Manage Apache Spark libraries in Microsoft Fabric

A library is a collection of prewritten code that developers can import to provide functionality. By using libraries, you can save time and effort by not having to write code from scratch to do common tasks. Instead, import the library and use its functions and classes to achieve the desired functionality. Microsoft Fabric provides multiple mechanisms to help you manage and use libraries.

  • Built-in libraries: Each Fabric Spark runtime provides a rich set of popular preinstalled libraries. You can find the full built-in library list in Fabric Spark Runtime.
  • Public libraries: Public libraries are sourced from repositories such as PyPI and Conda, which are currently supported.
  • Custom libraries: Custom libraries refer to code that you or your organization build. Fabric supports them in the .whl, .jar, and .tar.gz formats. Fabric supports .tar.gz only for the R language. For Python custom libraries, use the .whl format.

Library management in workspace setting


Library management at the workspace setting is no longer supported. To migrate workspace libraries and Spark properties to a default environment, see Migrate the workspace libraries and Spark properties.

Summary of library management best practices

The following scenarios describe best practices.

Scenario 1: Admin sets default libraries for the workspace

To set default libraries, you have to be the administrator of the workspace. As admin, you can perform these tasks:

  1. Create a new environment
  2. Install the required libraries in the environment
  3. Attach this environment as the workspace default

The notebooks and Spark job definitions in the workspace are attached to the Workspace settings. They start sessions with the libraries installed in the workspace's default environment.

Scenario 2: Persist library specifications for one or multiple code items

If you want to persist the library specifications, install the libraries in an environment and attach it to the code items.

One benefit of this approach is that it saves effort of running the code that requires common libraries all the time. Once successfully installed in the environment, the libraries are effective in all Spark sessions if the environment is attached.

Another benefit is that the approach supports library configuration granularity lower than the workspace level. One environment can be attached to multiple code artifacts. If you have a subset of notebooks or Spark job definitions in one workspace that require the same libraries, attach them to the same environment. An administrator, member, or contributor of the workspace can create, edit, and attach the environment.

Scenario 3: Inline installation in interactive run

If you're interested in the one-time use, within an interactive notebook, of a library that isn't installed, inline installation is the most convenient option. Inline commands in Fabric allow you to have the library effective in the current notebook Spark session. The library doesn't persist across different sessions.

Users who have the permission to run the notebook can install other libraries in the Spark session.

Summary of supported library types

Library type Environment library management Inline installation
Python Public (PyPI & Conda) Supported Supported
Python Custom (.whl) Supported Supported
R Public (CRAN) Not supported Supported
R custom (.tar.gz) Supported Supported
Jar Supported as custom library Not supported


There are currently limitations on the .jar library.

  • For Scala users, the .jar file can install successfully in an environment but it isn't effective for your Spark/Scala sessions. The installation overrides the built-in library with a different library. The new .jar works in the sessions.
  • For Python users, all .jar files are currently not supported in an environment. They can install successfully in an environment but aren't effective in PySpark sessions.
  • You can install the .jar files at the notebook session level instead.

Inline installation

Inline commands support Python libraries and R libraries.

Python inline installation

The Python interpreter restarts to apply the change of libraries. Any variables defined before you run the command cell are lost. We strongly recommend that you put all the commands for adding, deleting, or updating Python packages at the beginning of your notebook.

The inline commands for managing Python libraries are disabled in notebook pipeline run by default. If you want to enable %pip install for pipeline, add "_inlineInstallationEnabled" as bool parameter equals True in the notebook activity parameters.

Screenshot showing the the configuration of enabling pip install for notebook pipeline run.


The %pip install may lead to inconsistent results from time to time. It's recommended to install library in an environment and use it in the pipeline.

We recommend %pip instead of !pip. !pip is an IPython built-in shell command, which has the following limitations:

  • !pip only installs a package on the driver node, not executor nodes.
  • Packages that install through !pip don't affect conflicts with built-in packages or whether packages are already imported in a notebook.

However, %pip handles these scenarios. Libraries installed through %pip are available on both driver and executor nodes and are still effective even the library is already imported.


The %conda install command usually takes longer than the %pip install command to install new Python libraries. It checks the full dependencies and resolves conflicts.

You might want to use %conda install for more reliability and stability. You can use %pip install if you are sure that the library you want to install doesn't conflict with the preinstalled libraries in the runtime environment.

For all available Python inline commands and clarifications, see %pip commands and %conda commands.

Manage Python public libraries through inline installation

In this example, see how to use inline commands to manage libraries. Suppose you want to use altair, a powerful visualization library for Python, for a one-time data exploration. Suppose the library isn't installed in your workspace. The following example uses conda commands to illustrate the steps.

You can use inline commands to enable altair on your notebook session without affecting other sessions of the notebook or other items.

  1. Run the following commands in a notebook code cell. The first command installs the altair library. Also, install vega_datasets, which contains a semantic model you can use to visualize.

    %conda install altair          # install latest version through conda command
    %conda install vega_datasets   # install latest version through conda command

    The output of the cell indicates the result of the installation.

  2. Import the package and semantic model by running the following code in another notebook cell.

    import altair as alt
    from vega_datasets import data
  3. Now you can play around with the session-scoped altair library.

    # load a simple dataset as a pandas DataFrame
    cars =

Manage Python custom libraries through inline installation

You can upload your Python custom libraries to the File folder of the lakehouse attached to your notebook. Go to your lakehouse, select the icon on the File folder, and upload the custom library.

After your upload, use the following command to install the custom library to your notebook session.

# install the .whl through pip command
%pip install /lakehouse/default/Files/wheel_file_name.whl             

R inline installation

To manage R libraries, Fabric supports the install.packages(), remove.packages(), and devtools:: commands. For all available R inline commands and clarifications, see install.packages command and remove.package command.

Manage R public libraries through inline installation

Follow this example to walk through the steps of installing an R public library.

To install an R feed library:

  1. Switch the working language to SparkR (R) in the notebook ribbon.

  2. Install the caesar library by running the following command in a notebook cell.

  3. Now you can play around with the session-scoped caesar library with a Spark job.

    hello <- function(x) {
    spark.lapply(c("hello world", "good morning", "good evening"), hello)

Manage Jar libraries through inline installation

The .jar files are support at notebook sessions with following command.

%%configure -f
    "conf": {
        "spark.jars": "abfss://<<Lakehouse prefix>><<path to JAR file>>/<<JAR file name>>.jar",

The code cell is using Lakehouse's storage as an example. At the notebook explorer, you can copy the full file ABFS path and replace in the code. Screenshot of get the ABFS path.