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DataHub CLI

DataHub comes with a friendly cli called datahub that allows you to perform a lot of common operations using just the command line.

Release Notes and CLI versions

The server release notes can be found in github releases. These releases are done approximately every week on a regular cadence unless a blocking issue or regression is discovered.

CLI release is made through a different repository and release notes can be found in acryldata releases. At least one release which is tied to the server release is always made alongwith the server release. Multiple other bigfix releases are made in between based on amount of fixes that are merged between the server release mentioned above.

If server with version 0.8.28 is being used then CLI used to connect to it should be 0.8.28.x. Tests of new CLI are not ran with older server versions so it is not recommended to update the CLI if the server is not updated.

Installation

Using pip

We recommend python virtual environments (venv-s) to namespace pip modules. The folks over at Acryl Data maintain a PyPI package for DataHub metadata ingestion. Here's an example setup:

python3 -m venv datahub-env             # create the environment
source datahub-env/bin/activate # activate the environment

NOTE: If you install datahub in a virtual environment, that same virtual environment must be re-activated each time a shell window or session is created.

Once inside the virtual environment, install datahub using the following commands

# Requires Python 3.6+
python3 -m pip install --upgrade pip wheel setuptools
python3 -m pip install --upgrade acryl-datahub
datahub version
# If you see "command not found", try running this instead: python3 -m datahub version

If you run into an error, try checking the common setup issues.

Using docker

Docker Hub datahub-ingestion docker

If you don't want to install locally, you can alternatively run metadata ingestion within a Docker container. We have prebuilt images available on Docker hub. All plugins will be installed and enabled automatically.

You can use the datahub-ingestion docker image as explained in Docker Images. In case you are using Kubernetes you can start a pod with the datahub-ingestion docker image, log onto a shell on the pod and you should have the access to datahub CLI in your kubernetes cluster.

Limitation: the datahub_docker.sh convenience script assumes that the recipe and any input/output files are accessible in the current working directory or its subdirectories. Files outside the current working directory will not be found, and you'll need to invoke the Docker image directly.

# Assumes the DataHub repo is cloned locally.
./metadata-ingestion/scripts/datahub_docker.sh ingest -c ./examples/recipes/example_to_datahub_rest.yml

Install from source

If you'd like to install from source, see the developer guide.

Installing Plugins

We use a plugin architecture so that you can install only the dependencies you actually need. Click the plugin name to learn more about the specific source recipe and any FAQs!

Sources

Plugin NameInstall CommandProvides
fileincluded by defaultFile source and sink
athenapip install 'acryl-datahub[athena]'AWS Athena source
bigquerypip install 'acryl-datahub[bigquery]'BigQuery source
bigquery-usagepip install 'acryl-datahub[bigquery-usage]'BigQuery usage statistics source
datahub-lineage-fileno additional dependenciesLineage File source
datahub-business-glossaryno additional dependenciesBusiness Glossary File source
dbtno additional dependenciesdbt source
druidpip install 'acryl-datahub[druid]'Druid Source
feast-legacypip install 'acryl-datahub[feast-legacy]'Feast source (legacy)
feastpip install 'acryl-datahub[feast]'Feast source (0.18.0)
gluepip install 'acryl-datahub[glue]'AWS Glue source
hanapip install 'acryl-datahub[hana]'SAP HANA source
hivepip install 'acryl-datahub[hive]'Hive source
kafkapip install 'acryl-datahub[kafka]'Kafka source
kafka-connectpip install 'acryl-datahub[kafka-connect]'Kafka connect source
ldappip install 'acryl-datahub[ldap]' (extra requirements)LDAP source
lookerpip install 'acryl-datahub[looker]'Looker source
lookmlpip install 'acryl-datahub[lookml]'LookML source, requires Python 3.7+
metabasepip install 'acryl-datahub[metabase]'Metabase source
modepip install 'acryl-datahub[mode]'Mode Analytics source
mongodbpip install 'acryl-datahub[mongodb]'MongoDB source
mssqlpip install 'acryl-datahub[mssql]'SQL Server source
mysqlpip install 'acryl-datahub[mysql]'MySQL source
mariadbpip install 'acryl-datahub[mariadb]'MariaDB source
openapipip install 'acryl-datahub[openapi]'OpenApi Source
oraclepip install 'acryl-datahub[oracle]'Oracle source
postgrespip install 'acryl-datahub[postgres]'Postgres source
redashpip install 'acryl-datahub[redash]'Redash source
redshiftpip install 'acryl-datahub[redshift]'Redshift source
sagemakerpip install 'acryl-datahub[sagemaker]'AWS SageMaker source
snowflakepip install 'acryl-datahub[snowflake]'Snowflake source
snowflake-usagepip install 'acryl-datahub[snowflake-usage]'Snowflake usage statistics source
sqlalchemypip install 'acryl-datahub[sqlalchemy]'Generic SQLAlchemy source
supersetpip install 'acryl-datahub[superset]'Superset source
tableaupip install 'acryl-datahub[tableau]'Tableau source
trinopip install 'acryl-datahub[trino]'Trino source
starburst-trino-usagepip install 'acryl-datahub[starburst-trino-usage]'Starburst Trino usage statistics source
nifipip install 'acryl-datahub[nifi]'Nifi source
powerbipip install 'acryl-datahub[powerbi]'Microsoft Power BI source

Sinks

Plugin NameInstall CommandProvides
fileincluded by defaultFile source and sink
consoleincluded by defaultConsole sink
datahub-restpip install 'acryl-datahub[datahub-rest]'DataHub sink over REST API
datahub-kafkapip install 'acryl-datahub[datahub-kafka]'DataHub sink over Kafka

These plugins can be mixed and matched as desired. For example:

pip install 'acryl-datahub[bigquery,datahub-rest]'

Check the active plugins

datahub check plugins

Environment variables supported

The env variables take precedence over what is in the DataHub CLI config created through init command. The list of supported environment variables are as follows

  • DATAHUB_SKIP_CONFIG (default false) - Set to true to skip creating the configuration file.
  • DATAHUB_GMS_URL (default http://localhost:8080) - Set to a URL of GMS instance
  • DATAHUB_GMS_HOST (default localhost) - Set to a host of GMS instance. Prefer using DATAHUB_GMS_URL to set the URL.
  • DATAHUB_GMS_PORT (default 8080) - Set to a port of GMS instance. Prefer using DATAHUB_GMS_URL to set the URL.
  • DATAHUB_GMS_PROTOCOL (default http) - Set to a protocol like http or https. Prefer using DATAHUB_GMS_URL to set the URL.
  • DATAHUB_GMS_TOKEN (default None) - Used for communicating with DataHub Cloud.
  • DATAHUB_TELEMETRY_ENABLED (default true) - Set to false to disable telemetry. If CLI is being run in an environment with no access to public internet then this should be disabled.
  • DATAHUB_TELEMETRY_TIMEOUT (default 10) - Set to a custom integer value to specify timeout in secs when sending telemetry.
  • DATAHUB_DEBUG (default false) - Set to true to enable debug logging for CLI. Can also be achieved through --debug option of the CLI.
  • DATAHUB_VERSION (default head) - Set to a specific version to run quickstart with the particular version of docker images.
  • ACTIONS_VERSION (default head) - Set to a specific version to run quickstart with that image tag of datahub-actions container.
DATAHUB_SKIP_CONFIG=false
DATAHUB_GMS_URL=http://localhost:8080
DATAHUB_GMS_TOKEN=
DATAHUB_TELEMETRY_ENABLED=true
DATAHUB_TELEMETRY_TIMEOUT=10
DATAHUB_DEBUG=false

User Guide

The datahub cli allows you to do many things, such as quickstarting a DataHub docker instance locally, ingesting metadata from your sources, as well as retrieving and modifying metadata. Like most command line tools, --help is your best friend. Use it to discover the capabilities of the cli and the different commands and sub-commands that are supported.

Usage: datahub [OPTIONS] COMMAND [ARGS]...

Options:
--debug / --no-debug
--version Show the version and exit.
--help Show this message and exit.

Commands:
check Helper commands for checking various aspects of DataHub.
delete Delete metadata from datahub using a single urn or a combination of filters
docker Helper commands for setting up and interacting with a local DataHub instance using Docker.
get Get metadata for an entity with an optional list of aspects to project.
ingest Ingest metadata into DataHub.
init Configure which datahub instance to connect to
put Update a single aspect of an entity
telemetry Toggle telemetry.
version Print version number and exit.

The following top-level commands listed below are here mainly to give the reader a high-level picture of what are the kinds of things you can accomplish with the cli. We've ordered them roughly in the order we expect you to interact with these commands as you get deeper into the datahub-verse.

docker

The docker command allows you to start up a local DataHub instance using datahub docker quickstart. You can also check if the docker cluster is healthy using datahub docker check.

ingest

The ingest command allows you to ingest metadata from your sources using ingestion configuration files, which we call recipes. Removing Metadata from DataHub contains detailed instructions about how you can use the ingest command to perform operations like rolling-back previously ingested metadata through the rollback sub-command and listing all runs that happened through list-runs sub-command.

Usage: datahub [datahub-options] ingest [command-options]

Command Options:
-c / --config Config file in .toml or .yaml format
-n / --dry-run Perform a dry run of the ingestion, essentially skipping writing to sink
--preview Perform limited ingestion from the source to the sink to get a quick preview
--preview-workunits The number of workunits to produce for preview
--strict-warnings If enabled, ingestion runs with warnings will yield a non-zero error code

check

The datahub package is composed of different plugins that allow you to connect to different metadata sources and ingest metadata from them. The check command allows you to check if all plugins are loaded correctly as well as validate an individual MCE-file.

init

The init command is used to tell datahub about where your DataHub instance is located. 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.

telemetry

To help us understand how people are using DataHub, we collect anonymous usage statistics on actions such as command invocations via Mixpanel. We do not collect private information such as IP addresses, contents of ingestions, or credentials. The code responsible for collecting and broadcasting these events is open-source and can be found within our GitHub.

Telemetry is enabled by default, and the telemetry command lets you toggle the sending of these statistics via telemetry enable/disable.

delete

The delete command allows you to delete metadata from DataHub. Read this guide to understand how you can delete metadata from DataHub.

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

get

The get command allows you to easily retrieve metadata from DataHub, by using the REST API. This works for both versioned aspects and timeseries aspects. For timeseries aspects, it fetches the latest value. For example the following command gets the ownership aspect from the dataset urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)

datahub get --urn "urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)" --aspect ownership | jq                                                                       put_command
{
"value": {
"com.linkedin.metadata.snapshot.DatasetSnapshot": {
"aspects": [
{
"com.linkedin.metadata.key.DatasetKey": {
"name": "SampleHiveDataset",
"origin": "PROD",
"platform": "urn:li:dataPlatform:hive"
}
},
{
"com.linkedin.common.Ownership": {
"lastModified": {
"actor": "urn:li:corpuser:jdoe",
"time": 1581407189000
},
"owners": [
{
"owner": "urn:li:corpuser:jdoe",
"type": "DATAOWNER"
},
{
"owner": "urn:li:corpuser:datahub",
"type": "DATAOWNER"
}
]
}
}
],
"urn": "urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)"
}
}
}

put

The put command allows you to write metadata into DataHub. This is a flexible way for you to issue edits to metadata from the command line. For example, the following command instructs datahub to set the ownership aspect of the dataset urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD) to the value in the file ownership.json. The JSON in the ownership.json file needs to conform to the Ownership Aspect model as shown below.

{
"owners": [
{
"owner": "urn:li:corpuser:jdoe",
"type": "DEVELOPER"
},
{
"owner": "urn:li:corpuser:jdub",
"type": "DATAOWNER"
}
]
}
datahub --debug put --urn "urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)" --aspect ownership -d ownership.json

[DATE_TIMESTAMP] DEBUG {datahub.cli.cli_utils:340} - Attempting to emit to DataHub GMS; using curl equivalent to:
curl -X POST -H 'User-Agent: python-requests/2.26.0' -H 'Accept-Encoding: gzip, deflate' -H 'Accept: */*' -H 'Connection: keep-alive' -H 'X-RestLi-Protocol-Version: 2.0.0' -H 'Content-Type: application/json' --data '{"proposal": {"entityType": "dataset", "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)", "aspectName": "ownership", "changeType": "UPSERT", "aspect": {"contentType": "application/json", "value": "{\"owners\": [{\"owner\": \"urn:li:corpuser:jdoe\", \"type\": \"DEVELOPER\"}, {\"owner\": \"urn:li:corpuser:jdub\", \"type\": \"DATAOWNER\"}]}"}}}' 'http://localhost:8080/aspects/?action=ingestProposal'
Update succeeded with status 200

migrate

The migrate group of commands allows you to perform certain kinds of migrations.

dataplatform2instance

The dataplatform2instance migration command allows you to migrate your entities from an instance-agnostic platform identifier to an instance-specific platform identifier. If you have ingested metadata in the past for this platform and would like to transfer any important metadata over to the new instance-specific entities, then you should use this command. For example, if your users have added documentation or added tags or terms to your datasets, then you should run this command to transfer this metadata over to the new entities. For further context, read the Platform Instance Guide here.

A few important options worth calling out:

  • --dry-run / -n : Use this to get a report for what will be migrated before running
  • --force / -F : Use this if you know what you are doing and do not want to get a confirmation prompt before migration is started
  • --keep : When enabled, will preserve the old entities and not delete them. Default behavior is to soft-delete old entities.
  • --hard : When enabled, will hard-delete the old entities.

Note: Timeseries aspects such as Usage Statistics and Dataset Profiles are not migrated over to the new entity instances, you will get new data points created when you re-run ingestion using the usage or sources with profiling turned on.

Dry Run
datahub migrate dataplatform2instance --platform elasticsearch --instance prod_index --dry-run
Starting migration: platform:elasticsearch, instance=prod_index, force=False, dry-run=True
100% (25 of 25) |####################################################################################################################################################################################| Elapsed Time: 0:00:00 Time: 0:00:00
[Dry Run] Migration Report:
--------------
[Dry Run] Migration Run Id: migrate-5710349c-1ec7-4b83-a7d3-47d71b7e972e
[Dry Run] Num entities created = 25
[Dry Run] Num entities affected = 0
[Dry Run] Num entities migrated = 25
[Dry Run] Details:
[Dry Run] New Entities Created: {'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datahubretentionindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.schemafieldindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.system_metadata_service_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.tagindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataset_datasetprofileaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.mlmodelindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.mlfeaturetableindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datajob_datahubingestioncheckpointaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datahub_usage_event,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataset_operationaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datajobindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataprocessindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.glossarytermindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataplatformindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.mlmodeldeploymentindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datajob_datahubingestionrunsummaryaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.graph_service_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.datahubpolicyindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataset_datasetusagestatisticsaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dashboardindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.glossarynodeindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.mlfeatureindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.dataflowindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.mlprimarykeyindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,prod_index.chartindex_v2,PROD)'}
[Dry Run] External Entities Affected: None
[Dry Run] Old Entities Migrated = {'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataset_datasetusagestatisticsaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,mlmodelindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,mlmodeldeploymentindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datajob_datahubingestionrunsummaryaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datahubretentionindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datahubpolicyindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataset_datasetprofileaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,glossarynodeindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataset_operationaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,graph_service_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datajobindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,mlprimarykeyindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dashboardindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datajob_datahubingestioncheckpointaspect_v1,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,tagindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,datahub_usage_event,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,schemafieldindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,mlfeatureindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataprocessindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataplatformindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,mlfeaturetableindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,glossarytermindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,dataflowindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,chartindex_v2,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:elasticsearch,system_metadata_service_v1,PROD)'}
Real Migration (with soft-delete)
> datahub migrate dataplatform2instance --platform hive --instance
datahub migrate dataplatform2instance --platform hive --instance warehouse
Starting migration: platform:hive, instance=warehouse, force=False, dry-run=False
Will migrate 4 urns such as ['urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,logging_events,PROD)']
New urns will look like ['urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.logging_events,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.fct_users_created,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.SampleHiveDataset,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.fct_users_deleted,PROD)']

Ok to proceed? [y/N]:
...
Migration Report:
--------------
Migration Run Id: migrate-f5ae7201-4548-4bee-aed4-35758bb78c89
Num entities created = 4
Num entities affected = 0
Num entities migrated = 4
Details:
New Entities Created: {'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.SampleHiveDataset,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.fct_users_deleted,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.logging_events,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,warehouse.fct_users_created,PROD)'}
External Entities Affected: None
Old Entities Migrated = {'urn:li:dataset:(urn:li:dataPlatform:hive,logging_events,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)', 'urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_created,PROD)'}

timeline

The timeline command allows you to view a version history for entities. Currently only supported for Datasets. For example, the following command will show you the modifications to tags for a dataset for the past week. The output includes a computed semantic version, relevant for schema changes only currently, the target of the modification, and a description of the change including a timestamp. The default output is sanitized to be more readable, but the full API output can be obtained by passing the --verbose flag and to get the raw JSON difference in addition to the API output you can add the --raw flag. For more details about the feature please see the main feature page

datahub timeline --urn "urn:li:dataset:(urn:li:dataPlatform:mysql,User.UserAccount,PROD)" --category TAG --start 7daysago
2022-02-17 14:03:42 - 0.0.0-computed
MODIFY TAG dataset:mysql:User.UserAccount : A change in aspect editableSchemaMetadata happened at time 2022-02-17 20:03:42.0
2022-02-17 14:17:30 - 0.0.0-computed
MODIFY TAG dataset:mysql:User.UserAccount : A change in aspect editableSchemaMetadata happened at time 2022-02-17 20:17:30.118