Azure Synapse Analytics - Connect to Serverless SQL Pool via Spark using Managed Identity

@Graeme Cash

Thanks for reaching out to Microsoft Q&A.

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While it's not directly possible to connect to the serverless SQL pool via Spark in Synapse using managed identity with pyodbc, here are alternative approaches and troubleshooting steps:

1. Use the Azure Synapse Dedicated SQL Pool Connector for Apache Spark:

• This connector is designed for secure and efficient data transfer between Spark and Dedicated SQL Pools.
• It also supports managed identity authentication, eliminating the need for passwords or connection strings.
• Although primarily for Dedicated SQL Pools, it can often be used with serverless SQL pools as well.

Steps:

1. Install the connector: pip install synapse-dedicated-sqlpool-connector
2. Configure the Spark session with managed identity:

Python

spark.conf.set("spark.sql.connector.synapse.sql.option.enableSystemTokenAuth", "true")

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1. Create a DataFrame using the connector:

Python

df = spark.read.format("com.microsoft.spark.sqlanalytics.connector.SynapseSQLPoolConnector").option("url", server).option("database", database).load()

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2. Use mssparkutils for token-based authentication:

• Retrieve an access token using mssparkutils.credentials.getConnectionStringOrCreds.
• Construct the connection string with the token.

Steps:

1. Retrieve the connection string:

Python

conn_str = mssparkutils.credentials.getConnectionStringOrCreds(linked_service="<your-linked-service-name>")

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1. Connect using pyodbc with the token:

Python

cnxn = pyodbc.connect(conn_str)

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3. Troubleshooting the timeout error:

• Network connectivity: Ensure stable network connectivity between Spark and the serverless SQL pool.
• Firewall rules: Verify that firewall rules allow traffic from Spark to the serverless SQL pool.
• Managed identity permissions: Confirm that the managed identity has the necessary permissions to access the serverless SQL pool.
• Driver version: Consider trying different ODBC driver versions.
• Timeout settings: Adjust timeout settings in the connection string if needed.
• If using a private endpoint for the serverless SQL pool, ensure Spark is configured to use the private endpoint.
• For complex scenarios, consider using Azure Data Factory, which supports managed identity connections to serverless SQL pools.

Hope this helps. Do let us know if you any further queries.

@Graeme Cash We haven’t heard from you on the last response and was just checking back to see if you have a resolution yet. In case if you have any resolution please do share that same with the community as it can be helpful to others. Otherwise, will respond with more details and we will try to help.