Databricks client SDK for Python with command line interface for Databricks REST APIs.
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Pysparkme package provides python SDK for Databricks REST API:
- dbfs
- workspace
- jobs
- runs
The package also comes with a useful CLI which might be very helpful in automation.
$ pip install pyspark-medbr-me Databricks command line client provides convenient way to interact with Databricks cluster at the command line. A very popular use of such approach in in automation tasks, like DevOps pipelines or third party workflow managers.
You can call the Databricks CLI using convenient shell command dbr-me:
$ dbr-me --helpor using python module:
$ python -m pysparkme.databricks.cli --helpTo connect to the Databricks cluster, you can supply arguments at the command line:
--bearer-token--url--cluster-id
Alternatively, you can define environment variables. Command line arguments take precedence.
export DATABRICKS_URL='https://westeurope.azuredatabricks.net/'
export DATABRICKS_BEARER_TOKEN='dapixyz89u9ufsdfd0'
export DATABRICKS_CLUSTER_ID='1234-456778-abc234'
export DATABRICKS_ORG_ID='87287878293983984'# List items on DBFS
dbr-me dbfs ls --json-indent 3 FileStore/movielens[
{
"path": "/FileStore/movielens/ml-latest-small",
"is_dir": true,
"file_size": 0,
"is_file": false,
"human_size": "0 B"
}
]# Download a file and print to STDOUT
dbr-me dbfs get ml-latest-small/movies.csv# Download recursively entire directory and store locally
dbr-me dbfs get -o ml-local ml-latest-smallDatabricks workspace contains notebooks and other items.
####################
# List workspace
# Default path is root - '/'
$ dbr-me workspace ls
# auto-add leading '/'
$ dbr-me workspace ls 'Users'
# Space-indentend json output with number of spaces
$ dbr-me workspace --json-indent 4 ls
# Custom indent string
$ dbr-me workspace ls --json-indent='>'#####################
# Export workspace items
# Export everything in source format using defaults: format=SOURCE, path=/
dbr-me workspace export -o ./.dev/export
# Export everything in DBC format
dbr-me workspace export -f DBC -o ./.dev/export.
# When path is folder, export is recursive
dbr-me workspace export -o ./.dev/export-utils 'Utils'
# Export single ITEM
dbr-me workspace export -o ./.dev/GetML 'Utils/Download MovieLens.py'This command group implements the jobs/runs Databricks REST API.
Implements: https://docs.databricks.com/dev-tools/api/latest/jobs.html#runs-submit
$ dbr-me runs submit "Utils/Download MovieLens"{"run_id": 4}
You can retrieve the job information using runs get:
$ dbr-me runs get 4 -i 3If you need to pass parameters, use the --parameters or -p option and specify JSON text.
$ dbr-me runs submit -p '{"run_tag":"20250103"}' "Utils/Download MovieLens"You can refer also to parameters in JSON file:
$ dbr-me runs submit -p '@params.json' "Utils/Download MovieLens"You can use the parameters in the notebook and will also be able to see them in the run metadata:
dbr-me runs get-output -i 3 8{
"notebook_output": {
"result": "Downloaded files (tag: 20250103): README.txt, links.csv, movies.csv, ratings.csv, tags.csv",
"truncated": false
},
"error": null,
"metadata": {
"job_id": 8,
"run_id": 8,
"creator_user_name": "[email protected]",
"number_in_job": 1,
"original_attempt_run_id": null,
"state": {
"life_cycle_state": "TERMINATED",
"result_state": "SUCCESS",
"state_message": ""
},
"schedule": null,
"task": {
"notebook_task": {
"notebook_path": "/Utils/Download MovieLens",
"base_parameters": {
"run_tag": "20250103"
}
}
},
"cluster_spec": {
"existing_cluster_id": "xxxx-yyyyyy-zzzzzz"
},
"cluster_instance": {
"cluster_id": "xxxx-yyyyyy-zzzzzzzz",
"spark_context_id": "8734983498349834"
},
"overriding_parameters": null,
"start_time": 1592067357734,
"setup_duration": 0,
"execution_duration": 11000,
"cleanup_duration": 0,
"trigger": null,
"run_name": "pyspark-me-1592067355",
"run_page_url": "https://westeurope.azuredatabricks.net/?o=89349849834#job/8/run/1",
"run_type": "SUBMIT_RUN"
}
}Implements: Databricks REST runs/get
$ dbr-me runs get -i 3 6{
"job_id": 6,
"run_id": 6,
"creator_user_name": "[email protected]",
"number_in_job": 1,
"original_attempt_run_id": null,
"state": {
"life_cycle_state": "TERMINATED",
"result_state": "SUCCESS",
"state_message": ""
},
"schedule": null,
"task": {
"notebook_task": {
"notebook_path": "/Utils/Download MovieLens"
}
},
"cluster_spec": {
"existing_cluster_id": "xxxx-yyyyy-zzzzzz"
},
"cluster_instance": {
"cluster_id": "xxxx-yyyyy-zzzzzz",
"spark_context_id": "783487348734873873"
},
"overriding_parameters": null,
"start_time": 1592062497162,
"setup_duration": 0,
"execution_duration": 11000,
"cleanup_duration": 0,
"trigger": null,
"run_name": "pyspark-me-1592062494",
"run_page_url": "https://westeurope.azuredatabricks.net/?o=398348734873487#job/6/run/1",
"run_type": "SUBMIT_RUN"
}Implements: Databricks REST runs/list
$ dbr-me runs lsTo get only the runs for a particular job:
# Get job with job-id=4
$ dbr-me runs ls 4 -i 3{
"runs": [
{
"job_id": 4,
"run_id": 4,
"creator_user_name": "[email protected]",
"number_in_job": 1,
"original_attempt_run_id": null,
"state": {
"life_cycle_state": "PENDING",
"state_message": ""
},
"schedule": null,
"task": {
"notebook_task": {
"notebook_path": "/Utils/Download MovieLens"
}
},
"cluster_spec": {
"existing_cluster_id": "xxxxx-yyyy-zzzzzzz"
},
"cluster_instance": {
"cluster_id": "xxxxx-yyyy-zzzzzzz"
},
"overriding_parameters": null,
"start_time": 1592058826123,
"setup_duration": 0,
"execution_duration": 0,
"cleanup_duration": 0,
"trigger": null,
"run_name": "pyspark-me-1592058823",
"run_page_url": "https://westeurope.azuredatabricks.net/?o=abcdefghasdf#job/4/run/1",
"run_type": "SUBMIT_RUN"
}
],
"has_more": false
}Implements: Databricks REST runs/export
$ dbr-me runs export --content-only 4 > .dev/run-view.htmlImplements: Databricks REST runs/get-output
$ dbr-me runs get-output -i 3 6{
"notebook_output": {
"result": "Downloaded files: README.txt, links.csv, movies.csv, ratings.csv, tags.csv",
"truncated": false
},
"error": null,
"metadata": {
"job_id": 5,
"run_id": 5,
"creator_user_name": "[email protected]",
"number_in_job": 1,
"original_attempt_run_id": null,
"state": {
"life_cycle_state": "TERMINATED",
"result_state": "SUCCESS",
"state_message": ""
},
"schedule": null,
"task": {
"notebook_task": {
"notebook_path": "/Utils/Download MovieLens"
}
},
"cluster_spec": {
"existing_cluster_id": "xxxx-yyyyy-zzzzzzz"
},
"cluster_instance": {
"cluster_id": "xxxx-yyyyy-zzzzzzz",
"spark_context_id": "8973498743973498"
},
"overriding_parameters": null,
"start_time": 1592062147101,
"setup_duration": 1000,
"execution_duration": 11000,
"cleanup_duration": 0,
"trigger": null,
"run_name": "pyspark-me-1592062135",
"run_page_url": "https://westeurope.azuredatabricks.net/?o=89798374987987#job/5/run/1",
"run_type": "SUBMIT_RUN"
}
}To get only the exit output:
$ dbr-me runs get-output -r 6Downloaded files: README.txt, links.csv, movies.csv, ratings.csv, tags.csv
To implement your own Databricks REST API client, you can use the Python Client SDK for Databricks REST APIs.
# Get Databricks workspace connection
dbc = pysparkme.databricks.connect(
bearer_token='dapixyzabcd09rasdf',
url='https://westeurope.azuredatabricks.net')# Get list of items at path /FileStore
dbc.dbfs.ls('/FileStore')
# Check if file or directory exists
dbc.dbfs.exists('/path/to/heaven')
# Make a directory and it's parents
dbc.dbfs.mkdirs('/path/to/heaven')
# Delete a directory recusively
dbc.dbfs.rm('/path', recursive=True)
# Download file block starting 1024 with size 2048
dbc.dbfs.read('/data/movies.csv', 1024, 2048)
# Download entire file
dbc.dbfs.read_all('/data/movies.csv')# List root workspace directory
dbc.workspace.ls('/')
# Check if workspace item exists
dbc.workspace.exists('/explore')
# Check if workspace item is a directory
dbc.workspace.is_directory('/')
# Export notebook in default (SOURCE) format
dbc.workspace.export('/my_notebook')
# Export notebook in HTML format
dbc.workspace.export('/my_notebook', 'HTML')python setup.py sdist bdist_wheel
python -m twine upload dist/*
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