> For the complete documentation index, see [llms.txt](https://documentation.immuta.com/2024.3/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.immuta.com/2024.3/integrations/databricks-spark/reference-guides/configuration-settings/cluster-policies/python-sql-r-lib-support.md).

# Python & SQL & R with Library Support

{% hint style="info" %}
**Py4j security disabled**: In addition to support for Python, SQL, and R, this configuration adds support for additional Python libraries and utilities by disabling Databricks-native Py4j security.
{% endhint %}

This configuration does not rely on Databricks-native Py4j security to secure the cluster, while process isolation is still enabled to secure filesystem and network access from within Python processes. On an Immuta-enabled cluster, once Py4J security is disabled the Immuta SecurityManager is installed to prevent nefarious actions from Python in the JVM. Disabling Py4J security also allows for expanded Python library support, including many Python ML classes (such as `LogisticRegression`, `StringIndexer`, and `DecisionTreeClassifier`) and dbutils.fs.

By default, all actions in R will execute as the root user. Among other things, this permits access to the entire filesystem (including sensitive configuration data). And without iptable restrictions, a user may freely access the cluster’s cloud storage credentials. To properly support the use of the R language, Immuta’s initialization script wraps the R and Rscript binaries to launch each command as a temporary, non-privileged user. This user has limited filesystem and network access. The Immuta SecurityManager is also installed to prevent users from bypassing policies and protects against the above vulnerabilities from within the JVM.

The SecurityManager will incur a small increase in performance overhead; average latency will vary depending on whether the cluster is homogeneous or heterogeneous. (In homogeneous clusters, all users are at the same level of groups/authorizations; this is enforced externally, rather than directly by Immuta.)

When users install third-party Java/Scala libraries, they will be denied access to sensitive resources by default. However, cluster administrators can specify which of the installed Databricks libraries should be [trusted](/2024.3/integrations/databricks-spark/reference-guides/databricks-libraries.md#databricks-trusted-libraries) by Immuta.

A homogeneous cluster is recommended for configurations where Py4J security is disabled. If all users have the same level of authorization, there would not be any data leakage, even if a nefarious action was taken.

For full details on Databricks’ best practices in configuring clusters, read their [governance documentation](https://docs.databricks.com/security/data-governance.html).


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