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Jupyter metakernel for apache spark and scala

Project description

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This is an extremely early proof of concept for using the metakernel in combination with py4j to make a simpler kernel for scala.

Installation

On python 3.5+

pip install .

Installing the jupyter kernel

To install the jupyter kernel install it using

python -m spylon_kernel install

Using the kernel

The scala spark metakernl prodived a scala kernel by default. At the first scala cell that is run 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

%%init_spark
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 ernvironment.

%%python
df = spark.read.json("examples/src/main/resources/people.json")

To get completions for python, make sure that you have installed jedi

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
register_ipython_magics()
%%scala
val x = 8
x

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.
interp.interpret("""
    val x = 8
    x
    """)

interp.last_result()

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