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Jupyter metakernel for apache spark and scala

Project description

# spylon-kernel
[![Build Status](](

A Scala [Jupyter kernel]( that uses [metakernel]( in combination with [py4j](

## Prerequisites

* Apache Spark 2.1.1 compiled for Scala 2.11
* Jupyter Notebook
* Python 3.5+

## Install

You can install the spylon-kernel package using `pip` or `conda`.

pip install spylon-kernel
# or
conda install -c conda-forge spylon-kernel

## Using it as a Scala Kernel

You can use spylon-kernel as Scala kernel for Jupyter Notebook. Do this when you want
to work with Spark in Scala with a bit of Python code mixed in.

Create a kernel spec for Jupyter notebook by running the following command:

python -m spylon_kernel install

Launch `jupyter notebook` and you should see a `spylon-kernel` as an option
in the *New* dropdown menu.

See [the basic example notebook](./examples/basic_example.ipynb) for information
about how to intiialize a Spark session and use it both in Scala and Python.

## Using it as an IPython Magic

You can also use spylon-kernel as a magic in an IPython notebook. Do this when
you want to mix a little bit of Scala into your primarily Python notebook.

from spylon_kernel import register_ipython_magics

val x = 8

## Using it as a Library

Finally, you can use spylon-kernel as a Python library. Do this when you
want to evaluate a string of Scala code in a Python script or shell.

from spylon_kernel import get_scala_interpreter

interp = get_scala_interpreter()

# Evaluate the result of a scala code block.
val x = 8


# Release Process

Push a tag and submit a source dist to PyPI.

git commit -m 'REL: 0.2.1' --allow-empty
git tag -a 0.2.1 # and enter the same message as the commit
git push origin master # or send a PR

# if everything builds / tests cleanly, release to pypi
make release

Then update

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