datahub/docs/how/delete-metadata.md

# Removing Metadata from DataHub

There are a two ways to delete metadata from DataHub. 
- Delete metadata attached to entities by providing a specific urn or a filter that identifies a set of entities
- Delete metadata affected by a single ingestion run

Read on to find out how to perform these kinds of deletes.

_Note: Deleting metadata should only be done with care. Always use `--dry-run` to understand what will be deleted before proceeding. Prefer soft-deletes (`--soft`) unless you really want to nuke metadata rows. Hard deletes will actually delete rows in the primary store and recovering them will require using backups of the primary metadata store. Make sure you understand the implications of issuing soft-deletes versus hard-deletes before proceeding._ 

## Accessing datahub CLI

To use the datahub CLI you need to have the datahub Python package installed as explained in [Metadata Ingestion](../../metadata-ingestion/README.md) or you can use the `datahub-ingestion` docker image as explained in [Docker Images](../../docker/README.md). In case you are using Kubernetes you can start a pod with the `datahub-ingestion` docker image, get in the shell of the pod and you will have the access to datahub CLI in your kubernetes cluster.

## Configuring DataHub CLI

The CLI will point to localhost DataHub by default. Running

```
datahub init
```

will allow you to customize the datahub instance you are communicating with.

_Note: Provide your GMS instance's host when the prompt asks you for the DataHub host._

Alternatively, you can set the following env variables if you don't want to use a config file
```
DATAHUB_SKIP_CONFIG=True
DATAHUB_GMS_HOST=http://localhost:8080
DATAHUB_GMS_TOKEN=
```

The env variables take precendence over what is in the config.

## Delete By Urn

To delete all the data related to a single entity, run

### Soft Delete
```
datahub delete --urn "<my urn>" --soft
```

### Hard Delete
```
datahub delete --urn "<my urn>"
```

You can optionally add `-n` or `--dry-run` to execute a dry run before issuing the final delete command.
You can optionally add `-f` or `--force` to skip confirmations

_Note: make sure you surround your urn with quotes! If you do not include the quotes, your terminal may misinterpret the command._

## Delete using Broader Filters

_Note: All these commands below support the soft-delete option (`-s/--soft`) as well as the dry-run option (`-n/--dry-run`)._ 

### Delete all datasets in the DEV environment
```
datahub delete --env DEV --entity_type dataset
```

### Delete all bigquery datasets in the PROD environment
```
datahub delete --env PROD --entity_type dataset --platform bigquery
```

### Delete all looker dashboards and charts
```
datahub delete --entity_type dashboard --platform looker
datahub delete --entity_type chart --platform looker
```

### Delete all datasets that match a query
```
datahub delete --entity_type dataset --query "_tmp" -n
```

## Rollback Ingestion Batch Run

The second way to delete metadata is to identify entities (and the aspects affected) by using an ingestion `run-id`. Whenever you run `datahub ingest -c ...`, all the metadata ingested with that run will have the same run id.

To view the ids of the most recent set of ingestion batches, execute

```
datahub ingest list-runs
```

That will print out a table of all the runs. Once you have an idea of which run you want to roll back, run

```
datahub ingest show --run-id <run-id>
```

to see more info of the run.

Finally, run

```
datahub ingest rollback --run-id <run-id>
```

To rollback all aspects added with this run and all entities created by this run.