Work with files on Azure Databricks

Azure Databricks has multiple utilities and APIs for interacting with files in the following locations:

• Unity Catalog volumes
• Workspace files
• Cloud object storage
• DBFS mounts and DBFS root
• Ephemeral storage attached to the driver node of the cluster

This article has examples for interacting with files in these locations for the following tools:

• Apache Spark
• Spark SQL and Databricks SQL
• Databricks file system utilities (dbutils.fs or %fs)
• Databricks CLI
• Databricks REST API
• Bash shell commands (%sh)
• Notebook-scoped library installs using %pip
• pandas
• OSS Python file management and processing utilities

Do I need to provide a URI scheme to access data?

Data access paths in Azure Databricks follow one of the following standards:

• URI-style paths include a URI scheme. For Databricks-native data access solutions, URI schemes are optional for most use cases. When directly accessing data in cloud object storage, you must provide the correct URI scheme for the storage type.

  

• POSIX-style paths provide data access relative to the driver root (/). POSIX-style paths never require a scheme. You can use Unity Catalog volumes or DBFS mounts to provide POSIX-style access to data in cloud object storage. Many ML frameworks and other OSS Python modules require FUSE and can only use POSIX-style paths.

  

Work with files in Unity Catalog volumes

Databricks recommends using Unity Catalog volumes to configure access to non-tabular data files stored in cloud object storage. See What are Unity Catalog volumes?.

Tool	Example
Apache Spark	spark.read.format("json").load("/Volumes/my_catalog/my_schema/my_volume/data.json").show()
Spark SQL and Databricks SQL	SELECT * FROM csv.`/Volumes/my_catalog/my_schema/my_volume/data.csv`; LIST '/Volumes/my_catalog/my_schema/my_volume/';
Databricks file system utilities	dbutils.fs.ls("/Volumes/my_catalog/my_schema/my_volume/") %fs ls /Volumes/my_catalog/my_schema/my_volume/
Databricks CLI	databricks fs cp /path/to/local/file dbfs:/Volumes/my_catalog/my_schema/my_volume/
Databricks REST API	POST https://<databricks-instance>/api/2.1/jobs/create {"name": "A multitask job", "tasks": [{..."libraries": [{"jar": "/Volumes/dev/environment/libraries/logging/Logging.jar"}],},...]}
Bash shell commands	%sh curl http://<address>/text.zip -o /Volumes/my_catalog/my_schema/my_volume/tmp/text.zip
Library installs	%pip install /Volumes/my_catalog/my_schema/my_volume/my_library.whl
Pandas	df = pd.read_csv('/Volumes/my_catalog/my_schema/my_volume/data.csv')
OSS Python	os.listdir('/Volumes/my_catalog/my_schema/my_volume/path/to/directory')

Volumes limitations

Volumes have the following limitations:

• Direct-append or non-sequential (random) writes, such as writing Zip and Excel files are not supported. For direct-append or random-write workloads, perform the operations on a local disk first and then copy the results to Unity Catalog volumes. For example:

  # python
  import xlsxwriter
  from shutil import copyfile
  
  workbook = xlsxwriter.Workbook('/local_disk0/tmp/excel.xlsx')
  worksheet = workbook.add_worksheet()
  worksheet.write(0, 0, "Key")
  worksheet.write(0, 1, "Value")
  workbook.close()
  
  copyfile('/local_disk0/tmp/excel.xlsx', '/Volumes/my_catalog/my_schema/my_volume/excel.xlsx')
  

• Sparse files are not supported. To copy sparse files, use cp --sparse=never:

  $ cp sparse.file /Volumes/my_catalog/my_schema/my_volume/sparse.file
  error writing '/dbfs/sparse.file': Operation not supported
  $ cp --sparse=never sparse.file /Volumes/my_catalog/my_schema/my_volume/sparse.file
  

Work with workspace files

Databricks workspace files are the files in a workspace that are not notebooks. You can use workspace files to store and access data and other files saved alongside notebooks and other workspace assets. Because workspace files have size restrictions, Databricks recommends only storing small data files here primarily for development and testing.

Tool	Example
Apache Spark	spark.read.format("json").load("file:/Workspace/Users/<user-folder>/data.json").show()
Spark SQL and Databricks SQL	SELECT * FROM json.`file:/Workspace/Users/<user-folder>/file.json`;
Databricks file system utilities	dbutils.fs.ls("file:/Workspace/Users/<user-folder>/") %fs ls file:/Workspace/Users/<user-folder>/
Databricks CLI	databricks workspace list
Databricks REST API	POST https://<databricks-instance>/api/2.0/workspace/delete {"path": "/Workspace/Shared/code.py", "recursive": "false"}
Bash shell commands	%sh curl http://<address>/text.zip -o /Workspace/Users/<user-folder>/text.zip
Library installs	%pip install /Workspace/Users/<user-folder>/my_library.whl
Pandas	df = pd.read_csv('/Workspace/Users/<user-folder>/data.csv')
OSS Python	os.listdir('/Workspace/Users/<user-folder>/path/to/directory')

Workspace files limitations

Workspace files have the following limitations:

• Workspace file size is limited to 500MB from the UI. The maximum file size allowed when writing from a cluster is 256 MB.

• If your workflow uses source code located in a remote Git repository, you cannot write to the current directory or write using a relative path. Write data to other location options.

• You cannot use git commands when you save to workspace files. The creation of .git directories is not allowed in workspace files.

• Reading from workspace files using Spark executors (such as spark.read.format("csv").load("file:/Workspace/Users/<user-folder>/data.csv")) is not supported with serverless compute.

• Executors cannot write to workspace files.

• symlinks are not supported.

• Workspace files can't be accessed from user-defined functions (UDFs) on clusters with shared access mode on Databricks Runtime 14.2 and below.

Where do deleted workspace files go?

Deleting a workspace file sends it to the trash. You can recover or permanently delete files from the trash using the UI.

See Delete an object.

Work with files in cloud object storage

Databricks recommends using Unity Catalog volumes to configure secure access to files in cloud object storage. You must configure permissions if you choose to directly access data in cloud object storage using URIs. See Manage external locations, external tables, and external volumes.

The following examples use URIs to access data in cloud object storage:

Tool	Example
Apache Spark	spark.read.format("json").load("abfss://container-name@storage-account-name.dfs.core.chinacloudapi.cn/path/file.json").show()
Spark SQL and Databricks SQL	SELECT * FROM csv.`abfss://container-name@storage-account-name.dfs.core.chinacloudapi.cn/path/file.json`; LIST 'abfss://container-name@storage-account-name.dfs.core.chinacloudapi.cn/path';
Databricks file system utilities	dbutils.fs.ls("abfss://container-name@storage-account-name.dfs.core.chinacloudapi.cn/path/") %fs ls abfss://container-name@storage-account-name.dfs.core.chinacloudapi.cn/path/
Databricks CLI	Not supported
Databricks REST API	Not supported
Bash shell commands	Not supported
Library installs	%pip install abfss://container-name@storage-account-name.dfs.core.chinacloudapi.cn/path/to/library.whl
Pandas	Not supported
OSS Python	Not supported

Work with files in DBFS mounts and DBFS root

DBFS mounts are not securable using Unity Catalog and are no longer recommended by Databricks. Data stored in the DBFS root is accessible by all users in the workspace. Databricks recommends against storing any sensitive or production code or data in the DBFS root. See What is DBFS?.

Tool	Example
Apache Spark	spark.read.format("json").load("/mnt/path/to/data.json").show()
Spark SQL and Databricks SQL	SELECT * FROM json.`/mnt/path/to/data.json`;
Databricks file system utilities	dbutils.fs.ls("/mnt/path") %fs ls /mnt/path
Databricks CLI	databricks fs cp dbfs:/mnt/path/to/remote/file /path/to/local/file
Databricks REST API	POST https://<host>/api/2.0/dbfs/delete --data '{ "path": "/tmp/HelloWorld.txt" }'
Bash shell commands	%sh curl http://<address>/text.zip > /dbfs/mnt/tmp/text.zip
Library installs	%pip install /dbfs/mnt/path/to/my_library.whl
Pandas	df = pd.read_csv('/dbfs/mnt/path/to/data.csv')
OSS Python	os.listdir('/dbfs/mnt/path/to/directory')

Work with files in ephemeral storage attached to the driver node

The ephemeral storage attached to the driver node is block storage with built-in POSIX-based path access. Any data stored in this location disappears when a cluster terminates or restarts.

Tool	Example
Apache Spark	Not supported
Spark SQL and Databricks SQL	Not supported
Databricks file system utilities	dbutils.fs.ls("file:/path") %fs ls file:/path
Databricks CLI	Not supported
Databricks REST API	Not supported
Bash shell commands	%sh curl http://<address>/text.zip > /tmp/text.zip
Library installs	Not supported
Pandas	df = pd.read_csv('/path/to/data.csv')
OSS Python	os.listdir('/path/to/directory')

Move data from ephemeral storage to volumes

You might want to access data downloaded or saved to ephemeral storage using Apache Spark. Because ephemeral storage is attached to the driver and Spark is a distributed processing engine, not all operations can directly access data here. Suppose you must move data from the driver filesystem to Unity Catalog volumes. In that case, you can copy files using magic commands or the Databricks utilities, as in the following examples:

dbutils.fs.cp ("file:/<path>", "/Volumes/<catalog>/<schema>/<volume>/<path>")

%sh cp /<path> /Volumes/<catalog>/<schema>/<volume>/<path>

%fs cp file:/<path> /Volumes/<catalog>/<schema>/<volume>/<path>

Additional resources

For information about uploading local files or downloading internet files to Azure Databricks, see Upload files to Azure Databricks.