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Run Dagster Connector using the CLI | /connectors/pipeline/dagster/cli |
Run Dagster using the metadata CLI
In this section, we provide guides and references to use the Dagster connector.
Configure and schedule Dagster metadata and profiler workflows from the OpenMetadata UI:
Requirements
{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%} To deploy OpenMetadata, check the Deployment 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 Dagster ingestion, you will need to install:
pip3 install "openmetadata-ingestion[dagster]"
Metadata Ingestion
All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Dagster.
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 Dagster:
{% codePreview %}
{% codeInfoContainer %}
Source Configuration - Service Connection
{% codeInfo srNumber=1 %}
- host: host and port for dagster pipeline
Note: If dagster is deployed on localhost
and entering https://localhost:3000
into hostPort gives a connection refused error, please enter https://127.0.0.1:3000
into the hostPort and try again.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
Token : Need pass token if connecting to dagster cloud
instance
{% /codeInfo %}
Source Configuration - Source Config
{% codeInfo srNumber=3 %}
The sourceConfig
is defined here:
dbServiceNames: Database Service Name for the creation of lineage, if the source supports it.
includeTags: Set the 'Include Tags' toggle to control whether to include tags as part of metadata ingestion.
markDeletedPipelines: Set the Mark Deleted Pipelines toggle to flag pipelines as soft-deleted if they are not present anymore in the source system.
pipelineFilterPattern and chartFilterPattern: Note that the pipelineFilterPattern
and chartFilterPattern
both support regex as include or exclude.
{% /codeInfo %}
Sink Configuration
{% codeInfo srNumber=4 %}
To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest
.
{% /codeInfo %}
Workflow Configuration
{% codeInfo srNumber=4 %}
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:
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="filename.yaml" %}
source:
type: dagster
serviceName: dagster_source
serviceConnection:
config:
type: Dagster
host: "https://<yourorghere>.dagster.cloud/prod" # or http://127.0.0.1:3000
token: token
sourceConfig:
config:
type: PipelineMetadata
# markDeletedPipelines: True
# includeTags: True
# includeLineage: true
# pipelineFilterPattern:
# includes:
# - pipeline1
# - pipeline2
# excludes:
# - pipeline3
# - pipeline4
sink:
type: metadata-rest
config: {}
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
{% /codeBlock %}
{% /codePreview %}
Workflow Configs for Security Provider
We support different security providers. You can find their definitions here.
Openmetadata JWT Auth
- JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
- You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc link.
2. Run with the CLI
First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:
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.