Custom Spark Extension in SQL Warehouse

[naveenanto]

I understand only a limited spark configurations are supported in SQL Warehouse but is it possible to add spark extensions to SQL Warehouse clusters?

Use Case: We've a few restricted table properties. We prevent that with spark extensions installed in clusters as part of init script. Is it possible to achieve the same in SQL Warehouse?

[Kaniz]

Hi @naveenanto, While SQL Data Warehouse (now known as Azure Synapse Analytics) has some limitations when it comes to Spark configurations, you can indeed extend its capabilities by adding custom Spark extensions.

Let me provide you with some information on how you can achieve this:

1. Custom Spark Extensions in SQL Warehouse:

   • SQL Warehouse allows you to add custom Spark extensions to enhance its functionality. These extensions can be used to modify Spark behaviour, add new features, or customize existing ones.
   • To add a custom Spark extension, you’ll need to follow these steps:
     • Create a JAR File: First, create a JAR file containing your custom Spark extension code.
     • Upload the JAR File: Upload the JAR file to a location accessible by your SQL Warehouse cluster.
     • Configure Spark: Configure your SQL Warehouse cluster to use the custom extension by specifying the JAR file path in the Spark configuration.
     • Restart the Cluster: Restart the cluster to apply the changes.

2. Example: Using Apache Iceberg as a Spark Extension:

   • One common use case is integrating Apache Iceberg with Spark. Iceberg is an open source table format that provides features like schema evolution, time travel, and ACID transactions.
   • You can add Iceberg as a Spark extension in SQL Warehouse by setting the following Spark configuration properties:
     • spark.sql.extensions: Specify the class name of the Iceberg Spark extension.
     • spark.sql.catalog.glue_catalog: Set this to org.apache.iceberg.spark.SparkCatalog.
     • spark.sql.catalog.glue_catalog.warehouse: Specify the location of your Iceberg tables (e.g., an S3 path).
   • Here’s an example command to start Spark with Iceberg support:

     spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.5.1 \
       --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \
       --conf spark.sql.catalog.glue_catalog=org.apache.iceberg.spark.SparkCatalog \
       --conf spark.sql.catalog.glue_catalog.warehouse=s3://<your-warehouse-dir>/
     

3. Additional Resources:

   • If you’d like to explore more about using Spark extensions with SQL Warehouse, you can refer to the following resources:
     • Custom Spark Extension in SQL Warehouse (Databricks community forum)
     • Spark and Iceberg Quickstart (Apache Iceberg documentation)

Feel free to explore these resources and adapt them to your specific use case! 😊¹²

Let me know if you need further assistance or have any other questions! 🚀