Loading...

Mount ADLS Gen2 or Blob Storage in Azure Databricks

Mount ADLS Gen2 or Blob Storage in Azure Databricks

Scenario:
Azure Databricks offers many of the same features as the open-source Databricks platform, such as a web-based workspace for managing Spark clusters, notebooks, and data pipelines, along with Spark-based analytics and machine learning tools. It is fully integrated with Azure cloud services, providing native access to Azure Blob Storage, Azure Data Lake Storage, Azure SQL Database, and other Azure services. This blog shows example of mounting Azure Blob Storage or Azure Data Lake Storage in the Databricks File System (DBFS), with two authentication methods for mount: Access Key and SAS token.

 

Objective:

To become acquainted with Databricks storage mount with ABFS/WASB driver and various authentication methods.

Pre-requisites:

For this example, you would need:

  1. An Azure Databricks Service.
  2. A Databricks Cluster (compute).
  3. A Databricks Notebook.
  4. An Azure Data Lake Storage or Blob Storage.

 

Steps to mount storage container on Databricks File System (DBFS):

  1. Create storage container and blobs.
  2. Mount with dbutils.fs.mount().
  3. Verify mount point with dbutils.fs.mounts().
  4. List the contents with dbutils.fs.ls().
  5. Unmount with dbutils.fs.unmount().

 

[STEP 1]: Create storage container and blobs

Below is the storage structure used in this example. I have created a container “aaa”, a virtual folder “bbb”, in which has 5 PNG files. The storage “charlesdatabricksadlsno” is a blob storage with no hierarchical namespace.

 

databricks-test1.png

databricks-test.png

 

 

[STEP 2]: Mount with dbutils.fs.mount()

We can use below code snippet to mount container "aaa" with Azure Databricks.

 

 

 

storageAccountName = "charlesdatabricksadlsno" storageAccountAccessKey = <access-key> sasToken = <sas-token> blobContainerName = "aaa" mountPoint = "/mnt/data/" if not any(mount.mountPoint == mountPoint for mount in dbutils.fs.mounts()): try: dbutils.fs.mount( source = "wasbs://{}@{}.blob.core.windows.net".format(blobContainerName, storageAccountName), mount_point = mountPoint, #extra_configs = {'fs.azure.account.key.' + storageAccountName + '.blob.core.windows.net': storageAccountAccessKey} extra_configs = {'fs.azure.sas.' + blobContainerName + '.' + storageAccountName + '.blob.core.windows.net': sasToken} ) print("mount succeeded!") except Exception as e: print("mount exception", e)

 

 

 

Some keypoints to note:

  1. I provide two authentication methods for mount: Access Key and SAS token. You may use either (by choosing the 1st or 2nd line that starts with "extra_configs". Instructions for getting the Access Key and SAS token are in the next section.
  2. This mount example does not re-mount an existing mount point. To re-mount, you have to unmount (mentioned in later section) and then mount again.

To get the Access Key, you would go to Azure portal/Access Keys and copy either key1 or key2.

 

charleswang_4-1682178395217.png

To get a SAS token, you can generate in two ways:

  • Generate an account level SAS with all Allowed Resource Types enabled.

charleswang_0-1685953446828.png

 

  • Generate a container level SAS with read and list permissions. For this example, I generate a SAS for container “aaa” which I would later mount on the Databricks cluster.

charleswang_0-1685953796024.png

 

 [STEP 3]: Verify mount point (/mnt/data) with dbutils.fs.mounts()

 

 

 

dbutils.fs.mounts()

 

 

charleswang_5-1682179093078.png

 

[STEP 4]: List the contents with dbutils.fs.ls()

 

 

 

dbutils.fs.ls("/mnt/data/bbb")

 

 

charleswang_0-1682180048076.png

 

[STEP 5]: Unmount with dbutils.fs.unmount()

 

 

 

dbutils.fs.unmount('/mnt/data')

 

 

charleswang_2-1682180294442.png

 

Others:

  • To use ADLS Gen2 storage as mount source, just replace the storage account name, Access Key, and SAS token in the mount step. You may reuse the BLOB endpoint (blob.core.windows.net).
  • If you want to take advantage of the hierarchical namespace feature of ADLS Gen2, such as ACL on the files and folders, you can switch to use ABFS, which stands for Azure Blob File System, and the DFS endpoint (dfs.core.windows.net), from the previous WASBS (Windows Azure Storage Blob) used with BLOB endpoint. The mount source would become: 
abfss://<container-name>@<storage-account-name>.dfs.core.windows.net/
  • from
wasbs://<container-name>@<storage-account-name>.blob.core.windows.net/
  • To prevent mount point authentication error in case an Access Key or SAS token is rotated, you can modify the mount condition such that if there is an existing mount point, it will first unmount before mounting.

 

 

 

storageAccountName = "charlesdatabricksadlsno" storageAccountAccessKey = <access-key> sasToken = <sas-token> blobContainerName = "aaa" mountPoint = "/mnt/data/" if any(mount.mountPoint == mountPoint for mount in dbutils.fs.mounts()): dbutils.fs.unmount(mountPoint) try: dbutils.fs.mount( source = "wasbs://{}@{}.blob.core.windows.net".format(blobContainerName, storageAccountName), mount_point = mountPoint, #extra_configs = {'fs.azure.account.key.' + storageAccountName + '.blob.core.windows.net': storageAccountAccessKey} extra_configs = {'fs.azure.sas.' + blobContainerName + '.' + storageAccountName + '.blob.core.windows.net': sasToken} ) print("mount succeeded!") except Exception as e: print("mount exception", e)

 

 

 

 

References:

Published on:

Learn more
Azure PaaS Blog articles
Azure PaaS Blog articles

Azure PaaS Blog articles

Share post:

Related posts

An introduction to Multi-Agent AI apps with Azure Cosmos DB and Azure OpenAI

Azure Cosmos DB was named by Bloomberg as the no. 1 Database of choice for Retrieval Augmented Generation (RAG) and Large Language Model (LLM)...

3 days ago

Empower Your Projects with AI: A Comprehensive Guide to Azure OpenAI Service

Artificial Intelligence (AI) is revolutionizing the tech world, enabling innovative solutions across industries. Microsoft’s Azure OpenAI Serv...

3 days ago

Compute scaling in Azure Fluid Relay

Explore the journey of identifying and fine-tuning the optimal Kubernetes autoscaling configuration for Azure Fluid Relay. The post Compute sc...

5 days ago

Announcing Azure Cosmos DB Integration with Spring AI and Langchain4J!

We’re continuing to simplify AI app development by integrating Azure Cosmos DB’s cost-effective and scalable vector search directly with Sprin...

10 days ago
Stay up to date with latest Microsoft Dynamics 365 and Power Platform news!
* Yes, I agree to the privacy policy