Sparkling-water – keeping the web UI alive

Spark is a great way to make use of the available RAM on a Hadoop cluster to run fast in-memory analysis and queries, and H2O is a great project for running distributed machine learning algorithms on data stored in Hadoop.  Together they form “Sparkling Water” (Spark + H2O, obviously!).

Easy to follow instructions for setting up Sparkling Water are available here:

Running spark on Yarn is a good way to utilise an existing Hadoop cluster, however it’s challenging using the “live” method below to keep the Sparkling Water H2O Flow interface running permanently.  Doing so can let a number of data scientists use the notebook style interface to run machine learning tasks.  Luckily, using the spark-submit invocation with the water.SparklingWaterDriver class can ensure the web UI remains online even after the shell session which kicked it off exits (see below Persistent method).

Live method – doesn’t stay online after exiting shell session

  1. Create a shell script:

    export SPARK_HOME=’/usr/hdp/current/spark-client/’
    export HADOOP_CONF_DIR=/etc/hadoop/conf
    export MASTER=”yarn-client”
    sparkling-water-1.3.5/bin/sparkling-shell –num-executors 3 –executor-memory 2g –master yarn-client

  2. Run sparkling-shell

    import org.apache.spark.h2o._
    val h2oContext = new H2OContext(sc).start()
    import h2oContext._

Persistent method – stays online even after exiting shell session

To start a “persistent” H2O cluster on Yarn (i.e. one which doesn’t exit immediately) simply run this command at the command line of a node where the spark client and sparkling water is installed:

nohup bin/spark-submit –class water.SparklingWaterDriver –master yarn-client –num-executors 3 –driver-memory 4g –executor-memory 2g –executor-cores 1 ../sparkling-water-0.2.1-58/assembly/build/libs/*.jar &

The Spark UI should be available on it’s usual port (http://XXX.XXX.XXX.XXX:54321) and should remain there even if the shell session which started the UI dies!

Thanks to the helpful and responsive folks at H2Oai for the above tip (originally answered here)!


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