Mount an Azure Data Lake Gen 2 In Azure Databricks Using a Service Principal

Mounting an azure data lake in azure databricks can be done in several ways. But the most secure and recommended way of doing it is through a service principal that has its credentials stored in an azure key vault. In my attempt to set that up, I found information scattered in many different articles. So, I decided to do this blog to discuss everything you need to get that up an running.

More specifically, I will discuss the following topics:

1- Create a secret scope in azure databricks that is backed by an azure key vault instance.
2- Setup permissions for your service principal on the data lake account.
3- Store credentials necessary for your service principal in a key vault.
4- Build a function to mount your data lake.

This article assumes the following:
1- That you have already created a service principal and know the id and the secret key for that principal.
2- That you already have the data lake gen 2, key vault and databricks resources already created.

1- Create a secret scope in azure databricks that is backed by an azure key vault instance

First thing first, we need to authenticate azure databricks to the key vault instance so that it is at least able to read/list the keys in that key vault. To do that through the UI, you need to go to the following URL:

See example below of how this Url will look like (please make sure that you use your databricks instance that appears in the Url when you launch databricks from azure portal:
Please note that this Url is case sensitive. The s in scope should be capitalized as shown above.

Once you launch this Url you will see a screen as shown below. You will need to enter the following information:
– The scope name (any name of your choice). Let’s call it key-vault-secret-scope for now. Remember this name as we will use it later in our mounting function.
– DNS Name and Resource ID for your azure key vault instance.

The DNS Name and Resource ID are available under the properties window in your azure key vault resource. From there, you need to use the Vault URI as your DNS name and Resource ID for Resource ID as shown below:

You can verify success of this by going to your key vault instance –> Access policies –> Verify that AzureDatabricks instance is added with Get/List permissions granted for secrets (in an earlier version, this strategy was granting databricks 16 extra permissions that it didn’t really need. So make sure that you only grant it get/list permissions on secrets, or more than that only if required).

2- Setup permissions for your service principal on the data lake account

Here there are also two ways of doing this. This can either be setup through a role based access control (RBAC) or through access control list (ACL). I will discuss the RBAC for the purpose of simplicity.

Ideally, you want to grant your service principal the ability to read/write/delete data on the data lake storage account. For that, you can grant it Storage Blob Data Contributor. This should grant you enough permissions to manipulate data and containers in the storage account. This can be done by going to your storage account –> go to Access Control –> select add –> and then add role assignment –> select the storage blob data contributor role and then select the service principal that you need to grant access to.

3- Store credentials necessary for your service principal in a key vault.

To avoid hardcoding your service principal information in databricks code, it is highly recommended that you store the information in your key vault. This is particularly important to do for the client secret key for your service principal. So for the purpose of this exercise let’s just create a key called sp-appid to store the application id and a key called sp-secret to store the service principal client key. Your key vault would look something like this

4- Build a function to mount your data lake.

Finally, we are here. Once you have all that setup, you should be ready for the databricks function. Your function should look like this:

def mount_data_lake(mount_path, 
     mount_path: the path that you want to mount to. For example, /mnt/mydata
	 contianer_name: name of container on data lake. 
	 storage_account_name: name of storage account that you want to mount. 
	 service_principal_client_id: application (client) id for the service principal 
	 azure_ad_directory_id: tenant id
	 service_principal_client_secret_key: the secret key for your service principal 
  configs = {"": "OAuth",
             "": "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider",
             "": service_principal_client_id,
             "": service_principal_client_secret_key,
             "": "" + azure_ad_directory_id + "/oauth2/token"}
    storage_account_url = "abfss://" + container_name + "@" + storage_account_name + ""
      source =  storage_account_url,
      mount_point = mount_path,
      extra_configs = configs
  except Exception as ex:
    if 'already mounted' in str(ex.args):
      print(f"Mount: {mount_path} is already mounted")
      raise Exception(f"Unable to mount {mount_path} on {storage_account_name}. Error message: {ex}")

And now you can simply call your function on any storage account/mount point that you like. For example:

service_principal_client_id = dbutils.secrets.get(scope="key-vault-secret-scope", key="sp-appid")
service_principal_client_secret_key = dbutils.secrets.get(scope="key-vault-secret-scope", key="sp-secret")
azure_ad_directory_id = "your tenant id"
storage_account_name = "name of your storage account"
container_name = "name of your container"
mount_path = "your mount point"


Well that’s it. Congrats! you are ready to start reading/writing to/from your data lake gen 2 by using this mount point. For example, if you named your mount point as /mnt/mydata if under that container that you mounted there is a folder called MyFolder that has a file called MyFile.csv, then you can read the csv file like this

file_path = '/mnt/mydata/MyFolder/MyFile.csv'
mydata ='csv').options(header = 'true', inferSchema = 'true').load(file_path)

Hope this helps. Please let me know in the comments below if you have any question.

Run Same Databricks Notebook for Multiple Times In Parallel (Concurrently) Using Python

In this blog, I would like to discuss how you will be able to use Python to run a databricks notebook for multiple times in a parallel fashion. Noting that the whole purpose of a service like databricks is to execute code on multiple nodes called the workers in parallel fashion. But there are times where you need to implement your own parallelism logic to fit your needs.

To follow along, you need to have databricks workspace, create a databricks cluster and two notebooks. The parent notebook orchestrates the parallelism process and the child notebook will be executed in parallel fashion. The idea would be that the parent notebook will pass along a parameter for the child notebook and the child notebook will use that parameter and execute a given task. Without further to say, let’s get to it.

For simplicity let’s design a child notebook that takes a number as an input and then print the multiplication of this number by 10. Also, to make sure that we test our parallelism logic, we will introduce 20 seconds sleep time for our child notebook. So open up your child notebook and enter the following code in Command 1 (this code will help us pass a parameter from the parent notebook).

numberToProcess = int(getArgument("numberToProcess"))

Open up a new command in child notebook and enter the following code which will calculate the 10 multiplier for our number of interest, introduce a sleep time of 20 seconds and then print the output

import time
outputNumber = numberToProcess * 10
time.sleep(20) # sleep for 20 seconds 
print('The desired output is {}'.format(outputNumber))

So your child notebook will look something like this

Now that you have your child notebook setup, go to your parent notebook and paste the following code to import the multithreading packages

from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor

Then we define the function that will execute a child notebook passing in the number parameter

def processAnIntegerNumber(numberToProcess): = "/Shared/ChildNotebook",
                                        timeout_seconds = 300, 
                                        arguments = {"numberToProcess":numberToProcess})

Finally, the magic command, which will take a list of numbers, spin up a multithreading executor and then map the list of numbers to that executor. This command will execute the childnotebook instance for 5 times (because the list of numbers contains five numbers) in a parallel fashion.

listOfNumbers = [87,90,12,34,78]
with ThreadPoolExecutor() as executor:
  results =, listOfNumbers)

Executing the parent notebook, you will notice that 5 databricks jobs will run concurrently each one of these jobs will execute the child notebook with one of the numbers in the list. This is a snapshot of the parent notebook after execution

Notice how the overall time to execute the five jobs is about 40 seconds. Now, if we open the link to any of these jobs, we will notice that the time was also about 32 seconds indicating that jobs were run in parallel

Hope this blog helps you run your jobs faster and satisfies your “Need for Speed”.

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Mount a Blob Storage in Azure DataBricks Only if Not Mounted Already (Using Python)

As discussed in this article by Databricks that during your work in a notebook, you can mount a Blob Storage container or a folder inside a container to Databricks File System. The whole point of mounting to a blob storage container is simply to use an abbreviated link to your data using the databricks file system rather than having to refer to the whole URL to your blob container every time you need to read/write data from that blob container. More details on mounting and its usage, can be found in the articles referenced above.

The purpose of this article is to suggest a way to check if the mountpoint has been created already and only attempt to create it if it doesn’t exist using python.

This can simply be done if we knew how to list existing mountpoints using python. Luckily, databricks offers this to us using the dbutils.fs.mounts() command. To access the actual mountpoint we can do something like this:

for mount in dbutils.fs.mounts():
  print (mount.mountPoint)

Knowing how to access mountpoints enables us to write some Python syntax to only mount if the mountpoint doesn’t exist. The code should look like the following:

storageAccountName = "your storage account name"
storageAccountAccessKey = "your storage account access key"
blobContainerName = "your blob container name"
if not any(mount.mountPoint == '/mnt/FileStore/MountFolder/' for mount in dbutils.fs.mounts()):
    source = "wasbs://{}@{}".format(blobContainerName, storageAccountName),
    mount_point = "/mnt/FileStore/MountFolder/",
    extra_configs = {'' + storageAccountName + '': storageAccountAccessKey}
except Exception as e:
  print("already mounted. Try to unmount first")

Or, you can add an error handler to print an error message if the the blob is mounted already, as such:

storageAccountName = "your storage account name"
storageAccountAccessKey = "your storage account access key"
blobContainerName = "your blob container name"

  source = "wasbs://{}@{}".format(blobContainerName, storageAccountName),
  mount_point = "/mnt/FileStore/MountFolder/",
  extra_configs = {'' + storageAccountName + '': storageAccountAccessKey}
except Exception as e:
  print("already mounted. Try to unmount first")

Read a CSV file stored in blob container using python in DataBricks

Le’ts say that you have a csv file, a blob container and access to a DataBricks workspace. The purpose of this mini blog is to show how easy is the process from having a file on your local computer to reading the data into databricks. I will go through the process of uploading the csv file manually to a an azure blob container and then read it in DataBricks using python code.

Step 1: Upload the file to your blob container

This can be done simply by navigating to your blob container. From there, you can click the upload button and select the file you are interested in. Once selected, you need to click the upload button that in the upload blade. See screenshot below.

Once uploaded, you will be able to see the file available in your blob container as shown below:

Step 2: Get credentials necessary for databricks to connect to your blob container

From your azure portal, you need to navigate to all resources then select your blob storage account and from under the settings select account keys. Once their, copy the key under Key1 to a local notepad.

Step 3: Configure DataBricks to read the file

Here, you need to navigate to your databricks work space (create one if you don’t have one already) and launch it. Once launched, go to workspace and create a new python notebook.

To start reading the data, first, you need to configure your spark session to use credentials for your blob container. This can simply be done through the spark.conf.set command. More precisely, we start with the following

storage_account_name = 'nameofyourstorageaccount' 
storage_account_access_key = 'thekeyfortheblobcontainer'
spark.conf.set('' + storage_account_name + '', storage_account_access_key)

Once done, we need to build the file path in the blob container and read the file as a spark dataframe.

blob_container = 'yourblobcontainername'
filePath = "wasbs://" + blob_container + "@" + storage_account_name + ""
salesDf ="csv").load(filePath, inferSchema = True, header = True)

And congrats, we are done. You can use the display command to have a sneak peak at our data as shown below.