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Jupyter metakernel for apache spark and scala

Project description

# spylon-kernel
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This is a beta level concept for using metakernel in combination with py4j to make a simpler kernel for scala.

## Installation

On python 3.5+

:exclamation: Due to [SPARK-19019]( Apache Spark does not current work
in Python 3.6

pip install .

For runtime purposes you need to have Apache Spark installed. Minimum required is Apache Spark 2.0 compiled for Scala 2.11.
See [examples](./examples/basic_example.ipynb) for how to configure your spark instance.

## Installing the jupyter kernel

python -m spylon_kernel install

## Using the kernel

The scala spark metakernel provides a scala kernel by default. On the first execution of scala code, a spark session
will be constructed so that a user can interact with the interpreter.

### Customizing the spark context

The launch arguments can be customized using the `%%init_spark` magic as follows

launcher.jars = ["file://some/jar.jar"]
launcher.master = "local[4]"
launcher.conf.spark.executor.cores = 8

### Other languages

Since this makes use of metakernel you can evaluate normal python code using the `%%python` magic. In addition once
the spark context has been created the `spark` variable will be added to your python environment.

df ="examples/src/main/resources/people.json")

## Using as a magic

Spylon-kernel can be used as a magic in an existing ipykernel. This is the recommended solution when you want to write
relatively small blocks of scala.

from spylon_kernel import register_ipython_magics

val x = 8

## Using as a library

If you just want to send a string of scala code to the interpreter and evaluate it you can
do that too.

from spylon_kernel import get_scala_interpreter

interp = get_scala_interpreter()

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


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