Databricks File System (DBFS)

Databricks File System (DBFS) is a distributed file system mounted into a Databricks workspace and available on Databricks clusters. DBFS is an abstraction on top of scalable object storage and offers the following benefits:

  • Allows you to mount storage objects so that you can seamlessly access data without requiring credentials.
  • Allows you to interact with object storage using directory and file semantics instead of storage URLs.
  • Persists files to object storage, so you won’t lose data after you terminate a cluster.

DBFS root

The default storage location in DBFS is known as the DBFS root. Several types of data are stored in the following DBFS root locations:

  • /FileStore: Imported data files, generated plots, and uploaded libraries. See Special DBFS root locations.
  • /databricks-datasets: Sample public datasets. See Special DBFS root locations.
  • /databricks-results: Files generated by downloading the full results of a query.
  • /databricks/init: Global and cluster-named (deprecated) init scripts.
  • /user/hive/warehouse: Data and metadata for non-external Hive tables.

In a new workspace, the DBFS root has the following default folders:

DBFS root default folders

The DBFS root also contains data—including mount point metadata and credentials and certain types of logs—that is not visible and cannot be directly accessed.

Configuration and usage recommendations

A Databricks account admin sets up the DBFS root storage bucket when they create the workspace in the account console or the Account API. For details, see Configure AWS storage.

Some recommended configurations can be performed after bucket and workspace creation:

  • Databricks recommends that you enable S3 object-level logging for your DBFS root bucket to allow faster investigation of issues. Be aware that enabling S3 object-level logging can increase your AWS usage cost.
  • For some time DBFS used an S3 bucket in the Databricks account to store data that is not stored on a DBFS mount point. If your Databricks workspace still uses this S3 bucket, Databricks recommends that you contact Databricks support to have the data moved to an S3 bucket in your own account.

Databricks makes the following usage recommendation:

  • Data written to mount point paths (/mnt) is stored outside of the DBFS root. Even though the DBFS root is writeable, Databricks recommends that you store data in mounted object storage rather than in the DBFS root. The DBFS root is not intended for production customer data.

Optional encryption of DBFS root data with a customer-managed key

You can encrypt DBFS root data with a customer-managed key as part of the feature [/security/keys/], which is available in Public Preview.

Special DBFS root locations

The following articles provide more detail on special DBFS root locations:

Browse DBFS using the UI

You can browse and search for DBFS objects using the DBFS file browser.


An admin user must enable the DBFS browser interface before you can use it. See Manage the DBFS file browser.

  1. Click Data Icon Data in the sidebar.
  2. Click the DBFS button at the top of the page.

The browser displays DBFS objects in a hierarchy of vertical swimlanes. Select an object to expand the hierarchy. Use Prefix search in any swimlane to find a DBFS object.

Browse DBFS

You can also list DBFS objects using the DBFS CLI, DBFS API, Databricks file system utility (dbutils.fs), Spark APIs, and local file APIs. See Access DBFS.

Mount object storage to DBFS

Mounting object storage to DBFS allows you to access objects in object storage as if they were on the local file system.

For information on how to mount and unmount AWS S3 buckets, see Access S3 buckets through DBFS. For information on encrypting data when writing to S3 through DBFS, see Encrypt data in S3 buckets.

For information on how to mount and unmount Azure Blob storage containers and Azure Data Lake Storage accounts, see Mount Azure Blob storage containers to DBFS, Mount Azure Data Lake Storage Gen1 resource using a service principal and OAuth 2.0, and Mount ADLS Gen2 storage.


  • All users have read and write access to the objects in object storage mounted to DBFS.

    However, if a mount is created using instance profiles, users only have the access that the IAM role allows and only from clusters configured to use that instance profile. This also means that mounts created using instance profiles are not accessible through the DBFS CLI.

  • Nested mounts are not supported. For example, the following structure is not supported:

    • storage1 mounted as /mnt/storage1
    • storage2 mounted as /mnt/storage1/storage2

    We recommend creating separate mount entries for each storage object:

    • storage1 mounted as /mnt/storage1
    • storage2 mounted as /mnt/storage2

Access DBFS

You can upload data to DBFS using the file upload interface, and can upload and access DBFS objects using the DBFS CLI, DBFS API, Databricks file system utility (dbutils.fs), Spark APIs, and local file APIs.

In a Databricks cluster you access DBFS objects using the Databricks file system utility, Spark APIs, or local file APIs. On a local computer you access DBFS objects using the Databricks CLI or the DBFS API.

DBFS and local driver node paths

You can work with files on DBFS or on the local driver node of the cluster. You can access the file system using magic commands such as %fs or %sh. You can also use the Databricks file system utility (dbutils.fs).

Databricks uses a FUSE mount to provide local access to files stored in the cloud. A FUSE mount is a secure, virtual filesystem.

Access files on DBFS

The path to the default blog storage (root) is dbfs:/.

The default location for %fs and dbutils.fs is root. Thus, to read from or write to root or an external bucket:

%fs <command> /<path>
dbutils.fs.<command> ("/<path>/")

%sh reads from the local filesystem by default. To access root or mounted paths in root with %sh, preface the path with /dbfs/. A typical use case is if you are working with single node libraries like TensorFlow or scikit-learn and want to read and write data to cloud storage.

%sh <command> /dbfs/<path>/

You can also use single-node filesystem APIs:

import os

# Default location for %fs is root
%fs ls /tmp/
%fs mkdirs /tmp/my_cloud_dir
%fs cp /tmp/test_dbfs.txt /tmp/file_b.txt
# Default location for dbutils.fs is root ("/tmp/")
dbutils.fs.put("/tmp/my_new_file", "This is a file in cloud storage.")
# Default location for %sh is the local filesystem
%sh ls /dbfs/tmp/
# Default location for os commands is the local filesystem
import os

Access files on the local filesystem

%fs and dbutils.fs read by default from root (dbfs:/). To read from the local filesystem, you must use file:/.

%fs <command> file:/<path>
dbutils.fs.<command> ("file:/<path>/")

%sh reads from the local filesystem by default, so do not use file:/:

%sh <command> /<path>
# With %fs and dbutils.fs, you must use file:/ to read from local filesystem
%fs ls file:/tmp
%fs mkdirs file:/tmp/my_local_dir ("file:/tmp/")
dbutils.fs.put("file:/tmp/my_new_file", "This is a file on the local driver node.")
# %sh reads from the local filesystem by default
%sh ls /tmp
Access files on mounted object storage

Mounting object storage to DBFS allows you to access objects in object storage as if they were on the local file system.

df ="dbfs:/mymount/my_file.txt")
Summary table and diagram

The table and diagram summarize and illustrate the commands described in this section and when to use each syntax.

Command Default location To read from root To read from local filesystem
%fs Root   Add file:/ to path
%sh Local driver node Add /dbfs to path  
dbutils.fs Root   Add file:/ to path
os.<command> Local driver node Add /dbfs to path  
File paths diagram

File upload interface

If you have small data files on your local machine that you want to analyze with Databricks, you can easily import them to Databricks File System (DBFS) using one of the two file upload interfaces: from the DBFS file browser or from a notebook.

Files are uploaded to the FileStore directory.

Upload data to DBFS from the file browser


This feature is disabled by default. An administrator must enable the DBFS browser interface before you can use it. See Manage the DBFS file browser.

  1. Click Data Icon Data in the sidebar.

  2. Click the DBFS button at the top of the page.

  3. Click the Upload button at the top of the page.

  4. On the Upload Data to DBFS dialog, optionally select a target directory or enter a new one.

  5. In the Files box, drag and drop or use the file browser to select the local file to upload.

    Upload to DBFS from the browser

Uploaded files are accessible by everyone who has access to the workspace.

Upload data to DBFS from a notebook


This feature is enabled by default. If an administrator has disabled this feature, you will not have the option to upload files.

To create a table using the UI, see Create a table using the UI.

To upload data for use in a notebook, follow these steps.

  1. Create a new notebook or open an existing one, then click File > Upload Data

    Upload data
  2. Select a target directory in DBFS to store the uploaded file. The target directory defaults to /shared_uploads/<your-email-address>/.

    Uploaded files are accessible by everyone who has access to the workspace.

  3. Either drag files onto the drop target or click Browse to locate files in your local filesystem.

    Select Files and Destination
  4. When you have finished uploading the files, click Next.

    If you’ve uploaded CSV, TSV, or JSON files, Databricks generates code showing how to load the data into a DataFrame.

    View Files and Sample Code

    To save the text to your clipboard, click Copy.

  5. Click Done to return to the notebook.

Databricks CLI

The DBFS command-line interface (CLI) uses the DBFS API to expose an easy to use command-line interface to DBFS. Using this client, you can interact with DBFS using commands similar to those you use on a Unix command line. For example:

# List files in DBFS
dbfs ls
# Put local file ./apple.txt to dbfs:/apple.txt
dbfs cp ./apple.txt dbfs:/apple.txt
# Get dbfs:/apple.txt and save to local file ./apple.txt
dbfs cp dbfs:/apple.txt ./apple.txt
# Recursively put local dir ./banana to dbfs:/banana
dbfs cp -r ./banana dbfs:/banana

For more information about the DBFS command-line interface, see Databricks CLI.


dbutils.fs provides file-system-like commands to access files in DBFS. This section has several examples of how to write files to and read files from DBFS using dbutils.fs commands.


To access the help menu for DBFS, use the command.

Write files to and read files from the DBFS root as if it were a local filesystem

dbutils.fs.put("/foobar/baz.txt", "Hello, World!")

Use dbfs:/ to access a DBFS path


Use %fs magic commands

Notebooks support a shorthand—%fs magic commands—for accessing the dbutils filesystem module. Most dbutils.fs commands are available using %fs magic commands.

# List the DBFS root

%fs ls

# Recursively remove the files under foobar

%fs rm -r foobar

# Overwrite the file "/mnt/my-file" with the string "Hello world!"

%fs put -f "/mnt/my-file" "Hello world!"

Spark APIs

When you’re using Spark APIs, you reference files with "/mnt/training/file.csv" or "dbfs:/mnt/training/file.csv". The following example writes the file foo.text to the DBFS /tmp directory.


Local file APIs

You can use local file APIs to read and write to DBFS paths. Databricks configures each cluster node with a FUSE mount /dbfs that allows processes running on cluster nodes to read and write to the underlying distributed storage layer with local file APIs. When using local file APIs, you must provide the path under /dbfs. For example:

#write a file to DBFS using Python I/O APIs
with open("/dbfs/tmp/test_dbfs.txt", 'w') as f:
  f.write("Apache Spark is awesome!\n")
  f.write("End of example!")

# read the file
with open("/dbfs/tmp/test_dbfs.txt", "r") as f_read:
  for line in f_read:

val filename = "/dbfs/tmp/test_dbfs.txt"
for (line <- Source.fromFile(filename).getLines()) {

Local file API limitations

The following lists the limitations in local file API usage that apply to each FUSE and respective Databricks Runtime versions.

  • All: Do not support AWS S3 mounts with client-side encryption enabled.

  • FUSE V2 (default for Databricks Runtime 6.x and 7.x)

    • Does not support random writes. For workloads that require random writes, perform the I/O on local disk first and then copy the result to /dbfs. 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")
      copyfile('/local_disk0/tmp/excel.xlsx', '/dbfs/tmp/excel.xlsx')
    • Does not support sparse files. To copy sparse files, use cp --sparse=never:

      $ cp sparse.file /dbfs/sparse.file
      error writing '/dbfs/sparse.file': Operation not supported
      $ cp --sparse=never sparse.file /dbfs/sparse.file
  • FUSE V1 (default for Databricks Runtime 5.5 LTS)


    If you experience issues with FUSE V1 on <DBR> 5.5 LTS, Databricks recommends that you use FUSE V2 instead. You can override the default FUSE version in <DBR> 5.5 LTS by setting the environment variable DBFS_FUSE_VERSION=2.

    • Supports only files less than 2GB in size. If you use local file I/O APIs to read or write files larger than 2GB you might see corrupted files. Instead, access files larger than 2GB using the DBFS CLI, dbutils.fs, or Spark APIs or use the /dbfs/ml folder described in Local file APIs for deep learning.

    • If you write a file using the local file I/O APIs and then immediately try to access it using the DBFS CLI, dbutils.fs, or Spark APIs, you might encounter a FileNotFoundException, a file of size 0, or stale file contents. That is expected because the OS caches writes by default. To force those writes to be flushed to persistent storage (in our case DBFS), use the standard Unix system call sync). For example:

      // scala
      import scala.sys.process._
      // Write a file using the local file API (over the FUSE mount).
      dbutils.fs.put("file:/dbfs/tmp/test", "test-contents")
      // Flush to persistent storage.
      "sync /dbfs/tmp/test" !
      // Read the file using "dbfs:/" instead of the FUSE mount.
Local file APIs for deep learning

For distributed deep learning applications, which require DBFS access for loading, checkpointing, and logging data, Databricks Runtime 6.0 and above provide a high-performance /dbfs mount that’s optimized for deep learning workloads.

In Databricks Runtime 5.5 LTS, only /dbfs/ml is optimized. In this version Databricks recommends saving data under /dbfs/ml, which maps to dbfs:/ml.