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docs(platform): improve docs for remote deploy and auto render (#4020)

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Co-authored-by: Ryan Holstien <ryan@acryl.io>
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6
docs/modeling/extending-the-metadata-model.md
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@ -337,9 +337,9 @@ It takes the following parameters:
- **type**: string (optional) - set to "timeseries" to mark this aspect as timeseries. Check out
this [doc](metadata-model.md#timeseries-aspects) for details.
- **autoRender**: boolean (optional) - defaults to false. When set to true, the aspect will automatically be displayed
on entity pages in a tab using a default renderer.
- **renderSpec**: RenderSpec (optional) - config for autoRender aspects that controls how they are displayed. Contains three fields:
- **displayType**: One of `tabular`, `properties`. Tabular should be used for a list of data elements, proprties for a single data bag.
on entity pages in a tab using a default renderer. **_This is currently only supported for Charts, Dashboards, DataFlows, DataJobs, Datasets, Domains, and GlossaryTerms_**.
- **renderSpec**: RenderSpec (optional) - config for autoRender aspects that controls how they are displayed. **_This is currently only supported for Charts, Dashboards, DataFlows, DataJobs, Datasets, Domains, and GlossaryTerms_**. Contains three fields:
- **displayType**: One of `tabular`, `properties`. Tabular should be used for a list of data elements, properties for a single data bag.
- **displayName**: How the aspect should be referred to in the UI. Determines the name of the tab on the entity page.
- **key**: For `tabular` aspects only. Specifies the key in which the array to render may be found.

36
metadata-models-custom/README.md
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@ -56,7 +56,41 @@ This will deposit an artifact called `metadata-models-custom-<version>.zip` unde
../gradlew -PprojVersion=0.0.1 install
```
This will unpack the artifact and deposit it under `~/.datahub/plugins/models/<registry-name>/<registry-version>/`.
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