Sparklyr

Audience: System Administrators

Content Summary: This page describes the sparklyr cluster policy.

Two cluster types can be configured with sparklyr: Single-User Clusters (recommended) and Multi-User Clusters (discouraged).

• Single-User Clusters: Credential Passthrough (required on Databricks) allows a single-user cluster to be created. This setting automatically configures the cluster to assume the role of the attached user when reading from storage (S3). Because Immuta requires that raw data is readable by the cluster, the instance profile associated with the cluster should be used rather than a role assigned to the attached user.

• Multi-User Clusters: Because Immuta cannot guarantee user isolation in a multi-user sparklyr cluster, it is not recommended to deploy a multi-user cluster. To force all users to act under the same set of attributes, groups, and purposes with respect to their data access and eliminate the risk of a data leak, all sparklyr multi-user clusters must be equalized either by convention (all users able to attach to the cluster have the same level of data access in Immuta) or by configuration (detailed below).

Single-User Cluster Configuration

1 - Enable sparklyr

In addition to the configuration for an Immuta cluster with R, add this environment variable to the Environment Variables section of the cluster:

IMMUTA_DATABRICKS_SPARKLYR_SUPPORT_ENABLED=true

This configuration makes changes to the iptables rules on the cluster to allow the sparklyr client to connect to the required ports on the JVM used by the sparklyr backend service.

2 - Set Up a sparklyr Connection in Databricks

1. Install and load libraries into a notebook. Databricks includes the stable version of sparklyr, so library(sparklyr) in an R notebook is sufficient, but you may opt to install the latest version of sparklyr from CRAN. Additionally, loading library(DBI) will allow you to execute SQL queries.

2. Set up a sparklyr connection:

   sc <- spark_connect(method = "databricks")
   

3. Pass the connection object to execute queries:

   dbGetQuery(sc, "show tables in immuta")
   

3 - Configure a Single-User Cluster

Add the following items to the Spark Config section of the cluster:

spark.databricks.passthrough.enabled true

spark.databricks.pyspark.trustedFilesystems com.databricks.s3a.S3AFileSystem,shaded.databricks.azurebfs.org.apache.hadoop.fs.azurebfs.SecureAzureBlobFileSystem,shaded.databricks.v20180920_b33d810.org.apache.hadoop.fs.azurebfs.SecureAzureBlobFileSystem,com.databricks.adl.AdlFileSystem,shaded.databricks.V2_1_4.com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem,shaded.databricks.org.apache.hadoop.fs.azure.NativeAzureFileSystem,shaded.databricks.org.apache.hadoop.fs.s3a.S3AFileSystem,org.apache.hadoop.fs.ImmutaSecureFileSystemWrapper

spark.hadoop.fs.s3a.aws.credentials.provider com.amazonaws.auth.InstanceProfileCredentialsProvider

The trustedFileSystems setting is required to allow Immuta’s wrapper FileSystem (used in conjunction with the ImmutaSecurityManager for data security purposes) to be used with credential passthrough. Additionally, the InstanceProfileCredentialsProvider must be configured to continue using the cluster’s instance profile for data access, rather than a role associated with the attached user.

Multi-User Cluster Configuration

The configurations in this section enable sparklyr, require project equalization, map sparklyr sessions to the correct Immuta user, and prevent users from accessing Immuta native workspaces.

1. Add the following environment variables to the Environment Variables section of your cluster configuration:

   IMMUTA_DATABRICKS_SPARKLYR_SUPPORT_ENABLED=true
   
   IMMUTA_SPARK_REQUIRE_EQUALIZATION=true
   
   IMMUTA_SPARK_CURRENT_USER_SCIM_FALLBACK=false
   

2. Add the following items to the Spark Config section:

   immuta.spark.acl.assume.not.privileged true
   
   immuta.api.key=<user’s API key>
   

Limitations

Immuta’s integration with sparklyr does not currently support

• spark-submit jobs,
• UDFs, or
• Databricks Runtimes 5, 6, or 7.