org.apache.spark.examples.streaming.StatefulNetworkWordCount.scala Maven / Gradle / Ivy

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.examples.streaming

import org.apache.spark.SparkConf
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._

/**
 * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every
 * second.
 * Usage: StatefulNetworkWordCount  
 *    and  describe the TCP server that Spark Streaming would connect to receive
 *   data.
 *
 * To run this on your local machine, you need to first run a Netcat server
 *    `$ nc -lk 9999`
 * and then run the example
 *    `$ bin/run-example
 *      org.apache.spark.examples.streaming.StatefulNetworkWordCount localhost 9999`
 */
object StatefulNetworkWordCount {
  def main(args: Array[String]) {
    if (args.length < 2) {
      System.err.println("Usage: StatefulNetworkWordCount  ")
      System.exit(1)
    }

    StreamingExamples.setStreamingLogLevels()

    val updateFunc = (values: Seq[Int], state: Option[Int]) => {
      val currentCount = values.foldLeft(0)(_ + _)

      val previousCount = state.getOrElse(0)

      Some(currentCount + previousCount)
    }

    val sparkConf = new SparkConf().setAppName("StatefulNetworkWordCount")
    // Create the context with a 1 second batch size
    val ssc = new StreamingContext(sparkConf, Seconds(1))
    ssc.checkpoint(".")

    // Create a NetworkInputDStream on target ip:port and count the
    // words in input stream of \n delimited test (eg. generated by 'nc')
    val lines = ssc.socketTextStream(args(0), args(1).toInt)
    val words = lines.flatMap(_.split(" "))
    val wordDstream = words.map(x => (x, 1))

    // Update the cumulative count using updateStateByKey
    // This will give a Dstream made of state (which is the cumulative count of the words)
    val stateDstream = wordDstream.updateStateByKey[Int](updateFunc)
    stateDstream.print()
    ssc.start()
    ssc.awaitTermination()
  }
}