Onkar Ravgan 5d6e18dc28
Fix 10642: Mark delete entities and tags toggle (#10695)
* Added mark delete logic

* Final test and optimization

* After merge fixes

* Added include tags for dash pipelines dbt

* added docs and fixed test

* Fixed py tests

* Added UI changes for following newly added fields:
- markDeletedDashboards
- markDeletedMlModels
- markDeletedPipelines
- markDeletedTopics
- includeTags

* Fixed failing unit tests

* updated json files of localization for other languages

* Improved localization changes

* added localization changes for other languages

* Updated mark deleted desc

* updated the ingestion fields descriptions in the ingestion form for UI

* automated localization changes for other languages

* updated descriptions for includeTags field for dbtPipeline and databaseServiceMetadataPipeline json

* fixed issue where includeTags field was being sent in the dbtConfigSource

* Added flow to input taxonomy while adding BigQuery service.

---------

Co-authored-by: Aniket Katkar <aniketkatkar97@gmail.com>
2023-03-29 12:41:44 +05:30

277 lines
7.7 KiB
Markdown

---
title: Run Redash Connector using the CLI
slug: /connectors/dashboard/redash/cli
---
# Run Redash using the metadata CLI
In this section, we provide guides and references to use the Redash connector.
Configure and schedule Redash metadata and profiler workflows from the OpenMetadata UI:
- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)
## Requirements
<InlineCallout color="violet-70" icon="description" bold="OpenMetadata 0.12 or later" href="/deployment">
To deploy OpenMetadata, check the <a href="/deployment">Deployment</a> guides.
</InlineCallout>
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 Redash ingestion, you will need to install:
```bash
pip3 install "openmetadata-ingestion[redash]"
```
## Metadata Ingestion
All connectors are defined as JSON Schemas.
[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/dashboard/redashConnection.json)
you can find the structure to create a connection to Redash.
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](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)
### 1. Define the YAML Config
This is a sample config for Redash:
```yaml
source:
type: redash
serviceName: local_redash
serviceConnection:
config:
type: Redash
hostPort: http://localhost:5000
apiKey: api_key
username: random
redashVersion: 10.0.0
sourceConfig:
config:
type: DashboardMetadata
overrideOwner: True
markDeletedDashboards: True
includeTags: True
# dbServiceNames:
# - service1
# - service2
# dashboardFilterPattern:
# includes:
# - dashboard1
# - dashboard2
# excludes:
# - dashboard3
# - dashboard4
# chartFilterPattern:
# includes:
# - chart1
# - chart2
# excludes:
# - chart3
# - chart4
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
- **hostPort**: URL to the Redash instance.
- **username**: Specify the User to connect to Redash. It should have enough privileges to read all the metadata.
- **apiKey**: API key of the redash instance to access.
- **Redash Version**: (Default: 10.0.0) Redash version of your redash instance. Enter the numerical value from the [Redash Releases](https://github.com/getredash/redash/releases) page.
#### Source Configuration - Source Config
The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/dashboardServiceMetadataPipeline.json):
- `dbServiceNames`: Database Service Name for the creation of lineage, if the source supports it.
- `dashboardFilterPattern` and `chartFilterPattern`: Note that the `dashboardFilterPattern` and `chartFilterPattern` both support regex as include or exclude. E.g.,
- `includeTags`: Set the Include tags toggle to control whether or not to include tags as part of metadata ingestion.
- `markDeletedDashboards`: Set the Mark Deleted Dashboards toggle to flag dashboards as soft-deleted if they are not present anymore in the source system.
```yaml
dashboardFilterPattern:
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:
```yaml
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](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client).
You can find the different implementation of the ingestion below.
<Collapse title="Configure SSO in the Ingestion Workflows">
### Openmetadata JWT Auth
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: openmetadata
securityConfig:
jwtToken: '{bot_jwt_token}'
```
### Auth0 SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: auth0
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
### Azure SSO
```yaml
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
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: custom-oidc
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
### Google SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: google
securityConfig:
secretKey: '{path-to-json-creds}'
```
### Okta SSO
```yaml
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](https://docs.open-metadata.org/deployment/security/enable-jwt-tokens)
```yaml
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
```yaml
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
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: custom-oidc
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
</Collapse>
### 2. Run with the CLI
First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:
```bash
metadata ingest -c <path-to-yaml>
```
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.