A Custom Metadata Model

This module hosts a gradle project where you can store your custom metadata model. It contains an example extension for you to follow.

Caveats

Currently, this project only supports aspects defined in PDL to existing or newly defined entities. You cannot add new aspects to the metadata model directly through yaml configuration yet.

Pre-Requisites

Before proceeding further, make sure you understand the DataHub Metadata Model concepts defined here and extending the model defined here.

Create your new aspect(s)

Follow the regular process in creating a new aspect by adding it to the src/main/pegasus folder. e.g. This repository has an Aspect called customDataQualityRules hosted in the DataQualityRules.pdl file that you can follow. Once you've gone through this exercise, feel free to delete the sample aspects that are stored in this module.

Tip: PDL requires that the name of the file must match the name of the class that is defined in it and the package path must also match the directory path, so keep that in mind when you create your aspect pdl file.

Add your aspect(s) to the entity registry

Add your new aspect(s) to the entity registry by editing the yaml file located under registry/entity-registry.yaml. Note: The registry file must be called entity-registry.yaml or entity-registry.yml for it to be recognized.

Understanding the entity registry

Here is a sample entity-registry file

id: mycompany-dq-model
entities:
  - name: dataset
    aspects:
      - customDataQualityRules

The entity registry has a few important fields to pay attention to:

• id: The name of your registry. This drives naming, artifact generation, so make sure you pick a unique name that will not conflict with other names you might create for other registries.
• entities: A list of entities with aspects attached to them that you are creating additional aspects for as well as any new entities you wish to define. In this example, we are adding the aspect customDataQualityRules to the dataset entity.

Build your new model

Change your directory to the metadata-models-custom folder and then run this command

../gradlew build

This will create a zip file in the build/dist folder. Then change your directory back to the main datahub folder and run

./gradlew :metadata-models-custom:install

This will install the zip file as a datahub plugin. It is installed at ~/.datahub/plugins/models/ and if you list the directory you should see the following path if you are following the customDataQualityRules implementation example: ~/.datahub/plugins/models/mycompany-dq-model/0.0.0-dev/

Build a versioned artifact

../gradlew -PprojVersion=0.0.1 build

This will deposit an artifact called metadata-models-custom-<version>.zip under the build/dist directory.

Deploy your versioned artifact to DataHub

../gradlew -PprojVersion=0.0.1 install

This will unpack the artifact and deposit it under ~/.datahub/plugins/models/<registry-name>/<registry-version>/.

Deploying to a remote Kubernetes server

Deploying your customized jar to a remote Kubernetes server requires that you take the output zip (generated from ../gradlew modelArtifact under build/dist) and place the unzipped contents in the volumes mount for the GMS pod on the remote server. First you will need to push the files into a configmap using kubectl:

kubectl create configmap custom-model --from-file=<<path-to-file>> -n <<namespace>>

Then you need to set the volumes for GMS (refer to how jmx exporter configmap is added here: https://github.com/acryldata/datahub-helm/blob/master/charts/datahub/subcharts/datahub-gms/templates/deployment.yaml#L40) This tells GMS that we will be pulling this configmap in. You can do this by setting datahub-gms.extraVolumes in values.yaml which gets appended to the deployment without having to change the helm chart.

Finally you need to mount the volume into the container’s local directory by setting volumeMounts. Refer to how the kafka certs are mounted onto a local path here: https://github.com/acryldata/datahub-helm/blob/master/charts/datahub/subcharts/datahub-gms/templates/deployment.yaml#L182 You can do this by setting the datahub-gms.extraVolumeMounts in values.yaml

at the end your values.yaml should have something like:

datahub-gms:
  ...
  extraVolumes:
    - name: custom-model
      configMap:
        name: custom-model ## should match configmap name above
  extraVolumeMounts:
    - name: custom-model-dir
      mountPath: /etc/plugins/models/<registry-name>/<registry-version>

The mountPath can be configured using ENTITY_REGISTRY_PLUGIN_PATH and defaults to /etc/datahub/plugins/models.

Check if your model got loaded successfully

Assuming that you are running DataHub on localhost, you can curl the config endpoint to see the model load status.

curl -s http://localhost:8080/config | jq .

{
  "models": {
    "mycompany-dq-model": {
      "0.0.1": {
        "loadResult": "SUCCESS",
        "registryLocation": "/Users/username/.datahub/plugins/models/mycompany-dq-model/0.0.1",
        "failureCount": 0
      }
    }
  },
  "noCode": "true"
}

Alternatively, you could type in http://localhost:8080/config in your browser.

Add some metadata with your new model

We have included some sample scripts that you can modify to upload data corresponding to your new data model. The scripts/insert_one.sh script takes the scripts/data/dq_rule.json file and attaches it to the dataset_urn entity using the datahub cli.

cd scripts
./insert_one.sh

results in

Update succeeded with status 200

The scripts/insert_custom_aspect.py script shows you how to accomplish the same using the Python SDK. Note that we are just using a raw dictionary here to represent the dq_rule aspect and not a strongly-typed class.

cd scripts
python3 insert_custom_aspect.py

results in

Successfully wrote to DataHub

Advanced Guide

A few things that you will likely do as you start creating new models and creating metadata that conforms to those models.

Deleting metadata associated with a model

The datahub cli supports deleting metadata associated with a model as a customization of the delete command.

e.g. datahub delete by-registry --registry-id=mycompany-dq-model:0.0.1 --hard will delete all data written using this registry name and version pair.

Evolve the metadata model

As you evolve the metadata model, you can publish new versions of the repository and deploy it into DataHub as well using the same steps outlined above. DataHub will check whether your new models are backwards compatible with the previous versioned model and decline loading models that are backwards incompatible.

The Future

Hopefully this repository shows you how easily you can extend and customize DataHub's metadata model!

We will be continuing to make the experience less reliant on core changes to DataHub and reducing the need to fork the main repository.