org.apache.spark.SparkConf Scala Examples

package io.univalence.deltaqa.kpialgebra

import org.apache.spark.rdd.RDD
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import shapeless.contrib.spire._
import spire.algebra._
import spire.implicits._

import scala.reflect.ClassTag

case class DeltaPart[T: AdditiveMonoid](
  count: Long,
  part: T
)

case class DeltaCommon[T: AdditiveMonoid](
  count: Long,
  countZero: Long,
  diff: T,
  error: T,
  left: T,
  right: T
)

case class Delta[L: AdditiveMonoid, R: AdditiveMonoid, C: AdditiveMonoid](
  left: DeltaPart[L],
  right: DeltaPart[R],
  common: DeltaCommon[C]
)

object KpiAlgebra {

  def computeCommon[LRC: AdditiveAbGroup: MultiplicativeSemigroup](left: LRC, right: LRC): DeltaCommon[LRC] = {
    val diff  = left - right
    val error = diff * diff
    DeltaCommon(
      count     = 1,
      countZero = if (diff == Monoid.additive[LRC].id) 1 else 0,
      diff      = diff,
      error     = error,
      left      = left,
      right     = right
    )
  }

  def monoid[LM: AdditiveMonoid, RM: AdditiveMonoid, LRC: AdditiveMonoid]: Monoid[Delta[LM, RM, LRC]] =
    Monoid.additive[Delta[LM, RM, LRC]]

  def compare[
    K: ClassTag,
    L: ClassTag,
    R: ClassTag,
    LM: AdditiveMonoid: ClassTag,
    RM: AdditiveMonoid: ClassTag,
    LRC: AdditiveAbGroup: MultiplicativeSemigroup: ClassTag
  ](
    left: RDD[(K, L)],
    right: RDD[(K, R)]
  )(flm: L => LM, frm: R => RM, flc: L => LRC, frc: R => LRC): Delta[LM, RM, LRC] = {

    val map: RDD[Delta[LM, RM, LRC]] = left
      .fullOuterJoin(right)
      .map({
        case (_, (Some(l), None)) =>
          monoid[LM, RM, LRC].id
            .copy(left = DeltaPart(count = 1, part = flm(l)))
        case (_, (None, Some(r))) =>
          monoid[LM, RM, LRC].id
            .copy(right = DeltaPart(count = 1, part = frm(r)))
        case (_, (Some(l), Some(r))) =>
          monoid[LM, RM, LRC].id.copy(common = computeCommon(flc(l), frc(r)))
      })

    map.reduce((x, y) => monoid[LM, RM, LRC].op(x, y))
  }
}

case class KpiLeaf(l1: Long, l2: Long, l3: Long)

object KpiAlgebraTest {

  def main(args: Array[String]) {
    val sc = new SparkContext(new SparkConf().setMaster("local[*]").setAppName("smoketest"))

    val parallelize: RDD[(Int, Int)] = sc.parallelize((1 to 4).zipWithIndex)

    

    // Delta(DeltaPart(0,0),DeltaPart(0,0),DeltaCommon(4,4,0,0,6,6))

    val p2: RDD[(Int, KpiLeaf)] =
      sc.parallelize((1 to 4)).map(_ -> KpiLeaf(1, 2, 3))

    import spire.implicits._
    import shapeless.contrib.spire._

    ////println(((KpiAlgebra.compare(p2, p2)(identity, identity, identity, identity))

  }
} 

package com.vita.spark.test

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDD

object Test1 {
  def main(args: Array[String]): Unit = {

    val conf: SparkConf = new SparkConf()
    conf.setMaster("local")
    conf.setAppName("TransformationOperator")
    val sc: SparkContext = new SparkContext(conf)
    val list: List[String] = List("张无忌", "赵敏", "周芷若")
    val rdd: RDD[String] = sc.parallelize(list)


    val list1: List[(Int, String)] = List((1, "东方不败"), (2, "令狐冲"), (3, "林平之"))
    val list2: List[(Int, Int)] = List((1, 99), (2, 98), (3, 97))

    val rdd1: RDD[(Int, String)] = sc.parallelize(list1)
    val rdd2: RDD[(Int, Int)] = sc.parallelize(list2)
    rdd1.join(rdd2).foreach(x => println("学号： " + x._1 + "名字：" + x._2._1 + " 分数：" + x._2._2))

  }
} 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics}
// $example off$

object SummaryStatisticsExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("SummaryStatisticsExample")
    val sc = new SparkContext(conf)

    // $example on$
    val observations = sc.parallelize(
      Seq(
        Vectors.dense(1.0, 10.0, 100.0),
        Vectors.dense(2.0, 20.0, 200.0),
        Vectors.dense(3.0, 30.0, 300.0)
      )
    )

    // Compute column summary statistics.
    val summary: MultivariateStatisticalSummary = Statistics.colStats(observations)
    println(summary.mean)  // a dense vector containing the mean value for each column
    println(summary.variance)  // column-wise variance
    println(summary.numNonzeros)  // number of nonzeros in each column
    // $example off$

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.log4j.{Level, Logger}
import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.clustering.KMeans
import org.apache.spark.mllib.linalg.Vectors


object DenseKMeans {

  object InitializationMode extends Enumeration {
    type InitializationMode = Value
    val Random, Parallel = Value
  }

  import InitializationMode._

  case class Params(
      input: String = null,
      k: Int = -1,
      numIterations: Int = 10,
      initializationMode: InitializationMode = Parallel) extends AbstractParams[Params]

  def main(args: Array[String]) {
    val defaultParams = Params()

    val parser = new OptionParser[Params]("DenseKMeans") {
      head("DenseKMeans: an example k-means app for dense data.")
      opt[Int]('k', "k")
        .required()
        .text(s"number of clusters, required")
        .action((x, c) => c.copy(k = x))
      opt[Int]("numIterations")
        .text(s"number of iterations, default: ${defaultParams.numIterations}")
        .action((x, c) => c.copy(numIterations = x))
      opt[String]("initMode")
        .text(s"initialization mode (${InitializationMode.values.mkString(",")}), " +
        s"default: ${defaultParams.initializationMode}")
        .action((x, c) => c.copy(initializationMode = InitializationMode.withName(x)))
      arg[String]("<input>")
        .text("input paths to examples")
        .required()
        .action((x, c) => c.copy(input = x))
    }

    parser.parse(args, defaultParams) match {
      case Some(params) => run(params)
      case _ => sys.exit(1)
    }
  }

  def run(params: Params): Unit = {
    val conf = new SparkConf().setAppName(s"DenseKMeans with $params")
    val sc = new SparkContext(conf)

    Logger.getRootLogger.setLevel(Level.WARN)

    val examples = sc.textFile(params.input).map { line =>
      Vectors.dense(line.split(' ').map(_.toDouble))
    }.cache()

    val numExamples = examples.count()

    println(s"numExamples = $numExamples.")

    val initMode = params.initializationMode match {
      case Random => KMeans.RANDOM
      case Parallel => KMeans.K_MEANS_PARALLEL
    }

    val model = new KMeans()
      .setInitializationMode(initMode)
      .setK(params.k)
      .setMaxIterations(params.numIterations)
      .run(examples)

    val cost = model.computeCost(examples)

    println(s"Total cost = $cost.")

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.streaming

import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.{Seconds, StreamingContext, Time}


object SparkSessionSingleton {

  @transient  private var instance: SparkSession = _

  def getInstance(sparkConf: SparkConf): SparkSession = {
    if (instance == null) {
      instance = SparkSession
        .builder
        .config(sparkConf)
        .getOrCreate()
    }
    instance
  }
}
// scalastyle:on println 

package org.apache.spark.deploy.yarn.security

import java.io.{ByteArrayInputStream, DataInputStream}

import scala.collection.JavaConverters._

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier
import org.apache.hadoop.mapred.Master
import org.apache.hadoop.security.Credentials

import org.apache.spark.{SparkConf, SparkException}
import org.apache.spark.deploy.yarn.config._
import org.apache.spark.internal.Logging
import org.apache.spark.internal.config._

private[security] class HDFSCredentialProvider extends ServiceCredentialProvider with Logging {
  // Token renewal interval, this value will be set in the first call,
  // if None means no token renewer specified, so cannot get token renewal interval.
  private var tokenRenewalInterval: Option[Long] = null

  override val serviceName: String = "hdfs"

  override def obtainCredentials(
      hadoopConf: Configuration,
      sparkConf: SparkConf,
      creds: Credentials): Option[Long] = {
    // NameNode to access, used to get tokens from different FileSystems
    nnsToAccess(hadoopConf, sparkConf).foreach { dst =>
      val dstFs = dst.getFileSystem(hadoopConf)
      logInfo("getting token for namenode: " + dst)
      dstFs.addDelegationTokens(getTokenRenewer(hadoopConf), creds)
    }

    // Get the token renewal interval if it is not set. It will only be called once.
    if (tokenRenewalInterval == null) {
      tokenRenewalInterval = getTokenRenewalInterval(hadoopConf, sparkConf)
    }

    // Get the time of next renewal.
    tokenRenewalInterval.map { interval =>
      creds.getAllTokens.asScala
        .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND)
        .map { t =>
          val identifier = new DelegationTokenIdentifier()
          identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier)))
          identifier.getIssueDate + interval
      }.foldLeft(0L)(math.max)
    }
  }

  private def getTokenRenewalInterval(
      hadoopConf: Configuration, sparkConf: SparkConf): Option[Long] = {
    // We cannot use the tokens generated with renewer yarn. Trying to renew
    // those will fail with an access control issue. So create new tokens with the logged in
    // user as renewer.
    sparkConf.get(PRINCIPAL).map { renewer =>
      val creds = new Credentials()
      nnsToAccess(hadoopConf, sparkConf).foreach { dst =>
        val dstFs = dst.getFileSystem(hadoopConf)
        dstFs.addDelegationTokens(renewer, creds)
      }
      val t = creds.getAllTokens.asScala
        .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND)
        .head
      val newExpiration = t.renew(hadoopConf)
      val identifier = new DelegationTokenIdentifier()
      identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier)))
      val interval = newExpiration - identifier.getIssueDate
      logInfo(s"Renewal Interval is $interval")
      interval
    }
  }

  private def getTokenRenewer(conf: Configuration): String = {
    val delegTokenRenewer = Master.getMasterPrincipal(conf)
    logDebug("delegation token renewer is: " + delegTokenRenewer)
    if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) {
      val errorMessage = "Can't get Master Kerberos principal for use as renewer"
      logError(errorMessage)
      throw new SparkException(errorMessage)
    }

    delegTokenRenewer
  }

  private def nnsToAccess(hadoopConf: Configuration, sparkConf: SparkConf): Set[Path] = {
    sparkConf.get(NAMENODES_TO_ACCESS).map(new Path(_)).toSet +
      sparkConf.get(STAGING_DIR).map(new Path(_))
        .getOrElse(FileSystem.get(hadoopConf).getHomeDirectory)
  }
} 

package org.apache.spark.streaming.scheduler

import java.io.ObjectInputStream
import java.util.concurrent.atomic.AtomicLong

import scala.concurrent.{ExecutionContext, Future}

import org.apache.spark.SparkConf
import org.apache.spark.streaming.scheduler.rate.RateEstimator
import org.apache.spark.util.{ThreadUtils, Utils}


  private def computeAndPublish(time: Long, elems: Long, workDelay: Long, waitDelay: Long): Unit =
    Future[Unit] {
      val newRate = rateEstimator.compute(time, elems, workDelay, waitDelay)
      newRate.foreach { s =>
        rateLimit.set(s.toLong)
        publish(getLatestRate())
      }
    }

  def getLatestRate(): Long = rateLimit.get()

  override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) {
    val elements = batchCompleted.batchInfo.streamIdToInputInfo

    for {
      processingEnd <- batchCompleted.batchInfo.processingEndTime
      workDelay <- batchCompleted.batchInfo.processingDelay
      waitDelay <- batchCompleted.batchInfo.schedulingDelay
      elems <- elements.get(streamUID).map(_.numRecords)
    } computeAndPublish(processingEnd, elems, workDelay, waitDelay)
  }
}

object RateController {
  def isBackPressureEnabled(conf: SparkConf): Boolean =
    conf.getBoolean("spark.streaming.backpressure.enabled", false)
} 

package com.bigchange.mllib

import org.apache.spark.mllib.feature.{HashingTF, IDF}
import org.apache.spark.mllib.linalg.{SparseVector => SV}
import org.apache.spark.{SparkConf, SparkContext}

import scala.io.Source


object TFIDF {
  def main(args: Array[String]) {

    val conf = new SparkConf().setAppName("TfIdfTest")
      .setMaster("local")
    val sc = new SparkContext(conf)

    // Load documents (one per line).要求每行作为一个document,这里zipWithIndex将每一行的行号作为doc id
    val documents = sc.parallelize(Source.fromFile("J:\\github\\dataSet\\TFIDF-DOC").getLines()
      .filter(_.trim.length > 0).toSeq)
      .map(_.split(" ").toSeq)
      .zipWithIndex()


    // feature number
    val hashingTF = new HashingTF(Math.pow(2, 18).toInt)
    //line number for doc id，每一行的分词结果生成tf vector
    val idAndTFVector = documents.map {
      case (seq, num) =>
        val tf = hashingTF.transform(seq)
        (num + 1, tf)
    }
    idAndTFVector.cache()
    // build idf model
    val idf = new IDF().fit(idAndTFVector.values)
    // transform tf vector to tf-idf vector
    val idAndTFIDFVector = idAndTFVector.mapValues(v => idf.transform(v))
    // broadcast tf-idf vectors
    val idAndTFIDFVectorBroadCast = sc.broadcast(idAndTFIDFVector.collect())

    // cal doc cosineSimilarity
    val docSims = idAndTFIDFVector.flatMap {
      case (id1, idf1) =>
        // filter the same doc id
        val idfs = idAndTFIDFVectorBroadCast.value.filter(_._1 != id1)
        val sv1 = idf1.asInstanceOf[SV]
        import breeze.linalg._
        val bsv1 = new SparseVector[Double](sv1.indices, sv1.values, sv1.size)
        idfs.map {
          case (id2, idf2) =>
            val sv2 = idf2.asInstanceOf[SV]
            val bsv2 = new SparseVector[Double](sv2.indices, sv2.values, sv2.size)
            val cosSim = bsv1.dot(bsv2) / (norm(bsv1) * norm(bsv2))
            (id1, id2, cosSim)
        }
    }
    docSims.foreach(println)

    sc.stop()

  }
} 

package com.cloudera.sa.spark.unittest.sql

import org.apache.spark.sql.Row
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.{SparkConf, SparkContext}
import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite}

import scala.collection.mutable

class SqlUnitTest extends FunSuite with
BeforeAndAfterEach with BeforeAndAfterAll{

  @transient var sc: SparkContext = null
  @transient var hiveContext: HiveContext = null

  override def beforeAll(): Unit = {

    val envMap = Map[String,String](("Xmx", "512m"))

    val sparkConfig = new SparkConf()
    sparkConfig.set("spark.broadcast.compress", "false")
    sparkConfig.set("spark.shuffle.compress", "false")
    sparkConfig.set("spark.shuffle.spill.compress", "false")
    sparkConfig.set("spark.io.compression.codec", "lzf")
    sc = new SparkContext("local[2]", "unit test", sparkConfig)
    hiveContext = new HiveContext(sc)
  }

  override def afterAll(): Unit = {
    sc.stop()
  }

  test("Test table creation and summing of counts") {
    val personRDD = sc.parallelize(Seq(Row("ted", 42, "blue"),
      Row("tj", 11, "green"),
      Row("andrew", 9, "green")))

    hiveContext.sql("create table person (name string, age int, color string)")

    val emptyDataFrame = hiveContext.sql("select * from person limit 0")

    val personDataFrame = hiveContext.createDataFrame(personRDD, emptyDataFrame.schema)
    personDataFrame.registerTempTable("tempPerson")

    val ageSumDataFrame = hiveContext.sql("select sum(age) from tempPerson")

    val localAgeSum = ageSumDataFrame.take(10)

    assert(localAgeSum(0).get(0) == 62, "The sum of age should equal 62 but it equaled " + localAgeSum(0).get(0))
  }
} 

package com.github.maxpumperla.ml_spark.graphs

import org.apache.spark.graphx.lib.TriangleCount
import org.apache.spark.graphx.util.GraphGenerators
import org.apache.spark.graphx.{Graph, GraphLoader, PartitionStrategy, VertexId}
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}


object GraphGeneration extends App {

  val conf = new SparkConf()
    .setAppName("Graph generation")
    .setMaster("local[4]")
  val sc = new SparkContext(conf)

  val edgeListGraph = GraphLoader.edgeListFile(sc, "./edge_list.txt")

  val rawEdges: RDD[(VertexId, VertexId)] = sc.textFile("./edge_list.txt").map {
    line =>
      val field = line.split(" ")
      (field(0).toLong, field(1).toLong)
  }
  val edgeTupleGraph = Graph.fromEdgeTuples(
    rawEdges=rawEdges, defaultValue="")

  val gridGraph = GraphGenerators.gridGraph(sc, 5, 5)
  val starGraph = GraphGenerators.starGraph(sc, 11)
  val logNormalGraph  = GraphGenerators.logNormalGraph(
    sc, numVertices = 20, mu=1, sigma = 3
  )
  logNormalGraph.outDegrees.map(_._2).collect().sorted

  val actorGraph = GraphLoader.edgeListFile(
    sc, "./ca-hollywood-2009.txt", true
  ).partitionBy(PartitionStrategy.RandomVertexCut)
  actorGraph.edges.count()

  val actorComponents = actorGraph.connectedComponents().cache
  actorComponents.vertices.map(_._2).distinct().count

  val clusterSizes =actorComponents.vertices.map(
    v => (v._2, 1)).reduceByKey(_ + _)
  clusterSizes.map(_._2).max
  clusterSizes.map(_._2).min

  val smallActorGraph = GraphLoader.edgeListFile(sc, "./ca-hollywood-2009.txt")
  val strongComponents = smallActorGraph.stronglyConnectedComponents(numIter = 5)
  strongComponents.vertices.map(_._2).distinct().count

  val canonicalGraph = actorGraph.mapEdges(e => 1).removeSelfEdges().convertToCanonicalEdges()
  val partitionedGraph = canonicalGraph.partitionBy(PartitionStrategy.RandomVertexCut)

  actorGraph.triangleCount()
  val triangles = TriangleCount.runPreCanonicalized(partitionedGraph)

  actorGraph.staticPageRank(10)
  val actorPrGraph: Graph[Double, Double] = actorGraph.pageRank(0.0001)
  actorPrGraph.vertices.reduce((v1, v2) => {
    if (v1._2 > v2._2) v1 else v2
  })

  actorPrGraph.inDegrees.filter(v => v._1 == 33024L).collect.foreach(println)

  actorPrGraph.inDegrees.map(_._2).collect().sorted.takeRight(10)

  actorPrGraph.inDegrees.map(_._2).filter(_ >= 62).count

} 

package com.ibm.aardpfark.pfa

import com.holdenkarau.spark.testing.DataFrameSuiteBase
import org.apache.spark.SparkConf
import org.apache.spark.ml.Transformer
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import org.apache.spark.sql.functions.udf
import org.apache.spark.sql.{DataFrame, Row, SparkSession}
import org.scalactic.Equality
import org.scalatest.FunSuite

abstract class SparkPFASuiteBase extends FunSuite with DataFrameSuiteBase with PFATestUtils {

  val sparkTransformer: Transformer
  val input: Array[String]
  val expectedOutput: Array[String]

  val sparkConf =  new SparkConf().
    setMaster("local[*]").
    setAppName("test").
    set("spark.ui.enabled", "false").
    set("spark.app.id", appID).
    set("spark.driver.host", "localhost")
  override lazy val spark = SparkSession.builder().config(sparkConf).getOrCreate()
  override val reuseContextIfPossible = true

  // Converts column containing a vector to an array
  def withColumnAsArray(df: DataFrame, colName: String) = {
    val vecToArray = udf { v: Vector => v.toArray }
    df.withColumn(colName, vecToArray(df(colName)))
  }

  def withColumnAsArray(df: DataFrame, first: String, others: String*) = {
    val vecToArray = udf { v: Vector => v.toArray }
    var result = df.withColumn(first, vecToArray(df(first)))
    others.foreach(c => result = result.withColumn(c, vecToArray(df(c))))
    result
  }

  // Converts column containing a vector to a sparse vector represented as a map
  def getColumnAsSparseVectorMap(df: DataFrame, colName: String) = {
    val vecToMap = udf { v: Vector => v.toSparse.indices.map(i => (i.toString, v(i))).toMap }
    df.withColumn(colName, vecToMap(df(colName)))
  }

}

abstract class Result

object ApproxEquality extends ApproxEquality

trait ApproxEquality {

  import org.scalactic.Tolerance._
  import org.scalactic.TripleEquals._

  implicit val seqApproxEq: Equality[Seq[Double]] = new Equality[Seq[Double]] {
    override def areEqual(a: Seq[Double], b: Any): Boolean = {
      b match {
        case d: Seq[Double] =>
          a.zip(d).forall { case (l, r) => l === r +- 0.001 }
        case _ =>
          false
      }
    }
  }

  implicit val vectorApproxEq: Equality[Vector] = new Equality[Vector] {
    override def areEqual(a: Vector, b: Any): Boolean = {
      b match {
        case v: Vector =>
          a.toArray.zip(v.toArray).forall { case (l, r) => l === r +- 0.001 }
        case _ =>
          false
      }
    }
  }
} 

package org.apress.prospark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD.rddToOrderedRDDFunctions
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions
import kafka.serializer.StringDecoder
import org.apache.spark.streaming.kafka.KafkaUtils

object StationJourneyCountDirectApp {

  def main(args: Array[String]) {
    if (args.length != 7) {
      System.err.println(
        "Usage: StationJourneyCountApp <appname> <brokerUrl> <topic> <consumerGroupId> <zkQuorum> <checkpointDir> <outputPath>")
      System.exit(1)
    }

    val Seq(appName, brokerUrl, topic, consumerGroupId, zkQuorum, checkpointDir, outputPath) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(10))
    ssc.checkpoint(checkpointDir)

    val topics = Set(topic)
    val params = Map[String, String](
      "zookeeper.connect" -> zkQuorum,
      "group.id" -> consumerGroupId,
      "bootstrap.servers" -> brokerUrl)
    KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, params, topics).map(_._2)
      .map(rec => rec.split(","))
      .map(rec => ((rec(3), rec(7)), 1))
      .reduceByKey(_ + _)
      .repartition(1)
      .map(rec => (rec._2, rec._1))
      .transform(rdd => rdd.sortByKey(ascending = false))
      .saveAsTextFiles(outputPath)

    ssc.start()
    ssc.awaitTermination()
  }

} 

package examples

import org.apache.spark.{ SparkConf, SparkContext, HashPartitioner }
import org.apache.spark.rdd.RDD
import org.apache.spark.rdd.RDD.rddToPairRDDFunctions
import scala.Iterator



object TestJoins {
	def main(args: Array[String]): Unit = {
		val sc = new SparkContext(new SparkConf().setAppName("TestJoinJob"))

		val x = sc.parallelize(List((1, 2), (1, 3), (2, 3), (2, 4))).partitionBy(new HashPartitioner(2)).cache
		val y = sc.parallelize(List((2, 5), (2, 6))).partitionBy(new HashPartitioner(2)).cache

		inspectRDD(x)
		inspectRDD(y)

		println(">>> joining x with y")
		val joinRDD = x.join(y).cache
		joinRDD.collect().foreach(println)
		inspectRDD(joinRDD)

		println(">>> left outer join of x with y")
		val leftJoin = x.leftOuterJoin(y).cache
		leftJoin.collect().foreach(println)
		inspectRDD(leftJoin)

		println(">>> right outer join of x with y")
		val rightJoin = x.rightOuterJoin(y).cache
		rightJoin.collect().foreach(println)
		inspectRDD(rightJoin)
	}
	
	def inspectRDD[T](rdd: RDD[T]): Unit = {
		
		println(">>> Partition length...")
		rdd.mapPartitions(f => Iterator(f.length), true).foreach(println)
		
		println(">>> Partition data...")
		rdd.foreachPartition(f => f.foreach(println))
	}
} 

package examples.streaming

import org.apache.spark.streaming.{ StreamingContext, Seconds }
import org.apache.spark.SparkConf


object TestAdditionInWindow {
	def main(args: Array[String]): Unit = {
		val ssc = new StreamingContext(new SparkConf().setAppName("TestAdditionJob"), Seconds(1))

		val msg = ssc.socketTextStream("localhost", 9999)

		msg
			.map(data => ("sum", data.toInt))
			.reduceByKey(_ + _)
			.window(Seconds(3), Seconds(2))
			.print()

		ssc.start()
		ssc.awaitTermination()
	}
} 

package examples.streaming

import org.apache.spark.streaming.{StreamingContext, Duration}
import org.apache.spark.SparkConf


object TestUpdateStateByKey {
  val checkpointDir: String = "hdfs://localhost:9000/user/hduser/spark-chkpt"

  def main(args: Array[String]): Unit = {
    val ssc = StreamingContext.getOrCreate(checkpointDir, createFunc _)

    ssc.start()
    ssc.awaitTermination()
  }

  def updateFunc(values: Seq[Int], state: Option[Int]): Option[Int] = {
    Some(values.size + state.getOrElse(0))
  }

  def createFunc(): StreamingContext = {
    val ssc = new StreamingContext(new SparkConf().setAppName("TestUpdateStateByKeyJob"),
      Duration(2000))

    ssc.checkpoint(checkpointDir)

    ssc.socketTextStream("localhost", 9999)
      .flatMap(_.split(" "))
      .map((_, 1))
      .updateStateByKey(updateFunc _)
      .checkpoint(Duration(10000))
      .print()

    ssc
  }
} 

package examples.streaming


import org.apache.spark.streaming.{ StreamingContext, Seconds }
import org.apache.spark.streaming.scheduler.{
	StreamingListener,
	StreamingListenerBatchStarted,
	StreamingListenerBatchCompleted
}
import org.apache.spark.SparkConf

object TestStreamingListener {
	def main(args: Array[String]): Unit = {

		val ssc = new StreamingContext(new SparkConf().setAppName("TestStreamingListenerJob"),
			Seconds(5))

		ssc.addStreamingListener(new MyStreamingListener())

		ssc
			.socketTextStream("localhost", 9999)
			.flatMap(_.split(" "))
			.count()
			.print()

		ssc.start()
		ssc.awaitTermination()
	}
}

class MyStreamingListener extends StreamingListener {

	override def onBatchStarted(batchStarted: StreamingListenerBatchStarted): Unit = {
		println(">>> Batch started...records in batch = " + batchStarted.batchInfo.numRecords)
	}

	override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted): Unit = {
		println(">>> Batch completed...time taken (ms) = " + batchCompleted.batchInfo.totalDelay)
	}
} 

package examples.streaming

import org.apache.spark.streaming.StreamingContext
import org.apache.spark.SparkConf
import org.apache.spark.streaming.{ Seconds, State, StateSpec }


  def mappingFunc(key: String, value: Option[Int], state: State[Int]): Option[(String, Int)] = {
    val sum = value.getOrElse(0) + state.getOption().getOrElse(0)

    // updating the state of non-idle keys...
    // To call State.update(...) we need to check State.isTimingOut() == false, 
    // else there will be NoSuchElementException("Cannot update the state that is timing out")
    if (state.isTimingOut())
      println(key + " key is timing out...will be removed.")
    else
      state.update(sum)

    Some((key, sum))
  }
} 

package com.redislabs.provider.redis.env

import com.redislabs.provider.redis.{RedisConfig, RedisEndpoint}
import org.apache.spark.SparkConf


trait RedisStandaloneEnv extends Env {

  override val conf: SparkConf = new SparkConf()
    .setMaster("local[*]").setAppName(getClass.getName)
    .set("spark.redis.host", redisHost)
    .set("spark.redis.port", s"$redisPort")
    .set("spark.redis.auth", redisAuth)
    .set("spark.streaming.stopGracefullyOnShutdown", "true")
    .set("spark.driver.bindAddress", "127.0.0.1")

  override val redisConfig: RedisConfig =
    new RedisConfig(RedisEndpoint(redisHost, redisPort, redisAuth))
} 

package com.redislabs.provider.redis.env

import com.redislabs.provider.redis.RedisConfig
import org.apache.spark.sql.SparkSession
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.{SparkConf, SparkContext}

trait Env {

  val conf: SparkConf
  var spark: SparkSession = _
  var sc: SparkContext = _
  var ssc: StreamingContext = _

  val redisHost = "127.0.0.1"
  val redisPort = 6379
  val redisAuth = "passwd"
  val redisConfig: RedisConfig
} 

package com.redislabs.provider.redis.env

import com.redislabs.provider.redis.{RedisConfig, RedisEndpoint}
import org.apache.spark.SparkConf

trait RedisStandaloneSSLEnv extends Env {

  override val redisPort = 6380
  
  override val conf: SparkConf = new SparkConf()
    .setMaster("local[*]").setAppName(getClass.getName)
    .set("spark.redis.host", redisHost)
    .set("spark.redis.port", s"$redisPort")
    .set("spark.redis.auth", redisAuth)
    .set("spark.redis.ssl", "true")
    .set("spark.streaming.stopGracefullyOnShutdown", "true")
    .set("spark.driver.bindAddress", "127.0.0.1")

  override val redisConfig: RedisConfig =
    new RedisConfig(RedisEndpoint(redisHost, redisPort, redisAuth, ssl=true))
} 

package org.apache.spark.graphx

import org.apache.spark.SparkConf

import org.apache.spark.graphx.impl._
import org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap

import org.apache.spark.util.collection.{OpenHashSet, BitSet}
import org.apache.spark.util.BoundedPriorityQueue

object GraphXUtils {
  
  def registerKryoClasses(conf: SparkConf) {
    conf.registerKryoClasses(Array(
      classOf[Edge[Object]],
      classOf[(VertexId, Object)],
      classOf[EdgePartition[Object, Object]],
      classOf[BitSet],
      classOf[VertexIdToIndexMap],
      classOf[VertexAttributeBlock[Object]],
      classOf[PartitionStrategy],
      classOf[BoundedPriorityQueue[Object]],
      classOf[EdgeDirection],
      classOf[GraphXPrimitiveKeyOpenHashMap[VertexId, Int]],
      classOf[OpenHashSet[Int]],
      classOf[OpenHashSet[Long]]))
  }
} 

import java.io.{BufferedReader, InputStreamReader}
import java.net.Socket
import java.nio.charset.StandardCharsets

import org.apache.spark.SparkConf
import org.apache.spark.internal.Logging
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.receiver.Receiver


  private def receive() {
   var socket: Socket = null
   var userInput: String = null
   try {
     println("Connecting to " + host + ":" + port)
     socket = new Socket(host, port)
     println("Connected to " + host + ":" + port)
     val reader = new BufferedReader(
       new InputStreamReader(socket.getInputStream(), StandardCharsets.UTF_8))
     userInput = reader.readLine()
     while(!isStopped && userInput != null) {
       store(userInput)
       userInput = reader.readLine()
     }
     reader.close()
     socket.close()
     println("Stopped receiving")
     restart("Trying to connect again")
   } catch {
     case e: java.net.ConnectException =>
       restart("Error connecting to " + host + ":" + port, e)
     case t: Throwable =>
       restart("Error receiving data", t)
   }
  }
} 

import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import org.apache.http.HttpResponse;
import org.apache.http.client.ClientProtocolException;
import org.apache.http.client.methods.HttpGet;
import org.apache.http.impl.client.DefaultHttpClient;
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.receiver.Receiver

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}

object TFLCustomReceiver {
  private val url = "https://api.tfl.gov.uk/Line/circle/Arrivals?stopPointId=940GZZLUERC&app_id=a73727f3&app_key=dc8150560a2422afae2b70cf291c4327"
  def main(args: Array[String]) {
    // Create the context with a 1 second batch size
    val sparkConf = new SparkConf().setAppName("TFLCustomReceiver")
    val ssc = new StreamingContext(sparkConf, Seconds(300))
    
    val lines = ssc.receiverStream(new TFLCustomReceiver(url))
    lines.print()
    ssc.start()
    ssc.awaitTermination()
  }
}

class TFLCustomReceiver(url: String)
  extends Receiver[String](StorageLevel.MEMORY_AND_DISK_2) {

  def onStart() {
    // Start the thread that receives data over a connection
    new Thread("Http Receiver") {
      override def run() { receive() }
    }.start()
  }

  def onStop() {
   // There is nothing much to do as the thread calling receive()
   // is designed to stop by itself if isStopped() returns false
  }

  
  
  private def receive() {
    var userInput: String = null
    var httpClient: DefaultHttpClient = null
    var getRequest: HttpGet = null
    
    try {
     // Connect to host:port
     httpClient = new DefaultHttpClient();
     getRequest = new HttpGet(url);
     getRequest.addHeader("accept", "application/json");

     while(!isStopped) {
        val response = httpClient.execute(getRequest);
        if (response.getStatusLine().getStatusCode() != 200) {
                        throw new RuntimeException("Failed : HTTP error code : "+ response.getStatusLine().getStatusCode());
        }
        val reader = new BufferedReader(new InputStreamReader((response.getEntity().getContent())));
        userInput = reader.readLine()
        while(userInput != null) {
           store(userInput)
          //println(userInput)
          userInput = reader.readLine()
        }
       reader.close()
       Thread.sleep(60*1000)
     }
     httpClient.close()
     // Restart in an attempt to connect again when server is active again
     //restart("Trying to connect again")
    } catch {
     case e: java.net.ConnectException =>
       // restart if could not connect to server
       restart("Error connecting to " + url, e)
     case t: Throwable =>
       // restart if there is any other error
       restart("Error receiving data", t)
    }
  }

} 

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}

object TFLStreamingApp {
  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("TFLStreaming")
    val ssc = new StreamingContext(conf, Seconds(300))
    val stream = ssc.receiverStream(new TFLArrivalPredictionsByLine())
    println("Before")
    stream.print()
    println("After")
    if (args.length > 2) {
      stream.saveAsTextFiles(args(2))
    }
    ssc.start() 
    ssc.awaitTermination()
  }
} 

package com.ggstar.example

import com.ggstar.ctrmodel._
import com.ggstar.features.FeatureEngineering
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}

object ModelSerialization {
  def main(args: Array[String]): Unit = {

    Logger.getLogger("org").setLevel(Level.ERROR)

    val conf = new SparkConf()
      .setMaster("local")
      .setAppName("ctrModel")
      .set("spark.submit.deployMode", "client")

    val spark = SparkSession.builder.config(conf).getOrCreate()

    val resourcesPath = this.getClass.getResource("/samples.snappy.orc")
    val rawSamples = spark.read.format("orc").option("compression", "snappy").load(resourcesPath.getPath)


    //transform array to vector for following vectorAssembler
    val samples = FeatureEngineering.transferArray2Vector(rawSamples)

    samples.printSchema()
    samples.show(5, false)


    //model training
    println("Neural Network Ctr Prediction Model:")
    val innModel = new InnerProductNNCtrModel()
    innModel.train(samples)
    val transformedData = innModel.transform(samples)

    transformedData.show(1,false)

    //model serialization by mleap
    val mleapModelSerializer = new com.ggstar.serving.mleap.serialization.ModelSerializer()
    mleapModelSerializer.serializeModel(innModel._pipelineModel, "jar:file:/Users/zhwang/Workspace/CTRmodel/model/inn.model.mleap.zip", transformedData)

    //model serialization by JPMML
    val jpmmlModelSerializer = new com.ggstar.serving.jpmml.serialization.ModelSerializer()
    jpmmlModelSerializer.serializeModel(innModel._pipelineModel, "model/inn.model.jpmml.xml", transformedData)
  }
} 

package com.ggstar.example

import com.ggstar.ctrmodel._
import com.ggstar.evaluation.Evaluator
import com.ggstar.features.FeatureEngineering
import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.log4j.{Level, Logger}

object ModelSelection {
  def main(args: Array[String]): Unit = {

    Logger.getLogger("org").setLevel(Level.ERROR)

    val conf = new SparkConf()
      .setMaster("local")
      .setAppName("ctrModel")
      .set("spark.submit.deployMode", "client")

    val spark = SparkSession.builder.config(conf).getOrCreate()

    val resourcesPath = this.getClass.getResource("/samples.snappy.orc")
    val rawSamples = spark.read.format("orc").option("compression", "snappy").load(resourcesPath.getPath)
    rawSamples.printSchema()
    rawSamples.show(10)

    //transform array to vector for following vectorAssembler
    val samples = FeatureEngineering.transferArray2Vector(rawSamples)

    //split samples into training samples and validation samples
    val Array(trainingSamples, validationSamples) = samples.randomSplit(Array(0.7, 0.3))
    val evaluator = new Evaluator

    
  }
} 

package jp.gihyo.spark.ch08

// scalastyle:off println
import org.apache.log4j.{Level, Logger}

import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.rdd.RDD


object GenerateVerticesExample {

  def main(args: Array[String]): Unit = {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    // ログレベルをWARNに設定
    Logger.getLogger("org").setLevel(Level.WARN)

    // SparkContextの生成
    val conf = new SparkConf().setAppName("GenerateVerticesExample")
    val sc = new SparkContext(conf)

    // 引数から設定値を取得
    val (numProducts, numUsers): (Int, Int) = (args(0).toInt, args(1).toInt)
    implicit val recOpts: RecommendLogOptions = RecommendLogOptions(numProducts, numUsers)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext)
         (implicit recOpts: RecommendLogOptions)
  : Unit = {

    // 商品リスト、ユーザリストのRDDを生成
    val products: RDD[VertexProperty] = sc.parallelize(PurchaseLogGenerator.genProductList)
    val users: RDD[VertexProperty] = sc.parallelize(PurchaseLogGenerator.genUserList)

    // 商品リスト20件を表示
    println("===================================")
    println("get top 20 products:")
    products.take(20).foreach(x => println(s"id: ${x.id},\ttype: ${x.kind},\tname: ${x.name}"))

    // ユーザリスト20件を表示
    println("===================================")
    println("get top 20 users:")
    users.take(20).foreach(x => println(s"id: ${x.id},\ttype: ${x.kind},\tname: ${x.name}"))

  }
}
// scalastyle:on println 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_2_1_Sample {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)

    val wordCounts = run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String]) {
    val words = stream.flatMap(_.split(" "))
    val pairs = words.map(word => (word, 1))
    val wordCounts = pairs.reduceByKey(_ + _)
    wordCounts.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_Join {
  def main(args: Array[String]) {
    if (args.length != 4) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost1 = args(0)
    val targetHostPort1 = args(1).toInt
    val targetHost2 = args(2)
    val targetHostPort2 = args(3).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))
    val lines1 = ssc.socketTextStream(targetHost1, targetHostPort1)
    val lines2 = ssc.socketTextStream(targetHost2, targetHostPort2)
    run(lines1, lines2)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String], joinStream: InputDStream[String]) {
    val lines1KV = stream.map(x => (x, "attribute1"))
    val lines2KV = joinStream.map(x => (x, Array("attribute2", "attribute3", "attribute4")))
    val linesKVW = lines1KV.join(lines2KV)
    linesKVW.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_Reduce {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String]) {
    val windowLineCount = stream.reduce((x, y) => x + "," + y)
    windowLineCount.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_reduceByWindow {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val sc = new SparkContext(conf)
    val ssc = new StreamingContext(sc, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String], windowLength: Int = 10, slideInterval: Int = 5) {
    val userList = stream.reduceByWindow((x, y) =>
      x + y, Seconds(windowLength), Seconds(slideInterval))
    userList.print
  }
} 

package jp.gihyo.spark.ch06

// scalastyle:off println
import kafka.serializer.StringDecoder
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_KafkaStream {
  def main(args: Array[String]) {
    if (args.length != 4) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val brokerList = args(0)
    val consumeTopic = args(1)
    val checkpointDir = args(2)
    val saveDir = args(3)

    val f = createStreamingContext(brokerList, consumeTopic, checkpointDir, saveDir)
    // StreamingContextの取得
    val ssc = StreamingContext.getOrCreate(checkpointDir, f)

    sys.ShutdownHookThread {
      System.out.println("Gracefully stopping SparkStreaming Application")
      ssc.stop(true, true)
      System.out.println("SparkStreaming Application stopped")
    }
    ssc.start
    ssc.awaitTermination
  }

  def createStreamingContext(brokerList: String,
      consumeTopic: String,
      checkpointDir: String,
      saveDir: String): () => StreamingContext = { () => {
      
    System.out.println(values)
    Some(running.getOrElse(0) + values.length)
  }

  def run(stream: InputDStream[(String, String)],
    saveDir: String, windowLength: Int = 30, slideInterval: Int = 5) {
    val baseStream = stream.transform(rdd => {
      val t = (Long.MaxValue - System.currentTimeMillis)
      rdd.map(x => (x._1, x._2 + ", " + t))
    }).map(x => {
      val splitVal = x._2.split(",")
      val userVal = splitVal(0).split(":")
      val actionVal = splitVal(1).split(":")
      val pageVal = splitVal(2).split(":")
      val timestamp = splitVal(3)
      (actionVal(1), userVal(1), pageVal(1), timestamp)
    })
    baseStream.persist()

    val accountStream = baseStream.filter(_._1 == "view")
      .map(x => x._2)
      .countByValue()

    val totalUniqueUser = accountStream
      .updateStateByKey[Int](updateStateByKeyFunction _)
      .count()
      .map(x => "totalUniqueUser:" + x)

    val baseStreamPerTirty = baseStream
      .window(Seconds(windowLength), Seconds(slideInterval))
      .filter(_._1 == "view")
    baseStreamPerTirty.persist()

    val pageViewPerTirty = baseStreamPerTirty
      .count()
      .map(x => "PageView:" + x)

    val uniqueUserPerTirty = baseStreamPerTirty
      .map(x => x._2)
      .countByValue()
      .count()
      .map(x => "UniqueUser:" + x)

    val pageViewStream = baseStream
      .filter(_._1 == "view")
      .map(x => x._3)
      .count()
      .map(x => "PageView:" + x)

    val outputStream = totalUniqueUser
      .union(pageViewPerTirty)
      .union(uniqueUserPerTirty)
      .union(pageViewStream)
      .reduce((x, y) => x + ", " + y)
      .saveAsTextFiles(saveDir)
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch06

// scalastyle:off println

import org.atilika.kuromoji.Token
import twitter4j.Status

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.twitter.TwitterUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

object gihyo_6_3_TwitterStream {
  def main(args: Array[String]) {
    if (args.length != 7) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }

    val Array(cKey, cSecret, aToken, aSecret, cDir, tagDir, wordDir) = args

    System.setProperty("twitter4j.oauth.consumerKey", cKey)
    System.setProperty("twitter4j.oauth.consumerSecret", cSecret)
    System.setProperty("twitter4j.oauth.accessToken", aToken)
    System.setProperty("twitter4j.oauth.accessTokenSecret", aSecret)
    val f = createStreamingContext(cDir, tagDir, wordDir)
    val ssc = StreamingContext.getOrCreate(cDir, f)

    sys.ShutdownHookThread {
      System.out.println("Gracefully stopping SparkStreaming Application")
      ssc.stop(true, true)
      System.out.println("SparkStreaming Application stopped")
    }
    ssc.start
    ssc.awaitTermination
  }

  def createStreamingContext(checkpointDir: String,
      tagDir: String,
      wordDir: String): () => StreamingContext = { () => {
    
    val conf = new SparkConf().setAppName("gihyoSample_Application")
      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    conf.registerKryoClasses(Array(classOf[UserDic]))
    val sc = new SparkContext(conf)
    val ssc = new StreamingContext(sc, Seconds(5))
    ssc.checkpoint(checkpointDir)
    val twitterStream = TwitterUtils.createStream(ssc, None)
    run(sc, twitterStream, tagDir, wordDir)
    ssc
  }
  }

  def run(sc: SparkContext, stream: InputDStream[Status], tagDir: String, wordDir: String) {
    val tokenizer = sc.broadcast(UserDic.getInstance)
    val tweets = stream.map(tweet => tweet.getText())
    tweets.persist()
    val TweetText = tweets
      .flatMap(text => {
        val tokens = tokenizer.value.tokenize(text).toArray
        tokens.filter(t => {
          val token = t.asInstanceOf[Token]
          ((token.getPartOfSpeech.indexOf("名詞") > -1 &&
            token.getPartOfSpeech.indexOf("一般") > -1) ||
            token.getPartOfSpeech.indexOf("カスタム名詞") > -1) &&
            token.getSurfaceForm.length > 1 &&
            !(token.getSurfaceForm matches "^[a-zA-Z]+$|^[0-9]+$")
        }).map(t => t.asInstanceOf[Token].getSurfaceForm)
      })
      .countByValue()
      .map(x => (x._2, x._1))
      .transform(_.sortByKey(false))
      .map(x => (x._2, x._1))

    val TweetTags = tweets
      .flatMap(tweet => tweet.split(" ").filter(_.startsWith("#")))
      .countByValue()
      .map(x => (x._2, x._1))
      .transform(_.sortByKey(false))
      .map(x => (x._2, x._1))

    TweetText.saveAsTextFiles(wordDir)
    TweetTags.saveAsTextFiles(tagDir)
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.ReceiverInputDStream
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka.KafkaUtils

object gihyo_6_3_Union {
  def main(args: Array[String]) {
    if (args.length != 3) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHosts = args(0)
    val consumerGroup = args(1)
    val targetTopics = args(2)

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))

    val KafkaStreams = (1 to 5).map { i =>
      KafkaUtils.createStream(ssc, targetHosts, consumerGroup, Map(targetTopics -> 1))
    }
    run(ssc, KafkaStreams)

    ssc.start
    ssc.awaitTermination
  }

  def run(ssc: StreamingContext, streams: IndexedSeq[InputDStream[(String, String)]]) {
    val unionedStream = ssc.union(streams)
    unionedStream.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_flatMap {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String]) {
    val words = stream.flatMap(line => line.split(" "))
    words.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_Repartition {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String]) {
    val repartitionData = stream.repartition(3)
    // scalastyle:off println
    repartitionData.foreachRDD(rdd => println(s"partition size: ${rdd.partitions.size.toString}"))
    // scalastyle:on println
    repartitionData.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_Count {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String], windowLength: Int = 10, slideInterval: Int = 5) {
    val lineCount = stream.window(Seconds(windowLength), Seconds(slideInterval)).count
    lineCount.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_Map {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String]) {
    val lineCount = stream.map(line => (line, 1))
    lineCount.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream


object gihyo_6_3_Cogroup {
  def main(args: Array[String]) {
    if (args.length != 4) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost1 = args(0)
    val targetHostPort1 = args(1).toInt
    val targetHost2 = args(2)
    val targetHostPort2 = args(3).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))
    val lines1 = ssc.socketTextStream(targetHost1, targetHostPort1)
    val lines2 = ssc.socketTextStream(targetHost2, targetHostPort2)
    run(lines1, lines2)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String], otherStream: InputDStream[String]) {
    val lines1KV = stream.map(x => (x, "attribute1"))
    val lines2KV = otherStream.map(x => (x, "attribute2"))
    val linesKVW = lines1KV.cogroup(lines2KV)
    linesKVW.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_reduceByKey {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String]) {
    val countKeyValue = stream.map(x => (x, 1)).reduceByKey((x, y) => x + y)
    countKeyValue.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_reduceByKeyAndWindow_efficient {
  def main(args: Array[String]) {
    if (args.length != 3) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt
    val checkpointDir = args(2)

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val sc = new SparkContext(conf)
    val ssc = new StreamingContext(sc, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    ssc.checkpoint(checkpointDir)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String], windowLength: Int = 10, slideInterval: Int = 5) {
    val userList = stream.map(x => (x, 1))
      .reduceByKeyAndWindow(
        (a: Int, b: Int) => a + b,
        (a: Int, b: Int) => a - b, Seconds(windowLength), Seconds(slideInterval))
    userList.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_Transform {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val sc = new SparkContext(conf)
    val ssc = new StreamingContext(sc, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    val blackList = sc.parallelize(Array(("user002", "rockLogin"), ("user003", "rockPayment")))
    run(lines, blackList)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String], blackList: RDD[(String, String)]) {
    val userList = stream.map(x => (x, "action:Login")).transform(rdd => {
      val tmpUserList = rdd.leftOuterJoin(blackList)
      tmpUserList.filter(user => (user._2._2 == None))
    })
    userList.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_reduceByKeyAndWindow {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val sc = new SparkContext(conf)
    val ssc = new StreamingContext(sc, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String], windowLength: Int = 10, slideInterval: Int = 5) {
    val userList = stream.map(x => (x, 1))
      .reduceByKeyAndWindow((a: Int, b: Int) =>
        a + b, Seconds(windowLength), Seconds(slideInterval))
    userList.print
  }
} 

package jp.gihyo.spark.ch06

// scalastyle:off println
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_countByValueAndWindow {
  def main(args: Array[String]) {
    if (args.length != 3) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt
    val checkpointDir = args(2)

    val f = createStreamingContext(targetHost, targetHostPort, checkpointDir)
    val ssc = StreamingContext.getOrCreate(checkpointDir, f)

    sys.ShutdownHookThread {
      System.out.println("Gracefully stopping SparkStreaming Application")
      ssc.stop(true, true)
      System.out.println("SparkStreaming Application stopped")
    }
    ssc.start
    ssc.awaitTermination
  }

  def createStreamingContext(
      targetHost: String,
      targetHostPort: Int, checkpointDir: String): () => StreamingContext = { () => {
    
    val conf = new SparkConf().setAppName("gihyoSample_Application")
    val sc = new SparkContext(conf)
    val ssc = new StreamingContext(sc, Seconds(5))
    ssc.checkpoint(checkpointDir)

    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)
    ssc
  }
  }

  def run(stream: InputDStream[String], windowLength: Int = 10, slideInterval: Int = 5) {
    val userList = stream.countByValueAndWindow(Seconds(windowLength), Seconds(slideInterval))
    userList.print
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch06

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_updateStateByKey {
  def main(args: Array[String]) {
    if (args.length != 3) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt
    val checkpointDir = args(2)

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val sc = new SparkContext(conf)
    val ssc = new StreamingContext(sc, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    ssc.checkpoint(checkpointDir)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String]) {
    val userList = stream.map(x => (x, 1)).updateStateByKey[Int](updateStateByKeyFunction _)
    userList.print
  }

  def updateStateByKeyFunction(values: Seq[Int], running: Option[Int]): Option[Int] = {
    
    Some(running.getOrElse(0) + values.size)
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_Filter {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String]) {
    val overData = stream.filter(line => line.length > 5)
    overData.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_countByWindow {
  def main(args: Array[String]) {
    if (args.length != 3) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt
    val checkpointDir = args(2)

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val sc = new SparkContext(conf)
    val ssc = new StreamingContext(sc, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    ssc.checkpoint(checkpointDir)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String], windowLength: Int = 10, slideInterval: Int = 5) {
    val userList = stream.countByWindow(Seconds(windowLength), Seconds(slideInterval))
    userList.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream

object gihyo_6_3_Window {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val sc = new SparkContext(conf)
    val ssc = new StreamingContext(sc, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String], windowLength: Int = 10, slideInterval: Int = 5) {
    val userList = stream.window(Seconds(windowLength), Seconds(slideInterval)).countByValue()
    userList.print
  }
} 

package jp.gihyo.spark.ch06

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{StreamingContext, Seconds}
import org.apache.spark.streaming.dstream.InputDStream


object gihyo_6_3_countByValue {
  def main(args: Array[String]) {
    if (args.length != 2) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val targetHost = args(0)
    val targetHostPort = args(1).toInt

    val conf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(conf, Seconds(5))
    val lines = ssc.socketTextStream(targetHost, targetHostPort)
    run(lines)

    ssc.start
    ssc.awaitTermination
  }

  def run(stream: InputDStream[String]) {
    val countValue = stream.countByValue()
    countValue.print
  }
} 

package jp.gihyo.spark.ch03.basic_action

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object ReduceExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("ReduceExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val nums = sc.parallelize(Array(3, 2, 4, 1, 2, 1, 6, 3), 3)
    nums.reduce((x, y) => x + y)

    println(s"""nums: ${nums.collect().mkString(", ")}""")
    println(s"""sum:  ${nums.reduce((x, y) => x + y)}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_action

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object StatsExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("StatsExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val nums = sc.parallelize(Array.range(1, 11))
    val stats = nums.stats()

    println(s"""nums:   ${nums.collect().mkString(", ")}""")
    println(s"""count:  ${stats.count}""")
    println(s"""mean:   ${stats.mean}""")
    println(s"""stdev:  ${stats.stdev}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_action

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object FoldExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("FoldExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val nums = sc.parallelize(Array(3, 2, 4, 1, 2, 1, 6, 3), 3)
    nums.reduce((x, y) => x + y)

    println(s"""nums: ${nums.collect().mkString(", ")}""")
    println(s"""sum:  ${nums.fold(0)((x, y) => x + y)}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_action

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object OrderExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("OrderExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val nums = sc.parallelize(Array(3, 2, 4, 1, 2, 1))

    println(s"""nums:          ${nums.collect().mkString(", ")}""")
    println(s"""top3:          ${nums.top(3).mkString(", ")}""")
    println(s"""takeOredered3: ${nums.takeOrdered(3).mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_action

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object AggregateExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("AggregateExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  private[basic_action]
  def run(sc: SparkContext) {
    val nums = sc.parallelize(Array.range(1, 11), 3)

    val acc = nums.aggregate(zeroValue = (0.0, 0))(
      seqOp = (partAcc, n) => (partAcc._1 + n, partAcc._2 + 1),
      combOp = (acc1, acc2) => (acc1._1 + acc2._1, acc1._2 + acc2._2)
    )
    val avg = acc._1 / acc._2

    println(s"""nums: ${nums.collect().mkString(", ")}""")
    println(s"""sum:  ${nums.fold(0)((x, y) => x + y)}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.pairrdd_action

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object CollectAsMapExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("CollectAsMapExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(
      Array(
        ("Apple", 1), ("Orange", 1), ("Peach", 1), ("Orange", 1), ("PineApple", 1), ("Orange", 1)
      ), 3
    )
    val fruitsAsMap = fruits.collectAsMap()

    println(s"""fruits:      ${fruits.collect().mkString(", ")}""")
    println(s"""fruitsAsMap: $fruitsAsMap""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.persistence

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

object PersistExample {
  def main(args: Array[String]) {
    if (args.length != 1) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }

    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("PersistExample")
    val sc = new SparkContext(conf)

    run(sc, args(0))
    sc.stop()
  }

  def run(sc: SparkContext, inputFile: String) {
    val lines = sc.textFile(inputFile)
    lines.count()
    lines.collect()

    val persistedLines = sc.textFile(inputFile).persist()
    persistedLines.collect()
    persistedLines.count()

    persistedLines.unpersist()
    persistedLines.collect()
  }
} 

package jp.gihyo.spark.ch03.partition

import org.apache.log4j.{Level, Logger}
import org.apache.spark.Partitioner
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object CustomPartitionerExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("CustomPartitionerExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(Array("Apple", "Orange", "Peach", "Orange", "PineApple", "Orange"))

    val defaultPartitioned = fruits.map((_, 1)).reduceByKey(_ + _)
    val customPartitioned = fruits.map((_, 1)).reduceByKey(
      new FirstLetterPartitioner(sc.defaultParallelism), _ + _)

    println(s"""fruits:\n  ${fruits.collect().mkString(", ")}""")
    println()

    println("partitioned by default partitioner")
    defaultPartitioned.glom().mapPartitionsWithIndex((p, it) =>
      it.map(n => s"""  Par$p: ${n.mkString(",")}""")
    ).foreach(println)
    println()

    println("partitioned by first letter partitioner")
    customPartitioned.glom().mapPartitionsWithIndex((p, it) =>
      it.map(n => s"""  Par$p: ${n.mkString(",")}""")
    ).foreach(println)
  }
}

private[partition]
class FirstLetterPartitioner(numParts: Int) extends Partitioner {
  override def numPartitions: Int = numParts

  override def getPartition(key: Any): Int = {
    key.toString.charAt(0).hashCode % numPartitions match {
      case p if p < 0 => p + numPartitions
      case p => p
    }
  }

  override def equals(other: Any): Boolean = {
    other match {
      case p: FirstLetterPartitioner => p.numPartitions == numPartitions
      case _ => false
    }
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.partition

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object PartitionExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("Partition")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val nums = sc.parallelize(Array(3, 2, 4, 1, 2, 1), 1)
    println(s"""nums:\n  ${nums.collect().mkString(", ")}""")
    println()

    println("original:")
    nums.glom().mapPartitionsWithIndex((p, it) =>
      it.map(n => s"""  Par$p: ${n.mkString(",")}""")
    ).foreach(println)
    println()

    val numsPar3 = nums.repartition(3)
    println("repartition to 3:")
    numsPar3.glom().mapPartitionsWithIndex((p, it) =>
      it.map(n => s"""  Par$p: ${n.mkString(",")}""")
    ).foreach(println)
    println()

    val numsPar2 = numsPar3.coalesce(2)
    println("coalesce to 2:")
    numsPar2.glom().mapPartitionsWithIndex((p, it) =>
      it.map(n => s"""  Par$p: ${n.mkString(",")}""")
    ).foreach(println)
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.shared_variable

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object WordCountExample {
  def main(args: Array[String]) {
    if (args.length != 1) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }

    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("WordCountExample")
    val sc = new SparkContext(conf)

    run(sc, args(0))
    sc.stop()
  }

  def run(sc: SparkContext, inputFile: String) {
    val stopWordCount = sc.accumulator(0L)
    val stopWords = sc.broadcast(Set("a", "an", "for", "in", "on"))

    val lines = sc.textFile(inputFile)
    val words = lines.flatMap(_.split(" ")).filter(!_.isEmpty)
    val wordCounts = words.map(w => (w, 1)).reduceByKey(_ + _).filter { w =>
      val result = !stopWords.value.contains(w._1)
      if (!result) stopWordCount += 1L
      result
    }
    val sortedWordCounts = wordCounts.sortBy(_._2, ascending = false)

    println(s"""wordCounts:     ${sortedWordCounts.take(10).mkString(", ")}""")
    println(s"""stopWordCounts: ${stopWordCount.value}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.pairrdd_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object AggregateByKeyExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("AggregateByKeyExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(
      Array(("Apple", 6), ("Orange", 1), ("Apple", 2), ("Orange", 5), ("PineApple", 1)))
    val fruitCountAvgs = fruits.aggregateByKey(zeroValue = Acc(0.0, 0))(
      seqOp = (partAcc, n) => partAcc += n,
      combOp = (acc1, acc2) => acc1 ++= acc2
    ).mapValues(acc => acc.sum / acc.count)

    println(s"""fruits:         ${fruits.collect().mkString(", ")}""")
    println(s"""fruitCountAvgs: ${fruitCountAvgs.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.pairrdd_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object MapValuesExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("MapValuesExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(Array(("Apple", 1), ("Orange", 4), ("Apple", 2), ("Peach", 1)))
    val plusOnes = fruits.mapValues(v => v + 1)

    println(s"""fruits:   ${fruits.collect().mkString(", ")}""")
    println(s"""plusOnes: ${plusOnes.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.pairrdd_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object SortByKeyExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("SortByKeyExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(
      Array(("Apple", 6), ("Orange", 1), ("Apple", 2), ("Orange", 5), ("PineApple", 1)))
    val sortedByKeyAsc = fruits.sortByKey(ascending = false)

    println(s"""fruits:         ${fruits.collect().mkString(", ")}""")
    println(s"""sortedByKeyAsc: ${sortedByKeyAsc.collect().mkString(", ")}""")

    val nums = sc.parallelize(
      Array(("One", 1), ("Hundred", 100), ("Three", 3), ("Thousand", 1000)))
    implicit val sortByStrLen = new Ordering[String] {
      def compare(x: String, y: String): Int = x.length - y.length
    }
    val sortedByKeyLength = nums.sortByKey()

    println()
    println(s"""nums:              ${nums.collect().mkString(", ")}""")
    println(s"""sortedByKeyLength: ${sortedByKeyLength.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.pairrdd_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object CoGroupExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("CoGroupExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val persons = sc.parallelize(Array(
      ("Adam", "San francisco"),
      ("Bob", "San francisco"),
      ("Taro", "Tokyo"),
      ("Charles", "New York")
    ))
    val cities = sc.parallelize(Array(
      ("Tokyo", "Japan"),
      ("San francisco", "America"),
      ("Beijing", "China")
    ))
    val grouped = persons.map(_.swap).cogroup(cities)

    println(s"""persons: ${persons.collect().mkString(", ")}""")
    println(s"""cities:  ${cities.collect().mkString(", ")}""")
    println()
    println(s"""grouped:\n${grouped.collect().mkString("\n")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.pairrdd_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object JoinExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("JoinExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val persons = sc.parallelize(Array(
      ("Adam", "San francisco"),
      ("Bob", "San francisco"),
      ("Taro", "Tokyo"),
      ("Charles", "New York")
    ))
    val cities = sc.parallelize(Array(
      ("Tokyo", "Japan"),
      ("San francisco", "America"),
      ("Beijing", "China")
    ))

    val leftJoined = persons.map(_.swap).join(cities)
    val leftOuterJoined = persons.map(_.swap).leftOuterJoin(cities)
    val rightOuterJoined = persons.map(_.swap).rightOuterJoin(cities)
    val fullOuterJoined = persons.map(_.swap).fullOuterJoin(cities)

    println(s"""persons: ${persons.collect().mkString(", ")}""")
    println(s"""cities:  ${cities.collect().mkString(", ")}""")
    println()
    println(s"""leftJoined:\n${leftJoined.collect().mkString("\n")}""")
    println()
    println(s"""leftOuterJoined:\n${leftOuterJoined.collect().mkString("\n")}""")
    println()
    println(s"""rightOuterJoined:\n${rightOuterJoined.collect().mkString("\n")}""")
    println()
    println(s"""fullOuterJoined:\n${fullOuterJoined.collect().mkString("\n")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.pairrdd_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object GroupByKeyExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("GroupByKeyExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(
      Array(("Apple", 6), ("Orange", 1), ("Apple", 2), ("Orange", 5), ("PineApple", 1)))
    val grouped = fruits.groupByKey()

    println(s"""fruits:  ${fruits.collect().mkString(", ")}""")
    println(s"""grouped: ${grouped.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.pairrdd_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object ReduceByKeyExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("ReduceByKeyExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(Array(
      ("Apple", 1), ("Orange", 1), ("Peach", 1), ("Orange", 1), ("PineApple", 1), ("Orange", 1)))
    val fruitCounts = fruits.reduceByKey((x, y) => x + y)

    println(s"""fruits:      ${fruits.collect().mkString(", ")}""")
    println(s"""fruitCounts: ${fruitCounts.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.pairrdd_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object CombineByKeyExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("CombineByKeyExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(
      Array(("Apple", 6), ("Orange", 1), ("Apple", 2), ("Orange", 5), ("PineApple", 1)))
    val fruitCountAvgs = fruits.combineByKey(
      createCombiner = (v: Int) => Acc(v.toDouble, 1),
      mergeValue = (partAcc: Acc, n: Int) => partAcc += n,
      mergeCombiners = (acc1: Acc, acc2: Acc) => acc1 ++= acc2
    ).mapValues(acc => acc.sum / acc.count)

    println(s"""fruits:         ${fruits.collect().mkString(", ")}""")
    println(s"""fruitCountAvgs: ${fruitCountAvgs.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.pairrdd_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object FoldByKeyExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("FoldByKeyExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(Array(
      ("Apple", 1), ("Orange", 1), ("Peach", 1), ("Orange", 1), ("PineApple", 1), ("Orange", 1)))
    val fruitCounts = fruits.foldByKey(0)((x, y) => x + y)

    println(s"""fruits:      ${fruits.collect().mkString(", ")}""")
    println(s"""fruitCounts: ${fruitCounts.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_transformation

import com.fasterxml.jackson.databind.ObjectMapper
import com.fasterxml.jackson.module.scala.DefaultScalaModule
import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object MapPartitionsExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("MapPartitionsExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val jsonLines = sc.parallelize(Array(
      """{"name": "Apple",  "num": 1}""",
      """{"name": "Orange", "num": 4}""",
      """{"name": "Apple",  "num": 2}""",
      """{"name": "Peach",  "num": 1}"""
    ))

    val parsed = jsonLines.mapPartitions { lines =>
      val mapper = new ObjectMapper()
      mapper.registerModule(DefaultScalaModule)
      lines.map { line =>
        val f = mapper.readValue(line, classOf[Map[String, String]])
        (f("name"), f("num"))
      }
    }

    println(s"""json:\n${jsonLines.collect().mkString("\n")}""")
    println()
    println(s"""parsed:\n${parsed.collect().mkString("\n")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object FlatMapExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("FlatMapExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val lines = sc.parallelize(Array("Apple is red", "PineApple is yellow"))
    val words = lines.flatMap(line => line.split(" "))

    println(s"""lines: ${lines.collect().mkString(", ")}""")
    println(s"""words: ${words.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object SetOperationsExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("SetOperationsExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits1 = sc.parallelize(Array("Apple", "Orange", "Peach", "Orange", "PineApple", "Orange"))
    val fruits2 = sc.parallelize(Array("Grape", "Apple", "Banana", "Orange"))

    val union = fruits1.union(fruits2)
    val subtract = fruits1.subtract(fruits2)
    val intersection = fruits1.intersection(fruits2)
    val cartesian = fruits1.cartesian(fruits2)

    println(s"""fruits1: ${fruits1.collect().mkString(", ")}""")
    println(s"""fruits2: ${fruits2.collect().mkString(", ")}""")
    println(s"""union: ${union.collect().mkString(", ")}""")
    println(s"""subtract: ${subtract.collect().mkString(", ")}""")
    println(s"""intersection: ${intersection.collect().mkString(", ")}""")
    println(s"""cartesian: ${cartesian.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object MapExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("MapExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(Array("Apple", "Orange", "Peach", "Orange", "PineApple", "Orange"))
    val lengths = fruits.map(fruit => fruit.length)

    println(s"""fruits:  ${fruits.collect().mkString(", ")}""")
    println(s"""lengths: ${lengths.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object ZipExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("ZipExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits1 = sc.parallelize(
      Array("Apple", "Orange", "Peach", "Orange", "PineApple", "Orange"))
    val fruits2 = sc.parallelize(
      Array("りんご", "オレンジ", "桃", "オレンジ", "パイナップル", "オレンジ"))
    val zipped = fruits1.zip(fruits2)

    println(s"""fruits1: ${fruits1.collect().mkString(", ")}""")
    println(s"""fruits2: ${fruits2.collect().mkString(", ")}""")
    println(s"""zipped:  ${zipped.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object DistinctExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("DistinctExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(Array("Apple", "Orange", "Peach", "Orange", "PineApple", "Orange"))
    val uniques = fruits.distinct()

    println(s"""fruits:  ${fruits.collect().mkString(", ")}""")
    println(s"""uniques: ${uniques.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object SampleExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("SampleExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(Array("Apple", "Orange", "Peach", "Orange", "PineApple", "Orange"))
    val samples = fruits.sample(withReplacement = false, 0.5, 1)

    println(s"""fruits:  ${fruits.collect().mkString(", ")}""")
    println(s"""samples: ${samples.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch03.basic_transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}

// scalastyle:off println

object FilterExample {
  def main(args: Array[String]) {
    Logger.getLogger("org").setLevel(Level.WARN)

    val conf = new SparkConf().setAppName("FilterExample")
    val sc = new SparkContext(conf)

    run(sc)
    sc.stop()
  }

  def run(sc: SparkContext) {
    val fruits = sc.parallelize(Array("Apple", "Orange", "Peach", "Orange", "PineApple", "Orange"))
    val startWithPs = fruits.filter(fruit => fruit.startsWith("P"))

    println(s"""fruits:      ${fruits.collect().mkString(", ")}""")
    println(s"""startWithPs: ${startWithPs.collect().mkString(", ")}""")
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch05

// scalastyle:off println
import java.util.Properties

import org.apache.spark.sql.{DataFrame, SQLContext}
import org.apache.spark.{SparkConf, SparkContext}

object JdbcExample {

  
  def main(args: Seq[String]): Unit = {
    if (args.length != 3) {
      new IllegalArgumentException("Invalid arguments")
      System.exit(1)
    }
    val url = args(0)
    val user = args(1)
    val pass = args(2)

    val conf = new SparkConf().setAppName("JdbcExample")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)

    run(sc, sqlContext, url, user, pass)

    sc.stop()
  }

  def run(sc: SparkContext, sqlContext: SQLContext,
      url: String, user: String, pass: String): Unit = {
    val prop = new Properties()
    prop.setProperty("user", user)
    prop.setProperty("password", pass)

    val df: DataFrame = sqlContext.read.jdbc(url, "gihyo_spark.person", prop)
    df.printSchema()
    println("# Rows: " + df.count())
  }
}
// scalastyle:on println 

package jp.gihyo.spark.ch05

// scalastyle:off println
import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}


object DataFrameNaFunctionExample {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("BasicDataFrameExample")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    run(sc, sqlContext)
    sc.stop()
  }

  def run(
      sc: SparkContext,
      sqlContext: SQLContext): Unit = {
    import sqlContext.implicits._

    val nullDF = Seq[(String, java.lang.Integer, java.lang.Double)](
      ("Bob", 16, 176.5),
      ("Alice", null, 164.3),
      ("", 60, null),
      ("UNKNOWN", 25, Double.NaN),
      ("Amy", null, null),
      (null, null, Double.NaN)
    ).toDF("name", "age", "height")

    // drop
    nullDF.na.drop("any").show()
    nullDF.na.drop("all").show()
    nullDF.na.drop(Array("age")).show()
    nullDF.na.drop(Seq("age", "height")).show()
    nullDF.na.drop("any", Array("name", "age")).show()
    nullDF.na.drop("all", Array("age", "height")).show()

    // fill
    nullDF.na.fill(0.0, Array("name", "height")).show()
    nullDF.na.fill(Map(
      "name" -> "UNKNOWN",
      "height" -> 0.0
    )).show()

    // replace
    nullDF.na.replace("name", Map("" -> "UNKNOWN")).show()
  }
}

// scalastyle:on println 

package jp.gihyo.spark.ch05

import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.sql.{Dataset, SQLContext}
import org.apache.spark.sql.functions._

private case class Person(id: Int, name: String, age: Int)

object DatasetExample {

  
  def main(args: Seq[String]): Unit = {
    val conf = new SparkConf().setAppName("DatasetExample")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    run(sc, sqlContext)
    sc.stop()
  }

  def run(sc: SparkContext, sqlContext: SQLContext): Unit = {
    import sqlContext.implicits._

    // Creates a Dataset from a `Seq`
    val seq = Seq((1, "Bob", 23), (2, "Tom", 23), (3, "John", 22))
    val ds1: Dataset[(Int, String, Int)] = sqlContext.createDataset(seq)
    val ds2: Dataset[(Int, String, Int)] = seq.toDS()

    // Creates a Dataset from a `RDD`
    val rdd = sc.parallelize(seq)
    val ds3: Dataset[(Int, String, Int)] = sqlContext.createDataset(rdd)
    val ds4: Dataset[(Int, String, Int)] = rdd.toDS()

    // Creates a Dataset from a `DataFrame`
    val df = rdd.toDF("id", "name", "age")
    val ds5: Dataset[Person] = df.as[Person]

    // Selects a column
    ds5.select(expr("name").as[String]).show()

    // Filtering
    ds5.filter(_.name == "Bob").show()
    ds5.filter(person => person.age == 23).show()

    // Groups and counts the number of rows
    ds5.groupBy(_.age).count().show()
  }
} 

package jp.gihyo.spark

import org.scalatest.{BeforeAndAfterEach, Suite}

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{StreamingContext, Seconds}
import jp.gihyo.spark.ch06.UserDic

private[spark]
trait TestStreamingContext extends BeforeAndAfterEach { self: Suite =>
  @transient var ssc: StreamingContext = _
  @transient var sc: SparkContext = _
  val master = "local[2]"
  val appN = "StreamingUnitTest"
  val bd = Seconds(1)

  override def beforeEach() {
    super.beforeEach()
    val conf = new SparkConf().setMaster(master)
      .setAppName(appN)
      .set("spark.streaming.clock", "org.apache.spark.util.ManualClock")
      .registerKryoClasses(Array(classOf[UserDic]))

    ssc = new StreamingContext(conf, bd)
    sc = ssc.sparkContext
  }

  override def afterEach() {
    try {
      if (ssc != null) {
        // stop with sc
        ssc.stop(true)
      }
      ssc = null;
    } finally {
      super.afterEach()
    }
  }
} 

package jp.gihyo.spark

import org.scalatest.{BeforeAndAfterAll, Suite}

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SQLContext

private[spark]
trait TestSparkContext extends BeforeAndAfterAll { self: Suite =>
  @transient var sc: SparkContext = _
  @transient var sqlContext: SQLContext = _

  override def beforeAll() {
    super.beforeAll()
    val conf = new SparkConf()
      .setMaster("local[2]")
      .setAppName("SparkUnitTest")
      .set("spark.sql.shuffle.partitions", "2")
    sc = new SparkContext(conf)
    SQLContext.clearActive()
    sqlContext = new SQLContext(sc)
    SQLContext.setActive(sqlContext)
  }

  override def afterAll() {
    try {
      sqlContext = null
      SQLContext.clearActive()
      if (sc != null) {
        sc.stop()
      }
      sc = null
    } finally {
      super.afterAll()
    }
  }
} 

import org.apache.spark.{SparkContext, SparkConf}

object TestMain {
  private val master = "Core1"
  private val port = "7077"
  private val appName = "hbase-rdd_spark"
  private val data = "hdfs://Master1:8020/test/spark/hbase/testhb"

  def main(args: Array[String]) {
    val sparkConf = new SparkConf()
      .setMaster(s"spark://$master:$port")
      .setAppName(appName).setJars(List("/home/lele/coding/hbrdd/out/artifacts/hbrdd_jar/hbrdd.jar"))

    val sc = new SparkContext(sparkConf)
    val ret = sc.textFile(data).map({ line =>
        val Array(k, col1, col2, _) = line split "\t"
        val content = Map("col1" -> col1, "col2" -> col2)
        k -> content
      })

    println(ret.count())

    sc.stop()
  }
} 

package com.twilio.open.odsc.realish

import com.holdenkarau.spark.testing.{LocalSparkContext, SparkContextProvider}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SparkSession
import org.scalatest.{BeforeAndAfterAll, Suite}

object TestUtils {

}

@SerialVersionUID(1L)
case class UserPersonality(uuid: String, name: String, tags: Array[String])
  extends Serializable

@SerialVersionUID(1L)
case class Author(uuid: String, name: String, age: Int) extends Serializable

@SerialVersionUID(1L)
case class LibraryBook(uuid: String, name: String, author: Author) extends Serializable

case class MockKafkaDataFrame(key: Array[Byte], value: Array[Byte])

trait SharedSparkSql extends BeforeAndAfterAll with SparkContextProvider {
  self: Suite =>

  @transient var _sparkSql: SparkSession = _
  @transient private var _sc: SparkContext = _

  override def sc: SparkContext = _sc

  def conf: SparkConf

  def sparkSql: SparkSession = _sparkSql

  override def beforeAll() {
    _sparkSql = SparkSession.builder().config(conf).getOrCreate()

    _sc = _sparkSql.sparkContext
    setup(_sc)
    super.beforeAll()
  }

  override def afterAll() {
    try {
      _sparkSql.close()
      _sparkSql = null
      LocalSparkContext.stop(_sc)
      _sc = null
    } finally {
      super.afterAll()
    }
  }

} 

package com.twilio.open.odsc.realish

import com.holdenkarau.spark.testing.{LocalSparkContext, SparkContextProvider}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SparkSession
import org.scalatest.{BeforeAndAfterAll, Suite}

object TestUtils {

}

@SerialVersionUID(1L)
case class UserPersonality(uuid: String, name: String, tags: Array[String])
  extends Serializable

@SerialVersionUID(1L)
case class Author(uuid: String, name: String, age: Int) extends Serializable

@SerialVersionUID(1L)
case class LibraryBook(uuid: String, name: String, author: Author) extends Serializable

case class MockKafkaDataFrame(key: Array[Byte], value: Array[Byte])

trait SharedSparkSql extends BeforeAndAfterAll with SparkContextProvider {
  self: Suite =>

  @transient var _sparkSql: SparkSession = _
  @transient private var _sc: SparkContext = _

  override def sc: SparkContext = _sc

  def conf: SparkConf

  def sparkSql: SparkSession = _sparkSql

  override def beforeAll() {
    _sparkSql = SparkSession.builder().config(conf).getOrCreate()

    _sc = _sparkSql.sparkContext
    setup(_sc)
    super.beforeAll()
  }

  override def afterAll() {
    try {
      _sparkSql.close()
      _sparkSql = null
      LocalSparkContext.stop(_sc)
      _sc = null
    } finally {
      super.afterAll()
    }
  }

} 

package es.alvsanand.spark_recommender

import es.alvsanand.spark_recommender.parser.{DatasetDownloader, DatasetIngestion}
import es.alvsanand.spark_recommender.utils.{ESConfig, Logging, MongoConfig}
import org.apache.spark.SparkConf
import scopt.OptionParser


object DatasetLoaderApp extends App with Logging {

  override def main(args: Array[String]) {
    val defaultParams = scala.collection.mutable.Map[String, Any]()
    defaultParams += "spark.cores" -> "local[*]"
    defaultParams += "spark.option" -> scala.collection.mutable.Map[String, String]()
    defaultParams += "mongo.uri" -> "mongodb://127.0.0.1:27017/spark_recommender"
    defaultParams += "mongo.db" -> "spark_recommender"
    defaultParams += "es.httpHosts" -> "127.0.0.1:9200"
    defaultParams += "es.transportHosts" -> "127.0.0.1:9300"
    defaultParams += "es.index" -> "spark_recommender"
    defaultParams += "dataset.tmp.dir" -> "%s/.spark_recommender".format(sys.env("HOME"))

    val parser = new OptionParser[scala.collection.mutable.Map[String, Any]]("ScaleDataset") {
      head("Spark Recommender Example")
      opt[String]("spark.cores")
        .text("Number of cores in the Spark cluster")
        .action((x, c) => {
          c += "spark.cores" -> x
        })
      opt[Map[String,String]]("spark.option")
        .text("Spark Config Option")
        .valueName("spark.property1=value1,spark.property2=value2,...")
        .action { (x, c) => {
          c("spark.option").asInstanceOf[scala.collection.mutable.Map[String, Any]] ++= x.toSeq
          c
        }
        }
      opt[String]("mongo.uri")
        .text("Mongo URI including the DB")
        .action((x, c) => {
          c += "mongo.uri" -> x
        })
      opt[String]("mongo.db")
        .text("Mongo Database")
        .action((x, c) => {
          c += "mongo.db" -> x
        })
      opt[String]("es.httpHosts")
        .text("ElasicSearch HTTP Hosts")
        .action((x, c) => {
          c += "es.httpHosts" -> x
        })
      opt[String]("es.transportHosts")
        .text("ElasicSearch Transport Hosts")
        .action((x, c) => {
          c += "es.transportHosts" -> x
        })
      opt[String]("es.index")
        .text("ElasicSearch index")
        .action((x, c) => {
          c += "es.index" -> x
        })
      opt[String]("dataset.tmp.dir")
        .text("Temporal directory to store the products dataset")
        .action((x, c) => {
          c += "dataset.tmp.dir" -> x
        })
      opt[String]("dataset.file")
        .text("Ingest only one dataset file")
        .action((x, c) => {
          c += "dataset.file" -> x
        })
      help("help") text("prints this usage text")
    }
    parser.parse(args, defaultParams).map { params =>
      run(params.toMap)
    } getOrElse {
      System.exit(1)
    }
  }

  private def run(params: Map[String, Any]): Unit = {
    implicit val conf = new SparkConf().setAppName("RecommenderTrainerApp").setMaster(params("spark.cores").asInstanceOf[String])
    params("spark.option").asInstanceOf[scala.collection.mutable.Map[String, Any]].foreach { case (key: String, value: String) => conf.set(key, value) }
    implicit val mongoConf = new MongoConfig(params("mongo.uri").asInstanceOf[String], params("mongo.db").asInstanceOf[String])
    implicit val esConf = new ESConfig(params("es.httpHosts").asInstanceOf[String], params("es.transportHosts").asInstanceOf[String], params("es.index").asInstanceOf[String])


    try {
      DatasetDownloader.download(params("dataset.tmp.dir").asInstanceOf[String])
      DatasetIngestion.storeData(DatasetDownloader.getFinalDstName(params("dataset.tmp.dir").asInstanceOf[String]), Option(params.getOrElse("dataset.file", null).asInstanceOf[String]))
    }
    catch {
      case e: Exception =>
        logger.error("Error executing DatasetLoaderApp", e)
        sys.exit(1)
    }

    sys.exit(0)
  }
} 

package es.alvsanand.spark_recommender

import es.alvsanand.spark_recommender.trainer.ALSTrainer
import es.alvsanand.spark_recommender.utils.{Logging, MongoConfig}
import org.apache.spark.SparkConf
import scopt.OptionParser


object RecommenderTrainerApp extends App with Logging {

  override def main(args: Array[String]) {
    val defaultParams = scala.collection.mutable.Map[String, Any]()
    defaultParams += "spark.cores" -> "local[*]"
    defaultParams += "spark.option" -> scala.collection.mutable.Map[String, String]()
    defaultParams += "mongo.uri" -> "mongodb://127.0.0.1:27017/spark_recommender"
    defaultParams += "mongo.db" -> "spark_recommender"
    defaultParams += "maxRecommendations" -> ALSTrainer.MAX_RECOMMENDATIONS.toString

    val parser = new OptionParser[scala.collection.mutable.Map[String, Any]]("RecommenderTrainerApp") {
      head("Recommendation System Trainer")
      opt[String]("spark.cores")
        .text("Number of cores in the Spark cluster")
        .action((x, c) => {
          c += "spark.cores" -> x
        })
      opt[Map[String,String]]("spark.option")
        .text("Spark Config Option")
        .valueName("spark.property1=value1,spark.property2=value2,...")
        .action { (x, c) => {
          c("spark.option").asInstanceOf[scala.collection.mutable.Map[String, Any]] ++= x.toSeq
          c
        }
        }
      opt[String]("mongo.uri")
        .text("Mongo Hosts")
        .action((x, c) => {
          c += "mongo.uri" -> x
        })
      opt[String]("mongo.db")
        .text("Mongo Database")
        .action((x, c) => {
          c += "mongo.db" -> x
        })
      opt[String]("maxRecommendations")
        .text("Maximum number of recommendations")
        .action((x, c) => {
          c += "maxRecommendations" -> x
        })
      help("help") text("prints this usage text")
    }
    parser.parse(args, defaultParams).map { params =>
      run(params.toMap)
    } getOrElse {
      System.exit(1)
    }
  }

  private def run(params: Map[String, Any]): Unit = {
    implicit val conf = new SparkConf().setAppName("RecommenderTrainerApp").setMaster(params("spark.cores").asInstanceOf[String])
    params("spark.option").asInstanceOf[scala.collection.mutable.Map[String, Any]].foreach { case (key: String, value: String) => conf.set(key, value) }

    implicit val mongoConf = new MongoConfig(params("mongo.uri").asInstanceOf[String], params("mongo.db").asInstanceOf[String])
    val maxRecommendations = params("maxRecommendations").asInstanceOf[String].toInt

    try {
      ALSTrainer.calculateRecs(maxRecommendations)
    }
    catch {
      case e: Exception =>
        logger.error("Error executing RecommenderTrainerApp", e)
        sys.exit(1)
    }

    sys.exit(0)
  }
} 

package com.mozilla.spark.sql.hyperloglog.test

import com.mozilla.spark.sql.hyperloglog.aggregates._
import com.mozilla.spark.sql.hyperloglog.functions._
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.functions._
import org.apache.spark.{SparkConf, SparkContext}
import org.scalatest.{FlatSpec, Matchers}

class HyperLogLogTest extends FlatSpec with Matchers{
 "Algebird's HyperLogLog" can "be used from Spark" in {
  val sparkConf = new SparkConf().setAppName("HyperLogLog")
  sparkConf.setMaster(sparkConf.get("spark.master", "local[1]"))

  val sc = new SparkContext(sparkConf)
  val sqlContext = new SQLContext(sc)
  import sqlContext.implicits._

  val hllMerge = new HyperLogLogMerge
  sqlContext.udf.register("hll_merge", hllMerge)
  sqlContext.udf.register("hll_create", hllCreate _)
  sqlContext.udf.register("hll_cardinality", hllCardinality _)

  val frame = sc.parallelize(List("a", "b", "c", "c"), 4).toDF("id")
  val count = frame
    .select(expr("hll_create(id, 12) as hll"))
    .groupBy()
    .agg(expr("hll_cardinality(hll_merge(hll)) as count"))
    .collect()
  count(0)(0) should be (3)
 }
} 

package it.agilelab.darwin.app.spark

import com.typesafe.config.Config
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.slf4j.{Logger, LoggerFactory}

import scala.collection.JavaConverters._

trait SparkManager {

  val sparkManagerLogger: Logger = LoggerFactory.getLogger("SparkManager")

  
  protected def defaultParallelism(implicit sparkSession: SparkSession, config: Config): Int = {
    sparkSession.conf.getOption(SparkConfigurationKeys.SPARK_EXECUTOR_INSTANCES) match {
      case Some(instances) =>
        sparkSession.conf.getOption(SparkConfigurationKeys.SPARK_CORES).getOrElse("1").toInt * instances.toInt
      case None =>
        sparkManagerLogger.info("Spark is configured with dynamic allocation, default parallelism will be gathered from app " +
          "conf: " +
          "next.process.parallelism")
        if (config.hasPath(SparkConfigurationKeys.PARALLELISM)) {
          config.getInt(SparkConfigurationKeys.PARALLELISM)
        } else {
          sparkManagerLogger.info("next.process.parallelism was not set fallback to sparkSession.defaultParallelism")
          sparkSession.sparkContext.defaultParallelism
        }
    }
  }
} 

package keystoneml.pipelines.images.cifar

import breeze.linalg.DenseVector
import keystoneml.evaluation.MulticlassClassifierEvaluator
import keystoneml.loaders.CifarLoader
import keystoneml.nodes.images.{GrayScaler, ImageExtractor, ImageVectorizer, LabelExtractor}
import keystoneml.nodes.learning.LinearMapEstimator
import keystoneml.nodes.util.{Cacher, ClassLabelIndicatorsFromIntLabels, MaxClassifier}
import org.apache.spark.{SparkConf, SparkContext}
import keystoneml.pipelines.Logging
import scopt.OptionParser
import keystoneml.utils.Image
import keystoneml.workflow.Pipeline


object LinearPixels extends Logging {
  val appName = "LinearPixels"
  case class LinearPixelsConfig(trainLocation: String = "", testLocation: String = "")

  def run(sc: SparkContext, config: LinearPixelsConfig): Pipeline[Image, Int] = {
    val numClasses = 10

    // Load and cache the training data.
    val trainData = CifarLoader(sc, config.trainLocation).cache()

    val trainImages = ImageExtractor(trainData)

    val labelExtractor = LabelExtractor andThen
        ClassLabelIndicatorsFromIntLabels(numClasses) andThen
        new Cacher[DenseVector[Double]]
    val trainLabels = labelExtractor(trainData)

    // A featurizer maps input images into vectors. For this pipeline, we'll also convert the image to grayscale.
    // We then estimate our model by calling a linear solver on our data.
    val predictionPipeline = GrayScaler andThen
      ImageVectorizer andThen
      (new LinearMapEstimator, trainImages, trainLabels) andThen
      MaxClassifier

    // Calculate training error.
    val evaluator = new MulticlassClassifierEvaluator(numClasses)
    val trainEval = evaluator.evaluate(predictionPipeline(trainImages), LabelExtractor(trainData))

    // Do testing.
    val testData = CifarLoader(sc, config.testLocation)
    val testImages = ImageExtractor(testData)
    val testLabels = labelExtractor(testData)

    val testEval = evaluator.evaluate(predictionPipeline(testImages), LabelExtractor(testData))

    logInfo(s"Training accuracy: \n${trainEval.totalAccuracy}")
    logInfo(s"Test accuracy: \n${testEval.totalAccuracy}")

    predictionPipeline
  }

  def parse(args: Array[String]): LinearPixelsConfig = new OptionParser[LinearPixelsConfig](appName) {
    head(appName, "0.1")
    help("help") text("prints this usage text")
    opt[String]("trainLocation") required() action { (x,c) => c.copy(trainLocation=x) }
    opt[String]("testLocation") required() action { (x,c) => c.copy(testLocation=x) }
  }.parse(args, LinearPixelsConfig()).get

  
  def main(args: Array[String]) = {
    val appConfig = parse(args)

    val conf = new SparkConf().setAppName(appName)
    conf.setIfMissing("spark.master", "local[2]") // This is a fallback if things aren't set via spark submit.
    val sc = new SparkContext(conf)
    run(sc, appConfig)

    sc.stop()
  }

} 

package keystoneml.pipelines.text

import breeze.linalg.SparseVector
import keystoneml.evaluation.BinaryClassifierEvaluator
import keystoneml.loaders.{AmazonReviewsDataLoader, LabeledData}
import keystoneml.nodes.learning.LogisticRegressionEstimator
import keystoneml.nodes.nlp._
import keystoneml.nodes.stats.TermFrequency
import keystoneml.nodes.util.CommonSparseFeatures
import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}
import keystoneml.pipelines.Logging
import scopt.OptionParser
import keystoneml.workflow.Pipeline

object AmazonReviewsPipeline extends Logging {
  val appName = "AmazonReviewsPipeline"

  def run(spark: SparkSession, conf: AmazonReviewsConfig): Pipeline[String, Double] = {
    val amazonTrainData = AmazonReviewsDataLoader(spark, conf.trainLocation, conf.threshold).labeledData
    val trainData = LabeledData(amazonTrainData.repartition(conf.numParts).cache())

    val training = trainData.data
    val labels = trainData.labels

    // Build the classifier estimator
    val predictor = Trim andThen
        LowerCase() andThen
        Tokenizer() andThen
        NGramsFeaturizer(1 to conf.nGrams) andThen
        TermFrequency(x => 1) andThen
        (CommonSparseFeatures[Seq[String]](conf.commonFeatures), training) andThen
        (LogisticRegressionEstimator[SparseVector[Double]](numClasses = 2, numIters = conf.numIters), training, labels)

    // Evaluate the classifier
    val amazonTestData = AmazonReviewsDataLoader(spark, conf.testLocation, conf.threshold).labeledData
    val testData = LabeledData(amazonTestData.repartition(conf.numParts).cache())
    val testLabels = testData.labels
    val testResults = predictor(testData.data)
    val eval = BinaryClassifierEvaluator.evaluate(testResults.get.map(_ > 0), testLabels.map(_ > 0))

    logInfo("\n" + eval.summary())
    predictor
  }

  case class AmazonReviewsConfig(
    trainLocation: String = "",
    testLocation: String = "",
    threshold: Double = 3.5,
    nGrams: Int = 2,
    commonFeatures: Int = 100000,
    numIters: Int = 20,
    numParts: Int = 512)

  def parse(args: Array[String]): AmazonReviewsConfig = new OptionParser[AmazonReviewsConfig](appName) {
    head(appName, "0.1")
    opt[String]("trainLocation") required() action { (x,c) => c.copy(trainLocation=x) }
    opt[String]("testLocation") required() action { (x,c) => c.copy(testLocation=x) }
    opt[Double]("threshold") action { (x,c) => c.copy(threshold=x)}
    opt[Int]("nGrams") action { (x,c) => c.copy(nGrams=x) }
    opt[Int]("commonFeatures") action { (x,c) => c.copy(commonFeatures=x) }
    opt[Int]("numIters") action { (x,c) => c.copy(numParts=x) }
    opt[Int]("numParts") action { (x,c) => c.copy(numParts=x) }
  }.parse(args, AmazonReviewsConfig()).get

  
  def main(args: Array[String]) = {
    val conf = new SparkConf().setAppName(appName)
    conf.setIfMissing("spark.master", "local[2]") // This is a fallback if things aren't set via spark submit.

    val spark = SparkSession.builder.config(conf).getOrCreate()

    val appConfig = parse(args)
    run(spark, appConfig)

    spark.stop()
  }
} 

package keystoneml.pipelines.text

import breeze.linalg.SparseVector
import keystoneml.evaluation.MulticlassClassifierEvaluator
import keystoneml.loaders.NewsgroupsDataLoader
import keystoneml.nodes.learning.NaiveBayesEstimator
import keystoneml.nodes.nlp._
import keystoneml.nodes.stats.TermFrequency
import keystoneml.nodes.util.{CommonSparseFeatures, MaxClassifier}
import org.apache.spark.{SparkConf, SparkContext}
import keystoneml.pipelines.Logging
import scopt.OptionParser
import keystoneml.workflow.Pipeline

object NewsgroupsPipeline extends Logging {
  val appName = "NewsgroupsPipeline"

  def run(sc: SparkContext, conf: NewsgroupsConfig): Pipeline[String, Int] = {

    val trainData = NewsgroupsDataLoader(sc, conf.trainLocation)
    val numClasses = NewsgroupsDataLoader.classes.length

    // Build the classifier estimator
    logInfo("Training classifier")
    val predictor = Trim andThen
        LowerCase() andThen
        Tokenizer() andThen
        NGramsFeaturizer(1 to conf.nGrams) andThen
        TermFrequency(x => 1) andThen
        (CommonSparseFeatures[Seq[String]](conf.commonFeatures), trainData.data) andThen
        (NaiveBayesEstimator[SparseVector[Double]](numClasses), trainData.data, trainData.labels) andThen
        MaxClassifier

    // Evaluate the classifier
    logInfo("Evaluating classifier")

    val testData = NewsgroupsDataLoader(sc, conf.testLocation)
    val testLabels = testData.labels
    val testResults = predictor(testData.data)
    val eval = new MulticlassClassifierEvaluator(numClasses).evaluate(testResults, testLabels)

    logInfo("\n" + eval.summary(NewsgroupsDataLoader.classes))

    predictor
  }

  case class NewsgroupsConfig(
    trainLocation: String = "",
    testLocation: String = "",
    nGrams: Int = 2,
    commonFeatures: Int = 100000)

  def parse(args: Array[String]): NewsgroupsConfig = new OptionParser[NewsgroupsConfig](appName) {
    head(appName, "0.1")
    opt[String]("trainLocation") required() action { (x,c) => c.copy(trainLocation=x) }
    opt[String]("testLocation") required() action { (x,c) => c.copy(testLocation=x) }
    opt[Int]("nGrams") action { (x,c) => c.copy(nGrams=x) }
    opt[Int]("commonFeatures") action { (x,c) => c.copy(commonFeatures=x) }
  }.parse(args, NewsgroupsConfig()).get

  
  def main(args: Array[String]) = {
    val conf = new SparkConf().setAppName(appName)
    conf.setIfMissing("spark.master", "local[2]") // This is a fallback if things aren't set via spark submit.

    val sc = new SparkContext(conf)

    val appConfig = parse(args)
    run(sc, appConfig)

    sc.stop()
  }

} 

import com.ibm.spark.ibmdataserver.Constants
import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkContext, SparkConf}

object HiSpeedRead {

  def main(args: Array[String]) {
    val DB2_CONNECTION_URL = "jdbc:db2://localhost:50700/sample:traceFile=C:\\1.txt;"

    val conf = new SparkConf().setMaster("local[2]").setAppName("read test")

    val sparkContext = new SparkContext(conf)

    val sqlContext = new SQLContext(sparkContext)

    Class.forName("com.ibm.db2.jcc.DB2Driver")

    val jdbcRdr = sqlContext.read.format("com.ibm.spark.ibmdataserver")
      .option("url", DB2_CONNECTION_URL)
      // .option(Constants.TABLE, tableName)
      .option("user", "pallavipr")
      .option("password", "9manjari")
      .option("dbtable", "employee")
      .load()

    jdbcRdr.show()
  }
} 

package org.apache.spark.sql.hive

import org.apache.hadoop.conf.Configuration

import org.apache.spark.SparkConf
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.catalog._
import org.apache.spark.sql.execution.command.DDLUtils
import org.apache.spark.sql.types.StructType


class HiveExternalCatalogSuite extends ExternalCatalogSuite {

  private val externalCatalog: HiveExternalCatalog = {
    val catalog = new HiveExternalCatalog(new SparkConf, new Configuration)
    catalog.client.reset()
    catalog
  }

  protected override val utils: CatalogTestUtils = new CatalogTestUtils {
    override val tableInputFormat: String = "org.apache.hadoop.mapred.SequenceFileInputFormat"
    override val tableOutputFormat: String = "org.apache.hadoop.mapred.SequenceFileOutputFormat"
    override def newEmptyCatalog(): ExternalCatalog = externalCatalog
    override val defaultProvider: String = "hive"
  }

  protected override def resetState(): Unit = {
    externalCatalog.client.reset()
  }

  import utils._

  test("SPARK-18647: do not put provider in table properties for Hive serde table") {
    val catalog = newBasicCatalog()
    val hiveTable = CatalogTable(
      identifier = TableIdentifier("hive_tbl", Some("db1")),
      tableType = CatalogTableType.MANAGED,
      storage = storageFormat,
      schema = new StructType().add("col1", "int").add("col2", "string"),
      provider = Some("hive"))
    catalog.createTable(hiveTable, ignoreIfExists = false)

    val rawTable = externalCatalog.client.getTable("db1", "hive_tbl")
    assert(!rawTable.properties.contains(HiveExternalCatalog.DATASOURCE_PROVIDER))
    assert(DDLUtils.isHiveTable(externalCatalog.getTable("db1", "hive_tbl")))
  }

  Seq("parquet", "hive").foreach { format =>
    test(s"Partition columns should be put at the end of table schema for the format $format") {
      val catalog = newBasicCatalog()
      val newSchema = new StructType()
        .add("col1", "int")
        .add("col2", "string")
        .add("partCol1", "int")
        .add("partCol2", "string")
      val table = CatalogTable(
        identifier = TableIdentifier("tbl", Some("db1")),
        tableType = CatalogTableType.MANAGED,
        storage = CatalogStorageFormat.empty,
        schema = new StructType()
          .add("col1", "int")
          .add("partCol1", "int")
          .add("partCol2", "string")
          .add("col2", "string"),
        provider = Some(format),
        partitionColumnNames = Seq("partCol1", "partCol2"))
      catalog.createTable(table, ignoreIfExists = false)

      val restoredTable = externalCatalog.getTable("db1", "tbl")
      assert(restoredTable.schema == newSchema)
    }
  }

  test("SPARK-22306: alter table schema should not erase the bucketing metadata at hive side") {
    val catalog = newBasicCatalog()
    externalCatalog.client.runSqlHive(
      """
        |CREATE TABLE db1.t(a string, b string)
        |CLUSTERED BY (a, b) SORTED BY (a, b) INTO 10 BUCKETS
        |STORED AS PARQUET
      """.stripMargin)

    val newSchema = new StructType().add("a", "string").add("b", "string").add("c", "string")
    catalog.alterTableDataSchema("db1", "t", newSchema)

    assert(catalog.getTable("db1", "t").schema == newSchema)
    val bucketString = externalCatalog.client.runSqlHive("DESC FORMATTED db1.t")
      .filter(_.contains("Num Buckets")).head
    assert(bucketString.contains("10"))
  }

  test("SPARK-23001: NullPointerException when running desc database") {
    val catalog = newBasicCatalog()
    catalog.createDatabase(newDb("dbWithNullDesc").copy(description = null), ignoreIfExists = false)
    assert(catalog.getDatabase("dbWithNullDesc").description == "")
  }
} 

package org.apache.spark.sql.hive.execution

import org.scalatest.BeforeAndAfterAll

import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
import org.apache.spark.sql.hive.test.TestHiveContext

class ConcurrentHiveSuite extends SparkFunSuite with BeforeAndAfterAll {
  ignore("multiple instances not supported") {
    test("Multiple Hive Instances") {
      (1 to 10).map { i =>
        val conf = new SparkConf()
        conf.set("spark.ui.enabled", "false")
        val ts =
          new TestHiveContext(new SparkContext("local", s"TestSQLContext$i", conf))
        ts.sparkSession.sql("SHOW TABLES").collect()
        ts.sparkSession.sql("SELECT * FROM src").collect()
        ts.sparkSession.sql("SHOW TABLES").collect()
      }
    }
  }
} 

package org.apache.spark.sql.hive

import java.net.URL

import org.apache.hadoop.hive.conf.HiveConf.ConfVars

import org.apache.spark.SparkConf
import org.apache.spark.deploy.SparkHadoopUtil
import org.apache.spark.sql.QueryTest
import org.apache.spark.sql.hive.test.TestHiveSingleton
import org.apache.spark.sql.test.{ExamplePoint, ExamplePointUDT, SQLTestUtils}
import org.apache.spark.util.{ChildFirstURLClassLoader, MutableURLClassLoader}

class HiveUtilsSuite extends QueryTest with SQLTestUtils with TestHiveSingleton {

  test("newTemporaryConfiguration overwrites listener configurations") {
    Seq(true, false).foreach { useInMemoryDerby =>
      val conf = HiveUtils.newTemporaryConfiguration(useInMemoryDerby)
      assert(conf(ConfVars.METASTORE_PRE_EVENT_LISTENERS.varname) === "")
      assert(conf(ConfVars.METASTORE_EVENT_LISTENERS.varname) === "")
      assert(conf(ConfVars.METASTORE_END_FUNCTION_LISTENERS.varname) === "")
    }
  }

  test("newTemporaryConfiguration respect spark.hadoop.foo=bar in SparkConf") {
    sys.props.put("spark.hadoop.foo", "bar")
    Seq(true, false) foreach { useInMemoryDerby =>
      val hiveConf = HiveUtils.newTemporaryConfiguration(useInMemoryDerby)
      assert(!hiveConf.contains("spark.hadoop.foo"))
      assert(hiveConf("foo") === "bar")
    }
  }

  test("ChildFirstURLClassLoader's parent is null, get spark classloader instead") {
    val conf = new SparkConf
    val contextClassLoader = Thread.currentThread().getContextClassLoader
    val loader = new ChildFirstURLClassLoader(Array(), contextClassLoader)
    try {
      Thread.currentThread().setContextClassLoader(loader)
      HiveUtils.newClientForMetadata(
        conf,
        SparkHadoopUtil.newConfiguration(conf),
        HiveUtils.newTemporaryConfiguration(useInMemoryDerby = true))
    } finally {
      Thread.currentThread().setContextClassLoader(contextClassLoader)
    }
  }

  test("toHiveString correctly handles UDTs") {
    val point = new ExamplePoint(50.0, 50.0)
    val tpe = new ExamplePointUDT()
    assert(HiveUtils.toHiveString((point, tpe)) === "(50.0, 50.0)")
  }
} 

package org.apache.spark.sql.hive.client

import java.io.File

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.util.VersionInfo

import org.apache.spark.SparkConf
import org.apache.spark.util.Utils

private[client] object HiveClientBuilder {
  // In order to speed up test execution during development or in Jenkins, you can specify the path
  // of an existing Ivy cache:
  private val ivyPath: Option[String] = {
    sys.env.get("SPARK_VERSIONS_SUITE_IVY_PATH").orElse(
      Some(new File(sys.props("java.io.tmpdir"), "hive-ivy-cache").getAbsolutePath))
  }

  private def buildConf(extraConf: Map[String, String]) = {
    lazy val warehousePath = Utils.createTempDir()
    lazy val metastorePath = Utils.createTempDir()
    metastorePath.delete()
    extraConf ++ Map(
      "javax.jdo.option.ConnectionURL" -> s"jdbc:derby:;databaseName=$metastorePath;create=true",
      "hive.metastore.warehouse.dir" -> warehousePath.toString)
  }

  // for testing only
  def buildClient(
      version: String,
      hadoopConf: Configuration,
      extraConf: Map[String, String] = Map.empty,
      sharesHadoopClasses: Boolean = true): HiveClient = {
    IsolatedClientLoader.forVersion(
      hiveMetastoreVersion = version,
      hadoopVersion = VersionInfo.getVersion,
      sparkConf = new SparkConf(),
      hadoopConf = hadoopConf,
      config = buildConf(extraConf),
      ivyPath = ivyPath,
      sharesHadoopClasses = sharesHadoopClasses).createClient()
  }
} 

package org.apache.spark.sql.hive

import org.scalatest.BeforeAndAfterEach

import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}


class HiveContextCompatibilitySuite extends SparkFunSuite with BeforeAndAfterEach {

  override protected val enableAutoThreadAudit = false
  private var sc: SparkContext = null
  private var hc: HiveContext = null

  override def beforeAll(): Unit = {
    super.beforeAll()
    sc = SparkContext.getOrCreate(new SparkConf().setMaster("local").setAppName("test"))
    HiveUtils.newTemporaryConfiguration(useInMemoryDerby = true).foreach { case (k, v) =>
      sc.hadoopConfiguration.set(k, v)
    }
    hc = new HiveContext(sc)
  }

  override def afterEach(): Unit = {
    try {
      hc.sharedState.cacheManager.clearCache()
      hc.sessionState.catalog.reset()
    } finally {
      super.afterEach()
    }
  }

  override def afterAll(): Unit = {
    try {
      sc = null
      hc = null
    } finally {
      super.afterAll()
    }
  }

  test("basic operations") {
    val _hc = hc
    import _hc.implicits._
    val df1 = (1 to 20).map { i => (i, i) }.toDF("a", "x")
    val df2 = (1 to 100).map { i => (i, i % 10, i % 2 == 0) }.toDF("a", "b", "c")
      .select($"a", $"b")
      .filter($"a" > 10 && $"b" > 6 && $"c")
    val df3 = df1.join(df2, "a")
    val res = df3.collect()
    val expected = Seq((18, 18, 8)).toDF("a", "x", "b").collect()
    assert(res.toSeq == expected.toSeq)
    df3.createOrReplaceTempView("mai_table")
    val df4 = hc.table("mai_table")
    val res2 = df4.collect()
    assert(res2.toSeq == expected.toSeq)
  }

  test("basic DDLs") {
    val _hc = hc
    import _hc.implicits._
    val databases = hc.sql("SHOW DATABASES").collect().map(_.getString(0))
    assert(databases.toSeq == Seq("default"))
    hc.sql("CREATE DATABASE mee_db")
    hc.sql("USE mee_db")
    val databases2 = hc.sql("SHOW DATABASES").collect().map(_.getString(0))
    assert(databases2.toSet == Set("default", "mee_db"))
    val df = (1 to 10).map { i => ("bob" + i.toString, i) }.toDF("name", "age")
    df.createOrReplaceTempView("mee_table")
    hc.sql("CREATE TABLE moo_table (name string, age int)")
    hc.sql("INSERT INTO moo_table SELECT * FROM mee_table")
    assert(
      hc.sql("SELECT * FROM moo_table order by name").collect().toSeq ==
      df.collect().toSeq.sortBy(_.getString(0)))
    val tables = hc.sql("SHOW TABLES IN mee_db").select("tableName").collect().map(_.getString(0))
    assert(tables.toSet == Set("moo_table", "mee_table"))
    hc.sql("DROP TABLE moo_table")
    hc.sql("DROP TABLE mee_table")
    val tables2 = hc.sql("SHOW TABLES IN mee_db").select("tableName").collect().map(_.getString(0))
    assert(tables2.isEmpty)
    hc.sql("USE default")
    hc.sql("DROP DATABASE mee_db CASCADE")
    val databases3 = hc.sql("SHOW DATABASES").collect().map(_.getString(0))
    assert(databases3.toSeq == Seq("default"))
  }

} 

package org.apache.spark.sql.hive.thriftserver

import java.io.PrintStream

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.internal.Logging
import org.apache.spark.sql.{SparkSession, SQLContext}
import org.apache.spark.sql.hive.{HiveExternalCatalog, HiveUtils}
import org.apache.spark.sql.internal.StaticSQLConf.CATALOG_IMPLEMENTATION
import org.apache.spark.util.Utils


  def stop() {
    logDebug("Shutting down Spark SQL Environment")
    // Stop the SparkContext
    if (SparkSQLEnv.sparkContext != null) {
      sparkContext.stop()
      sparkContext = null
      sqlContext = null
    }
  }
} 

package org.apache.spark.sql.hive.thriftserver

import org.apache.hadoop.hive.cli.CliSessionState
import org.apache.hadoop.hive.conf.HiveConf
import org.apache.hadoop.hive.ql.session.SessionState

import org.apache.spark.{SparkConf, SparkFunSuite}
import org.apache.spark.deploy.SparkHadoopUtil
import org.apache.spark.sql.hive.HiveUtils

class HiveCliSessionStateSuite extends SparkFunSuite {

  def withSessionClear(f: () => Unit): Unit = {
    try f finally SessionState.detachSession()
  }

  test("CliSessionState will be reused") {
    withSessionClear { () =>
      val hiveConf = new HiveConf(classOf[SessionState])
      HiveUtils.newTemporaryConfiguration(useInMemoryDerby = false).foreach {
        case (key, value) => hiveConf.set(key, value)
      }
      val sessionState: SessionState = new CliSessionState(hiveConf)
      SessionState.start(sessionState)
      val s1 = SessionState.get
      val sparkConf = new SparkConf()
      val hadoopConf = SparkHadoopUtil.get.newConfiguration(sparkConf)
      val s2 = HiveUtils.newClientForMetadata(sparkConf, hadoopConf).getState
      assert(s1 === s2)
      assert(s2.isInstanceOf[CliSessionState])
    }
  }

  test("SessionState will not be reused") {
    withSessionClear { () =>
      val sparkConf = new SparkConf()
      val hadoopConf = SparkHadoopUtil.get.newConfiguration(sparkConf)
      HiveUtils.newTemporaryConfiguration(useInMemoryDerby = false).foreach {
        case (key, value) => hadoopConf.set(key, value)
      }
      val hiveClient = HiveUtils.newClientForMetadata(sparkConf, hadoopConf)
      val s1 = hiveClient.getState
      val s2 = hiveClient.newSession().getState
      assert(s1 !== s2)
    }
  }
} 

package org.apache.spark.sql.xsql

import java.util.ServiceLoader

import scala.collection.JavaConverters._

import org.apache.hadoop.conf.Configuration

import org.apache.spark.{SparkConf, SparkException}
import org.apache.spark.util.Utils

object DataSourceManagerFactory {

  def create(
      datasourceType: String,
      conf: SparkConf,
      hadoopConf: Configuration): DataSourceManager = {
    val loader = Utils.getContextOrSparkClassLoader
    val serviceLoader = ServiceLoader.load(classOf[DataSourceManager], loader)
    var cls: Class[_] = null
    // As we use ServiceLoader to support creating any user provided DataSourceManager here,
    // META-INF/services/org.apache.spark.sql.sources.DataSourceRegister must be packaged properly
    // in user's jar, and the implementation of DataSourceManager must have a public parameterless
    // constructor. For scala language, def this() = this(null...) just work.
    try {
      cls = serviceLoader.asScala
        .filter(_.shortName().equals(datasourceType))
        .toList match {
        case head :: Nil =>
          head.getClass
        case _ =>
          throw new SparkException(s"error when instantiate datasource ${datasourceType}")
      }
    } catch {
      case _: Exception =>
        throw new SparkException(
          s"""Can't find corresponding DataSourceManager for ${datasourceType} type,
             |please check
             |1. META-INF/services/org.apache.spark.sql.sources.DataSourceRegister is packaged
             |2. your implementation of DataSourceManager's shortname is ${datasourceType}
             |3. your implementation of DataSourceManager must have a public parameterless
             |   constructor. For scala language, def this() = this(null, null, ...) just work.
           """.stripMargin)
    }
    try {
      val constructor = cls.getConstructor(classOf[SparkConf], classOf[Configuration])
      val newHadoopConf = new Configuration(hadoopConf)
      constructor.newInstance(conf, newHadoopConf).asInstanceOf[DataSourceManager]
    } catch {
      case _: NoSuchMethodException =>
        try {
          cls.getConstructor(classOf[SparkConf]).newInstance(conf).asInstanceOf[DataSourceManager]
        } catch {
          case _: NoSuchMethodException =>
            cls.getConstructor().newInstance().asInstanceOf[DataSourceManager]
        }
    }
  }
} 

package org.apache.spark.sql.xsql.test

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.internal.SessionState
import org.apache.spark.sql.internal.StaticSQLConf.CATALOG_IMPLEMENTATION
import org.apache.spark.sql.test.TestSparkSession
import org.apache.spark.sql.xsql.XSQLSessionStateBuilder


class XSQLTestSparkSession(sc: SparkContext) extends TestSparkSession(sc) { self =>
  def this(sparkConf: SparkConf) {
    this(
      new SparkContext(
        "local[2]",
        "test-sql-context",
        sparkConf.set("spark.sql.testkey", "true").set(CATALOG_IMPLEMENTATION, "xsql")))
  }

  @transient
  override lazy val sessionState: SessionState = {
    new XSQLSessionStateBuilder(this, None).build()
  }
} 

package org.apache.spark.sql.execution.ui

import org.apache.spark.SparkConf
import org.apache.spark.scheduler.SparkListener
import org.apache.spark.status.{AppHistoryServerPlugin, ElementTrackingStore}
import org.apache.spark.ui.SparkUI

class SQLHistoryServerPlugin extends AppHistoryServerPlugin {
  override def createListeners(conf: SparkConf, store: ElementTrackingStore): Seq[SparkListener] = {
    Seq(new SQLAppStatusListener(conf, store, live = false))
  }

  override def setupUI(ui: SparkUI): Unit = {
    val sqlStatusStore = new SQLAppStatusStore(ui.store.store)
    if (sqlStatusStore.executionsCount() > 0) {
      new SQLTab(sqlStatusStore, ui)
    }
  }
} 

package org.apache.spark.sql.execution.benchmark

import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.util.Benchmark

trait DataSourceWriteBenchmark {
  val conf = new SparkConf()
    .setAppName("DataSourceWriteBenchmark")
    .setIfMissing("spark.master", "local[1]")
    .set(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key, "true")

  val spark = SparkSession.builder.config(conf).getOrCreate()

  val tempTable = "temp"
  val numRows = 1024 * 1024 * 15

  def withTempTable(tableNames: String*)(f: => Unit): Unit = {
    try f finally tableNames.foreach(spark.catalog.dropTempView)
  }

  def withTable(tableNames: String*)(f: => Unit): Unit = {
    try f finally {
      tableNames.foreach { name =>
        spark.sql(s"DROP TABLE IF EXISTS $name")
      }
    }
  }

  def writeNumeric(table: String, format: String, benchmark: Benchmark, dataType: String): Unit = {
    spark.sql(s"create table $table(id $dataType) using $format")
    benchmark.addCase(s"Output Single $dataType Column") { _ =>
      spark.sql(s"INSERT OVERWRITE TABLE $table SELECT CAST(id AS $dataType) AS c1 FROM $tempTable")
    }
  }

  def writeIntString(table: String, format: String, benchmark: Benchmark): Unit = {
    spark.sql(s"CREATE TABLE $table(c1 INT, c2 STRING) USING $format")
    benchmark.addCase("Output Int and String Column") { _ =>
      spark.sql(s"INSERT OVERWRITE TABLE $table SELECT CAST(id AS INT) AS " +
        s"c1, CAST(id AS STRING) AS c2 FROM $tempTable")
    }
  }

  def writePartition(table: String, format: String, benchmark: Benchmark): Unit = {
    spark.sql(s"CREATE TABLE $table(p INT, id INT) USING $format PARTITIONED BY (p)")
    benchmark.addCase("Output Partitions") { _ =>
      spark.sql(s"INSERT OVERWRITE TABLE $table SELECT CAST(id AS INT) AS id," +
        s" CAST(id % 2 AS INT) AS p FROM $tempTable")
    }
  }

  def writeBucket(table: String, format: String, benchmark: Benchmark): Unit = {
    spark.sql(s"CREATE TABLE $table(c1 INT, c2 INT) USING $format CLUSTERED BY (c2) INTO 2 BUCKETS")
    benchmark.addCase("Output Buckets") { _ =>
      spark.sql(s"INSERT OVERWRITE TABLE $table SELECT CAST(id AS INT) AS " +
        s"c1, CAST(id AS INT) AS c2 FROM $tempTable")
    }
  }

  def runBenchmark(format: String): Unit = {
    val tableInt = "tableInt"
    val tableDouble = "tableDouble"
    val tableIntString = "tableIntString"
    val tablePartition = "tablePartition"
    val tableBucket = "tableBucket"
    withTempTable(tempTable) {
      spark.range(numRows).createOrReplaceTempView(tempTable)
      withTable(tableInt, tableDouble, tableIntString, tablePartition, tableBucket) {
        val benchmark = new Benchmark(s"$format writer benchmark", numRows)
        writeNumeric(tableInt, format, benchmark, "Int")
        writeNumeric(tableDouble, format, benchmark, "Double")
        writeIntString(tableIntString, format, benchmark)
        writePartition(tablePartition, format, benchmark)
        writeBucket(tableBucket, format, benchmark)
        benchmark.run()
      }
    }
  }
} 

package org.apache.spark.sql.execution.datasources

import org.apache.spark.SparkConf
import org.apache.spark.sql.SaveMode
import org.apache.spark.sql.test.SharedSQLContext

class SaveIntoDataSourceCommandSuite extends SharedSQLContext {

  test("simpleString is redacted") {
    val URL = "connection.url"
    val PASS = "mypassword"
    val DRIVER = "mydriver"

    val dataSource = DataSource(
      sparkSession = spark,
      className = "jdbc",
      partitionColumns = Nil,
      options = Map("password" -> PASS, "url" -> URL, "driver" -> DRIVER))

    val logicalPlanString = dataSource
      .planForWriting(SaveMode.ErrorIfExists, spark.range(1).logicalPlan)
      .treeString(true)

    assert(!logicalPlanString.contains(URL))
    assert(!logicalPlanString.contains(PASS))
    assert(logicalPlanString.contains(DRIVER))
  }
} 

package org.apache.spark.sql.execution

import org.apache.hadoop.fs.Path

import org.apache.spark.SparkConf
import org.apache.spark.sql.QueryTest
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.test.SharedSQLContext


class DataSourceScanExecRedactionSuite extends QueryTest with SharedSQLContext {

  override protected def sparkConf: SparkConf = super.sparkConf
    .set("spark.redaction.string.regex", "file:/[\\w_]+")

  test("treeString is redacted") {
    withTempDir { dir =>
      val basePath = dir.getCanonicalPath
      spark.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
      val df = spark.read.parquet(basePath)

      val rootPath = df.queryExecution.sparkPlan.find(_.isInstanceOf[FileSourceScanExec]).get
        .asInstanceOf[FileSourceScanExec].relation.location.rootPaths.head
      assert(rootPath.toString.contains(dir.toURI.getPath.stripSuffix("/")))

      assert(!df.queryExecution.sparkPlan.treeString(verbose = true).contains(rootPath.getName))
      assert(!df.queryExecution.executedPlan.treeString(verbose = true).contains(rootPath.getName))
      assert(!df.queryExecution.toString.contains(rootPath.getName))
      assert(!df.queryExecution.simpleString.contains(rootPath.getName))

      val replacement = "*********"
      assert(df.queryExecution.sparkPlan.treeString(verbose = true).contains(replacement))
      assert(df.queryExecution.executedPlan.treeString(verbose = true).contains(replacement))
      assert(df.queryExecution.toString.contains(replacement))
      assert(df.queryExecution.simpleString.contains(replacement))
    }
  }

  private def isIncluded(queryExecution: QueryExecution, msg: String): Boolean = {
    queryExecution.toString.contains(msg) ||
    queryExecution.simpleString.contains(msg) ||
    queryExecution.stringWithStats.contains(msg)
  }

  test("explain is redacted using SQLConf") {
    withTempDir { dir =>
      val basePath = dir.getCanonicalPath
      spark.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
      val df = spark.read.parquet(basePath)
      val replacement = "*********"

      // Respect SparkConf and replace file:/
      assert(isIncluded(df.queryExecution, replacement))

      assert(isIncluded(df.queryExecution, "FileScan"))
      assert(!isIncluded(df.queryExecution, "file:/"))

      withSQLConf(SQLConf.SQL_STRING_REDACTION_PATTERN.key -> "(?i)FileScan") {
        // Respect SQLConf and replace FileScan
        assert(isIncluded(df.queryExecution, replacement))

        assert(!isIncluded(df.queryExecution, "FileScan"))
        assert(isIncluded(df.queryExecution, "file:/"))
      }
    }
  }

} 

package org.apache.spark.sql

import org.apache.spark.{SparkConf, SparkFunSuite}
import org.apache.spark.serializer.JavaSerializer
import org.apache.spark.sql.test.SharedSQLContext

class SerializationSuite extends SparkFunSuite with SharedSQLContext {

  test("[SPARK-5235] SQLContext should be serializable") {
    val spark = SparkSession.builder.getOrCreate()
    new JavaSerializer(new SparkConf()).newInstance().serialize(spark.sqlContext)
  }

  test("[SPARK-26409] SQLConf should be serializable") {
    val spark = SparkSession.builder.getOrCreate()
    new JavaSerializer(new SparkConf()).newInstance().serialize(spark.sessionState.conf)
  }
} 

package org.apache.spark.sql.test

import scala.concurrent.duration._

import org.scalatest.{BeforeAndAfterEach, Suite}
import org.scalatest.concurrent.Eventually

import org.apache.spark.{DebugFilesystem, SparkConf}
import org.apache.spark.sql.{SparkSession, SQLContext}
import org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation
import org.apache.spark.sql.internal.SQLConf


  protected override def afterAll(): Unit = {
    try {
      super.afterAll()
    } finally {
      try {
        if (_spark != null) {
          try {
            _spark.sessionState.catalog.reset()
          } finally {
            _spark.stop()
            _spark = null
          }
        }
      } finally {
        SparkSession.clearActiveSession()
        SparkSession.clearDefaultSession()
      }
    }
  }

  protected override def beforeEach(): Unit = {
    super.beforeEach()
    DebugFilesystem.clearOpenStreams()
  }

  protected override def afterEach(): Unit = {
    super.afterEach()
    // Clear all persistent datasets after each test
    spark.sharedState.cacheManager.clearCache()
    // files can be closed from other threads, so wait a bit
    // normally this doesn't take more than 1s
    eventually(timeout(10.seconds), interval(2.seconds)) {
      DebugFilesystem.assertNoOpenStreams()
    }
  }
} 

package org.apache.spark.sql.test

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.internal.{SessionState, SessionStateBuilder, SQLConf, WithTestConf}


  val overrideConfs: Map[String, String] =
    Map(
      // Fewer shuffle partitions to speed up testing.
      SQLConf.SHUFFLE_PARTITIONS.key -> "5")
}

private[sql] class TestSQLSessionStateBuilder(
    session: SparkSession,
    state: Option[SessionState])
  extends SessionStateBuilder(session, state) with WithTestConf {
  override def overrideConfs: Map[String, String] = TestSQLContext.overrideConfs
  override def newBuilder: NewBuilder = new TestSQLSessionStateBuilder(_, _)
} 

package org.apache.spark.sql

import org.scalatest.BeforeAndAfter

import org.apache.spark.SparkConf

class SingleLevelAggregateHashMapSuite extends DataFrameAggregateSuite with BeforeAndAfter {
  override protected def sparkConf: SparkConf = super.sparkConf
    .set("spark.sql.codegen.fallback", "false")
    .set("spark.sql.codegen.aggregate.map.twolevel.enabled", "false")

  // adding some checking after each test is run, assuring that the configs are not changed
  // in test code
  after {
    assert(sparkConf.get("spark.sql.codegen.fallback") == "false",
      "configuration parameter changed in test body")
    assert(sparkConf.get("spark.sql.codegen.aggregate.map.twolevel.enabled") == "false",
      "configuration parameter changed in test body")
  }
}

class TwoLevelAggregateHashMapSuite extends DataFrameAggregateSuite with BeforeAndAfter {
  override protected def sparkConf: SparkConf = super.sparkConf
    .set("spark.sql.codegen.fallback", "false")
    .set("spark.sql.codegen.aggregate.map.twolevel.enabled", "true")

  // adding some checking after each test is run, assuring that the configs are not changed
  // in test code
  after {
    assert(sparkConf.get("spark.sql.codegen.fallback") == "false",
      "configuration parameter changed in test body")
    assert(sparkConf.get("spark.sql.codegen.aggregate.map.twolevel.enabled") == "true",
      "configuration parameter changed in test body")
  }
}

class TwoLevelAggregateHashMapWithVectorizedMapSuite
  extends DataFrameAggregateSuite
  with BeforeAndAfter {

  override protected def sparkConf: SparkConf = super.sparkConf
    .set("spark.sql.codegen.fallback", "false")
    .set("spark.sql.codegen.aggregate.map.twolevel.enabled", "true")
    .set("spark.sql.codegen.aggregate.map.vectorized.enable", "true")

  // adding some checking after each test is run, assuring that the configs are not changed
  // in test code
  after {
    assert(sparkConf.get("spark.sql.codegen.fallback") == "false",
      "configuration parameter changed in test body")
    assert(sparkConf.get("spark.sql.codegen.aggregate.map.twolevel.enabled") == "true",
      "configuration parameter changed in test body")
    assert(sparkConf.get("spark.sql.codegen.aggregate.map.vectorized.enable") == "true",
      "configuration parameter changed in test body")
  }
} 

package org.apache.spark.monitor

import java.util.ServiceLoader

import scala.collection.JavaConverters._

import org.apache.spark.{SparkConf, SparkException}
import org.apache.spark.alarm.Alarm
import org.apache.spark.util.Utils
import org.apache.spark.util.kvstore.KVStore

object MonitorFactory {

  def create(
      monitorName: String,
      alarms: Seq[Alarm],
      appStore: KVStore,
      conf: SparkConf): Monitor = {
    val loader = Utils.getContextOrSparkClassLoader
    val serviceLoader = ServiceLoader.load(classOf[Monitor], loader)
    val MonitorClass = serviceLoader.asScala
      .filter(_.item.equals(MonitorItem.withName(monitorName)))
      .toList match {
      case head :: Nil =>
        head.getClass
      case _ =>
        throw new SparkException("error when instantiate spark.xsql.monitor.items")
    }
    MonitorClass.newInstance().bind(alarms).bind(appStore).bind(conf)
  }
} 

package org.apache.spark.monitor

import scala.collection.mutable.ArrayBuffer

import org.apache.spark.SparkConf
import org.apache.spark.alarm.{Alarm, AlertMessage}
import org.apache.spark.alarm.AlertType.AlertType
import org.apache.spark.internal.config.ConfigBuilder
import org.apache.spark.monitor.MonitorItem.MonitorItem
import org.apache.spark.scheduler.SparkListenerEvent
import org.apache.spark.status.AppStatusStore
import org.apache.spark.util.kvstore.KVStore

trait Monitor {

  val alertType: Seq[AlertType]
  val item: MonitorItem
  val alarms: ArrayBuffer[Alarm] = ArrayBuffer()
  var kvStore: KVStore = null
  var appStore: AppStatusStore = null
  var conf: SparkConf = null

  def watchOut(event: SparkListenerEvent): Option[AlertMessage]
  def bind(alarm: Alarm): Monitor = {
    alarms.append(alarm)
    this
  }
  def bind(alarms: Seq[Alarm]): Monitor = {
    this.alarms.appendAll(alarms)
    this
  }
  def bind(kvStore: KVStore): Monitor = {
    this.kvStore = kvStore
    this.appStore = new AppStatusStore(kvStore)
    this
  }
  def bind(conf: SparkConf): Monitor = {
    this.conf = conf
    this
  }
  def onEvent(event: SparkListenerEvent): Unit = {
    val message = watchOut(event)
    if (message.isDefined) {
      alarms.foreach(_.alarm(message.get))
    }
  }
}
object Monitor {
  val commonClasses = Seq(
    "org.apache.spark.sql.xsql.shell.SparkXSQLShell",
    "org.apache.spark.repl.Main",
    "org.apache.spark.sql.hive.xitong.shell.SparkHiveShell",
    "org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver")
  val dateFormats = Seq("yyyy-MM-dd", "yyyy/MM/dd", "yyyyMMdd")
  val PREFIX = "spark.monitor"
  private[spark] val MONITOR_ITEMS =
    ConfigBuilder("spark.monitor.items")
      .internal()
      .doc("choose monitors to open, split with `,`")
      .stringConf
      .transform(_.toUpperCase)
      .toSequence
      .checkValue(
        _.toSet.subsetOf(MonitorItem.values.map(_.toString)),
        s"must be one of ${MonitorItem.values.map(_.toString)}")
      .createWithDefault(Seq.empty)
}
object MonitorItem extends Enumeration {
  type MonitorItem = Value
  val SQL_CHANGE_NOTIFIER = Value
  val APP_FINISH_NOTIFIER, EXECUTOR_NUM_NOTIFIER, DATASKEW_NOTIFIER, EXECUTOR_MEMORY_ADVISER =
    Value
  val SPARK_APPLICATION_SUMMARY, APP_IDLE_WARNER = Value
} 

package org.apache.spark.sql.hierarchy

import org.apache.spark.SparkConf
import org.apache.spark.serializer.JavaSerializer
import org.apache.spark.sql.Row
import org.apache.spark.sql.types._
import org.apache.spark.sql.types.Node
import org.scalatest.FunSuite

class HierarchyBuilderSuite extends FunSuite {

  val N = 5
  val rowFunctions = HierarchyRowFunctions(Seq.fill(N)(StringType))

  test("HierarchyRowFunctions.rowGet") {
    for (i <- 0 to 5) {
      val row = Row((0 to 5).map(_.toString): _*)
      assertResult(i.toString)(rowFunctions.rowGet(i)(row))
    }
  }

  test("HierarchyRowFunctions.rowInit") {
    for (i <- 0 to 5) {
      val row = Row((0 to 5).map(_.toString): _*)

      val result = rowFunctions.rowInit(rowFunctions.rowGet(i), StringType)(row, None)
      val expected = Row(row.toSeq :+ Node(List(i.toString), StringType): _*)
      assertResult(expected)(result)
    }
  }

  // scalastyle:off magic.number
  test("HierarchyRowFunctions.rowInitWithOrder") {
    for (i <- 0 to 5) {
      val row = Row((0 to 5).map(_.toString): _*)
      val result = rowFunctions.rowInit(rowFunctions.rowGet(i), StringType)(row, Some(42L))
      val expected = Row(row.toSeq :+ Node(List(i.toString),StringType, ordPath = List(42L)): _*)
      assertResult(expected)(result)
    }
  }
  // scalastyle:on magic.number

  test("HierarchyRowFunctions.rowModify") {
    for (i <- 0 to 5) {
      val rightRow = Row(0 to 5: _*)
      val leftRow = Row("foo", 0, "bar", Node(List(0),StringType))
      val result = rowFunctions.rowModify(
        rowFunctions.rowGet(i),StringType
      )(leftRow, rightRow)
      val expected = Row((0 to 5) :+ Node(List(0, i), StringType): _*)
      assertResult(expected)(result)
    }
  }

  // scalastyle:off magic.number
  test("HierarchyRowFunctions.rowModifyAndOrder") {
    for (i <- 0 to 5) {
      val rightRow = Row(0 to 5: _*)
      val leftRow = Row("foo", 0, "bar", Node(List(0),StringType))
      val result = rowFunctions.rowModifyAndOrder(
        rowFunctions.rowGet(i), StringType
      )(leftRow, rightRow, Some(42L))
      val expected = Row((0 to 5) :+ Node(List(0, i), StringType, ordPath = List(42L)): _*)
      assertResult(expected)(result)
    }
  }
  // scalastyle:on magic.number

  test("HierarchyBuilder closure is serializable") {
    val closureSerializer = new JavaSerializer(new SparkConf(loadDefaults = false)).newInstance()
    val serialized = closureSerializer.serialize(() =>
      HierarchyJoinBuilder(null, null, null, null, null, null))
  }

  test("HierarchyRowFunctions closure is serializable") {
    val closureSerializer = new JavaSerializer(new SparkConf(loadDefaults = false)).newInstance()
    val serialized = closureSerializer.serialize(() =>
      HierarchyRowJoinBuilder(null, null, null, null))
  }

} 

package com.sap.spark

import com.sap.spark.util.TestUtils._
import org.apache.spark.{SparkConf, SparkContext}
import org.scalatest.{BeforeAndAfterAll, Suite}

trait WithSparkContext extends BeforeAndAfterAll {
  self: Suite =>

  override def beforeAll(): Unit = {
    try {
      super.beforeAll()
      setUpSparkContext()
    } catch {
      case ex: Throwable =>
        tearDownSparkContext()
        throw ex
    }
  }

  override def afterAll(): Unit = {
    try {
      super.afterAll()
    } finally {
      tearDownSparkContext()
    }
  }

  
    conf.set("spark.sql.autoBroadcastJoinThreshold", "-1")
    conf.set("spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory")
    conf.set("spark.shuffle.spill", "false")
    conf.set("spark.shuffle.compress", "false")
    conf.set("spark.ui.enabled", "false")
    conf.set("spark.ui.showConsoleProgress", "false")
  }

  def sc: SparkContext

  protected def setUpSparkContext(): Unit

  protected def tearDownSparkContext(): Unit

} 

package com.sap.spark

import org.apache.spark.{SparkConf, SparkContext}
import org.scalatest.{BeforeAndAfterAll, Suite}


  }

}

object GlobalSparkContext {
  @transient private var _sc: SparkContext = _

  def init(sparkMaster: String, sparkConf: SparkConf): Unit = {
    if (_sc == null) {
      this.synchronized {
        if (_sc == null) {
          _sc = new SparkContext(sparkMaster, "test", sparkConf)
        }
      }
    }
  }

  def reset(): Unit = {
    if (_sc != null) {
      _sc.cancelAllJobs()
    }
  }

  def close(): Unit = {
    if (_sc != null) {
      _sc.stop()
      _sc = null
    }
  }

} 

package org.apache.spark.sql.hive.sap.thriftserver

import java.io.PrintStream

import org.apache.spark.scheduler.StatsReportListener
import org.apache.spark.sql.hive.{HiveContext, SapHiveContext}
import org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver
import org.apache.spark.sql.hive.thriftserver.SparkSQLEnv._
import org.apache.spark.util.Utils
import org.apache.spark.{Logging, SparkConf, SparkContext}

import scala.collection.JavaConversions._


object SapSQLEnv extends Logging {

  def init() {
    logDebug("Initializing SapSQLEnv")
    if (hiveContext == null) {
      logInfo("Creating SapSQLContext")
      val sparkConf = new SparkConf(loadDefaults = true)
      val maybeSerializer = sparkConf.getOption("spark.serializer")
      val maybeKryoReferenceTracking = sparkConf.getOption("spark.kryo.referenceTracking")
      // If user doesn't specify the appName, we want to get [SparkSQL::localHostName] instead of
      // the default appName [SparkSQLCLIDriver] in cli or beeline.
      val maybeAppName = sparkConf
        .getOption("spark.app.name")
        .filterNot(_ == classOf[SparkSQLCLIDriver].getName)

      sparkConf
        .setAppName(maybeAppName.getOrElse(s"SparkSQL::${Utils.localHostName()}"))
        .set("spark.serializer",
          maybeSerializer.getOrElse("org.apache.spark.serializer.KryoSerializer"))
        .set("spark.kryo.referenceTracking",
          maybeKryoReferenceTracking.getOrElse("false"))

      sparkContext = new SparkContext(sparkConf)
      sparkContext.addSparkListener(new StatsReportListener())
      hiveContext = new SapHiveContext(sparkContext)

      hiveContext.metadataHive.setOut(new PrintStream(System.out, true, "UTF-8"))
      hiveContext.metadataHive.setInfo(new PrintStream(System.err, true, "UTF-8"))
      hiveContext.metadataHive.setError(new PrintStream(System.err, true, "UTF-8"))

      hiveContext.setConf("spark.sql.hive.version", HiveContext.hiveExecutionVersion)

      if (log.isDebugEnabled) {
        hiveContext.hiveconf.getAllProperties.toSeq.sorted.foreach { case (k, v) =>
          logDebug(s"HiveConf var: $k=$v")
        }
      }
    }
  }
} 

package org.apache.spark.ml.optim

import java.util.Random

import scala.language.implicitConversions

import org.apache.hadoop.fs.{FileSystem, Path}

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.ml.optim.VectorFreeLBFGS.{Oracle, VectorSpace}
import org.apache.spark.ml.optim.VectorRDDFunctions._
import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors}
import org.apache.spark.mllib.random.RandomRDDs
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.rdd.{RDD, UnionRDD}
import org.apache.spark.storage.StorageLevel


  private def gradient(data: RDD[Array[LabeledPoint]], dx: RDD[Vector]): RDD[Vector] = {
    data.cartesian(dx).map { case (points, x) =>
      val g = Vectors.zeros(x.size)
      points.foreach { case LabeledPoint(b, a) =>
        val err = BLAS.dot(a, x) - b
        BLAS.axpy(err, a, g)
      }
      g
    }.treeSum()
  }

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("VLBFGS").setMaster("local[*]")
    val sc = new SparkContext(conf)
    sc.setCheckpointDir("/tmp/checkpoint")
    val n = 1000
    val p = 100
    val random = new Random(0L)
    val xExact = Vectors.dense(Array.fill(p)(random.nextDouble()))
    val data = RandomRDDs.normalVectorRDD(sc, n, p, 4, 11L).mapPartitionsWithIndex { (idx, part) =>
      val random = new Random(100 + idx)
      part.map { v =>
        val target = BLAS.dot(v, xExact) + 0.1 * random.nextGaussian()
        LabeledPoint(target, v)
      }
    }.glom()
    .cache()

    val x = solve(data).first()

    println(s"x_exact = $xExact")
    println(s"x_vlbfgs = $x")

    sc.stop()
  }
} 

package test.util

import org.apache.spark.{SparkConf, SparkContext}
import org.scalatest.BeforeAndAfterEach
import org.scalatest._

trait LocalSparkContext extends BeforeAndAfterEach { self: Suite =>

  @transient private var _sc: SparkContext = _

  val _sparkConf = new SparkConf(false)
    .set("spark.ui.showConsoleProgress", "false")

  def sc: SparkContext = _sc

  override def beforeEach() {
    _sc = new SparkContext("local[4]", "test", _sparkConf)
    super.beforeEach()
  }

  override def afterEach() {
    resetSparkContext()
    super.afterEach()
  }

  def resetSparkContext(): Unit = {
    LocalSparkContext.stop(_sc)
    _sc = null
  }

}

object LocalSparkContext {
  def stop(sc: SparkContext) {
    if (sc != null) {
      sc.stop()
    }
    // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
    System.clearProperty("spark.driver.port")
  }

  
  def withSpark[T](sc: SparkContext)(f: SparkContext => T): T = {
    try {
      f(sc)
    } finally {
      stop(sc)
    }
  }

} 

package test.util

import org.apache.spark.{SparkConf, SparkContext}
import org.scalatest.BeforeAndAfterEach
import org.scalatest._

trait LocalSparkContext extends BeforeAndAfterEach { self: Suite =>

  @transient private var _sc: SparkContext = _

  val _sparkConf = new SparkConf(false)
    .set("spark.ui.showConsoleProgress", "false")

  def sc: SparkContext = _sc

  override def beforeEach() {
    _sc = new SparkContext("local[4]", "test", _sparkConf)
    super.beforeEach()
  }

  override def afterEach() {
    resetSparkContext()
    super.afterEach()
  }

  def resetSparkContext(): Unit = {
    LocalSparkContext.stop(_sc)
    _sc = null
  }

}

object LocalSparkContext {
  def stop(sc: SparkContext) {
    if (sc != null) {
      sc.stop()
    }
    // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown
    System.clearProperty("spark.driver.port")
  }

  
  def withSpark[T](sc: SparkContext)(f: SparkContext => T): T = {
    try {
      f(sc)
    } finally {
      stop(sc)
    }
  }

} 

package com.granturing.spark.powerbi

import org.apache.spark.SparkConf
import scala.concurrent.duration._


  def fromSparkConf(conf: SparkConf): ClientConf = {
    val token = conf.get("spark.powerbi.token.uri", TOKEN_URI_DEFAULT)
    val resource = conf.get("spark.powerbi.token.resource", TOKEN_RESOURCE_DEFAULT)
    val api = conf.get("spark.powerbi.uri", API_URI_DEFAULT)
    val username = sys.env.getOrElse(POWERBI_USERNAME, conf.get("spark.powerbi.username"))
    val password = sys.env.getOrElse(POWERBI_PASSWORD, conf.get("spark.powerbi.password"))
    val clientid = sys.env.getOrElse(POWERBI_CLIENTID, conf.get("spark.powerbi.clientid"))
    val timeout = Duration(conf.get("spark.powerbi.timeout", "30").toInt, SECONDS)
    val maxPartitions = conf.get("spark.powerbi.max_partitions", MAX_PARTITIONS.toString).toInt
    val batchSize = conf.get("spark.powerbi.batch_size", BATCH_SIZE.toString).toInt

    ClientConf(token, resource, api, username, password, clientid, timeout, maxPartitions, batchSize)
  }
} 

package com.granturing.spark.powerbi

import org.apache.spark.SparkConf
import org.scalatest.{BeforeAndAfterAll, Matchers, FunSuite}
import scala.concurrent.Await

class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll {

  val clientConf = ClientConf.fromSparkConf(new SparkConf())
  val client = new Client(clientConf)

  val dataset = "PowerBI Spark Test"
  var datasetId: String = _
  val group = sys.env.get("POWERBI_GROUP")
  var groupId: Option[String] = None
  val table = "People"
  val tableSchema = Table(
    table, Seq(
      Column("name", "string"),
      Column("age", "Int64"),
      Column("birthday", "Datetime"),
      Column("timestamp", "Datetime")
    ))

  override def beforeAll = {
    groupId = group match {
      case Some(grp) => {
        val grpOpt = Await.result(client.getGroups, clientConf.timeout).filter(g => grp.equals(g.name)).map(_.id).headOption

        grpOpt match {
          case Some(g) => Some(g)
          case None => sys.error(s"group $grp not found")
        }
      }
      case None => None
    }
  }

  test("client can list groups") {
    val groups = Await.result(client.getGroups, clientConf.timeout)

    groups should not be null
  }

  test("client can list datasets") {
    val ds = Await.result(client.getDatasets(groupId), clientConf.timeout)

    ds should not be null
  }

} 

package org.apache.spark.sql.execution.streaming.http

import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.TimestampType
import org.apache.spark.SparkConf
import org.apache.commons.io.IOUtils
import org.apache.spark.serializer.KryoSerializer
import java.io.InputStream
import com.esotericsoftware.kryo.io.Input
import java.io.ByteArrayOutputStream

class WrongArgumentException(name: String, value: Any)
		extends RuntimeException(s"wrong argument: $name=$value") {
}

class MissingRequiredArgumentException(map: Map[String, String], paramName: String)
		extends RuntimeException(s"missing required argument: $paramName, all parameters=$map") {
}

class InvalidSerializerNameException(serializerName: String)
		extends RuntimeException(s"invalid serializer name: $serializerName") {
}

object SchemaUtils {
	def buildSchema(schema: StructType, includesTimestamp: Boolean, timestampColumnName: String = "_TIMESTAMP_"): StructType = {
		if (!includesTimestamp)
			schema;
		else
			StructType(schema.fields.toSeq :+ StructField(timestampColumnName, TimestampType, false));
	}
}

object Params {
	
	def deserialize(bytes: Array[Byte]): Any = {
		val kryo = kryoSerializer.newKryo();
		val input = new Input();
		input.setBuffer(bytes);
		kryo.readClassAndObject(input);
	}
} 

package org.apache.spark.sql.execution.streaming.http

import java.nio.ByteBuffer
import org.apache.spark.serializer.SerializerInstance
import org.apache.spark.serializer.DeserializationStream
import org.apache.spark.serializer.SerializationStream
import java.io.OutputStream
import java.io.InputStream
import scala.reflect.ClassTag
import com.fasterxml.jackson.databind.ObjectMapper
import org.apache.spark.SparkConf
import org.apache.spark.serializer.JavaSerializer
import org.apache.spark.serializer.KryoSerializer


object SerializerFactory {
	val DEFAULT = new SerializerFactory {
		override def getSerializerInstance(serializerName: String): SerializerInstance = {
			serializerName.toLowerCase() match {
				case "kryo" ⇒
					new KryoSerializer(new SparkConf()).newInstance();
				case "java" ⇒
					new JavaSerializer(new SparkConf()).newInstance();
				case _ ⇒ throw new InvalidSerializerNameException(serializerName);
			}
		}
	}
}

trait SerializerFactory {
	def getSerializerInstance(serializerName: String): SerializerInstance;
} 

import java.sql.Date

import org.apache.spark.SparkConf
import org.apache.spark.serializer.KryoSerializer
import org.apache.spark.sql.SparkSession
import org.junit.Assert
import org.junit.Test
import java.io.ByteArrayOutputStream
import java.io.InputStream
import org.apache.commons.io.IOUtils
import com.esotericsoftware.kryo.io.Input
import org.apache.spark.sql.execution.streaming.http.KryoSerializerUtils

class UtilsTest {
	@Test
	def testKryoSerDe() {
		val d1 = new Date(30000);
		val bytes = KryoSerializerUtils.serialize(d1);
		val d2 = KryoSerializerUtils.deserialize(bytes);
		Assert.assertEquals(d1, d2);

		val d3 = Map('x' -> Array("aaa", "bbb"), 'y' -> Array("ccc", "ddd"));
		println(d3);
		val bytes2 = KryoSerializerUtils.serialize(d3);
		val d4 = KryoSerializerUtils.deserialize(bytes2).asInstanceOf[Map[String, Any]];
		println(d4);
	}

	@Test
	def testEncoderSchema() {
		val spark = SparkSession.builder.master("local[4]")
			.getOrCreate();
		val sqlContext = spark.sqlContext;
		import sqlContext.implicits._
		import org.apache.spark.sql.catalyst.encoders.encoderFor
		val schema1 = encoderFor[String].schema;
		val schema2 = encoderFor[(String)].schema;
		val schema3 = encoderFor[((String))].schema;

		Assert.assertEquals(schema1, schema2);
		Assert.assertEquals(schema1, schema3);
	}

	@Test
	def testDateInTuple() {
		val spark = SparkSession.builder.master("local[4]")
			.getOrCreate();
		val sqlContext = spark.sqlContext;
		import sqlContext.implicits._

		val d1 = new Date(30000);
		val ds = sqlContext.createDataset(Seq[(Int, Date)]((1, d1)));
		val d2 = ds.collect()(0)._2;

		//NOTE: d1!=d2, maybe a bug
		println(d1.equals(d2));
	}
} 

import org.apache.spark.SparkConf
import org.apache.spark.serializer.KryoSerializer
import org.apache.spark.sql.Row
import org.apache.spark.sql.execution.streaming.http.HttpStreamClient
import org.junit.Assert
import org.junit.Test
import org.apache.spark.sql.types.LongType
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.types.DoubleType
import org.apache.spark.sql.types.BooleanType
import org.apache.spark.sql.types.FloatType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.ByteType
import org.apache.spark.sql.execution.streaming.http.HttpStreamServer
import org.apache.spark.sql.execution.streaming.http.StreamPrinter
import org.apache.spark.sql.execution.streaming.http.HttpStreamServerSideException


class HttpStreamServerClientTest {
	val ROWS1 = Array(Row("hello1", 1, true, 0.1f, 0.1d, 1L, '1'.toByte),
		Row("hello2", 2, false, 0.2f, 0.2d, 2L, '2'.toByte),
		Row("hello3", 3, true, 0.3f, 0.3d, 3L, '3'.toByte));

	val ROWS2 = Array(Row("hello"),
		Row("world"),
		Row("bye"),
		Row("world"));

	@Test
	def testHttpStreamIO() {
		//starts a http server
		val kryoSerializer = new KryoSerializer(new SparkConf());
		val server = HttpStreamServer.start("/xxxx", 8080);

		val spark = SparkSession.builder.appName("testHttpTextSink").master("local[4]")
			.getOrCreate();
		spark.conf.set("spark.sql.streaming.checkpointLocation", "/tmp/");

		val sqlContext = spark.sqlContext;
		import spark.implicits._
		//add a local message buffer to server, with 2 topics registered
		server.withBuffer()
			.addListener(new StreamPrinter())
			.createTopic[(String, Int, Boolean, Float, Double, Long, Byte)]("topic-1")
			.createTopic[String]("topic-2");

		val client = HttpStreamClient.connect("http://localhost:8080/xxxx");
		//tests schema of topics
		val schema1 = client.fetchSchema("topic-1");
		Assert.assertArrayEquals(Array[Object](StringType, IntegerType, BooleanType, FloatType, DoubleType, LongType, ByteType),
			schema1.fields.map(_.dataType).asInstanceOf[Array[Object]]);

		val schema2 = client.fetchSchema("topic-2");
		Assert.assertArrayEquals(Array[Object](StringType),
			schema2.fields.map(_.dataType).asInstanceOf[Array[Object]]);

		//prepare to consume messages
		val sid1 = client.subscribe("topic-1")._1;
		val sid2 = client.subscribe("topic-2")._1;

		//produces some data
		client.sendRows("topic-1", 1, ROWS1);

		val sid4 = client.subscribe("topic-1")._1;
		val sid5 = client.subscribe("topic-2")._1;

		client.sendRows("topic-2", 1, ROWS2);

		//consumes data
		val fetched = client.fetchStream(sid1).map(_.originalRow);
		Assert.assertArrayEquals(ROWS1.asInstanceOf[Array[Object]], fetched.asInstanceOf[Array[Object]]);
		//it is empty now
		Assert.assertArrayEquals(Array[Object](), client.fetchStream(sid1).map(_.originalRow).asInstanceOf[Array[Object]]);
		Assert.assertArrayEquals(ROWS2.asInstanceOf[Array[Object]], client.fetchStream(sid2).map(_.originalRow).asInstanceOf[Array[Object]]);
		Assert.assertArrayEquals(Array[Object](), client.fetchStream(sid4).map(_.originalRow).asInstanceOf[Array[Object]]);
		Assert.assertArrayEquals(ROWS2.asInstanceOf[Array[Object]], client.fetchStream(sid5).map(_.originalRow).asInstanceOf[Array[Object]]);
		Assert.assertArrayEquals(Array[Object](), client.fetchStream(sid5).map(_.originalRow).asInstanceOf[Array[Object]]);

		client.unsubscribe(sid4);
		try {
			client.fetchStream(sid4);
			//exception should be thrown, because subscriber id is invalidated
			Assert.assertTrue(false);
		}
		catch {
			case e: Throwable ⇒
				e.printStackTrace();
				Assert.assertEquals(classOf[HttpStreamServerSideException], e.getClass);
		}

		server.stop();
	}
} 

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.execution.streaming.http.HttpStreamServer
import org.apache.spark.SparkConf
import org.apache.spark.sql.execution.streaming.http.StreamPrinter
import org.apache.spark.sql.execution.streaming.http.HttpStreamSourceProvider
import org.apache.spark.sql.execution.streaming.http.HttpStreamSinkProvider



object HttpStreamDemo {

	def printUsage() {
		println("USAGE:");
		val name = this.getClass.getSimpleName;
		println(s"\t$name start-server-on 8080 /xxxx");
		println(s"\t$name write-into http://localhost:8080/xxxx");
		println(s"\t$name read-from http://localhost:8080/xxxx");
	}

	def main(args: Array[String]) {
		if (args.length == 0) {
			printUsage();
		}
		else {
			args(0) match {
				case "write-into" ⇒ runAsSink(args(1));
				case "start-server-on" ⇒ runAsReceiver(args(2), args(1).toInt);
				case "read-from" ⇒ runAsSource(args(1));
				case s: String ⇒ printUsage();
			}
		}
	}

	def runAsSink(httpServletURL: String) {
		val spark = SparkSession.builder.appName("StructuredNetworkWordCount").master("local[4]")
			.getOrCreate();

		println(s"reading from tcp://localhost:9999");
		println(s"writing into $httpServletURL");

		val sqlContext = spark.sqlContext;

		//tcp->HttpStreamSink
		val lines = spark.readStream.
			format("socket").
			option("host", "localhost").
			option("port", 9999).
			load();

		spark.conf.set("spark.sql.streaming.checkpointLocation", "/tmp/");

		val query = lines.writeStream
			.format(classOf[HttpStreamSinkProvider].getName)
			.option("httpServletUrl", httpServletURL)
			.option("topic", "topic-1")
			.start();

		query.awaitTermination();
	}

	def runAsReceiver(servletPath: String, httpPort: Int) {
		val spark = SparkSession.builder.appName("StructuredNetworkWordCount").master("local[4]")
			.getOrCreate();

		import spark.implicits._

		//starts a http server with a buffer
		HttpStreamServer.start(servletPath, httpPort)
			.withBuffer()
			.addListener(new StreamPrinter())
			.createTopic[String]("topic-1");
	}

	def runAsSource(httpServletURL: String) {
		val spark = SparkSession.builder.appName("StructuredNetworkWordCount").master("local[4]")
			.getOrCreate();

		spark.conf.set("spark.sql.streaming.checkpointLocation", "/tmp/");

		//HttpStreamSource->map->console
		//HttpStreamSource as a source stream
		val lines = spark.readStream.format(classOf[HttpStreamSourceProvider].getName)
			.option("httpServletUrl", httpServletURL)
			.option("topic", "topic-1").load();

		import spark.implicits._
		val words = lines.as[String].flatMap(_.split(" "));
		val wordCounts = words.groupBy("value").count();

		val query = wordCounts.writeStream.
			outputMode("complete").
			format("console").
			start();

		query.awaitTermination();
	}
} 

package org.hammerlab.spark

import org.apache.spark.SparkConf
import org.hammerlab.paths.Path


object Conf {

  val propsLineRegex = """(\S+)\s+(.*)""".r

  def apply(loadDefaults: Boolean = true): SparkConf = {
    val envSparkPropertiesFiles =
      Option(System.getenv("SPARK_PROPERTIES_FILES"))
        .toList
        .flatMap(_.split(","))
        .filterNot(_.isEmpty)

    val sparkProperties =
      envSparkPropertiesFiles
        .flatMap {
          path ⇒
            Path(path)
              .lines
              .filter(_.trim.nonEmpty)
              .map {
                case propsLineRegex(key, value) ⇒
                  key → value
                case line ⇒
                  throw new IllegalArgumentException(
                    s"Invalid property line in $path: '$line'"
                  )
              }
        }

    val sparkConf = new SparkConf()

    for {
      (k, v) ← sparkProperties
    } {
      sparkConf.set(k, v)
    }

    sparkConf
  }
} 

package org.hammerlab.spark

import org.apache.spark.SparkConf

import scala.collection.mutable


trait SparkConfBase {
  private val _sparkConfs = mutable.Map[String, String]()

  protected def sparkConfs: Map[String, String] = _sparkConfs.toMap

  protected def makeSparkConf: SparkConf = {
    val sparkConf = new SparkConf()
    for {
      (k, v) ← _sparkConfs
    } {
      sparkConf.setIfMissing(k, v)
    }
    sparkConf
  }

  protected def sparkConf(confs: (String, String)*): Unit =
    for {
      (k, v) ← confs
    } {
      _sparkConfs(k) = v
    }
} 

package org.hammerlab.spark

import org.apache.spark.{ SparkConf, SparkContext }
import org.hammerlab.hadoop.Configuration


case class Context(@transient sc: SparkContext)
  extends Configuration(sc.hadoopConfiguration)

object Context {
  implicit def makeContext(sc: SparkContext): Context = Context(sc)
  implicit def deriveContext(implicit sc: SparkContext): Context = Context(sc)
  implicit def umakeContext(context: Context): SparkContext = context.sc

  def apply()(implicit conf: SparkConf): Context =
    Context(
      new SparkContext(
        conf
      )
    )
} 

package org.akozlov.chapter06

import java.io._

import java.time.ZoneOffset
import java.time.LocalDateTime
import java.time.format.DateTimeFormatter

import org.apache.spark.{SparkConf,SparkContext}
import org.apache.spark.storage.StorageLevel


object Sessionize extends App {
  val sc = new SparkContext("local[8]", "Sessionize", new SparkConf())

  val checkoutPattern = ".*>checkout.*".r.pattern

  // a basic page view structure
  case class PageView(ts: String, path: String) extends Serializable with Ordered[PageView] {
    override def toString: String = {
      s"($ts #$path)"
    }
    def compare(other: PageView) = ts compare other.ts
  }

  // represent a session
  case class Session[A  <: PageView](id: String, visits: Seq[A]) extends Serializable {
    override def toString: String = {
      val vsts = visits.mkString("[", ",", "]")
      s"($id -> $vsts)"
    }
  }

  def toEpochSeconds(str: String) = { LocalDateTime.parse(str, DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss")).toEpochSecond(ZoneOffset.UTC) }

  val sessions = sc.textFile("data/clickstream")
    .map(line => {val parts = line.split("\t"); (parts(4), new PageView(parts(0), parts(20)))})
    .groupByKey.map(x => { new Session(x._1, x._2.toSeq.sorted) } )
    .cache

  // sessions.take(100).foreach(println)

  def findAllCheckoutSessions(s: Session[PageView]) = {
    s.visits.tails.filter {
      _ match { case PageView(ts1, "mycompanycom>homepage") :: PageView(ts2, page) :: tail if (page != "mycompanycom>homepage" ) => true; case _ => false }
    }
    .foldLeft(Seq[Session[PageView]]()) {
      case (r, x) => {
        x.find(y => checkoutPattern.matcher(y.path).matches) match {
          case Some(checkout) if (toEpochSeconds(checkout.ts) > toEpochSeconds(x.head.ts) + 60) => r.:+(new Session(s.id, x.slice(0, x.indexOf(checkout))))
          case _ => r
        }
      }
    }
  }

  val prodLandingSessions = sessions.flatMap(findAllCheckoutSessions)

  prodLandingSessions.collect.foreach(println)

  sc.stop()
} 

package org.akozlov.chapter03

import org.apache.spark.SparkConf
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.flume._


object FlumeWordCount {
  def main(args: Array[String]) {
    // Create the context with a 2 second batch size
    val sparkConf = new SparkConf().setMaster("local[2]").setAppName("FlumeWordCount")
    val ssc = new StreamingContext(sparkConf, Seconds(2))
    ssc.checkpoint("/tmp/flume_check")
    val hostPort=args(0).split(":")
    System.out.println("Opening a sink at host: [" + hostPort(0) + "] port: [" + hostPort(1).toInt + "]")
    val lines = FlumeUtils.createPollingStream(ssc, hostPort(0), hostPort(1).toInt, StorageLevel.MEMORY_ONLY)
    val words = lines
      .map(e => new String(e.event.getBody.array)).map(_.toLowerCase).flatMap(_.split("\\W+"))
      .map(word => (word, 1L))
      .reduceByKeyAndWindow(_+_, _-_, Seconds(6), Seconds(2)).print
    ssc.start()
    ssc.awaitTermination()
  }
} 

package org.akozlov.chapter03

import org.apache.spark.SparkConf
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.kafka._


object KafkaWordCount {
  def main(args: Array[String]) {
    // Create the context with a 2 second batch size
    val sparkConf = new SparkConf().setMaster("local[2]").setAppName("KafkaWordCount")
    val ssc = new StreamingContext(sparkConf, Seconds(2))
    ssc.checkpoint("/tmp/kafka_check")
    System.out.println("Opening a Kafka consumer at zk: [" + args(0) + "] for group group-1 and topic example")
    val lines = KafkaUtils.createStream(ssc, args(0), "group-1", Map("example" -> 1), StorageLevel.MEMORY_ONLY)
    val words = lines
      .flatMap(_._2.toLowerCase.split("\\W+"))
      .map(word => (word, 1L))
      .reduceByKeyAndWindow(_+_, _-_, Seconds(6), Seconds(2)).print
    ssc.start()
    ssc.awaitTermination()
  }
} 

package org.apress.prospark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.graphx.Edge
import org.apache.spark.graphx.Graph
import org.apache.spark.graphx.Graph.graphToGraphOps
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.json4s.DefaultFormats
import org.json4s.jvalue2extractable
import org.json4s.jvalue2monadic
import org.json4s.native.JsonMethods.parse
import org.json4s.string2JsonInput

object UserRankApp {

  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: UserRankApp <appname> <batchInterval> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    ssc.socketTextStream(hostname, port.toInt)
      .map(r => {
        implicit val formats = DefaultFormats
        parse(r)
      })
      .foreachRDD(rdd => {
        val edges = rdd.map(jvalue => {
          implicit val formats = DefaultFormats
          ((jvalue \ "user_id").extract[String], (jvalue \ "friends").extract[Array[String]])
        })
          .flatMap(r => r._2.map(f => Edge(r._1.hashCode.toLong, f.hashCode.toLong, 1.0)))

        val vertices = rdd.map(jvalue => {
          implicit val formats = DefaultFormats
          ((jvalue \ "user_id").extract[String])
        })
          .map(r => (r.hashCode.toLong, r))

        val tolerance = 0.0001
        val graph = Graph(vertices, edges, "defaultUser")
          .subgraph(vpred = (id, idStr) => idStr != "defaultUser")
        val pr = graph.pageRank(tolerance).cache

        graph.outerJoinVertices(pr.vertices) {
          (userId, attrs, rank) => (rank.getOrElse(0.0).asInstanceOf[Number].doubleValue, attrs)
        }.vertices.top(10) {
          Ordering.by(_._2._1)
        }.foreach(rec => println("User id: %s, Rank: %f".format(rec._2._2, rec._2._1)))
      })

    ssc.start()
    ssc.awaitTermination()

  }

} 

package org.apress.prospark

import org.apache.spark.HashPartitioner
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions
import org.json4s.DefaultFormats
import org.json4s.JsonAST.JNothing
import org.json4s.jvalue2extractable
import org.json4s.jvalue2monadic
import org.json4s.native.JsonMethods.parse
import org.json4s.string2JsonInput

object DataProcApp {

  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: DataProcApp <appname> <batchInterval> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    ssc.socketTextStream(hostname, port.toInt)
      .map(r => {
        implicit val formats = DefaultFormats
        parse(r)
      })
      .filter(jvalue => {
        jvalue \ "attributes" \ "Wi-Fi" != JNothing
      })
      .map(jvalue => {
        implicit val formats = DefaultFormats
        ((jvalue \ "attributes" \ "Wi-Fi").extract[String], (jvalue \ "stars").extract[Int])
      })
      .combineByKey(
        (v) => (v, 1),
        (accValue: (Int, Int), v) => (accValue._1 + v, accValue._2 + 1),
        (accCombine1: (Int, Int), accCombine2: (Int, Int)) => (accCombine1._1 + accCombine2._1, accCombine1._2 + accCombine2._2),
        new HashPartitioner(ssc.sparkContext.defaultParallelism))
      .map({ case (k, v) => (k, v._1 / v._2.toFloat) })
      .print()

    ssc.start()
    ssc.awaitTermination()
  }

} 

package org.apress.prospark

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf

import org.apache.spark.streaming.{ Seconds, StreamingContext }
import org.apache.spark.streaming.dstream.PairDStreamFunctions

import java.util.Calendar

object TripByYearMultiApp {
  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: TripByYearMultiApp <appname> <hostname> <base_port> <num_of_sockets>")
      System.exit(1)
    }
    val Seq(appName, hostname, basePort, nSockets) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(10))

    val streams = (0 to nSockets.toInt - 1).map(i => ssc.socketTextStream(hostname, basePort.toInt + i))
    val uniStream = ssc.union(streams)

    uniStream
      .map(rec => rec.split(","))
      .map(rec => (rec(13), rec(0).toInt))
      .reduceByKey(_ + _)
      .map(pair => (pair._2, normalizeYear(pair._1)))
      .transform(rec => rec.sortByKey(ascending = false))
      .saveAsTextFiles("TripByYear")

    ssc.start()
    ssc.awaitTermination()
  }

  def normalizeYear(s: String): String = {
    try {
      (Calendar.getInstance().get(Calendar.YEAR) - s.toInt).toString
    } catch {
      case e: Exception => s
    }
  }
} 

package org.apress.prospark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD.rddToOrderedRDDFunctions
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions
import org.apache.spark.streaming.mqtt.MQTTUtils

object YearlyDistributionApp {
  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: YearlyDistributionApp <appname> <brokerUrl> <topic> <checkpointDir>")
      System.exit(1)
    }
    val Seq(appName, brokerUrl, topic, checkpointDir) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(10))
    ssc.checkpoint(checkpointDir)

    MQTTUtils.createStream(ssc, brokerUrl, topic, StorageLevel.MEMORY_ONLY_SER_2)
      .map(rec => rec.split(","))
      .map(rec => (rec(1).split(" ")(0), 1))
      .updateStateByKey(statefulCount)
      .map(pair => (pair._2, pair._1))
      .transform(rec => rec.sortByKey(ascending = false))
      .saveAsTextFiles("YearlyDistribution")

    ssc.start()
    ssc.awaitTermination()
  }

  val statefulCount = (values: Seq[Int], state: Option[Int]) => Some(values.sum + state.getOrElse(0))

} 

package org.apress.prospark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD.rddToOrderedRDDFunctions
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions
import org.apache.spark.streaming.flume.FlumeUtils

object DailyUserTypeDistributionApp2 {
  def main(args: Array[String]) {
    if (args.length != 5) {
      System.err.println(
        "Usage: DailyUserTypeDistributionApp <appname> <hostname> <port> <checkpointDir> <outputPath>")
      System.exit(1)
    }
    val Seq(appName, hostname, port, checkpointDir, outputPath) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(10))
    ssc.checkpoint(checkpointDir)

    FlumeUtils.createPollingStream(ssc, hostname, port.toInt, StorageLevel.MEMORY_ONLY_SER_2)
      .map(rec => new String(rec.event.getBody().array()).split(","))
      .map(rec => ((rec(1).split(" ")(0), rec(12)), 1))
      .updateStateByKey(statefulCount)
      .repartition(1)
      .transform(rdd => rdd.sortByKey(ascending = false))
      .saveAsTextFiles(outputPath)

    ssc.start()
    ssc.awaitTermination()
  }

  val statefulCount = (values: Seq[Int], state: Option[Int]) => Some(values.sum + state.getOrElse(0))

} 

package org.apress.prospark

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf

import org.apache.spark.streaming.{ Seconds, StreamingContext }
import org.apache.spark.streaming.dstream.PairDStreamFunctions

import java.util.Calendar

object TripByYearApp {
  def main(args: Array[String]) {
    if (args.length != 3) {
      System.err.println(
        "Usage: TripByYearApp <appname> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(10))

    ssc.socketTextStream(hostname, port.toInt)
      .map(rec => rec.split(","))
      .map(rec => (rec(13), rec(0).toInt))
      .reduceByKey(_ + _)
      .map(pair => (pair._2, normalizeYear(pair._1)))
      .transform(rec => rec.sortByKey(ascending = false))
      .saveAsTextFiles("TripByYear")

    ssc.start()
    ssc.awaitTermination()
  }

  def normalizeYear(s: String): String = {
    try {
      (Calendar.getInstance().get(Calendar.YEAR) - s.toInt).toString
    } catch {
      case e: Exception => s
    }
  }
} 

package org.apress.prospark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD.rddToOrderedRDDFunctions
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions
import org.apache.spark.streaming.twitter.TwitterUtils
import org.apache.spark.storage.StorageLevel
import twitter4j.conf.ConfigurationBuilder
import twitter4j.TwitterFactory

object TwitterApp {

  def main(args: Array[String]) {
    if (args.length != 2) {
      System.err.println(
        "Usage: TwitterApp <appname> <outputPath>")
      System.exit(1)
    }

    val Seq(appName, outputPath) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(10))

    val cb = new ConfigurationBuilder()
    cb.setOAuthConsumerKey("")
    cb.setOAuthConsumerSecret("")
    cb.setOAuthAccessToken("")
    cb.setOAuthAccessTokenSecret("")

    val twitterAuth = new TwitterFactory(cb.build()).getInstance().getAuthorization()

    val tweetStream = TwitterUtils.createStream(ssc, Some(twitterAuth), Array("nyc citi bike", "nyc bike share"))
    tweetStream.count().print()
    tweetStream.saveAsTextFiles(outputPath)

    ssc.start()
    ssc.awaitTermination()
  }

} 

package org.apress.prospark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD.rddToOrderedRDDFunctions
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions
import org.apache.spark.streaming.flume.FlumeUtils

object DailyUserTypeDistributionApp {
  def main(args: Array[String]) {
    if (args.length != 5) {
      System.err.println(
        "Usage: DailyUserTypeDistributionApp <appname> <hostname> <port> <checkpointDir> <outputPath>")
      System.exit(1)
    }
    val Seq(appName, hostname, port, checkpointDir, outputPath) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(10))
    ssc.checkpoint(checkpointDir)

    FlumeUtils.createStream(ssc, hostname, port.toInt, StorageLevel.MEMORY_ONLY_SER_2)
      .map(rec => new String(rec.event.getBody().array()).split(","))
      .map(rec => ((rec(1).split(" ")(0), rec(12)), 1))
      .updateStateByKey(statefulCount)
      .repartition(1)
      .transform(rdd => rdd.sortByKey(ascending = false))
      .saveAsTextFiles(outputPath)

    ssc.start()
    ssc.awaitTermination()
  }

  val statefulCount = (values: Seq[Int], state: Option[Int]) => Some(values.sum + state.getOrElse(0))

} 

package org.apress.prospark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD.rddToOrderedRDDFunctions
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions
import org.apache.spark.streaming.kafka.KafkaUtils

object StationJourneyCountApp {

  def main(args: Array[String]) {
    if (args.length != 7) {
      System.err.println(
        "Usage: StationJourneyCountApp <appname> <brokerUrl> <topic> <consumerGroupId> <zkQuorum> <checkpointDir> <outputPath>")
      System.exit(1)
    }

    val Seq(appName, brokerUrl, topic, consumerGroupId, zkQuorum, checkpointDir, outputPath) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)
    //.set("spark.streaming.receiver.writeAheadLog.enable", "true")

    val ssc = new StreamingContext(conf, Seconds(10))
    ssc.checkpoint(checkpointDir)

    val topics = Map[String, Int](
      topic -> 1)
    KafkaUtils.createStream(ssc, zkQuorum, consumerGroupId, topics, StorageLevel.MEMORY_ONLY_SER).map(_._2)
      .map(rec => rec.split(","))
      .map(rec => ((rec(3), rec(7)), 1))
      .reduceByKey(_ + _)
      .repartition(1)
      .map(rec => (rec._2, rec._1))
      .transform(rdd => rdd.sortByKey(ascending = false))
      .saveAsTextFiles(outputPath)

    ssc.start()
    ssc.awaitTermination()
  }

} 

package org.apress.prospark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.json4s.DefaultFormats
import org.json4s.JField
import org.json4s.jvalue2extractable
import org.json4s.jvalue2monadic
import org.json4s.native.JsonMethods.parse
import org.json4s.string2JsonInput

object HttpApp {

  def main(args: Array[String]) {
    if (args.length != 2) {
      System.err.println(
        "Usage: HttpApp <appname> <outputPath>")
      System.exit(1)
    }

    val Seq(appName, outputPath) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val batchInterval = 10

    val ssc = new StreamingContext(conf, Seconds(batchInterval))

    HttpUtils.createStream(ssc, url = "https://www.citibikenyc.com/stations/json", interval = batchInterval)
      .flatMap(rec => (parse(rec) \ "stationBeanList").children)
      .filter(rec => {
        implicit val formats = DefaultFormats
        (rec \ "statusKey").extract[Integer] != 1
      })
      .map(rec => rec.filterField {
        case JField("id", _) => true
        case JField("stationName", _) => true
        case JField("statusValue", _) => true
        case _ => false
      })
      .map(rec => {
        implicit val formats = DefaultFormats
        (rec(0)._2.extract[Integer], rec(1)._2.extract[String], rec(2)._2.extract[String])
      })
      .saveAsTextFiles(outputPath)

    ssc.start()
    ssc.awaitTermination()
  }

} 

package org.apress.prospark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD.rddToOrderedRDDFunctions
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions
import org.apache.spark.streaming.kafka.KafkaUtils
import kafka.serializer.StringDecoder
import org.apache.spark.storage.StorageLevel

object StationJourneyCountCustomApp {

  def main(args: Array[String]) {
    if (args.length != 7) {
      System.err.println(
        "Usage: StationJourneyCountApp <appname> <brokerUrl> <topic> <consumerGroupId> <zkQuorum> <checkpointDir> <outputPath>")
      System.exit(1)
    }

    val Seq(appName, brokerUrl, topic, consumerGroupId, zkQuorum, checkpointDir, outputPath) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)
      //.set("spark.streaming.receiver.writeAheadLog.enable", "true")

    val ssc = new StreamingContext(conf, Seconds(10))
    ssc.checkpoint(checkpointDir)

    val topics = Map[String, Int](
      topic -> 1)
    val params = Map[String, String](
      "zookeeper.connect" -> zkQuorum,
      "group.id" -> consumerGroupId,
      "bootstrap.servers" -> brokerUrl)
    KafkaUtils.createStream[String, String, StringDecoder, StringDecoder](ssc, params, topics, StorageLevel.MEMORY_ONLY_SER).map(_._2)
      .map(rec => rec.split(","))
      .map(rec => ((rec(3), rec(7)), 1))
      .reduceByKey(_ + _)
      .repartition(1)
      .map(rec => (rec._2, rec._1))
      .transform(rdd => rdd.sortByKey(ascending = false))
      .saveAsTextFiles(outputPath)

    ssc.start()
    ssc.awaitTermination()
  }

} 

package org.apress.prospark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD.rddToOrderedRDDFunctions
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions

object ReferrerApp {
  def main(args: Array[String]) {
    if (args.length != 7) {
      System.err.println(
        "Usage: ReferrerApp <appname> <hostname> <port> <tachyonUrl> <checkpointDir> <outputPathTop> <outputPathSpark>")
      System.exit(1)
    }
    val Seq(appName, hostname, port, tachyonUrl, checkpointDir, outputPathTop, outputPathSpark) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)
      .set("spark.externalBlockStore.url", tachyonUrl)

    val ssc = new StreamingContext(conf, Seconds(10))
    ssc.checkpoint(checkpointDir)

    val clickstream = ssc.socketTextStream(hostname, port.toInt)
      .map(rec => rec.split("\\t"))
      .persist(StorageLevel.OFF_HEAP)

    val topRefStream = clickstream
      .map(rec => {
        var prev_title = rec(3)
        if (!prev_title.startsWith("other")) {
          prev_title = "wikipedia"
        }
        (prev_title, 1)
      })

    val topSparkStream = clickstream
      .filter(rec => rec(4).equals("Apache_Spark"))
      .map(rec => (rec(3), 1))

    saveTopKeys(topRefStream, outputPathTop)

    saveTopKeys(topSparkStream, outputPathSpark)

    ssc.start()
    ssc.awaitTermination()
  }

  def saveTopKeys(clickstream: DStream[(String, Int)], outputPath: String) {
    clickstream.updateStateByKey((values, state: Option[Int]) => Some(values.sum + state.getOrElse(0)))
      .repartition(1)
      .map(rec => (rec._2, rec._1))
      .transform(rec => rec.sortByKey(ascending = false))
      .saveAsTextFiles(outputPath)
  }

} 

package org.apress.prospark

import java.util.concurrent.atomic.AtomicLong

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext

object SocialSearchApp {
  def main(args: Array[String]) {
    if (args.length != 3) {
      System.err.println(
        "Usage: SocialSearchApp <appname> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)
      //.set("spark.eventLog.enabled", "true")
      //.set("spark.eventLog.dir", "/tmp/historical")
      

    val countSearch = new AtomicLong(0)
    val countSocial = new AtomicLong(0)

    val ssc = new StreamingContext(conf, Seconds(1))
    
    val titleStream = ssc.socketTextStream(hostname, port.toInt)
      .map(rec => rec.split("\\t"))
      .filter(_(3) match {
        case "other-google" | "other-bing" | "other-yahoo" | "other-facebook" | "other-twitter" => true
        case _ => false
      })
      .map(rec => (rec(3), rec(4)))
      .cache()

    val searchStream = titleStream.filter(_._1 match {
      case "other-google" | "other-bing" | "other-yahoo" => true
      case _ => false
    })
      .map(rec => rec._2)

    val socialStream = titleStream.filter(_._1 match {
      case "other-facebook" | "other-twitter" => true
      case _ => false
    })
      .map(rec => rec._2)

    val exclusiveSearch = searchStream.transformWith(socialStream,
      (searchRDD: RDD[String], socialRDD: RDD[String]) => searchRDD.subtract(socialRDD))
      .foreachRDD(rdd => {
        countSearch.addAndGet(rdd.count())
        println("Exclusive count search engines: " + countSearch)
      })

    val exclusiveSocial = socialStream.transformWith(searchStream,
      (socialRDD: RDD[String], searchRDD: RDD[String]) => socialRDD.subtract(searchRDD))
      .foreachRDD(rdd => {
        countSocial.addAndGet(rdd.count())
        println("Exclusive count social media: " + countSocial)
      })

    ssc.start()
    ssc.awaitTermination()
  }

} 

package org.apress.prospark

import org.apache.hadoop.fs.Path
import org.apache.hadoop.io.LongWritable
import org.apache.hadoop.io.Text
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD.rddToOrderedRDDFunctions
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions

object VoyagerApp {
  def main(args: Array[String]) {
    if (args.length != 3) {
      System.err.println(
        "Usage: VoyagerApp <appname> <inputPath> <outputPath>")
      System.exit(1)
    }
    val Seq(appName, inputPath, outputPath) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)
      .set("spark.executor.extraJavaOptions", "-XX:+UseConcMarkSweepGC")

    val ssc = new StreamingContext(conf, Seconds(10))

    val voyager1 = ssc.fileStream[LongWritable, Text, TextInputFormat](inputPath, (f: Path) => true, newFilesOnly = false).map(pair => pair._2.toString)
    voyager1.map(rec => {
      val attrs = rec.split("\\s+")
      ((attrs(0).toInt), attrs.slice(18, 28).map(_.toDouble))
    }).filter(pflux => pflux._2.exists(_ > 1.0)).map(rec => (rec._1, 1))
      .reduceByKey(_ + _)
      .transform(rec => rec.sortByKey(ascending = false, numPartitions = 1)).saveAsTextFiles(outputPath)

    ssc.start()
    ssc.awaitTermination()
  }
} 

package org.apress.prospark

import org.apache.hadoop.fs.Path
import org.apache.hadoop.io.LongWritable
import org.apache.hadoop.io.Text
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD.rddToOrderedRDDFunctions
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions

object VoyagerAppKryo {
  def main(args: Array[String]) {
    if (args.length != 3) {
      System.err.println(
        "Usage: VoyagerAppKryo <appname> <inputPath> <outputPath>")
      System.exit(1)
    }
    val Seq(appName, inputPath, outputPath) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)
      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
      .registerKryoClasses(Array(classOf[ProtonFlux]))

    val ssc = new StreamingContext(conf, Seconds(10))

    val voyager1 = ssc.fileStream[LongWritable, Text, TextInputFormat](inputPath, (f: Path) => true, newFilesOnly = false).map(pair => pair._2.toString)
    val projected = voyager1.map(rec => {
      val attrs = rec.split("\\s+")
      new ProtonFlux(attrs(0), attrs(18), attrs(19), attrs(20), attrs(21),
        attrs(22), attrs(23), attrs(24), attrs(25), attrs(26), attrs(27),
        attrs(28))
    })
    val filtered = projected.filter(pflux => pflux.isSolarStorm)
    val yearlyBreakdown = filtered.map(rec => (rec.year, 1))
      .reduceByKey(_ + _)
      .transform(rec => rec.sortByKey(ascending = false))
    yearlyBreakdown.saveAsTextFiles(outputPath)

    ssc.start()
    ssc.awaitTermination()
  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.functions.desc
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext

object CdrDataframeApp {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: CdrDataframeApp <appname> <batchInterval> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val sqlC = new SQLContext(ssc.sparkContext)
    import sqlC.implicits._

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        val cdrs = seqToCdr(rdd).toDF()

        cdrs.groupBy("countryCode").count().orderBy(desc("count")).show(5)
      })

    ssc.start()
    ssc.awaitTermination()
  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.functions.desc
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.json4s.native.Serialization.write
import org.json4s.DefaultFormats

object DataframeCreationApp {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: CdrDataframeApp <appname> <batchInterval> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val sqlC = new SQLContext(ssc.sparkContext)
    import sqlC.implicits._

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        //val cdrs = sqlC.createDataFrame(seqToCdr(rdd))
        //val cdrs = sqlC.createDataFrame(seqToCdr(rdd).collect())
        //val cdrs = seqToCdr(rdd).toDF()
        val cdrsJson = seqToCdr(rdd).map(r => {
          implicit val formats = DefaultFormats
          write(r)
        })
        val cdrs = sqlC.read.json(cdrsJson)

        cdrs.groupBy("countryCode").count().orderBy(desc("count")).show(5)
      })

    ssc.start()
    ssc.awaitTermination()

  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SQLContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.json4s.DefaultFormats
import org.json4s.JDouble
import org.json4s.JObject
import org.json4s.jvalue2extractable
import org.json4s.jvalue2monadic
import org.json4s.native.JsonMethods.compact
import org.json4s.native.JsonMethods.parse
import org.json4s.native.JsonMethods.render
import org.json4s.string2JsonInput

object CdrDataframeExamples3App {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 5) {
      System.err.println(
        "Usage: CdrDataframeExamples3App <appname> <batchInterval> <hostname> <port> <gridJsonPath>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port, gridJsonPath) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val sqlC = new SQLContext(ssc.sparkContext)
    import sqlC.implicits._
    implicit val formats = DefaultFormats

    val gridFile = scala.io.Source.fromFile(gridJsonPath).mkString
    val gridGeo = (parse(gridFile) \ "features")
    val gridStr = gridGeo.children.map(r => {
      val c = (r \ "geometry" \ "coordinates").extract[List[List[List[Float]]]].flatten.flatten.map(r => JDouble(r))
      val l = List(("id", r \ "id"), ("x1", c(0)), ("y1", c(1)), ("x2", c(2)), ("y2", c(3)),
        ("x3", c(4)), ("y3", c(5)), ("x4", c(6)), ("y4", c(7)))
      compact(render(JObject(l)))
    })

    val gridDF = sqlC.read.json(ssc.sparkContext.makeRDD(gridStr))

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        val cdrs = seqToCdr(rdd).toDF()
        cdrs.join(gridDF, $"squareId" === $"id").show()
      })

    ssc.start()
    ssc.awaitTermination()
  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import java.nio.file.Paths
import org.apache.spark.SparkFiles

object CdrStreamingSparkRApp {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 7) {
      System.err.println(
        "Usage: CdrStreamingSparkRApp <appname> <batchInterval> <hostname> <port> <tableName> <RScriptPath> <RScriptLogsPath>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port, tableName, rScriptPath, logsPath) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val cl = Thread.currentThread().getContextClassLoader()
    val hiveC = new HiveContext(ssc.sparkContext)
    Thread.currentThread().setContextClassLoader(cl)

    import hiveC.implicits._

    ssc.sparkContext.addFile(rScriptPath)
    val rScriptName = SparkFiles.get(Paths.get(rScriptPath).getFileName.toString)
    val master = hiveC.sparkContext.getConf.get("spark.master")

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD((rdd, time) => {
        val iTableName = tableName + time.milliseconds
        seqToCdr(rdd).toDF().write.saveAsTable(iTableName)
        hiveC.sparkContext.parallelize(Array(iTableName)).pipe("%s %s".format(rScriptName, master)).saveAsTextFile(Paths.get(logsPath, iTableName).toString)
      })

    ssc.start()
    ssc.awaitTermination()
  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SaveMode
import org.apache.spark.sql.functions.desc
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apress.prospark.CdrDataframeExamplesActionsApp.Cdr
import org.json4s.DefaultFormats

object CdrDataframeExamplesActionsApp {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: CdrDataframeExamplesActionsApp <appname> <batchInterval> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val cl = Thread.currentThread().getContextClassLoader()
    val hiveC = new HiveContext(ssc.sparkContext)
    Thread.currentThread().setContextClassLoader(cl)
    import hiveC.implicits._
    implicit val formats = DefaultFormats

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        val cdrs = seqToCdr(rdd).toDF()

        val counts = cdrs.groupBy("countryCode").count().orderBy(desc("count"))
        counts.show(5)
        counts.show()
        println("head(5): " + counts.head(5))
        println("take(5): " + counts.take(5))
        println("head(): " + counts.head())
        println("first(5): " + counts.first())
        println("count(): " + counts.count())
        println("collect(): " + counts.collect())
        println("collectAsList(): " + counts.collectAsList())
        println("describe(): " + cdrs.describe("smsInActivity", "smsOutActivity", "callInActivity", "callOutActivity", "internetTrafficActivity").show())
        counts.write.format("parquet").save("/tmp/parquent" + rdd.id)
        counts.write.format("json").save("/tmp/json" + rdd.id)
        counts.write.parquet("/tmp/parquent2" + rdd.id)
        counts.write.json("/tmp/json2" + rdd.id)
        counts.write.saveAsTable("count_table")
        cdrs.groupBy("countryCode").count().orderBy(desc("count")).write.mode(SaveMode.Append).save("/tmp/counts")
        val prop: java.util.Properties = new java.util.Properties()
        counts.write.jdbc("jdbc:mysql://hostname:port/cdrsdb", "count_table", prop)
      })

    ssc.start()
    ssc.awaitTermination()
  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }
} 

package org.apress.prospark

import scala.io.Source
import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SQLContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.json4s.jackson.JsonMethods.parse
import org.json4s.jvalue2extractable
import org.json4s.string2JsonInput

object CdrUDFApp {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: CdrUDFApp <appname> <batchInterval> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val sqlC = new SQLContext(ssc.sparkContext)
    import sqlC.implicits._

    def getCountryCodeMapping() = {
      implicit val formats = org.json4s.DefaultFormats
      parse(Source.fromURL("http://country.io/phone.json").mkString).extract[Map[String, String]].map(_.swap)
    }

    def getCountryNameMapping() = {
      implicit val formats = org.json4s.DefaultFormats
      parse(Source.fromURL("http://country.io/names.json").mkString).extract[Map[String, String]]
    }

    def getCountryName(mappingPhone: Map[String, String], mappingName: Map[String, String], code: Int) = {
      mappingName.getOrElse(mappingPhone.getOrElse(code.toString, "NotFound"), "NotFound")
    }

    val getCountryNamePartial = getCountryName(getCountryCodeMapping(), getCountryNameMapping(), _: Int)

    sqlC.udf.register("getCountryNamePartial", getCountryNamePartial)

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        val cdrs = seqToCdr(rdd).toDF()
        cdrs.registerTempTable("cdrs")

        sqlC.sql("SELECT getCountryNamePartial(countryCode) AS countryName, COUNT(countryCode) AS cCount FROM cdrs GROUP BY countryCode ORDER BY cCount DESC LIMIT 5").show()

      })

    ssc.start()
    ssc.awaitTermination()
  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }

} 

package org.apress.prospark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.Row
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.functions.desc
import org.apache.spark.sql.types.DataType
import org.apache.spark.sql.types.StructType
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext

object DataframeCreationApp2 {

  def main(args: Array[String]) {
    if (args.length != 5) {
      System.err.println(
        "Usage: CdrDataframeApp2 <appname> <batchInterval> <hostname> <port> <schemaPath>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port, schemaFile) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val sqlC = new SQLContext(ssc.sparkContext)

    val schemaJson = scala.io.Source.fromFile(schemaFile).mkString
    val schema = DataType.fromJson(schemaJson).asInstanceOf[StructType]

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        val cdrs = sqlC.createDataFrame(rdd.map(c => Row(c: _*)), schema)
        
        cdrs.groupBy("countryCode").count().orderBy(desc("count")).show(5)
      })

    ssc.start()
    ssc.awaitTermination()

  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.functions._
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext

object CdrDataframeExamplesApp {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: CdrDataframeExamplesApp <appname> <batchInterval> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val sqlC = new SQLContext(ssc.sparkContext)
    import sqlC.implicits._

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        val cdrs = seqToCdr(rdd).toDF()

        cdrs.select("squareId", "timeInterval", "countryCode").show()
        cdrs.select($"squareId", $"timeInterval", $"countryCode").show()
        cdrs.filter("squareId = 5").show()
        cdrs.drop("countryCode").show()
        cdrs.select($"squareId", $"timeInterval", $"countryCode").where($"squareId" === 5).show()
        cdrs.limit(5).show()
        cdrs.groupBy("squareId").count().show()
        cdrs.groupBy("countryCode").avg("internetTrafficActivity").show()
        cdrs.groupBy("countryCode").max("callOutActivity").show()
        cdrs.groupBy("countryCode").min("callOutActivity").show()
        cdrs.groupBy("squareId").sum("internetTrafficActivity").show()
        cdrs.groupBy("squareId").agg(sum("callOutActivity"), sum("callInActivity"), sum("smsOutActivity"), sum("smsInActivity"), sum("internetTrafficActivity")).show()
        cdrs.groupBy("countryCode").sum("internetTrafficActivity").orderBy(desc("SUM(internetTrafficActivity)")).show()
        cdrs.agg(sum("callOutActivity"), sum("callInActivity"), sum("smsOutActivity"), sum("smsInActivity"), sum("internetTrafficActivity")).show()
        cdrs.rollup("squareId", "countryCode").count().orderBy(desc("squareId"), desc("countryCode")).rdd.saveAsTextFile("/tmp/rollup" + rdd.hashCode())
        cdrs.cube("squareId", "countryCode").count().orderBy(desc("squareId"), desc("countryCode")).rdd.saveAsTextFile("/tmp/cube" + rdd.hashCode())
        cdrs.dropDuplicates(Array("callOutActivity", "callInActivity")).show()
        cdrs.select("squareId", "countryCode", "internetTrafficActivity").distinct.show()
        cdrs.withColumn("endTime", cdrs("timeInterval") + 600000).show()
        cdrs.sample(true, 0.01).show()
      })

    ssc.start()
    ssc.awaitTermination()
  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.SQLContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext

object CdrDataframeExamples2App {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: CdrDataframeExamples2App <appname> <batchInterval> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val sqlC = new SQLContext(ssc.sparkContext)
    import sqlC.implicits._

    var previousCdrs: Option[DataFrame] = None

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        val cdrs = seqToCdr(rdd).toDF().select("squareId", "countryCode").dropDuplicates()
        previousCdrs match {
          case Some(prevCdrs) => cdrs.unionAll(prevCdrs).show()
          //case Some(prevCdrs) => cdrs.intersect(prevCdrs).show()
          //case Some(prevCdrs) => cdrs.except(prevCdrs).show()
          case None => Unit
        }
        previousCdrs = Some(cdrs)
      })

    ssc.start()
    ssc.awaitTermination()
  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SQLContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.json4s.DefaultFormats
import org.json4s.JDouble
import org.json4s.JObject
import org.json4s.jvalue2extractable
import org.json4s.jvalue2monadic
import org.json4s.native.JsonMethods.compact
import org.json4s.native.JsonMethods.parse
import org.json4s.native.JsonMethods.render
import org.json4s.string2JsonInput

object CdrDataframeExamplesNAApp {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: CdrDataframeExamplesNAApp <appname> <batchInterval> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val sqlC = new SQLContext(ssc.sparkContext)
    import sqlC.implicits._
    implicit val formats = DefaultFormats

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        val cdrs = seqToCdr(rdd).toDF()
        cdrs.na.drop("any").show()
        cdrs.na.fill(0, Array("squareId")).show()
        cdrs.na.replace("squareId", Map(0 -> 1)).show()
        println("Correlation: " + cdrs.stat.corr("smsOutActivity", "callOutActivity"))
        println("Covariance: " + cdrs.stat.cov("smsInActivity", "callInActivity"))
        cdrs.stat.crosstab("squareId", "countryCode").show()
        cdrs.stat.freqItems(Array("squareId", "countryCode"), 0.1).show()
        cdrs.stat.crosstab("callOutActivity", "callInActivity").show()
      })

    ssc.start()
    ssc.awaitTermination()
  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SQLContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext

object CdrSqlApp {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: CdrSqlApp <appname> <batchInterval> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val sqlC = new SQLContext(ssc.sparkContext)
    import sqlC.implicits._

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        val cdrs = seqToCdr(rdd).toDF()
        cdrs.registerTempTable("cdrs")

        sqlC.sql("SELECT countryCode, COUNT(countryCode) AS cCount FROM cdrs GROUP BY countryCode ORDER BY cCount DESC LIMIT 5").show()
        sqlC.dropTempTable("cdrs")
      })

    ssc.start()
    ssc.awaitTermination()
  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.types.DataType
import org.apache.spark.sql.types.StructType
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.json4s.DefaultFormats

object CdrDataframeExamplesRDDApp {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 5) {
      System.err.println(
        "Usage: CdrDataframeExamplesRDDApp <appname> <batchInterval> <hostname> <schemaPath>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port, schemaFile) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val sqlC = new SQLContext(ssc.sparkContext)
    import sqlC.implicits._
    implicit val formats = DefaultFormats

    val schemaJson = scala.io.Source.fromFile(schemaFile).mkString
    val schema = DataType.fromJson(schemaJson).asInstanceOf[StructType]

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        val cdrs = seqToCdr(rdd).toDF()
        val highInternet = sqlC.createDataFrame(cdrs.rdd.filter(r => r.getFloat(3) + r.getFloat(4) >= r.getFloat(5) + r.getFloat(6)), schema)
        val highOther = cdrs.except(highInternet)
        val highInternetGrid = highInternet.select("squareId", "countryCode").dropDuplicates()
        val highOtherGrid = highOther.select("squareId", "countryCode").dropDuplicates()
        highOtherGrid.except(highInternetGrid).show()
        highInternetGrid.except(highOtherGrid).show()
      })

    ssc.start()
    ssc.awaitTermination()
  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext

object CdrHiveqlApp {

  case class Cdr(squareId: Int, timeInterval: Long, countryCode: Int,
    smsInActivity: Float, smsOutActivity: Float, callInActivity: Float,
    callOutActivity: Float, internetTrafficActivity: Float)

  def main(args: Array[String]) {
    if (args.length != 4) {
      System.err.println(
        "Usage: CdrHiveqlApp <appname> <batchInterval> <hostname> <port>")
      System.exit(1)
    }
    val Seq(appName, batchInterval, hostname, port) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt))

    val cl = Thread.currentThread().getContextClassLoader()
    val hiveC = new HiveContext(ssc.sparkContext)
    Thread.currentThread().setContextClassLoader(cl)

    import hiveC.implicits._

    val cdrStream = ssc.socketTextStream(hostname, port.toInt)
      .map(_.split("\\t", -1))
      .foreachRDD(rdd => {
        seqToCdr(rdd).toDF().registerTempTable("cdrs")

        hiveC.sql("SET DATE_FMT='yy-MM-dd|HH'")
        hiveC.sql("SELECT from_unixtime(timeInterval, ${hiveconf:DATE_FMT}) AS TS, SUM(smsInActivity + smsOutActivity + callInActivity + callOutActivity + internetTrafficActivity) AS Activity FROM cdrs GROUP BY from_unixtime(timeInterval, ${hiveconf:DATE_FMT}) ORDER BY Activity DESC").show()
      })

    ssc.start()
    ssc.awaitTermination()
  }

  def seqToCdr(rdd: RDD[Array[String]]): RDD[Cdr] = {
    rdd.map(c => c.map(f => f match {
      case x if x.isEmpty() => "0"
      case x => x
    })).map(c => Cdr(c(0).toInt, c(1).toLong, c(2).toInt, c(3).toFloat,
      c(4).toFloat, c(5).toFloat, c(6).toFloat, c(7).toFloat))
  }
} 

package org.apress.prospark

import java.nio.charset.StandardCharsets

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.eclipse.paho.client.mqttv3.MqttClient
import org.eclipse.paho.client.mqttv3.MqttMessage
import org.eclipse.paho.client.mqttv3.persist.MemoryPersistence
import org.json4s.DefaultFormats
import org.json4s.JField
import org.json4s.JsonAST.JObject
import org.json4s.jvalue2extractable
import org.json4s.jvalue2monadic
import org.json4s.native.JsonMethods.parse
import org.json4s.string2JsonInput

object MqttSinkAppB {

  def main(args: Array[String]) {
    if (args.length != 3) {
      System.err.println(
        "Usage: MqttSinkApp <appname> <outputBrokerUrl> <topic>")
      System.exit(1)
    }

    val Seq(appName, outputBrokerUrl, topic) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val batchInterval = 10

    val ssc = new StreamingContext(conf, Seconds(batchInterval))

    HttpUtils.createStream(ssc, url = "https://query.yahooapis.com/v1/public/yql?q=select%20*%20from%20yahoo.finance.quotes%20where%20symbol%20in%20(%22IBM,GOOG,MSFT,AAPL,FB,ORCL,YHOO,TWTR,LNKD,INTC%22)%0A%09%09&format=json&diagnostics=true&env=http%3A%2F%2Fdatatables.org%2Falltables.env",
      interval = batchInterval)
      .flatMap(rec => {
        val query = parse(rec) \ "query"
        ((query \ "results" \ "quote").children).map(rec => JObject(JField("Timestamp", query \ "created")).merge(rec))
      })
      .map(rec => {
        implicit val formats = DefaultFormats
        rec.children.map(f => f.extract[String]) mkString ","
      })
      .foreachRDD { rdd =>
        rdd.foreach { rec =>
          {
            val client = new MqttClient(outputBrokerUrl, MqttClient.generateClientId(), new MemoryPersistence())
            client.connect()
            client.publish(topic, new MqttMessage(rec.getBytes(StandardCharsets.UTF_8)))
            client.disconnect()
            client.close()
          }
        }
      }

    ssc.start()
    ssc.awaitTermination()
  }

} 

package org.apress.prospark

import java.nio.charset.StandardCharsets

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.eclipse.paho.client.mqttv3.MqttClient
import org.eclipse.paho.client.mqttv3.MqttMessage
import org.eclipse.paho.client.mqttv3.persist.MemoryPersistence
import org.json4s.DefaultFormats
import org.json4s.JField
import org.json4s.JsonAST.JObject
import org.json4s.jvalue2extractable
import org.json4s.jvalue2monadic
import org.json4s.native.JsonMethods.parse
import org.json4s.string2JsonInput

object MqttSinkAppF {

  def main(args: Array[String]) {
    if (args.length != 3) {
      System.err.println(
        "Usage: MqttSinkApp <appname> <outputBrokerUrl> <topic>")
      System.exit(1)
    }

    val Seq(appName, outputBrokerUrl, topic) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val batchInterval = 10

    val ssc = new StreamingContext(conf, Seconds(batchInterval))

    val mqttSink = ssc.sparkContext.broadcast(MqttSinkLazy(outputBrokerUrl))

    HttpUtils.createStream(ssc, url = "https://query.yahooapis.com/v1/public/yql?q=select%20*%20from%20yahoo.finance.quotes%20where%20symbol%20in%20(%22IBM,GOOG,MSFT,AAPL,FB,ORCL,YHOO,TWTR,LNKD,INTC%22)%0A%09%09&format=json&diagnostics=true&env=http%3A%2F%2Fdatatables.org%2Falltables.env",
      interval = batchInterval)
      .flatMap(rec => {
        val query = parse(rec) \ "query"
        ((query \ "results" \ "quote").children).map(rec => JObject(JField("Timestamp", query \ "created")).merge(rec))
      })
      .map(rec => {
        implicit val formats = DefaultFormats
        rec.children.map(f => f.extract[String]) mkString ","
      })
      .foreachRDD { rdd =>
        rdd.foreachPartition { par =>
          par.foreach(message => mqttSink.value.client.publish(topic, new MqttMessage(message.getBytes(StandardCharsets.UTF_8))))
        }
      }

    ssc.start()
    ssc.awaitTermination()
  }

}

class MqttSinkLazy(brokerUrl: String) extends Serializable {
  lazy val client = {
    val client = new MqttClient(brokerUrl, MqttClient.generateClientId(), new MemoryPersistence())
    client.connect()
    sys.addShutdownHook {
      client.disconnect()
      client.close()
    }
    client
  }
}

object MqttSinkLazy {
  val brokerUrl = "tcp://localhost:1883"
  val client = new MqttSinkLazy(brokerUrl)

  def apply(brokerUrl: String): MqttSinkLazy = {
    client
  }
} 

package org.apress.prospark

import java.nio.charset.StandardCharsets

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.eclipse.paho.client.mqttv3.MqttClient
import org.eclipse.paho.client.mqttv3.MqttMessage
import org.eclipse.paho.client.mqttv3.persist.MemoryPersistence
import org.json4s.DefaultFormats
import org.json4s.JField
import org.json4s.JsonAST.JObject
import org.json4s.jvalue2extractable
import org.json4s.jvalue2monadic
import org.json4s.native.JsonMethods.parse
import org.json4s.string2JsonInput

object MqttSinkAppD {

  def main(args: Array[String]) {
    if (args.length != 3) {
      System.err.println(
        "Usage: MqttSinkApp <appname> <outputBrokerUrl> <topic>")
      System.exit(1)
    }

    val Seq(appName, outputBrokerUrl, topic) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val batchInterval = 10

    val ssc = new StreamingContext(conf, Seconds(batchInterval))

    HttpUtils.createStream(ssc, url = "https://query.yahooapis.com/v1/public/yql?q=select%20*%20from%20yahoo.finance.quotes%20where%20symbol%20in%20(%22IBM,GOOG,MSFT,AAPL,FB,ORCL,YHOO,TWTR,LNKD,INTC%22)%0A%09%09&format=json&diagnostics=true&env=http%3A%2F%2Fdatatables.org%2Falltables.env",
      interval = batchInterval)
      .flatMap(rec => {
        val query = parse(rec) \ "query"
        ((query \ "results" \ "quote").children).map(rec => JObject(JField("Timestamp", query \ "created")).merge(rec))
      })
      .map(rec => {
        implicit val formats = DefaultFormats
        rec.children.map(f => f.extract[String]) mkString ","
      })
      .foreachRDD { rdd =>
        rdd.foreachPartition { par =>
          par.foreach(message => MqttSink().publish(topic, new MqttMessage(message.getBytes(StandardCharsets.UTF_8))))
        }
      }

    ssc.start()
    ssc.awaitTermination()
  }
}

object MqttSink {
  val brokerUrl = "tcp://localhost:1883"
  val client = new MqttClient(brokerUrl, MqttClient.generateClientId(), new MemoryPersistence())
  client.connect()
  sys.addShutdownHook {
    client.disconnect()
    client.close()
  }

  def apply(): MqttClient = {
    client
  }
} 

package org.apress.prospark

import java.nio.charset.StandardCharsets
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.json4s.DefaultFormats
import org.json4s.JField
import org.json4s.JsonAST.JObject
import org.json4s.jvalue2extractable
import org.json4s.jvalue2monadic
import org.json4s.native.JsonMethods.parse
import org.json4s.string2JsonInput
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.io.Text
import java.nio.ByteBuffer
import org.apache.cassandra.hadoop.ColumnFamilyOutputFormat
import org.apache.cassandra.hadoop.ConfigHelper
import org.apache.cassandra.thrift.ColumnOrSuperColumn
import org.apache.cassandra.thrift.Column
import org.apache.cassandra.utils.ByteBufferUtil
import org.apache.cassandra.thrift.Mutation
import java.util.Arrays

object CassandraSinkApp {

  def main(args: Array[String]) {
    if (args.length != 6) {
      System.err.println(
        "Usage: CassandraSinkApp <appname> <cassandraHost> <cassandraPort> <keyspace> <columnFamilyName> <columnName>")
      System.exit(1)
    }

    val Seq(appName, cassandraHost, cassandraPort, keyspace, columnFamilyName, columnName) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)

    val batchInterval = 10
    val windowSize = 20
    val slideInterval = 10

    val ssc = new StreamingContext(conf, Seconds(batchInterval))

    HttpUtils.createStream(ssc, url = "https://query.yahooapis.com/v1/public/yql?q=select%20*%20from%20yahoo.finance.quotes%20where%20symbol%20in%20(%22IBM,GOOG,MSFT,AAPL,FB,ORCL,YHOO,TWTR,LNKD,INTC%22)%0A%09%09&format=json&diagnostics=true&env=http%3A%2F%2Fdatatables.org%2Falltables.env",
      interval = batchInterval)
      .flatMap(rec => {
        implicit val formats = DefaultFormats
        val query = parse(rec) \ "query"
        ((query \ "results" \ "quote").children)
          .map(rec => ((rec \ "symbol").extract[String], (rec \ "LastTradePriceOnly").extract[String].toFloat))
      })
      .reduceByKeyAndWindow((x: Float, y: Float) => (x + y), Seconds(windowSize), Seconds(slideInterval))
      .foreachRDD(rdd => {
        val jobConf = new Configuration()
        ConfigHelper.setOutputRpcPort(jobConf, cassandraPort)
        ConfigHelper.setOutputInitialAddress(jobConf, cassandraHost)
        ConfigHelper.setOutputColumnFamily(jobConf, keyspace, columnFamilyName)
        ConfigHelper.setOutputPartitioner(jobConf, "Murmur3Partitioner")
        rdd.map(rec => {
          val c = new Column()
          c.setName(ByteBufferUtil.bytes(columnName))
          c.setValue(ByteBufferUtil.bytes(rec._2 / (windowSize / batchInterval)))
          c.setTimestamp(System.currentTimeMillis)
          val m = new Mutation()
          m.setColumn_or_supercolumn(new ColumnOrSuperColumn())
          m.column_or_supercolumn.setColumn(c)
          (ByteBufferUtil.bytes(rec._1), Arrays.asList(m))
        }).saveAsNewAPIHadoopFile(keyspace, classOf[ByteBuffer], classOf[List[Mutation]], classOf[ColumnFamilyOutputFormat], jobConf)
      })

    ssc.start()
    ssc.awaitTermination()
  }
} 

package org.apress.prospark

import scala.reflect.runtime.universe

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions
import org.json4s.DefaultFormats
import org.json4s.jvalue2extractable
import org.json4s.jvalue2monadic
import org.json4s.native.JsonMethods.parse
import org.json4s.string2JsonInput

import com.datastax.spark.connector.SomeColumns
import com.datastax.spark.connector.cql.CassandraConnector
import com.datastax.spark.connector.streaming.toDStreamFunctions
import com.datastax.spark.connector.toNamedColumnRef

object CassandraConnectorSinkApp {

  def main(args: Array[String]) {
    if (args.length != 6) {
      System.err.println(
        "Usage: CassandraConnectorSinkApp <appname> <cassandraHost> <cassandraPort> <keyspace> <tableName> <columnName>")
      System.exit(1)
    }

    val Seq(appName, cassandraHost, cassandraPort, keyspace, tableName, columnName) = args.toSeq

    val conf = new SparkConf()
      .setAppName(appName)
      .setJars(SparkContext.jarOfClass(this.getClass).toSeq)
      .set("spark.cassandra.connection.host", cassandraHost)
      .set("spark.cassandra.connection.port", cassandraPort)

    val batchInterval = 10
    val windowSize = 20
    val slideInterval = 10

    val ssc = new StreamingContext(conf, Seconds(batchInterval))

    CassandraConnector(conf).withSessionDo { session =>
      session.execute(s"CREATE KEYSPACE IF NOT EXISTS %s WITH REPLICATION = {'class': 'SimpleStrategy', 'replication_factor': 1 }".format(keyspace))
      session.execute(s"CREATE TABLE IF NOT EXISTS %s.%s (key TEXT PRIMARY KEY, %s FLOAT)".format(keyspace, tableName, columnName))
    }

    HttpUtils.createStream(ssc, url = "https://query.yahooapis.com/v1/public/yql?q=select%20*%20from%20yahoo.finance.quotes%20where%20symbol%20in%20(%22IBM,GOOG,MSFT,AAPL,FB,ORCL,YHOO,TWTR,LNKD,INTC%22)%0A%09%09&format=json&diagnostics=true&env=http%3A%2F%2Fdatatables.org%2Falltables.env",
      interval = batchInterval)
      .flatMap(rec => {
        implicit val formats = DefaultFormats
        val query = parse(rec) \ "query"
        ((query \ "results" \ "quote").children)
          .map(rec => ((rec \ "symbol").extract[String], (rec \ "LastTradePriceOnly").extract[String].toFloat))
      })
      .reduceByKeyAndWindow((x: Float, y: Float) => (x + y), Seconds(windowSize), Seconds(slideInterval))
      .map(stock => (stock._1, stock._2 / (windowSize / batchInterval)))
      .saveToCassandra(keyspace, tableName)

    ssc.start()
    ssc.awaitTermination()
  }
} 

package com.cloudera.spark.examples

import org.apache.kafka.clients.consumer.ConsumerRecord

import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, LocationStrategies, KafkaUtils}
import org.apache.spark.streaming._

object DirectKafkaWordCount {
    def main(args: Array[String]) {
      if (args.length < 2) {
        System.err.println(s"""
                              |Usage: DirectKafkaWordCount <brokers> <topics>
                              |  <brokers> is a list of one or more Kafka brokers
                              |  <topics> is a list of one or more kafka topics to consume from
                              |  <ssl> true if using SSL, false otherwise.
                              |
        """.stripMargin)
        System.exit(1)
      }

      val Array(brokers, topics, ssl) = args

      // Create context with 2 second batch interval
      val sparkConf = new SparkConf().setAppName("DirectKafkaWordCount")
      val ssc = new StreamingContext(sparkConf, Seconds(2))
      val isUsingSsl = ssl.toBoolean

      // Create direct kafka stream with brokers and topics
      val topicsSet = topics.split(",").toSet
      val commonParams = Map[String, Object](
        "bootstrap.servers" -> brokers,
        "security.protocol" -> (if (isUsingSsl) "SASL_SSL" else "SASL_PLAINTEXT"),
        "sasl.kerberos.service.name" -> "kafka",
        "auto.offset.reset" -> "earliest",
        "key.deserializer" -> "org.apache.kafka.common.serialization.StringDeserializer",
        "value.deserializer" -> "org.apache.kafka.common.serialization.StringDeserializer",
        "group.id" -> "default",
        "enable.auto.commit" -> (false: java.lang.Boolean)
      )

      val additionalSslParams = if (isUsingSsl) {
        Map(
          "ssl.truststore.location" -> "/etc/cdep-ssl-conf/CA_STANDARD/truststore.jks",
          "ssl.truststore.password" -> "cloudera"
        )
      } else {
        Map.empty
      }

      val kafkaParams = commonParams ++ additionalSslParams

      val messages: InputDStream[ConsumerRecord[String, String]] =
        KafkaUtils.createDirectStream[String, String](
          ssc,
          LocationStrategies.PreferConsistent,
          ConsumerStrategies.Subscribe[String, String](topicsSet, kafkaParams)
        )

      // Get the lines, split them into words, count the words and print
      val lines = messages.map(_.value())
      val words = lines.flatMap(_.split(" "))
      val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _)
      wordCounts.print()

      // Start the computation
      ssc.start()
      ssc.awaitTermination()
    }
} 

package io.univalence

import org.apache.spark.SparkConf
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.ArrayType
import org.apache.spark.sql.types.StringType
import io.univalence.SparkLens._
import org.scalatest.FunSuite

case class Toto(name: String, age: Int)

case class Tata(toto: Toto)

class SparkLensTest extends FunSuite {

  val conf: SparkConf = new SparkConf()
  conf.setAppName("yo")
  conf.setMaster("local[*]")

  implicit val ss: SparkSession = SparkSession.builder.config(conf).getOrCreate

  import ss.implicits._

  test("testLensRegExp change string") {
    assert(lensRegExp(ss.createDataFrame(Seq(Toto("a", 1))))({
      case ("name", StringType) => true
      case _                    => false
    }, { case (a: String, d)    => a.toUpperCase }).as[Toto].first() == Toto("A", 1))
  }

  test("change Int") {
    assert(lensRegExp(ss.createDataFrame(Seq(Tata(Toto("a", 1)))))({
      case ("toto/age", _) => true
      case _               => false
    }, { case (a: Int, d)  => a + 1 }).as[Tata].first() == Tata(Toto("a", 2)))
  }

  ignore("null to nil") {

    val df: DataFrame = ss.read.parquet("/home/phong/daily_gpp_20180705")

    val yoho: DataFrame = lensRegExp(df)({
      case (_, ArrayType(_, _)) => true
      case _                    => false
    }, (a, b) => if (a == null) Nil else a)

  }

} 

package com.cloudera.sa.examples.tablestats

import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.expressions.GenericRow
import org.apache.spark.sql.types.{StringType, LongType, StructField, StructType}
import org.apache.spark.{SparkContext, SparkConf}

import scala.collection.mutable
import scala.util.Random



object ConfigurableDataGeneratorMain {
  def main(args: Array[String]): Unit = {

    if (args.length == 0) {
      println("ConfigurableDataGeneratorMain <outputPath> <numberOfColumns> <numberOfRecords> <numberOfPartitions> <local>")
      return
    }

    val outputPath = args(0)
    val numberOfColumns = args(1).toInt
    val numberOfRecords = args(2).toInt
    val numberOfPartitions = args(3).toInt
    val runLocal = (args.length == 5 && args(4).equals("L"))

    var sc: SparkContext = null
    if (runLocal) {
      val sparkConfig = new SparkConf()
      sparkConfig.set("spark.broadcast.compress", "false")
      sparkConfig.set("spark.shuffle.compress", "false")
      sparkConfig.set("spark.shuffle.spill.compress", "false")
      sc = new SparkContext("local", "test", sparkConfig)
    } else {
      val sparkConfig = new SparkConf().setAppName("ConfigurableDataGeneratorMain")
      sc = new SparkContext(sparkConfig)
    }

    val sqlContext = new org.apache.spark.sql.SQLContext(sc)

    //Part A
    val rowRDD = sc.parallelize( (0 until numberOfPartitions).map( i => i), numberOfPartitions)

    //Part B
    val megaDataRDD = rowRDD.flatMap( r => {
      val random = new Random()

      val dataRange = (0 until numberOfRecords/numberOfPartitions).iterator
      dataRange.map[Row]( x => {
        val values = new mutable.ArrayBuffer[Any]
        for (i <- 0 until numberOfColumns) {
          if (i % 2 == 0) {
            values.+=(random.nextInt(100).toLong)
          } else {
            values.+=(random.nextInt(100).toString)
          }
        }
        new GenericRow(values.toArray)
      })
    })

    //Part C
    val schema =
      StructType(
        (0 until numberOfColumns).map( i => {
          if (i % 2 == 0) {
            StructField("longColumn_" + i, LongType, true) }
          else {
            StructField("stringColumn_" + i, StringType, true)
          }
        })
      )
    val df = sqlContext.createDataFrame(megaDataRDD, schema)
    df.saveAsParquetFile(outputPath)

    //Part D
    sc.stop()
  }
} 

package com.cloudera.sa.examples.tablestats

import org.apache.spark.sql.Row
import org.apache.spark.sql.types._
import org.apache.spark.{SparkContext, SparkConf}


object SimpleDataGeneratorMain {
  def main(args: Array[String]): Unit = {


    if (args.length == 0) {
      println("SimpleDataGeneratorMain <outputPath> ")
      return
    }

    val outputPath = args(0)

    val sparkConfig = new SparkConf()
    sparkConfig.set("spark.broadcast.compress", "false")
    sparkConfig.set("spark.shuffle.compress", "false")
    sparkConfig.set("spark.shuffle.spill.compress", "false")
    var sc = new SparkContext("local", "test", sparkConfig)
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)

    val schema =
      StructType(
        Array(
          StructField("id", LongType, true),
          StructField("name", StringType, true),
          StructField("age", LongType, true),
          StructField("gender", StringType, true),
          StructField("height", LongType, true),
          StructField("job_title", StringType, true)
        )
      )

    val rowRDD = sc.parallelize(Array(
      Row(1l, "Name.1", 20l, "M", 6l, "dad"),
      Row(2l, "Name.2", 20l, "F", 5l, "mom"),
      Row(3l, "Name.3", 20l, "F", 5l, "mom"),
      Row(4l, "Name.4", 20l, "F", 5l, "mom"),
      Row(5l, "Name.5", 10l, "M", 4l, "kid"),
      Row(6l, "Name.6", 8l, "M", 3l, "kid")))

    val df = sqlContext.createDataFrame(rowRDD, schema)

    println("columns:")
    df.columns.foreach( c => println(" - " + c))

    df.saveAsParquetFile(outputPath)

    sc.stop()
  }
} 

package com.cloudera.sa.examples.tablestats


import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.sql.Row
import org.apache.spark.sql.types.{StringType, LongType, StructField, StructType}
import org.scalatest.{FunSuite, BeforeAndAfterEach, BeforeAndAfterAll}


class TestTableStatsSinglePathMain extends FunSuite with BeforeAndAfterEach with BeforeAndAfterAll{
  test("run table stats on sample data") {

    val sparkConfig = new SparkConf()
    sparkConfig.set("spark.broadcast.compress", "false")
    sparkConfig.set("spark.shuffle.compress", "false")
    sparkConfig.set("spark.shuffle.spill.compress", "false")
    var sc = new SparkContext("local", "test", sparkConfig)
    try {
      val sqlContext = new org.apache.spark.sql.SQLContext(sc)

      val schema =
        StructType(
          Array(
            StructField("id", LongType, true),
            StructField("name", StringType, true),
            StructField("age", LongType, true),
            StructField("gender", StringType, true),
            StructField("height", LongType, true),
            StructField("job_title", StringType, true)
          )
        )

      val rowRDD = sc.parallelize(Array(
        Row(1l, "Name.1", 20l, "M", 6l, "dad"),
        Row(2l, "Name.2", 20l, "F", 5l, "mom"),
        Row(3l, "Name.3", 20l, "F", 5l, "mom"),
        Row(4l, "Name.4", 20l, "M", 5l, "mom"),
        Row(5l, "Name.5", 10l, "M", 4l, "kid"),
        Row(6l, "Name.6", 8l, "M", 3l, "kid")))

      val df = sqlContext.createDataFrame(rowRDD, schema)

      val firstPassStats = TableStatsSinglePathMain.getFirstPassStat(df)

      assertResult(6l)(firstPassStats.columnStatsMap(0).maxLong)
      assertResult(1l)(firstPassStats.columnStatsMap(0).minLong)
      assertResult(21l)(firstPassStats.columnStatsMap(0).sumLong)
      assertResult(3l)(firstPassStats.columnStatsMap(0).avgLong)

      assertResult(2)(firstPassStats.columnStatsMap(3).topNValues.topNCountsForColumnArray.length)

      firstPassStats.columnStatsMap(3).topNValues.topNCountsForColumnArray.foreach { r =>
        if (r._1.equals("M")) {
          assertResult(4l)(r._2)
        } else if (r._1.equals("F")) {
          assertResult(2l)(r._2)
        } else {
          throw new RuntimeException("Unknown gender: " + r._1)
        }
      }
    } finally {
      sc.stop()
    }
  }
} 

// scalastyle:off println
package org.apache.spark.examples.graphx

import java.io.{FileOutputStream, PrintWriter}

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.graphx.{GraphXUtils, PartitionStrategy}
import org.apache.spark.graphx.util.GraphGenerators


  def main(args: Array[String]) {
    val options = args.map {
      arg =>
        arg.dropWhile(_ == '-').split('=') match {
          case Array(opt, v) => (opt -> v)
          case _ => throw new IllegalArgumentException("Invalid argument: " + arg)
        }
    }

    var app = "pagerank"
    var niter = 10
    var numVertices = 100000
    var numEPart: Option[Int] = None
    var partitionStrategy: Option[PartitionStrategy] = None
    var mu: Double = 4.0
    var sigma: Double = 1.3
    var degFile: String = ""
    var seed: Int = -1

    options.foreach {
      case ("app", v) => app = v
      case ("niters", v) => niter = v.toInt
      case ("nverts", v) => numVertices = v.toInt
      case ("numEPart", v) => numEPart = Some(v.toInt)
      case ("partStrategy", v) => partitionStrategy = Some(PartitionStrategy.fromString(v))
      case ("mu", v) => mu = v.toDouble
      case ("sigma", v) => sigma = v.toDouble
      case ("degFile", v) => degFile = v
      case ("seed", v) => seed = v.toInt
      case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt)
    }

    val conf = new SparkConf()
      .setAppName(s"GraphX Synth Benchmark (nverts = $numVertices, app = $app)")
    GraphXUtils.registerKryoClasses(conf)

    val sc = new SparkContext(conf)

    // Create the graph
    println(s"Creating graph...")
    val unpartitionedGraph = GraphGenerators.logNormalGraph(sc, numVertices,
      numEPart.getOrElse(sc.defaultParallelism), mu, sigma, seed)
    // Repartition the graph
    val graph = partitionStrategy.foldLeft(unpartitionedGraph)(_.partitionBy(_)).cache()

    var startTime = System.currentTimeMillis()
    val numEdges = graph.edges.count()
    println(s"Done creating graph. Num Vertices = $numVertices, Num Edges = $numEdges")
    val loadTime = System.currentTimeMillis() - startTime

    // Collect the degree distribution (if desired)
    if (!degFile.isEmpty) {
      val fos = new FileOutputStream(degFile)
      val pos = new PrintWriter(fos)
      val hist = graph.vertices.leftJoin(graph.degrees)((id, _, optDeg) => optDeg.getOrElse(0))
        .map(p => p._2).countByValue()
      hist.foreach {
        case (deg, count) => pos.println(s"$deg \t $count")
      }
    }

    // Run PageRank
    startTime = System.currentTimeMillis()
    if (app == "pagerank") {
      println("Running PageRank")
      val totalPR = graph.staticPageRank(niter).vertices.map(_._2).sum()
      println(s"Total PageRank = $totalPR")
    } else if (app == "cc") {
      println("Running Connected Components")
      val numComponents = graph.connectedComponents.vertices.map(_._2).distinct().count()
      println(s"Number of components = $numComponents")
    }
    val runTime = System.currentTimeMillis() - startTime

    println(s"Num Vertices = $numVertices")
    println(s"Num Edges = $numEdges")
    println(s"Creation time = ${loadTime/1000.0} seconds")
    println(s"Run time = ${runTime/1000.0} seconds")

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.feature.Normalizer
import org.apache.spark.mllib.util.MLUtils
// $example off$

object NormalizerExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("NormalizerExample")
    val sc = new SparkContext(conf)

    // $example on$
    val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")

    val normalizer1 = new Normalizer()
    val normalizer2 = new Normalizer(p = Double.PositiveInfinity)

    // Each sample in data1 will be normalized using $L^2$ norm.
    val data1 = data.map(x => (x.label, normalizer1.transform(x.features)))

    // Each sample in data2 will be normalized using $L^\infty$ norm.
    val data2 = data.map(x => (x.label, normalizer2.transform(x.features)))
    // $example off$

    println("data1: ")
    data1.foreach(x => println(x))

    println("data2: ")
    data2.foreach(x => println(x))

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.feature.PCA
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.rdd.RDD
// $example off$

object PCAOnSourceVectorExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("PCAOnSourceVectorExample")
    val sc = new SparkContext(conf)

    // $example on$
    val data: RDD[LabeledPoint] = sc.parallelize(Seq(
      new LabeledPoint(0, Vectors.dense(1, 0, 0, 0, 1)),
      new LabeledPoint(1, Vectors.dense(1, 1, 0, 1, 0)),
      new LabeledPoint(1, Vectors.dense(1, 1, 0, 0, 0)),
      new LabeledPoint(0, Vectors.dense(1, 0, 0, 0, 0)),
      new LabeledPoint(1, Vectors.dense(1, 1, 0, 0, 0))))

    // Compute the top 5 principal components.
    val pca = new PCA(5).fit(data.map(_.features))

    // Project vectors to the linear space spanned by the top 5 principal
    // components, keeping the label
    val projected = data.map(p => p.copy(features = pca.transform(p.features)))
    // $example off$
    val collect = projected.collect()
    println("Projected vector of principal component:")
    collect.foreach { vector => println(vector) }
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.linalg.Matrix
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.linalg.distributed.RowMatrix
// $example off$

object PCAOnRowMatrixExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("PCAOnRowMatrixExample")
    val sc = new SparkContext(conf)

    // $example on$
    val data = Array(
      Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))),
      Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
      Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0))

    val dataRDD = sc.parallelize(data, 2)

    val mat: RowMatrix = new RowMatrix(dataRDD)

    // Compute the top 4 principal components.
    // Principal components are stored in a local dense matrix.
    val pc: Matrix = mat.computePrincipalComponents(4)

    // Project the rows to the linear space spanned by the top 4 principal components.
    val projected: RowMatrix = mat.multiply(pc)
    // $example off$
    val collect = projected.rows.collect()
    println("Projected Row Matrix of principal component:")
    collect.foreach { vector => println(vector) }
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.classification.{NaiveBayes, NaiveBayesModel}
import org.apache.spark.mllib.util.MLUtils
// $example off$

object NaiveBayesExample {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("NaiveBayesExample")
    val sc = new SparkContext(conf)
    // $example on$
    // Load and parse the data file.
    val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")

    // Split data into training (60%) and test (40%).
    val Array(training, test) = data.randomSplit(Array(0.6, 0.4))

    val model = NaiveBayes.train(training, lambda = 1.0, modelType = "multinomial")

    val predictionAndLabel = test.map(p => (model.predict(p.features), p.label))
    val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count()

    // Save and load model
    model.save(sc, "target/tmp/myNaiveBayesModel")
    val sameModel = NaiveBayesModel.load(sc, "target/tmp/myNaiveBayesModel")
    // $example off$
  }
}

// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
import org.apache.spark.mllib.linalg.Vectors


object TallSkinnyPCA {
  def main(args: Array[String]) {
    if (args.length != 1) {
      System.err.println("Usage: TallSkinnyPCA <input>")
      System.exit(1)
    }

    val conf = new SparkConf().setAppName("TallSkinnyPCA")
    val sc = new SparkContext(conf)

    // Load and parse the data file.
    val rows = sc.textFile(args(0)).map { line =>
      val values = line.split(' ').map(_.toDouble)
      Vectors.dense(values)
    }
    val mat = new RowMatrix(rows)

    // Compute principal components.
    val pc = mat.computePrincipalComponents(mat.numCols().toInt)

    println("Principal components are:\n" + pc)

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.clustering.{GaussianMixture, GaussianMixtureModel}
import org.apache.spark.mllib.linalg.Vectors
// $example off$

object GaussianMixtureExample {

  def main(args: Array[String]) {

    val conf = new SparkConf().setAppName("GaussianMixtureExample")
    val sc = new SparkContext(conf)

    // $example on$
    // Load and parse the data
    val data = sc.textFile("data/mllib/gmm_data.txt")
    val parsedData = data.map(s => Vectors.dense(s.trim.split(' ').map(_.toDouble))).cache()

    // Cluster the data into two classes using GaussianMixture
    val gmm = new GaussianMixture().setK(2).run(parsedData)

    // Save and load model
    gmm.save(sc, "target/org/apache/spark/GaussianMixtureExample/GaussianMixtureModel")
    val sameModel = GaussianMixtureModel.load(sc,
      "target/org/apache/spark/GaussianMixtureExample/GaussianMixtureModel")

    // output parameters of max-likelihood model
    for (i <- 0 until gmm.k) {
      println("weight=%f\nmu=%s\nsigma=\n%s\n" format
        (gmm.weights(i), gmm.gaussians(i).mu, gmm.gaussians(i).sigma))
    }
    // $example off$

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel}
// $example off$

object Word2VecExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("Word2VecExample")
    val sc = new SparkContext(conf)

    // $example on$
    val input = sc.textFile("data/mllib/sample_lda_data.txt").map(line => line.split(" ").toSeq)

    val word2vec = new Word2Vec()

    val model = word2vec.fit(input)

    val synonyms = model.findSynonyms("1", 5)

    for((synonym, cosineSimilarity) <- synonyms) {
      println(s"$synonym $cosineSimilarity")
    }

    // Save and load model
    model.save(sc, "myModelPath")
    val sameModel = Word2VecModel.load(sc, "myModelPath")
    // $example off$

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.stat.Statistics
import org.apache.spark.mllib.util.MLUtils

spark-examples-*.jar \
        |  --input data/mllib/sample_linear_regression_data.txt
        """.stripMargin)
    }

    parser.parse(args, defaultParams) match {
      case Some(params) => run(params)
      case _ => sys.exit(1)
    }
  }

  def run(params: Params): Unit = {
    val conf = new SparkConf().setAppName(s"Correlations with $params")
    val sc = new SparkContext(conf)

    val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()

    println(s"Summary of data file: ${params.input}")
    println(s"${examples.count()} data points")

    // Calculate label -- feature correlations
    val labelRDD = examples.map(_.label)
    val numFeatures = examples.take(1)(0).features.size
    val corrType = "pearson"
    println()
    println(s"Correlation ($corrType) between label and each feature")
    println(s"Feature\tCorrelation")
    var feature = 0
    while (feature < numFeatures) {
      val featureRDD = examples.map(_.features(feature))
      val corr = Statistics.corr(labelRDD, featureRDD)
      println(s"$feature\t$corr")
      feature += 1
    }
    println()

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.fpm.FPGrowth


object FPGrowthExample {

  case class Params(
    input: String = null,
    minSupport: Double = 0.3,
    numPartition: Int = -1) extends AbstractParams[Params]

  def main(args: Array[String]) {
    val defaultParams = Params()

    val parser = new OptionParser[Params]("FPGrowthExample") {
      head("FPGrowth: an example FP-growth app.")
      opt[Double]("minSupport")
        .text(s"minimal support level, default: ${defaultParams.minSupport}")
        .action((x, c) => c.copy(minSupport = x))
      opt[Int]("numPartition")
        .text(s"number of partition, default: ${defaultParams.numPartition}")
        .action((x, c) => c.copy(numPartition = x))
      arg[String]("<input>")
        .text("input paths to input data set, whose file format is that each line " +
          "contains a transaction with each item in String and separated by a space")
        .required()
        .action((x, c) => c.copy(input = x))
    }

    parser.parse(args, defaultParams) match {
      case Some(params) => run(params)
      case _ => sys.exit(1)
    }
  }

  def run(params: Params): Unit = {
    val conf = new SparkConf().setAppName(s"FPGrowthExample with $params")
    val sc = new SparkContext(conf)
    val transactions = sc.textFile(params.input).map(_.split(" ")).cache()

    println(s"Number of transactions: ${transactions.count()}")

    val model = new FPGrowth()
      .setMinSupport(params.minSupport)
      .setNumPartitions(params.numPartition)
      .run(transactions)

    println(s"Number of frequent itemsets: ${model.freqItemsets.count()}")

    model.freqItemsets.collect().foreach { itemset =>
      println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
    }

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.log4j.{Level, Logger}
import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.optimization.{L1Updater, SimpleUpdater, SquaredL2Updater}
import org.apache.spark.mllib.regression.LinearRegressionWithSGD
import org.apache.spark.mllib.util.MLUtils

spark-examples-*.jar \
          |  data/mllib/sample_linear_regression_data.txt
        """.stripMargin)
    }

    parser.parse(args, defaultParams) match {
      case Some(params) => run(params)
      case _ => sys.exit(1)
    }
  }

  def run(params: Params): Unit = {
    val conf = new SparkConf().setAppName(s"LinearRegression with $params")
    val sc = new SparkContext(conf)

    Logger.getRootLogger.setLevel(Level.WARN)

    val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()

    val splits = examples.randomSplit(Array(0.8, 0.2))
    val training = splits(0).cache()
    val test = splits(1).cache()

    val numTraining = training.count()
    val numTest = test.count()
    println(s"Training: $numTraining, test: $numTest.")

    examples.unpersist(blocking = false)

    val updater = params.regType match {
      case NONE => new SimpleUpdater()
      case L1 => new L1Updater()
      case L2 => new SquaredL2Updater()
    }

    val algorithm = new LinearRegressionWithSGD()
    algorithm.optimizer
      .setNumIterations(params.numIterations)
      .setStepSize(params.stepSize)
      .setUpdater(updater)
      .setRegParam(params.regParam)

    val model = algorithm.run(training)

    val prediction = model.predict(test.map(_.features))
    val predictionAndLabel = prediction.zip(test.map(_.label))

    val loss = predictionAndLabel.map { case (p, l) =>
      val err = p - l
      err * err
    }.reduce(_ + _)
    val rmse = math.sqrt(loss / numTest)

    println(s"Test RMSE = $rmse.")

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.log4j.{Level, Logger}
import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.classification.{LogisticRegressionWithLBFGS, SVMWithSGD}
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.optimization.{L1Updater, SquaredL2Updater}
import org.apache.spark.mllib.util.MLUtils

spark-examples-*.jar \
          |  --algorithm LR --regType L2 --regParam 1.0 \
          |  data/mllib/sample_binary_classification_data.txt
        """.stripMargin)
    }

    parser.parse(args, defaultParams) match {
      case Some(params) => run(params)
      case _ => sys.exit(1)
    }
  }

  def run(params: Params): Unit = {
    val conf = new SparkConf().setAppName(s"BinaryClassification with $params")
    val sc = new SparkContext(conf)

    Logger.getRootLogger.setLevel(Level.WARN)

    val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()

    val splits = examples.randomSplit(Array(0.8, 0.2))
    val training = splits(0).cache()
    val test = splits(1).cache()

    val numTraining = training.count()
    val numTest = test.count()
    println(s"Training: $numTraining, test: $numTest.")

    examples.unpersist(blocking = false)

    val updater = params.regType match {
      case L1 => new L1Updater()
      case L2 => new SquaredL2Updater()
    }

    val model = params.algorithm match {
      case LR =>
        val algorithm = new LogisticRegressionWithLBFGS()
        algorithm.optimizer
          .setNumIterations(params.numIterations)
          .setUpdater(updater)
          .setRegParam(params.regParam)
        algorithm.run(training).clearThreshold()
      case SVM =>
        val algorithm = new SVMWithSGD()
        algorithm.optimizer
          .setNumIterations(params.numIterations)
          .setStepSize(params.stepSize)
          .setUpdater(updater)
          .setRegParam(params.regParam)
        algorithm.run(training).clearThreshold()
    }

    val prediction = model.predict(test.map(_.features))
    val predictionAndLabel = prediction.zip(test.map(_.label))

    val metrics = new BinaryClassificationMetrics(predictionAndLabel)

    println(s"Test areaUnderPR = ${metrics.areaUnderPR()}.")
    println(s"Test areaUnderROC = ${metrics.areaUnderROC()}.")

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.log4j.{Level, Logger}
import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.classification.NaiveBayes
import org.apache.spark.mllib.util.MLUtils


object SparseNaiveBayes {

  case class Params(
      input: String = null,
      minPartitions: Int = 0,
      numFeatures: Int = -1,
      lambda: Double = 1.0) extends AbstractParams[Params]

  def main(args: Array[String]) {
    val defaultParams = Params()

    val parser = new OptionParser[Params]("SparseNaiveBayes") {
      head("SparseNaiveBayes: an example naive Bayes app for LIBSVM data.")
      opt[Int]("numPartitions")
        .text("min number of partitions")
        .action((x, c) => c.copy(minPartitions = x))
      opt[Int]("numFeatures")
        .text("number of features")
        .action((x, c) => c.copy(numFeatures = x))
      opt[Double]("lambda")
        .text(s"lambda (smoothing constant), default: ${defaultParams.lambda}")
        .action((x, c) => c.copy(lambda = x))
      arg[String]("<input>")
        .text("input paths to labeled examples in LIBSVM format")
        .required()
        .action((x, c) => c.copy(input = x))
    }

    parser.parse(args, defaultParams) match {
      case Some(params) => run(params)
      case _ => sys.exit(1)
    }
  }

  def run(params: Params): Unit = {
    val conf = new SparkConf().setAppName(s"SparseNaiveBayes with $params")
    val sc = new SparkContext(conf)

    Logger.getRootLogger.setLevel(Level.WARN)

    val minPartitions =
      if (params.minPartitions > 0) params.minPartitions else sc.defaultMinPartitions

    val examples =
      MLUtils.loadLibSVMFile(sc, params.input, params.numFeatures, minPartitions)
    // Cache examples because it will be used in both training and evaluation.
    examples.cache()

    val splits = examples.randomSplit(Array(0.8, 0.2))
    val training = splits(0)
    val test = splits(1)

    val numTraining = training.count()
    val numTest = test.count()

    println(s"numTraining = $numTraining, numTest = $numTest.")

    val model = new NaiveBayes().setLambda(params.lambda).run(training)

    val prediction = model.predict(test.map(_.features))
    val predictionAndLabel = prediction.zip(test.map(_.label))
    val accuracy = predictionAndLabel.filter(x => x._1 == x._2).count().toDouble / numTest

    println(s"Test accuracy = $accuracy.")

    sc.stop()
  }
}
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package org.apache.spark.examples.mllib

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.feature.PCA
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.{LabeledPoint, LinearRegressionWithSGD}
// $example off$

@deprecated("Deprecated since LinearRegressionWithSGD is deprecated.  Use ml.feature.PCA", "2.0.0")
object PCAExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("PCAExample")
    val sc = new SparkContext(conf)

    // $example on$
    val data = sc.textFile("data/mllib/ridge-data/lpsa.data").map { line =>
      val parts = line.split(',')
      LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
    }.cache()

    val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
    val training = splits(0).cache()
    val test = splits(1)

    val pca = new PCA(training.first().features.size / 2).fit(data.map(_.features))
    val training_pca = training.map(p => p.copy(features = pca.transform(p.features)))
    val test_pca = test.map(p => p.copy(features = pca.transform(p.features)))

    val numIterations = 100
    val model = LinearRegressionWithSGD.train(training, numIterations)
    val model_pca = LinearRegressionWithSGD.train(training_pca, numIterations)

    val valuesAndPreds = test.map { point =>
      val score = model.predict(point.features)
      (score, point.label)
    }

    val valuesAndPreds_pca = test_pca.map { point =>
      val score = model_pca.predict(point.features)
      (score, point.label)
    }

    val MSE = valuesAndPreds.map { case (v, p) => math.pow((v - p), 2) }.mean()
    val MSE_pca = valuesAndPreds_pca.map { case (v, p) => math.pow((v - p), 2) }.mean()

    println("Mean Squared Error = " + MSE)
    println("PCA Mean Squared Error = " + MSE_pca)
    // $example off$

    sc.stop()
  }
}
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package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.stat.Statistics
import org.apache.spark.rdd.RDD
// $example off$

object HypothesisTestingKolmogorovSmirnovTestExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("HypothesisTestingKolmogorovSmirnovTestExample")
    val sc = new SparkContext(conf)

    // $example on$
    val data: RDD[Double] = sc.parallelize(Seq(0.1, 0.15, 0.2, 0.3, 0.25))  // an RDD of sample data

    // run a KS test for the sample versus a standard normal distribution
    val testResult = Statistics.kolmogorovSmirnovTest(data, "norm", 0, 1)
    // summary of the test including the p-value, test statistic, and null hypothesis if our p-value
    // indicates significance, we can reject the null hypothesis.
    println(testResult)
    println()

    // perform a KS test using a cumulative distribution function of our making
    val myCDF = Map(0.1 -> 0.2, 0.15 -> 0.6, 0.2 -> 0.05, 0.3 -> 0.05, 0.25 -> 0.1)
    val testResult2 = Statistics.kolmogorovSmirnovTest(data, myCDF)
    println(testResult2)
    // $example off$

    sc.stop()
  }
}
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package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.random.RandomRDDs
import org.apache.spark.rdd.RDD


object RandomRDDGeneration {

  def main(args: Array[String]) {

    val conf = new SparkConf().setAppName(s"RandomRDDGeneration")
    val sc = new SparkContext(conf)

    val numExamples = 10000 // number of examples to generate
    val fraction = 0.1 // fraction of data to sample

    // Example: RandomRDDs.normalRDD
    val normalRDD: RDD[Double] = RandomRDDs.normalRDD(sc, numExamples)
    println(s"Generated RDD of ${normalRDD.count()}" +
      " examples sampled from the standard normal distribution")
    println("  First 5 samples:")
    normalRDD.take(5).foreach( x => println(s"    $x") )

    // Example: RandomRDDs.normalVectorRDD
    val normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows = numExamples, numCols = 2)
    println(s"Generated RDD of ${normalVectorRDD.count()} examples of length-2 vectors.")
    println("  First 5 samples:")
    normalVectorRDD.take(5).foreach( x => println(s"    $x") )

    println()

    sc.stop()
  }

}
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package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.fpm.FPGrowth
import org.apache.spark.rdd.RDD
// $example off$

object SimpleFPGrowth {

  def main(args: Array[String]) {

    val conf = new SparkConf().setAppName("SimpleFPGrowth")
    val sc = new SparkContext(conf)

    // $example on$
    val data = sc.textFile("data/mllib/sample_fpgrowth.txt")

    val transactions: RDD[Array[String]] = data.map(s => s.trim.split(' '))

    val fpg = new FPGrowth()
      .setMinSupport(0.2)
      .setNumPartitions(10)
    val model = fpg.run(transactions)

    model.freqItemsets.collect().foreach { itemset =>
      println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
    }

    val minConfidence = 0.8
    model.generateAssociationRules(minConfidence).collect().foreach { rule =>
      println(
        rule.antecedent.mkString("[", ",", "]")
          + " => " + rule.consequent .mkString("[", ",", "]")
          + ", " + rule.confidence)
    }
    // $example off$
  }
}
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package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.stat.KernelDensity
import org.apache.spark.rdd.RDD
// $example off$

object KernelDensityEstimationExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("KernelDensityEstimationExample")
    val sc = new SparkContext(conf)

    // $example on$
    // an RDD of sample data
    val data: RDD[Double] = sc.parallelize(Seq(1, 1, 1, 2, 3, 4, 5, 5, 6, 7, 8, 9, 9))

    // Construct the density estimator with the sample data and a standard deviation
    // for the Gaussian kernels
    val kd = new KernelDensity()
      .setSample(data)
      .setBandwidth(3.0)

    // Find density estimates for the given values
    val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
    // $example off$

    densities.foreach(println)

    sc.stop()
  }
}
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package org.apache.spark.examples.mllib

import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.linalg.distributed.{MatrixEntry, RowMatrix}


object CosineSimilarity {
  case class Params(inputFile: String = null, threshold: Double = 0.1)
    extends AbstractParams[Params]

  def main(args: Array[String]) {
    val defaultParams = Params()

    val parser = new OptionParser[Params]("CosineSimilarity") {
      head("CosineSimilarity: an example app.")
      opt[Double]("threshold")
        .required()
        .text(s"threshold similarity: to tradeoff computation vs quality estimate")
        .action((x, c) => c.copy(threshold = x))
      arg[String]("<inputFile>")
        .required()
        .text(s"input file, one row per line, space-separated")
        .action((x, c) => c.copy(inputFile = x))
      note(
        """
          |For example, the following command runs this app on a dataset:
          |
          | ./bin/spark-submit  --class org.apache.spark.examples.mllib.CosineSimilarity \
          | examplesjar.jar \
          | --threshold 0.1 data/mllib/sample_svm_data.txt
        """.stripMargin)
    }

    parser.parse(args, defaultParams) match {
      case Some(params) => run(params)
      case _ => sys.exit(1)
    }
  }

  def run(params: Params): Unit = {
    val conf = new SparkConf().setAppName("CosineSimilarity")
    val sc = new SparkContext(conf)

    // Load and parse the data file.
    val rows = sc.textFile(params.inputFile).map { line =>
      val values = line.split(' ').map(_.toDouble)
      Vectors.dense(values)
    }.cache()
    val mat = new RowMatrix(rows)

    // Compute similar columns perfectly, with brute force.
    val exact = mat.columnSimilarities()

    // Compute similar columns with estimation using DIMSUM
    val approx = mat.columnSimilarities(params.threshold)

    val exactEntries = exact.entries.map { case MatrixEntry(i, j, u) => ((i, j), u) }
    val approxEntries = approx.entries.map { case MatrixEntry(i, j, v) => ((i, j), v) }
    val MAE = exactEntries.leftOuterJoin(approxEntries).values.map {
      case (u, Some(v)) =>
        math.abs(u - v)
      case (u, None) =>
        math.abs(u)
    }.mean()

    println(s"Average absolute error in estimate is: $MAE")

    sc.stop()
  }
}
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package org.apache.spark.examples.mllib

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.feature.ElementwiseProduct
import org.apache.spark.mllib.linalg.Vectors
// $example off$

object ElementwiseProductExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("ElementwiseProductExample")
    val sc = new SparkContext(conf)

    // $example on$
    // Create some vector data; also works for sparse vectors
    val data = sc.parallelize(Array(Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0)))

    val transformingVector = Vectors.dense(0.0, 1.0, 2.0)
    val transformer = new ElementwiseProduct(transformingVector)

    // Batch transform and per-row transform give the same results:
    val transformedData = transformer.transform(data)
    val transformedData2 = data.map(x => transformer.transform(x))
    // $example off$

    println("transformedData: ")
    transformedData.foreach(x => println(x))

    println("transformedData2: ")
    transformedData2.foreach(x => println(x))

    sc.stop()
  }
}
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package org.apache.spark.examples.mllib

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.linalg.Matrix
import org.apache.spark.mllib.linalg.SingularValueDecomposition
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.linalg.distributed.RowMatrix
// $example off$

object SVDExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("SVDExample")
    val sc = new SparkContext(conf)

    // $example on$
    val data = Array(
      Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))),
      Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
      Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0))

    val dataRDD = sc.parallelize(data, 2)

    val mat: RowMatrix = new RowMatrix(dataRDD)

    // Compute the top 5 singular values and corresponding singular vectors.
    val svd: SingularValueDecomposition[RowMatrix, Matrix] = mat.computeSVD(5, computeU = true)
    val U: RowMatrix = svd.U  // The U factor is a RowMatrix.
    val s: Vector = svd.s  // The singular values are stored in a local dense vector.
    val V: Matrix = svd.V  // The V factor is a local dense matrix.
    // $example off$
    val collect = U.rows.collect()
    println("U factor is:")
    collect.foreach { vector => println(vector) }
    println(s"Singular values are: $s")
    println(s"V factor is:\n$V")
  }
}
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package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}

object StratifiedSamplingExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("StratifiedSamplingExample")
    val sc = new SparkContext(conf)

    // $example on$
    // an RDD[(K, V)] of any key value pairs
    val data = sc.parallelize(
      Seq((1, 'a'), (1, 'b'), (2, 'c'), (2, 'd'), (2, 'e'), (3, 'f')))

    // specify the exact fraction desired from each key
    val fractions = Map(1 -> 0.1, 2 -> 0.6, 3 -> 0.3)

    // Get an approximate sample from each stratum
    val approxSample = data.sampleByKey(withReplacement = false, fractions = fractions)
    // Get an exact sample from each stratum
    val exactSample = data.sampleByKeyExact(withReplacement = false, fractions = fractions)
    // $example off$

    println("approxSample size is " + approxSample.collect().size.toString)
    approxSample.collect().foreach(println)

    println("exactSample its size is " + exactSample.collect().size.toString)
    exactSample.collect().foreach(println)

    sc.stop()
  }
}
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package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
import org.apache.spark.mllib.linalg.Vectors


object TallSkinnySVD {
  def main(args: Array[String]) {
    if (args.length != 1) {
      System.err.println("Usage: TallSkinnySVD <input>")
      System.exit(1)
    }

    val conf = new SparkConf().setAppName("TallSkinnySVD")
    val sc = new SparkContext(conf)

    // Load and parse the data file.
    val rows = sc.textFile(args(0)).map { line =>
      val values = line.split(' ').map(_.toDouble)
      Vectors.dense(values)
    }
    val mat = new RowMatrix(rows)

    // Compute SVD.
    val svd = mat.computeSVD(mat.numCols().toInt)

    println("Singular values are " + svd.s)

    sc.stop()
  }
}
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package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.fpm.PrefixSpan
// $example off$

object PrefixSpanExample {

  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("PrefixSpanExample")
    val sc = new SparkContext(conf)

    // $example on$
    val sequences = sc.parallelize(Seq(
      Array(Array(1, 2), Array(3)),
      Array(Array(1), Array(3, 2), Array(1, 2)),
      Array(Array(1, 2), Array(5)),
      Array(Array(6))
    ), 2).cache()
    val prefixSpan = new PrefixSpan()
      .setMinSupport(0.5)
      .setMaxPatternLength(5)
    val model = prefixSpan.run(sequences)
    model.freqSequences.collect().foreach { freqSequence =>
      println(
        freqSequence.sequence.map(_.mkString("[", ", ", "]")).mkString("[", ", ", "]") +
          ", " + freqSequence.freq)
    }
    // $example off$
  }
}
// scalastyle:off println 

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package org.apache.spark.examples.mllib

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.feature.{StandardScaler, StandardScalerModel}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.util.MLUtils
// $example off$

object StandardScalerExample {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("StandardScalerExample")
    val sc = new SparkContext(conf)

    // $example on$
    val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")

    val scaler1 = new StandardScaler().fit(data.map(x => x.features))
    val scaler2 = new StandardScaler(withMean = true, withStd = true).fit(data.map(x => x.features))
    // scaler3 is an identical model to scaler2, and will produce identical transformations
    val scaler3 = new StandardScalerModel(scaler2.std, scaler2.mean)

    // data1 will be unit variance.
    val data1 = data.map(x => (x.label, scaler1.transform(x.features)))

    // data2 will be unit variance and zero mean.
    val data2 = data.map(x => (x.label, scaler2.transform(Vectors.dense(x.features.toArray))))
    // $example off$

    println("data1: ")
    data1.foreach(x => println(x))

    println("data2: ")
    data2.foreach(x => println(x))

    sc.stop()
  }
}
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package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.clustering.{KMeans, KMeansModel}
import org.apache.spark.mllib.linalg.Vectors
// $example off$

object KMeansExample {

  def main(args: Array[String]) {

    val conf = new SparkConf().setAppName("KMeansExample")
    val sc = new SparkContext(conf)

    // $example on$
    // Load and parse the data
    val data = sc.textFile("data/mllib/kmeans_data.txt")
    val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble))).cache()

    // Cluster the data into two classes using KMeans
    val numClusters = 2
    val numIterations = 20
    val clusters = KMeans.train(parsedData, numClusters, numIterations)

    // Evaluate clustering by computing Within Set Sum of Squared Errors
    val WSSSE = clusters.computeCost(parsedData)
    println("Within Set Sum of Squared Errors = " + WSSSE)

    // Save and load model
    clusters.save(sc, "target/org/apache/spark/KMeansExample/KMeansModel")
    val sameModel = KMeansModel.load(sc, "target/org/apache/spark/KMeansExample/KMeansModel")
    // $example off$

    sc.stop()
  }
}
// scalastyle:on println 

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package org.apache.spark.examples.mllib

import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
import org.apache.spark.mllib.util.MLUtils

spark-examples-*.jar \
        |  --input data/mllib/sample_linear_regression_data.txt
        """.stripMargin)
    }

    parser.parse(args, defaultParams) match {
      case Some(params) => run(params)
      case _ => sys.exit(1)
    }
  }

  def run(params: Params): Unit = {
    val conf = new SparkConf().setAppName(s"MultivariateSummarizer with $params")
    val sc = new SparkContext(conf)

    val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()

    println(s"Summary of data file: ${params.input}")
    println(s"${examples.count()} data points")

    // Summarize labels
    val labelSummary = examples.aggregate(new MultivariateOnlineSummarizer())(
      (summary, lp) => summary.add(Vectors.dense(lp.label)),
      (sum1, sum2) => sum1.merge(sum2))

    // Summarize features
    val featureSummary = examples.aggregate(new MultivariateOnlineSummarizer())(
      (summary, lp) => summary.add(lp.features),
      (sum1, sum2) => sum1.merge(sum2))

    println()
    println(s"Summary statistics")
    println(s"\tLabel\tFeatures")
    println(s"mean\t${labelSummary.mean(0)}\t${featureSummary.mean.toArray.mkString("\t")}")
    println(s"var\t${labelSummary.variance(0)}\t${featureSummary.variance.toArray.mkString("\t")}")
    println(
      s"nnz\t${labelSummary.numNonzeros(0)}\t${featureSummary.numNonzeros.toArray.mkString("\t")}")
    println(s"max\t${labelSummary.max(0)}\t${featureSummary.max.toArray.mkString("\t")}")
    println(s"min\t${labelSummary.min(0)}\t${featureSummary.min.toArray.mkString("\t")}")
    println()

    sc.stop()
  }
}
// scalastyle:on println 

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package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.mllib.recommendation.Rating
// $example off$

object RecommendationExample {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("CollaborativeFilteringExample")
    val sc = new SparkContext(conf)
    // $example on$
    // Load and parse the data
    val data = sc.textFile("data/mllib/als/test.data")
    val ratings = data.map(_.split(',') match { case Array(user, item, rate) =>
      Rating(user.toInt, item.toInt, rate.toDouble)
    })

    // Build the recommendation model using ALS
    val rank = 10
    val numIterations = 10
    val model = ALS.train(ratings, rank, numIterations, 0.01)

    // Evaluate the model on rating data
    val usersProducts = ratings.map { case Rating(user, product, rate) =>
      (user, product)
    }
    val predictions =
      model.predict(usersProducts).map { case Rating(user, product, rate) =>
        ((user, product), rate)
      }
    val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>
      ((user, product), rate)
    }.join(predictions)
    val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
      val err = (r1 - r2)
      err * err
    }.mean()
    println("Mean Squared Error = " + MSE)

    // Save and load model
    model.save(sc, "target/tmp/myCollaborativeFilter")
    val sameModel = MatrixFactorizationModel.load(sc, "target/tmp/myCollaborativeFilter")
    // $example off$
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.fpm.AssociationRules
import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
// $example off$

object AssociationRulesExample {

  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("AssociationRulesExample")
    val sc = new SparkContext(conf)

    // $example on$
    val freqItemsets = sc.parallelize(Seq(
      new FreqItemset(Array("a"), 15L),
      new FreqItemset(Array("b"), 35L),
      new FreqItemset(Array("a", "b"), 12L)
    ))

    val ar = new AssociationRules()
      .setMinConfidence(0.8)
    val results = ar.run(freqItemsets)

    results.collect().foreach { rule =>
      println("[" + rule.antecedent.mkString(",")
        + "=>"
        + rule.consequent.mkString(",") + "]," + rule.confidence)
    }
    // $example off$
  }

}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.regression.LinearRegressionModel
import org.apache.spark.mllib.regression.LinearRegressionWithSGD
// $example off$

@deprecated("Use ml.regression.LinearRegression or LBFGS", "2.0.0")
object LinearRegressionWithSGDExample {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("LinearRegressionWithSGDExample")
    val sc = new SparkContext(conf)

    // $example on$
    // Load and parse the data
    val data = sc.textFile("data/mllib/ridge-data/lpsa.data")
    val parsedData = data.map { line =>
      val parts = line.split(',')
      LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
    }.cache()

    // Building the model
    val numIterations = 100
    val stepSize = 0.00000001
    val model = LinearRegressionWithSGD.train(parsedData, numIterations, stepSize)

    // Evaluate model on training examples and compute training error
    val valuesAndPreds = parsedData.map { point =>
      val prediction = model.predict(point.features)
      (point.label, prediction)
    }
    val MSE = valuesAndPreds.map{ case(v, p) => math.pow((v - p), 2) }.mean()
    println("training Mean Squared Error = " + MSE)

    // Save and load model
    model.save(sc, "target/tmp/scalaLinearRegressionWithSGDModel")
    val sameModel = LinearRegressionModel.load(sc, "target/tmp/scalaLinearRegressionWithSGDModel")
    // $example off$

    sc.stop()
  }
}
// scalastyle:on println 

// scalastyle:off println
package org.apache.spark.examples.mllib

import scopt.OptionParser

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.util.MLUtils

spark-examples-*.jar
        """.stripMargin)
    }

    parser.parse(args, defaultParams) match {
      case Some(params) => run(params)
      case _ => sys.exit(1)
    }
  }

  def run(params: Params): Unit = {
    val conf = new SparkConf().setAppName(s"SampledRDDs with $params")
    val sc = new SparkContext(conf)

    val fraction = 0.1 // fraction of data to sample

    val examples = MLUtils.loadLibSVMFile(sc, params.input)
    val numExamples = examples.count()
    if (numExamples == 0) {
      throw new RuntimeException("Error: Data file had no samples to load.")
    }
    println(s"Loaded data with $numExamples examples from file: ${params.input}")

    // Example: RDD.sample() and RDD.takeSample()
    val expectedSampleSize = (numExamples * fraction).toInt
    println(s"Sampling RDD using fraction $fraction.  Expected sample size = $expectedSampleSize.")
    val sampledRDD = examples.sample(withReplacement = true, fraction = fraction)
    println(s"  RDD.sample(): sample has ${sampledRDD.count()} examples")
    val sampledArray = examples.takeSample(withReplacement = true, num = expectedSampleSize)
    println(s"  RDD.takeSample(): sample has ${sampledArray.length} examples")

    println()

    // Example: RDD.sampleByKey() and RDD.sampleByKeyExact()
    val keyedRDD = examples.map { lp => (lp.label.toInt, lp.features) }
    println(s"  Keyed data using label (Int) as key ==> Orig")
    //  Count examples per label in original data.
    val keyCounts = keyedRDD.countByKey()

    //  Subsample, and count examples per label in sampled data. (approximate)
    val fractions = keyCounts.keys.map((_, fraction)).toMap
    val sampledByKeyRDD = keyedRDD.sampleByKey(withReplacement = true, fractions = fractions)
    val keyCountsB = sampledByKeyRDD.countByKey()
    val sizeB = keyCountsB.values.sum
    println(s"  Sampled $sizeB examples using approximate stratified sampling (by label)." +
      " ==> Approx Sample")

    //  Subsample, and count examples per label in sampled data. (approximate)
    val sampledByKeyRDDExact =
      keyedRDD.sampleByKeyExact(withReplacement = true, fractions = fractions)
    val keyCountsBExact = sampledByKeyRDDExact.countByKey()
    val sizeBExact = keyCountsBExact.values.sum
    println(s"  Sampled $sizeBExact examples using exact stratified sampling (by label)." +
      " ==> Exact Sample")

    //  Compare samples
    println(s"   \tFractions of examples with key")
    println(s"Key\tOrig\tApprox Sample\tExact Sample")
    keyCounts.keys.toSeq.sorted.foreach { key =>
      val origFrac = keyCounts(key) / numExamples.toDouble
      val approxFrac = if (sizeB != 0) {
        keyCountsB.getOrElse(key, 0L) / sizeB.toDouble
      } else {
        0
      }
      val exactFrac = if (sizeBExact != 0) {
        keyCountsBExact.getOrElse(key, 0L) / sizeBExact.toDouble
      } else {
        0
      }
      println(s"$key\t$origFrac\t$approxFrac\t$exactFrac")
    }

    sc.stop()
  }
}
// scalastyle:on println