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apache / incubator-toree / v0.1.5-assembly
commitecfa5dd2abe8f992e54b12f4c5e411855b72e898[log] [tgz]
authorChip Senkbeil <chip.senkbeil@gmail.com>Wed Nov 25 11:26:18 2015 -0600
committerChip Senkbeil <chip.senkbeil@gmail.com>Wed Nov 25 11:26:18 2015 -0600
tree7cf9bb2ec54d41bb32d76ddf86da8ec9f36a95d1
parent6f46c20fbfd4a7eb7e9fe4dd559e6d404bdca4e4 [diff]
parentbc834e873d9668d8060b43bd871bd861368e3a54 [diff]
Merge pull request #196 from ibm-et/SPARK_HOME_REQUIRED

Require SPARK-HOME to run and do not pack spark dependencies
tree: 7cf9bb2ec54d41bb32d76ddf86da8ec9f36a95d1
  1. client/
  2. communication/
  3. etc/
  4. kernel/
  5. kernel-api/
  6. macros/
  7. project/
  8. protocol/
  9. pyspark-interpreter/
  10. resources/
  11. scala-interpreter/
  12. sparkr-interpreter/
  13. sql-interpreter/
  14. src/
  15. .gitignore
  16. .pairs
  17. .travis.yml
  18. Dockerfile
  19. LICENSE
  20. Makefile
  21. NOTICE
  22. README.md
  23. Vagrantfile
README.md

Build Status License Join the chat at https://gitter.im/ibm-et/spark-kernel

Spark Kernel

The main goal of the Spark Kernel is to provide the foundation for interactive applications to connect to and use Apache Spark.

Overview

The Spark Kernel provides an interface that allows clients to interact with a Spark Cluster. Clients can send libraries and snippets of code that are interpreted and ran against 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. ...

The kernel's main supported language is Scala, but it is also capable of processing both Python and R. It implements the latest Jupyter message protocol (5.0), so it can easily plug into the 3.x branch of Jupyter/IPython for quick, interactive data exploration.

Try It

A version of the Spark Kernel is deployed as part of the Try Jupyter! site. Select Scala 2.10.4 (Spark 1.4.1) under the New dropdown. Note that this version only supports Scala.

Develop

Vagrant is used to simplify the development experience. It is the only requirement to be able to build, package and test the Spark Kernel on your development machine.

To build and interact with the Spark Kernel using Jupyter, run

make dev

This will start a Jupyter notebook server accessible at http://192.168.44.44:8888. From here you can create notebooks that use the Spark Kernel configured for local mode.

Tests can be run by doing make test.

Build & Package

To build and package up the Spark Kernel, run

make dist

The resulting package of the kernel will be located at ./dist/spark-kernel-<VERSION>.tar.gz. The uncompressed package is what is used is ran by Jupyter when doing make dev.

Version

Our goal is to keep master up to date with the latest version of Spark. When new versions of Spark require code changes, we create a separate branch. The table below shows what is available now.

BranchSpark Kernel VersionApache Spark Version
master0.1.51.5.1
branch-0.1.40.1.41.4.1
branch-0.1.30.1.31.3.1

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

Resources

There is more detailed information available in our Wiki and our Getting Started guide.