Mirror of Apache Toree (Incubating)

Clone this repo:
  1. 413917c Fix typo in github workflow name by Kevin Bates · 8 weeks ago master
  2. 6ee5600 Remove cap on traitlets version by Kevin Bates · 8 weeks ago
  3. 2c174aa [TOREE-527] Enable GitHub Actions for CI builds by Luciano Resende · 3 months ago
  4. 833dcfb Update README.md (#191) by Sunbuhui · 10 weeks ago
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Apache Toree

Apache Toree is a Juypter Notebook kernel. The main goal of Toree is to provide the foundation for interactive applications that connect to and use Apache Spark using Scala language.


Toree provides an interface that allows clients to interact with a Spark Cluster. Clients can send libraries and snippets of code that are interpreted and executed using a preconfigured Spark context. These snippets can do a variety of things:

  1. Define and run spark jobs of all kinds
  2. Collect results from spark and push them to the client
  3. Load necessary dependencies for the running code
  4. Start and monitor a stream
  5. ...

Apache Toree supports the Scala programming language. It implements the latest Jupyter message protocol (5.0), so it can easily plug into the latest releases of Jupyter/IPython (3.2.x+ and up) for quick, interactive data exploration.


This project uses make as the entry point for build, test, and packaging. It supports 2 modes, local and vagrant. The default is local and all commands (i.e. sbt) will be run locally on your machine. This means that you need to install sbt, jupyter/ipython, and other development requirements locally on your machine. The 2nd mode uses Vagrant to simplify the development experience. In vagrant mode, all commands are sent to the vagrant box that has all necessary dependencies pre-installed. To run in vagrant mode, run export USE_VAGRANT=true.

To build and interact with Toree using Jupyter, run

make dev

This will start a Jupyter notebook server. Depending on your mode, it will be accessible at http://localhost:8888 or From here you can create notebooks that use Toree configured for Spark local mode.

Tests can be run by doing make test.

NOTE: Do not use sbt directly.

Build & Package

To build and package up Toree, run

make release

This results in 2 packages.

  • ./dist/toree-<VERSION>-binary-release.tar.gz is a simple package that contains JAR and executable
  • ./dist/toree-<VERSION>.tar.gz is a pip installable package that adds Toree as a Jupyter kernel.

NOTE: make release uses docker. Please refer to docker installation instructions for your system. USE_VAGRANT is not supported by this make target.

Run Examples

To play with the example notebooks, run

make jupyter

A notebook server will be launched in a Docker container with Toree and some other dependencies installed. Refer to your Docker setup for the ip address. The notebook will be at http://<ip>:8888/.


This requires you to have a distribution of Apache Spark downloaded to the system where Apache Toree will run. The following commands will install Apache Toree.

pip install --upgrade toree
jupyter toree install --spark_home=<YOUR_SPARK_PATH>

Dev snapshots of Toree are located at https://dist.apache.org/repos/dist/dev/incubator/toree. To install using one of those packages, you can use the following:

pip install <PIP_RELEASE_URL>
jupyter toree install --spark_home=<YOUR_SPARK_PATH>

where PIP_RELEASE_URL is one of the pip packages. For example:

pip install https://dist.apache.org/repos/dist/dev/incubator/toree/0.2.0/snapshots/dev1/toree-pip/toree-0.2.0.dev1.tar.gz
jupyter toree install --spark_home=<YOUR_SPARK_PATH>

Reporting Issues

Refer to and open issue here


You can reach us through gitter or our mailing list


We are working on publishing binary releases of Toree soon. As part of our move into Apache Incubator, Toree will start a new version sequence starting at 0.1.

Our goal is to keep master up to date with the latest version of Spark. When new versions of Spark require specific code changes to Toree, we will branch out older Spark version support.

As it stands, we maintain several branches for legacy versions of Spark. The table below shows what is available now.

BranchApache Spark Version

Please note that for the most part, new features will mainly be added to the master branch.


We are currently enhancing our documentation, which is available in our website.