Running Sparkling Water on Google Cloud Dataproc ------------------------------------------------ This section describes how to run Sparkling Water on `Google Cloud Dataproc `__. It is meant to get you up and running with Sparkling Water on Google Cloud Dataproc as fast as possible so you can try it out. For further usage and productionizing some adjustments like `initialization actions `__ will be required. In this tutorial we will use Dataproc image version 2.0-debian10 which has Spark 3.1 and Scala 2.12. 1. Install the `Google Cloud SDK `__. Login to your account. 2. Download `Sparkling water `__. 3. Create a Google Cloud Dataproc cluster. .. code:: bash DATAPROC_CLUSTER_NAME='sparkling-water-test' GCP_REGION='europe-central2' gcloud dataproc clusters create $DATAPROC_CLUSTER_NAME \ --region $GCP_REGION \ --image-version 2.0-debian10 \ --num-workers 3 \ --properties='^#^dataproc:pip.packages=tabulate==0.8.3,requests==2.21.0' .. content-tabs:: .. tab-container:: Scala :title: Scala Set the variables. .. code:: bash # we need spark jars to compile the example SPARK_JARS=$(echo "$SPARK_HOME"/jars/*.jar | tr ' ' ':') SPARKLING_WATER_JAR='sparkling-water-assembly_2.12-SUBST_SW_VERSION-all.jar' Copy the example job source into a file named SparklingWaterGcpExampleJob.scala .. code:: scala import java.net.URI import ai.h2o.sparkling._ import org.apache.spark.SparkFiles import org.apache.spark.sql.SparkSession object SparklingWaterGcpExampleJob extends App { // start the cluster val spark = SparkSession.builder.appName("Sparkling water example").getOrCreate() import spark.implicits._ val hc = H2OContext.getOrCreate() val expectedClusterSize = 3 val clusterSize = hc.getH2ONodes().length require(clusterSize != expectedClusterSize, s"H2O cluster should be of size $expectedClusterSize but is $clusterSize") // prepare the data spark.sparkContext.addFile("https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate/prostate.csv") val frame = H2OFrame(new URI("file://" + SparkFiles.get("prostate.csv"))) val sparkDF = hc.asSparkFrame(frame).withColumn("CAPSULE", $"CAPSULE" cast "string") val Array(trainingDF, testingDF) = sparkDF.randomSplit(Array(0.8, 0.2)) // train the model import ai.h2o.sparkling.ml.algos.H2OGBM val estimator = new H2OGBM().setLabelCol("CAPSULE") val model = estimator.fit(trainingDF) // run predictions model.transform(testingDF).collect() } Compile the code into a jar file having Spark and Sparkling Water on the classpath. .. code:: bash EXAMPLE_JOB_JAR='sparkling-water-gcp-example-job.jar' scalac SparklingWaterGcpExampleJob.scala \ -d $EXAMPLE_JOB_JAR \ -classpath "$SPARKLING_WATER_JAR:$SPARK_JARS" Submit the job to the cluster. .. code:: bash gcloud dataproc jobs submit spark \ --class=SparklingWaterGcpExampleJob \ --cluster=$DATAPROC_CLUSTER_NAME \ --region=$GCP_REGION \ --jars "$SPARKLING_WATER_JAR,$EXAMPLE_JOB_JAR" \ --properties=spark.dynamicAllocation.enabled=false,spark.scheduler.minRegisteredResourcesRatio=1,spark.executor.instances=3 .. tab-container:: Python :title: Python Set the variables. .. code:: bash PYSPARKLING_ZIP='h2o_pysparkling_3.1-SUBST_SW_VERSION.zip' Copy the example job source into a file named sparkling_water_gcp_example_job.py .. code:: python from pysparkling import * from pyspark.sql import SparkSession import h2o # start the cluster spark = SparkSession.builder.appName("Sparkling water example").getOrCreate() hc = H2OContext.getOrCreate() assert h2o.cluster().cloud_size == 3 # prepare the data frame = h2o.import_file("https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate/prostate.csv") sparkDF = hc.asSparkFrame(frame) sparkDF = sparkDF.withColumn("CAPSULE", sparkDF.CAPSULE.cast("string")) [trainingDF, testingDF] = sparkDF.randomSplit([0.8, 0.2]) # train the model from pysparkling.ml import H2OGBM estimator = H2OGBM(labelCol = "CAPSULE") model = estimator.fit(trainingDF) # run predictions model.transform(testingDF).collect() Submit the job to the cluster. .. code:: bash gcloud dataproc jobs submit pyspark sparkling_water_gcp_example_job.py \ --cluster=$DATAPROC_CLUSTER_NAME \ --region=$GCP_REGION \ --py-files $PYSPARKLING_ZIP \ --properties=spark.dynamicAllocation.enabled=false,spark.scheduler.minRegisteredResourcesRatio=1,spark.executor.instances=3