In Azure, I have a virtual network (vNET
) with following settings:
Address space: 10.200.0.0/20
subnet: 10.200.0.0/24
Inside the above vNET, I am trying to deploy an Azure Databricks
with the following Network
settings:
Public Subnet CIDR Range: 10.200.15.0/20
Private Subnet CIDR Range: 10.200.15.1/24
But on the Private Subnet CIDR Range
above I get the following error:
Public and private subnet ranges must be valid and non-conflicting
Question: What I may be doing wrong, and how can I resolve the above error?
Remarks:
- I have tried various variations of
10.200.15.1/24
(e.g. 10.200.15.0/24, 10.200.15.255/24 etc.) but I keep getting the same error. I am sure there must be a correctPrivate Subnet CIDR Range
that I am not using. - I noticed people have pointed out to some online tool such as the following, but I am not a networking expert, and I am not sure how exactly I can use these tools to get correct Private Subnet CIDR Range. CIRD Calculator, Subnet Calculator for IPV4, and IP Calculator.
UPDATE I’m following this tutorial from Azure team. When I tried the following settings, I get the error shown below:
2
Answers
The CIDR tool I like to use is https://www.ipaddressguide.com/cidr.
Your public subnet 10.200.15.0/20 has a starting IP of 10.200.0.0 and ends with 10.200.15.255.
Your private subnet 10.200.15.1/24 is not even valid. You can check this SO answer as to why that is.
Change the private subnet to 10.200.14.0/24. Keep the public subnet as is.
These are not overlapping and completely valid. 10.200.16.0/24 is outside the ip range of your vnet, so you can’t use that.
I tried to reproduce the same in my environment to create Azure Databricks Workspace with existing Vnet:
I have created Azure Databricks workspace with existing Virtual Network.
To resolve the issue, create different subnet range for both Public and Private CIDR, while you are creating the Azure Databricks workspace.
I created a virtual network, like below.
Created Azure Databricks workspace. like below.
Check the Azure Databricks IP address range, like below.
Go to Azure Databricks workspace > Select your Cluster > Select Spark UI > Executors
Refer the Document more about Azure Databricks workspace