h2oai / sparkling-water

Sparkling Water provides H2O functionality inside Spark cluster


Sparkling Water

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

  • Utilities to publish Spark data structures (RDDs, DataFrames, Datasets) 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.

Getting Started

Select right version

The Sparkling Water is developed in multiple parallel branches. Each branch corresponds to a Spark major release (e.g., branch rel-2.1 provides implementation of Sparkling Water for Spark 2.1).

Please, switch to the right branch:

  • For Spark 2.2 use branch rel-2.2

  • For Spark 2.1 use branch rel-2.1

  • For Spark 2.0 use branch rel-2.0

  • For Spark 1.6 use branch rel-1.6 (Only critical fixes)

    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 8+
  • Python 2.7+ For Python version of Sparkling Water (PySparkling)
  • Spark 2.2 and SPARK_HOME shell variable must point to your local Spark installation

Download Binaries

For each Sparkling Water you can download binaries here:


Each Sparkling Water release is published into Maven central. Published artifacts are provided with the following Scala versions:

  • Sparkling Water 2.1.x - Scala 2.11
  • Sparkling Water 2.0.x - Scala 2.11
  • Sparkling Water 1.6.x - Scala 2.10

The artifacts coordinates are:

  • ai.h2o:sparkling-water-core_{{scala_version}}:{{version}} - includes core of Sparkling Water.

  • ai.h2o:sparkling-water-examples_{{scala_version}}:{{version}} - includes example applications.

    Note: The {{version}} references to a release version of Sparkling Water, the {{scala_version}} references to Scala base version (2.10 or 2.11). For example: ai.h2o:sparkling-water-examples_2.11:2.1.0

The full list of published packages is available here.

Use Sparkling Water

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

Run Sparkling shell

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. Either download or build Sparkling Water

  2. Configure the location of Spark cluster:

    export SPARK_HOME="/path/to/spark/installation"
    export MASTER="local[*]"

    In this case, local[*] points to an embedded single node cluster.

  3. Run 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(spark)

    H2OContext starts H2O services on top of Spark cluster and provides primitives for transformations between H2O and Spark data structures.

Use Sparkling Water with PySpark

Sparkling Water can be also used directly from PySpark and the integration is called PySparkling.

See PySparkling README to learn about PySparkling.

Use Sparkling Water via Spark Packages

To see how Sparkling Water can be used as Spark package, please see Use as Spark Package.

Use Sparkling Water in Windows environments

See Windows Tutorial to learn how to use Sparkling Water in Windows environments.

Sparkling Water examples

To see how to run examples for Sparkling Water, please see Running Examples.

Sparkling Water Backends

Sparkling water supports two backend/deployment modes - internal and external. Sparkling Water applications are independent on the selected backend. The backend can be specified before creationg of the H2OContext.

For more details regarding the internal or external backend, please see Backends.


List of all Frequently Asked Questions is available at FAQ.


Complete development documentation is available at Development Documentation.

Build Sparkling Water

To see how to build Sparkling Water, please see Build Sparkling Water.

Develop applications with Sparkling Water

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/.

We also respond to questions tagged with sparkling-water and h2o tags on the Stack Overflow.

Change Logs

Change logs are available at Change Logs.