Sparkling Water integrates H₂O's fast scalable machine learning engine with Spark. It provides:

• Utilities to publish Spark data structures (RDDs, DataFrames) as H2O's frames and vice versa.
• DSL to use Spark data structures as input for H2O's algorithms
• Basic building blocks to create ML applications utilizing Spark and H2O APIs
• Python interface enabling use of Sparkling Water directly from pySpark

The Sparkling Water is developed in multiple parallel branches. Each branch corresponds to a Spark major release (e.g., branch rel-1.5 provides implementation of Sparkling Water for Spark 1.5).

Please, switch to the right branch:

• For Spark 1.6 use branch rel-1.6
• For Spark 1.5 use branch rel-1.5
• For Spark 1.4 use branch rel-1.4
• For Spark 1.3 use branch rel-1.3

Note: The master branch includes the latest changes for the latest Spark version. They are back-ported into older Sparkling Water versions.

• Linux/OS X/Windows
• Java 7+
• Spark 1.3+
  • SPARK_HOME shell variable must point to your local Spark installation

Download Spark installation and point environment variable SPARK_HOME to it. Then use the provided gradlew to build project:

./gradlew build

To avoid running tests, use the -x test -x integTest option.

For each Sparkling Water you can download binaries here:

• Sparkling Water - Latest version
• Sparkling Water - Latest 1.6 version
• Sparkling Water - Latest 1.5 version
• Sparkling Water - Latest 1.4 version
• Sparkling Water - Latest 1.3 version

Each Sparkling Water release is published into Maven central. Right now we publish artifacts only for Scala 2.10.

The artifacts coordinates are:

• ai.h2o:sparkling-water-core_2.10:{{version}} - includes core of Sparkling Water.
• ai.h2o:sparkling-water-examples_2.10:{{version}} - includes example applications.

Note: The {{version}} reference to a release version of Sparkling Water, for example: ai.h2o:sparkling-water-examples_2.10:1.5.10

The full list of published packages is available here.

Sparkling Water is distributed as a Spark application library which can be used by any Spark application. Furthermore, we provide also zip distribution which bundles the library and shell scripts.

There are several ways of using Sparkling Water:

• Sparkling Shell
• Sparkling Water driver
• Spark Shell and include Sparkling Water library via --jars or --packages option
• Spark Submit and include Sparkling Water library via --jars or --packages option
• pySpark with pySparkling

The Sparkling shell encapsulates a regular Spark shell and append Sparkling Water library on the classpath via --jars option. The Sparkling Shell supports creation of an H₂O cloud and execution of H₂O algorithms.

1. First, build a package containing Sparkling water:

./gradlew assemble

2. Configure the location of Spark cluster:

export SPARK_HOME="/path/to/spark/installation"
export MASTER="local-cluster[3,2,2048]"

In this case, local-cluster[3,2,2048] points to embedded cluster of 3 worker nodes, each with 2 cores and 2G of memory.

3. Run Sparkling Shell:

bin/sparkling-shell

Sparkling Shell accepts common Spark Shell arguments. For example, to increase memory allocated by each executor, use the spark.executor.memory parameter: bin/sparkling-shell --conf "spark.executor.memory=4g"

4. Initialize H2OContext

import org.apache.spark.h2o._
val hc = H2OContext.getOrCreate(sc)

H2OContext start H2O services on top of Spark cluster and provides primitives for transformations between H2O and Spark datastructures.

The Sparkling Water distribution includes also a set of examples. You can find there implementation in example folder. You can run them in the following way:

1. Build a package that can be submitted to Spark cluster:

./gradlew assemble

2. Set the configuration of the demo Spark cluster (for example, local-cluster[3,2,1024])

export SPARK_HOME="/path/to/spark/installation"
export MASTER="local-cluster[3,2,1024]"

In this example, the description local-cluster[3,2,1024] causes creation of a local cluster consisting of 3 workers.

3. And run the example:

bin/run-example.sh

For more details about examples, please see the README.md file in the examples directory.

You can find more examples in the examples folder.

Sparkling Water can be also used directly from pySpark.

1. First, build a package:

./gradlew build -x check

2. Configure the location of Spark distribution and cluster:

export SPARK_HOME="/path/to/spark/installation"
export MASTER="local-cluster[3,2,1024]"

3. And run pySparkling shell:

bin/pysparkling

The pysparkling shell accepts common pyspark arguments.

4. Initialize H2OContext

import pysparkling
hc = H2OContext(sc).start()

To run pysparkling on top of Spark cluster, H2O Python package is required. You can install it via pip or point to it via PYTHONPATH shell variable: export PYTHONPATH=$PYTHONPATH:$H2O_HOME/h2o-py

To use Python notebook with pysparkling you need to specify IPYTHON_OPTS shell variable: IPYTHON_OPTS="notebook" bin/pysparkling

To use iPython with pysparkling you need to specify PYSPARK_PYTHON shell variable: PYSPARK_PYTHON="ipython" bin/pysparkling

Sparkling Water is also published as a Spark package. You can use it directly from your Spark distribution.

For example, if you have Spark version 1.5 and would like to use Sparkling Water version 1.5.2 and launch example CraigslistJobTitlesStreamingApp, then you can use the following command:

$SPARK_HOME/bin/spark-submit --packages ai.h2o:sparkling-water-core_2.10:1.5.2,ai.h2o:sparkling-water-examples_2.10:1.5.2 --class org.apache.spark.examples.h2o.CraigslistJobTitlesStreamingApp /dev/null

The Spark option --packages points to published Sparkling Water packages in Maven repository.

The similar command works for spark-shell:

$SPARK_HOME/bin/spark-shell --packages ai.h2o:sparkling-water-core_2.10:1.5.2,ai.h2o:sparkling-water-examples_2.10:1.5.2 

The same command works for Python programs:

$SPARK_HOME/bin/spark-submit --packages ai.h2o:sparkling-water-core_2.10:1.5.2,ai.h2o:sparkling-water-examples_2.10:1.5.2 example.py

Note: When you are using Spark packages you do not need to download Sparkling Water distribution! Spark installation is sufficient!

See docker/README.md to learn about Docker support.

• In IDEA, install the Scala plugin for IDEA
• In a Terminal:

git clone https://github.com/h2oai/sparkling-water.git
cd sparkling-water
./gradlew idea
open sparkling-water.ipr

• In IDEA, open the file sparkling-water/core/src/main/scala/water/SparklingWaterDriver.scala
• [ Wait for IDEA indexing to complete so the Run and Debug choices are available ]
• In IDEA, Run or Debug SparklingWaterDriver (via right-click)

An application using Sparkling Water is regular Spark application which bundling Sparkling Water library. See Sparkling Water Droplet providing an example application here.

Look at our list of JIRA tasks for new contributors or send your idea to support@h2o.ai.

To report issues, please use our JIRA page at http://jira.h2o.ai/.

Follow our H2O Stream.

• Where do I find the Spark logs?

Standalone mode: Spark executor logs are located in the directory $SPARK_HOME/work/app-<AppName> (where <AppName> is the name of your application). The location contains also stdout/stderr from H2O.

YARN mode: The executors logs are available via yarn logs -applicationId <appId> command. Driver logs are by default printed to console, however, H2O also writes logs into current_dir/h2ologs.

The location of H2O driver logs can be controlled via Spark property spark.ext.h2o.client.log.dir (pass via --conf) option.

• Spark is very slow during initialization or H2O does not form a cluster. What should I do?

Configure the Spark variable SPARK_LOCAL_IP. For example:

export SPARK_LOCAL_IP='127.0.0.1'

• How do I increase the amount of memory assigned to the Spark executors in Sparkling Shell?

Sparkling Shell accepts common Spark Shell arguments. For example, to increase the amount of memory allocated by each executor, use the spark.executor.memory parameter: bin/sparkling-shell --conf "spark.executor.memory=4g"

• How do I change the base port H2O uses to find available ports?

  The H2O accepts spark.ext.h2o.port.base parameter via Spark configuration properties: bin/sparkling-shell --conf "spark.ext.h2o.port.base=13431". For a complete list of configuration options, refer to Devel Documentation.

• How do I use Sparkling Shell to launch a Scala test.script that I created?

Sparkling Shell accepts common Spark Shell arguments. To pass your script, please use -i option of Spark Shell: bin/sparkling-shell -i test.script

• How do I increase PermGen size for Spark driver?

Specify --conf spark.driver.extraJavaOptions="-XX:MaxPermSize=384m"

• How do I add Apache Spark classes to Python path?

Configure the Python path variable PYTHONPATH:

export PYTHONPATH=$SPARK_HOME/python:$SPARK_HOME/python/build:$PYTHONPATH
export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.9-src.zip:$PYTHONPATH

• Trying to import a class from the hex package in Sparkling Shell but getting weird error:

  error: missing arguments for method hex in object functions;
  follow this method with '_' if you want to treat it as a partially applied
  

  In this case you are probably using Spark 1.5 which is importing SQL functions into Spark Shell environment. Please use the following syntax to import a class from the hex package:

  import _root_.hex.tree.gbm.GBM