Databricks Spark Application Configuration

Audience: System Administrators

Content Summary: This page outlines configuration options for Immuta-enabled Databricks clusters. Databricks Administrators should place desired configuration in the immuta_conf.xml file.

Spark Application Configuration

• immuta.spark.acl.enabled

  • Default: true

  • Description: Immuta Access Control List (ACL). Controls whether Databricks users are blocked from accessing non-Immuta tables. Ignored if Databricks Table ACLs are enabled (i.e., spark.databricks.acl.dfAclsEnabled=true).

• immuta.spark.acl.whitelist

  • Description: Comma-separated list of Databricks usernames who may access raw tables when the Immuta ACL is in use.

• immuta.spark.acl.privileged.timeout.seconds

  • Default: 3600

  • Description: The number of seconds to cache privileged user status for the Immuta ACL. A privileged Databricks user is an admin or is whitelisted in immuta.spark.acl.whitelist.

• immuta.spark.acl.assume.not.privileged

  • Default: false

  • Description: Session property that overrides privileged user status when the Immuta ACL is in use. This should only be used in R scripts associated with spark-submit jobs.

• immuta.spark.resolve.raw.tables.enabled

  • Default: true

  • Description: Enables use of the underlying database and table name in queries against a table-backed Immuta data source. Note that this property is not set in immuta_conf.xml. Administrators or whitelisted users can set immuta.spark.session.resolve.raw.tables.enabled to false to bypass resolving raw databases or tables as Immuta data sources. This is useful if an admin wants to read raw data but is also an Immuta user. By default, data policies will be applied to a table even for an administrative user if that admin is also an Immuta user; however if they run set immuta.spark.session.resolve.raw.tables.enabled=false then they will see raw data only (not Immuta data policy-enforced data).

• immuta.spark.session.resolve.raw.tables.enabled

  • Default: true

  • Description: Same as above, but a session property that allows users to toggle this functionality. Ignored if immuta.spark.resolve.raw.tables.enabled=false.

• immuta.spark.databricks.local.scratch.dir.enabled

  • Default: true

  • Description: Creates a world-readable/writable scratch directory on local disk to facilitate the use of dbutils and 3rd party libraries that may write to local disk. Its location is non-configurable and is stored in the environment variable IMMUTA_LOCAL_SCRATCH_DIR. Note: Sensitive data should not be stored at this location.

• immuta.spark.databricks.py4j.strict.enabled

  • Default: true

  • Description: Disable to allow the use of the dbutils API in Python. Note: This setting should only be disabled for customers who employ a homogeneous access pattern (i.e., all users have the same level of data access).

• immuta.spark.databricks.scratch.paths

  • Description: Comma-separated list of remote paths that Databricks users are allowed to directly read/write. These paths amount to unprotected "scratch spaces." You can create a scratch database by configuring its specified location (or configure dbfs:/user/hive/warehouse/<db_name>.db for the default location).

    To create a scratch path to a location or a database stored at that location, configure

    <property>
        <name>immuta.spark.databricks.scratch.paths</name>
        <value>s3://path/to/the/dir</value>
    </property>
    

    To create a scratch path to a database created using the default location,

    <property>
        <name>immuta.spark.databricks.scratch.paths</name>
        <value>s3://path/to/the/dir, dbfs:/user/hive/warehouse/any_db_name.db</value>
    </property>
    

• immuta.spark.databricks.scratch.paths.create.db.enabled

  • Default: false

  • Description: Enables non-privileged users to create or drop scratch databases.

• immuta.spark.databricks.filesystem.blacklist

  • Default: hdfs

  • Description: A list of filesystem protocols that this instance of Immuta will not support for workspaces. This is useful in cases where a filesystem is available to a cluster but should not be used on that cluster.

• immuta.spark.acl.workspace.enabled

  • Default: true

  • Description: Enables enforcement of workspace operations in Databricks.

• immuta.spark.require.equalization

  • Default: false

  • Description: Requires that users act through a single, equalized project. A cluster should be equalized if users need to run Scala jobs on it, and it should be limited to Scala jobs only via spark.databricks.repl.allowedLanguages.

• immuta.user.context.class

  • Default: com.immuta.spark.OSUserContext

  • Description: The class name of the UserContext that will be used to determine the current user in immuta-spark-hive. The default implementation gets the OS user running the JVM for the Spark application.

• immuta.spark.databricks.jar.uri

  • Default: file:///databricks/jars/immuta-spark-hive.jar

  • Description: The location of immuta-spark-hive.jar on the filesystem for Databricks. This should not need to change unless a customer needs a custom initialization script that places immuta-spark-hive in a non-standard location.

• immuta.spark.databricks.submit.tag.job

  • Default: true

  • Description: Denotes whether the Spark job will be run that "tags" a Databricks cluster as being associated with Immuta.

• immuta.spark.databricks.dbfs.mount.enabled

  • Default: false

  • Description: Exposes the DBFS FUSE mount located at /dbfs. Granular permissions are not possible, so all users will have read/write access to all objects therein. Note: Raw, unfiltered source data should never be stored in DBFS.

• immuta.user.mapping.iamid

  • Default: bim

  • Description: Denotes which IAM in Immuta should be used when mapping the current Spark user's username to a userid in Immuta. This defaults to bim but should be updated to reflect an actual production IAM.

Accessing DBFS in Databricks

To allow general access to remote storage locations (e.g., S3) for non-sensitive data, opt to enable DBFS FUSE Mount or DBUtils with scratch paths.

1 - DBFS FUSE Mount

To enable the DBFS FUSE mount, set this configuration: immuta.spark.databricks.dbfs.mount.enabled=true.

This feature (provided by Databricks) mounts DBFS to the local cluster filesystem at /dbfs. Although disabled when using process isolation, this feature can safely be enabled if raw, unfiltered data is not stored in DBFS and all users on the cluster are authorized to see each other’s files. When enabled, the entirety of DBFS essentially becomes a scratch path where users can read and write files in /dfbs/path/to/my/file as though they were local files.

For example,

%sh echo "I'm creating a new file in DBFS" > /dbfs/my/newfile.txt

In Python,

%python
with open("/dbfs/my/newfile.txt", "w") as f:
  f.write("I'm creating a new file in DBFS")

Note: This solution also works in R and Scala.

2 - Scala DBUtils (and %fs magic) with Scratch Paths

To support %fs magic and Scala DBUtils with scratch paths, configure

shell <property> <name>immuta.spark.databricks.scratch.paths</name> <value>s3://my-bucket/my/scratch/path</value> </property>

Scratch paths will work when performing arbitrary remote filesystem operations with fs magic or Scala dbutils.fs functions. For example,

%fs put -f s3://my-bucket/my/scratch/path/mynewfile.txt "I'm creating a new file in S3"
%scala dbutils.fs.put("s3://my-bucket/my/scratch/path/mynewfile.txt", "I'm creating a new file in S3")

DBUtils in Python

To use dbutils in Python, set this configuration: immuta.spark.databricks.py4j.strict.enabled=false.

Example Workflow

This section illustrates the workflow for getting a file from a remote scratch path, editing it locally with Python, and writing it back to a remote scratch path.

%python
import os
import shutil

s3ScratchFile = "s3://some-bucket/path/to/scratch/file"
localScratchDir = os.environ['IMMUTA_LOCAL_SCRATCH_DIR']
localScratchFile = localScratchDir + "/myfile.txt"
localScratchFileCopy = localScratchDir + "/myfile_copy.txt"

1. Get the file from remote storage:

   dbutils.fs.cp(s3ScratchFile, "file://" + localScratchFile)`
   

2. Make a copy if want to explicitly edit localScratchFile, as it will be read-only and owned by root:

   shutil.copy(localScratchFile, localScratchFileCopy)
   with open(localScratchFileCopy, "a") as f:
       f.write("Some appended file content")
   

3. Write the new file back to remote storage:

   dbutils.fs.cp("file://" + localScratchFileCopy, s3ScratchFile)