection: false`, you'll need to explicitly
define the `serviceConnection`.
Moreover, see how we are not configuring any tests in the `processor`. You can do [that](/how-to-guides/data-quality-observability/quality/configure#full-yaml-config-example),
but even if nothing gets defined in the YAML, we will execute all the tests configured against the table.
{% note %}
You can find complete YAMLs in each connector [docs](/connectors) and find more information about the available
configurations.
{% /note %}
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**Preparing the Workflow**
Finally, we'll prepare a function that we can execute anywhere.
It will take care of instantiating the workflow, executing it and giving us the results.
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="ingestion.py" %}
```python {% isCodeBlock=true %}
import yaml
```
```python {% srNumber=1 %}
from metadata.workflow.data_quality import TestSuiteWorkflow
```
```python {% srNumber=2 %}
CONFIG = """
source:
type: TestSuite
serviceName: <service name>
sourceConfig:
config:
type: TestSuite
entityFullyQualifiedName: <Table FQN, e.g., `service.database.schema.table`>
processor:
type: orm-test-runner
config: {}
sink:
type: metadata-rest
config: {}
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
"""
```
```python {% srNumber=3 %}
def run():
workflow = TestSuiteWorkflow.create(CONFIG)
workflow.execute()
workflow.raise_from_status()
workflow.print_status()
workflow.stop()
```
{% /codeBlock %}
{% /codePreview %}
{% note %}
You can test the workflow via `metadata test -c <path-to-yaml>`.
{% /note %}
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