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Delta Lake on SQL Server Big Data Clusters

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Applies to: SQL Server 2019 (15.x)

Important

The Microsoft SQL Server 2019 Big Data Clusters add-on will be retired. Support for SQL Server 2019 Big Data Clusters will end on February 28, 2025. All existing users of SQL Server 2019 with Software Assurance will be fully supported on the platform and the software will continue to be maintained through SQL Server cumulative updates until that time. For more information, see the announcement blog post and Big data options on the Microsoft SQL Server platform.

In this guide, you'll learn:

  • The requisites and capabilities of Delta Lake on SQL Server Big Data Clusters.
  • How to load Delta Lake libraries on CU12 clusters to use with Spark 2.4 sessions and jobs.

Introduction

Linux Foundation Delta Lake is an open-source storage layer that brings ACID (atomicity, consistency, isolation, and durability) transactions to Apache Spark and big data workloads. To learn more about Delta Lake, see:

Delta Lake on SQL Server Big Data Clusters CU13 and above (Spark 3)

Delta Lake is installed and configured by default on SQL Server Big Data Clusters CU13 and above. No further action is required.

This article covers configuration of Delta Lake on SQL Server Big Data Clusters CU12 and below.

Configure Delta Lake on SQL Server Big Data Clusters CU12 and below (Spark 2.4)

On SQL Server Big Data Clusters CU12 or below, it is possible to load Delta Lake libraries using the Spark library management feature.

Note

As a general rule, use the most recent compatible library. The code in this guide was tested by using Delta Lake 0.6.1 on SQL Server Big Data Clusters CU12. Delta Lake 0.6.1 is compatible with Apache Spark 2.4.x, later versions are not. The examples are provided as-is, not a supportability statement.

Configure Delta Lake library and Spark configuration options

Set up your Delta Lake libraries with your application before you submit the jobs. The following library is required:

  • delta-core - This core library enables Delta Lake support.

The library must target Scala 2.11 and Spark 2.4.7. This SQL Server Big Data Clusters requirement is for SQL Server 2019 Cumulative Update 9 (CU9) or later.

It's also required to configure Spark to enable Delta Lake-specific Spark SQL commands and the metastore integration. The example below is how an Azure Data Studio notebook would configure Delta Lake support:

%%configure -f \
{
    "conf": {
        "spark.jars.packages": "io.delta:delta-core_2.11:0.6.1",
        "spark.sql.extensions":"io.delta.sql.DeltaSparkSessionExtension",
        "spark.sql.catalog.spark_catalog":"org.apache.spark.sql.delta.catalog.DeltaCatalog"
    }
}

Share library locations for jobs on HDFS

If multiple applications will use the Delta Lake library, copy the appropriate library JAR files to a shared location on HDFS. Then all jobs should reference the same library files.

Copy the libraries to the common location:

azdata bdc hdfs cp --from-path delta-core_2.11-0.6.1.jar --to-path "hdfs:/apps/jars/delta-core_2.11-0.6.1.jar"

Dynamically install the libraries

You can dynamically install packages when you submit a job by using the package management features of Big Data Clusters. There's a job startup time penalty because of the recurrent downloads of the library files on each job submission.

Submit the Spark job by using azdata

The following example uses the shared library JAR files on HDFS:

azdata bdc spark batch create -f hdfs:/apps/ETL-Pipelines/my-delta-lake-python-job.py \
-j '["/apps/jars/delta-core_2.11-0.6.1.jar"]' \
--config '{"spark.sql.extensions":"io.delta.sql.DeltaSparkSessionExtension","spark.sql.catalog.spark_catalog":"org.apache.spark.sql.delta.catalog.DeltaCatalog"}' \
-n MyETLPipelinePySpark --executor-count 2 --executor-cores 2 --executor-memory 1664m

This example uses dynamic package management to install the dependencies:

azdata bdc spark batch create -f hdfs:/apps/ETL-Pipelines/my-delta-lake-python-job.py \
--config '{"spark.jars.packages":"io.delta:delta-core_2.11:0.6.1","spark.sql.extensions":"io.delta.sql.DeltaSparkSessionExtension","spark.sql.catalog.spark_catalog":"org.apache.spark.sql.delta.catalog.DeltaCatalog"' \
-n MyETLPipelinePySpark --executor-count 2 --executor-cores 2 --executor-memory 1664m

Next steps

To learn how to effectively use Delta Lake, see the following articles.

To submit Spark jobs to SQL Server Big Data Clusters by using azdata or Livy endpoints, see Submit Spark jobs by using command-line tools.

For more information about SQL Server Big Data Clusters and related scenarios, see Introducing SQL Server Big Data Clusters.