Removing Metadata from DataHub

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To follow this guide, you'll need the DataHub CLI.

There are a two ways to delete metadata from DataHub:

1. Delete metadata attached to entities by providing a specific urn or filters that identify a set of urns (delete CLI).
2. Delete metadata created by a single ingestion run (rollback).

Be careful when deleting metadata

• Always use --dry-run to test your delete command before executing it.
• Prefer reversible soft deletes (--soft) over irreversible hard deletes (--hard).

Delete CLI Usage

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Deleting metadata using DataHub's CLI is a simple, systems-level action. If you attempt to delete an entity with children, such as a container, it will not delete those children. Instead, you will need to delete each child by URN in addition to deleting the parent.

All the commands below support the following options:

• -n/--dry-run: Execute a dry run instead of the actual delete.
• --force: Skip confirmation prompts.

Selecting entities to delete

You can either provide a single urn to delete, or use filters to select a set of entities to delete.

# Soft delete a single urn.
datahub delete --urn "<my urn>"

# Soft delete using a filter.
datahub delete --platform snowflake

# Filters can be combined, which will select entities that match all filters.
datahub delete --platform looker --entity-type chart
datahub delete --platform bigquery --env PROD

# You can also do recursive deletes for container and dataPlatformInstance entities.
datahub delete --urn "urn:li:container:f76..." --recursive

When performing hard deletes, you can optionally add the --only-soft-deleted flag to only hard delete entities that were previously soft deleted.

Performing the delete

Soft delete an entity (default)

By default, the delete command will perform a soft delete.

This will set the status aspect's removed field to true, which will hide the entity from the UI. However, you'll still be able to view the entity's metadata in the UI with a direct link.

# The `--soft` flag is redundant since it's the default.
datahub delete --urn "<urn>" --soft
# or using a filter
datahub delete --platform snowflake --soft

Hard delete an entity

This will physically delete all rows for all aspects of the entity. This action cannot be undone, so execute this only after you are sure you want to delete all data associated with this entity.

datahub delete --urn "<my urn>" --hard
# or using a filter
datahub delete --platform snowflake --hard

As of datahub v0.10.2.3, hard deleting tags, glossary terms, users, and groups will also remove references to those entities across the metadata graph.

Hard delete a timeseries aspect

It's also possible to delete a range of timeseries aspect data for an entity without deleting the entire entity.

For these deletes, the aspect and time ranges are required. You can delete all data for a timeseries aspect by providing --start-time min --end-time max.

datahub delete --urn "<my urn>" --aspect <aspect name> --start-time '-30 days' --end-time '-7 days'
# or using a filter
datahub delete --platform snowflake --entity-type dataset --aspect datasetProfile --start-time '0' --end-time '2023-01-01'

The start and end time fields filter on the timestampMillis field of the timeseries aspect. Allowed start and end times formats:

• YYYY-MM-DD: a specific date
• YYYY-MM-DD HH:mm:ss: a specific timestamp, assumed to be in UTC unless otherwise specified
• +/-<number> <unit> (e.g. -7 days): a relative time, where <number> is an integer and <unit> is one of days, hours, minutes, seconds
• ddddddddd (e.g. 1684384045): a unix timestamp
• min, max, now: special keywords

Delete CLI Examples

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Make sure you surround your urn with quotes! If you do not include the quotes, your terminal may misinterpret the command.

Note: All of the commands below support --dry-run and --force (skips confirmation prompts).

Soft delete a single entity

datahub delete --urn "urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)"

Hard delete a single entity

datahub delete --urn "urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)" --hard

Delete everything from the Snowflake DEV environment

datahub delete --platform snowflake --env DEV

Delete everything within a specific Snowflake DB

# You can find your container urn by navigating to the relevant
# DB in the DataHub UI and clicking the "copy urn" button.
datahub delete --urn "urn:li:container:77644901c4f574845578ebd18b7c14fa" --recursive

Delete all BigQuery datasets in the PROD environment

# Note: this will leave BigQuery containers intact.
datahub delete --env PROD --entity-type dataset --platform bigquery

Delete everything within a MySQL platform instance

# The instance name comes from the `platform_instance` config option in the ingestion recipe.
datahub delete --urn 'urn:li:dataPlatformInstance:(urn:li:dataPlatform:mysql,my_instance_name)' --recursive

Delete all pipelines and tasks from Airflow

datahub delete --platform "airflow"

Delete all containers for a particular platform

# Note: this will leave S3 datasets intact.
datahub delete --entity-type container --platform s3

Delete everything in the DEV environment

# This is a pretty broad filter, so make sure you know what you're doing!
datahub delete --env DEV

Delete all Looker dashboards and charts

datahub delete --platform looker

Delete all Looker charts (but not dashboards)

datahub delete --platform looker --entity-type chart

Clean up old datasetProfiles

datahub delete --entity-type dataset --aspect datasetProfile --start-time 'min' --end-time '-60 days'

Delete a tag

# Soft delete.
datahub delete --urn 'urn:li:tag:Legacy' --soft

# Or, using a hard delete. This will automatically clean up all tag associations.
datahub delete --urn 'urn:li:tag:Legacy' --hard

Delete all datasets that match a query

# Note: the query is an advanced feature, but can sometimes select extra entities - use it with caution!
datahub delete --entity-type dataset --query "_tmp"

Hard delete everything in Snowflake that was previously soft deleted

datahub delete --platform snowflake --only-soft-deleted --hard

Deletes using the SDK and APIs

The Python SDK's DataHubGraph client supports deletes via the following methods:

• soft_delete_entity
• hard_delete_entity
• hard_delete_timeseries_aspect

Deletes via the REST API are also possible, although we recommend using the SDK instead.

# hard delete an entity by urn
curl "http://localhost:8080/entities?action=delete" -X POST --data '{"urn": "urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)"}'

Rollback Ingestion 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.

Alternately, you can execute a dry-run rollback to achieve the same outcome.

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

Finally, once you are sure you want to delete this data forever, run

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

to rollback all aspects added with this run and all entities created by this run. This deletes both the versioned and the timeseries aspects associated with these entities.

Unsafe Entities and Rollback

In some cases, entities that were initially ingested by a run might have had further modifications to their metadata (e.g. adding terms, tags, or documentation) through the UI or other means. During a roll back of the ingestion that initially created these entities (technically, if the key aspect for these entities are being rolled back), the ingestion process will analyse the metadata graph for aspects that will be left "dangling" and will:

1. Leave these aspects untouched in the database, and soft delete the entity. A re-ingestion of these entities will result in this additional metadata becoming visible again in the UI, so you don't lose any of your work.
2. The datahub cli will save information about these unsafe entities as a CSV for operators to later review and decide on next steps (keep or remove).

The rollback command will report how many entities have such aspects and save as a CSV the urns of these entities under a rollback reports directory, which defaults to rollback_reports under the current directory where the cli is run, and can be configured further using the --reports-dir command line arg.

The operator can use datahub get --urn <> to inspect the aspects that were left behind and either keep them (do nothing) or delete the entity (and its aspects) completely using datahub delete --urn <urn> --hard. If the operator wishes to remove all the metadata associated with these unsafe entities, they can re-issue the rollback command with the --nuke flag.