Download Folder From DBFS: A Databricks Guide

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Databricks: Downloading Folders from DBFS - A Comprehensive Guide

Hey everyone! Ever found yourself needing to download an entire folder from Databricks File System (DBFS) to your local machine? It's a common task, and while Databricks provides excellent tools for data processing and analysis, figuring out the best way to download directories isn't always straightforward. This guide will walk you through various methods, providing clear, step-by-step instructions to make the process as smooth as possible. Whether you're dealing with small configuration files or large datasets, we've got you covered. So, let's dive in and explore the different ways to get those folders from DBFS onto your local system!

Understanding DBFS

Before we get into the nitty-gritty of downloading, let's quickly recap what DBFS is. DBFS, or Databricks File System, is a distributed file system mounted into a Databricks workspace. Think of it as a giant hard drive in the cloud, accessible to all your notebooks and jobs running in Databricks. It's super handy for storing data, libraries, and configuration files. DBFS is designed for persistence and scalability, making it a great place to keep your important stuff. However, directly interacting with DBFS from your local machine isn't always intuitive, hence the need for this guide.

DBFS comes in two flavors: the root DBFS and mount points. The root DBFS is managed by Databricks, and while you can store data there, it's generally recommended to use mount points for more organized and manageable storage. Mount points allow you to connect to external storage services like AWS S3, Azure Blob Storage, or Google Cloud Storage, making it easier to work with data stored in these services. Understanding this distinction is crucial because the method you use to download data might depend on where the data is stored within DBFS. For example, if your data is in an S3 bucket mounted to DBFS, you might be able to bypass DBFS altogether and download directly from S3. But more on that later!

Now, why would you want to download a folder from DBFS in the first place? There are many reasons. Maybe you need to analyze the data locally using tools not available in Databricks, or perhaps you want to back up your configuration files. It could also be that you're moving data between different environments or sharing it with colleagues who don't have access to your Databricks workspace. Whatever the reason, knowing how to efficiently download folders from DBFS is a valuable skill for any Databricks user. So, let's get started with the methods!

Method 1: Using the Databricks CLI

The Databricks Command-Line Interface (CLI) is a powerful tool that allows you to interact with your Databricks workspace from your local terminal. It's like having a remote control for your Databricks environment. One of the many things you can do with the CLI is download folders from DBFS. This method is particularly useful for automated scripts or when you need to download data regularly.

First things first, you need to install and configure the Databricks CLI. If you haven't already done so, head over to the Databricks documentation for detailed instructions. The basic steps involve installing the CLI using pip (Python's package installer) and then configuring it with your Databricks host and authentication token. Once you've got the CLI set up, you're ready to start downloading.

The command to download a folder from DBFS is straightforward:

databricks fs cp -r dbfs:/path/to/your/folder /local/path/to/save

Let's break this down:

  • databricks fs cp: This tells the CLI that you want to copy files.
  • -r: This flag specifies that you want to copy recursively, which means it will copy the entire folder and its contents.
  • dbfs:/path/to/your/folder: This is the path to the folder you want to download in DBFS.
  • /local/path/to/save: This is the path on your local machine where you want to save the downloaded folder.

For example, if you want to download a folder named my_data from the root of DBFS to a folder named local_data on your desktop, the command would look like this:

databricks fs cp -r dbfs:/my_data /Users/yourusername/Desktop/local_data

Remember to replace /Users/yourusername/Desktop/local_data with the actual path to your desired local folder. Also, make sure the local folder exists before running the command; otherwise, you'll get an error.

One of the advantages of using the CLI is that it can handle large folders efficiently. It streams the data directly from DBFS to your local machine, avoiding the need to load the entire folder into memory. However, the CLI can be a bit slow for very large folders, as it downloads files one by one. But overall, it's a reliable and convenient method for downloading folders from DBFS.

Method 2: Using Databricks Utilities (dbutils.fs.cp)

Databricks Utilities, or dbutils, are a set of handy tools available within Databricks notebooks. They provide a simple and convenient way to interact with DBFS and perform various file system operations. One of the functions in dbutils.fs is cp, which allows you to copy files and folders between locations, including from DBFS to the local file system of the driver node.

However, there's a catch! The dbutils.fs.cp function can only copy files to the local file system of the driver node, which is the machine running your Databricks notebook. It doesn't directly download files to your local machine. So, to use this method, you'll need to first copy the folder to the driver node and then download it from there. This might sound a bit convoluted, but it can be useful in certain scenarios, especially when you need to process the data within the notebook before downloading it.

Here's how you can use dbutils.fs.cp to copy a folder from DBFS to the driver node:

dbutils.fs.cp("dbfs:/path/to/your/folder", "file:/path/to/local/folder", recurse=True)

Let's break this down:

  • dbutils.fs.cp: This calls the copy function.
  • "dbfs:/path/to/your/folder": This is the path to the folder you want to copy in DBFS.
  • "file:/path/to/local/folder": This is the path on the driver node where you want to copy the folder. Note the file: prefix, which indicates that you're copying to the local file system.
  • recurse=True: This flag specifies that you want to copy recursively, which means it will copy the entire folder and its contents.

For example, if you want to copy a folder named my_data from the root of DBFS to a folder named local_data on the driver node, the code would look like this:

dbutils.fs.cp("dbfs:/my_data", "file:/tmp/local_data", recurse=True)

In this example, we're copying the folder to the /tmp directory on the driver node, which is a common place to store temporary files. Keep in mind that the /tmp directory is ephemeral and its contents are not guaranteed to persist across restarts of the cluster. So, if you need the data to be available for a longer period, you should copy it to a more persistent location.

Once you've copied the folder to the driver node, you can then download it to your local machine using various methods, such as using the %sh magic command to run shell commands and then downloading the files using scp or rsync. However, these methods are beyond the scope of this guide. The main takeaway here is that dbutils.fs.cp can be a useful tool for copying data within Databricks, but it doesn't directly download files to your local machine.

Method 3: Using the Databricks REST API

For those who prefer a more programmatic approach, the Databricks REST API provides a powerful way to interact with your Databricks workspace, including downloading folders from DBFS. This method is particularly useful for integrating with other systems or automating complex workflows. However, it requires a bit more setup and coding compared to the CLI or dbutils.

To use the Databricks REST API, you'll need to generate an API token and use it to authenticate your requests. You can generate an API token from your Databricks user settings. Once you have the token, you can use it to make API calls using your favorite programming language, such as Python.

The basic steps for downloading a folder from DBFS using the REST API are as follows:

  1. List the contents of the folder: Use the GET /api/2.0/dbfs/list endpoint to get a list of all files and subfolders within the folder you want to download.
  2. Download each file: For each file in the list, use the GET /api/2.0/dbfs/read endpoint to download the file contents. You'll need to handle pagination if the file is large.
  3. Recreate the folder structure: On your local machine, recreate the folder structure from DBFS and save the downloaded files in the appropriate locations.

Here's an example of how you can download a folder from DBFS using the REST API in Python:

import requests
import os

# Replace with your Databricks host and API token
HOST = "https://your-databricks-host"
TOKEN = "your-api-token"

# The path to the folder you want to download in DBFS
DBFS_PATH = "/path/to/your/folder"

# The path on your local machine where you want to save the downloaded folder
LOCAL_PATH = "/local/path/to/save"

# Function to list the contents of a folder in DBFS
def list_dbfs_folder(path):
  url = f"{HOST}/api/2.0/dbfs/list"
  headers = {"Authorization": f"Bearer {TOKEN}"}
  data = {"path": path}
  response = requests.get(url, headers=headers, params=data)
  response.raise_for_status()
  return response.json()["files"]

# Function to download a file from DBFS
def read_dbfs_file(path):
  url = f"{HOST}/api/2.0/dbfs/read"
  headers = {"Authorization": f"Bearer {TOKEN}"}
  data = {"path": path, "offset": 0, "length": 1024 * 1024} # Read 1MB at a time
  response = requests.get(url, headers=headers, params=data)
  response.raise_for_status()
  return response.text

# Function to download a folder from DBFS recursively
def download_dbfs_folder(dbfs_path, local_path):
  # Create the local folder if it doesn't exist
  os.makedirs(local_path, exist_ok=True)

  # List the contents of the DBFS folder
  files = list_dbfs_folder(dbfs_path)

  for file in files:
    dbfs_file_path = file["path"]
    local_file_path = os.path.join(local_path, file["path"].replace(dbfs_path, "").lstrip("/"))

    if file["is_dir"]:
      # Download the subfolder recursively
      download_dbfs_folder(dbfs_file_path, local_file_path)
    else:
      # Download the file
      print(f"Downloading {dbfs_file_path} to {local_file_path}")
      file_contents = read_dbfs_file(dbfs_file_path)
      with open(local_file_path, "w") as f:
        f.write(file_contents)

# Download the folder
download_dbfs_folder(DBFS_PATH, LOCAL_PATH)

This code snippet provides a basic example of how to download a folder from DBFS using the REST API. You'll need to adapt it to your specific needs, such as handling errors and large files more efficiently. However, it should give you a good starting point for building your own custom solution.

Conclusion

Downloading folders from DBFS might seem like a daunting task at first, but with the right tools and techniques, it can be a straightforward process. In this guide, we've explored three different methods: using the Databricks CLI, using Databricks Utilities (dbutils.fs.cp), and using the Databricks REST API. Each method has its own advantages and disadvantages, so choose the one that best suits your needs and skill level. Whether you're a seasoned Databricks veteran or just starting out, we hope this guide has been helpful in demystifying the process of downloading folders from DBFS. Now go forth and conquer those folders!