spark StatefulNetworkWordCount 源码
spark StatefulNetworkWordCount 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
/*
* 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.
*/
// scalastyle:off println
package org.apache.spark.examples.streaming
import org.apache.spark.SparkConf
import org.apache.spark.streaming._
/**
* Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every
* second starting with initial value of word count.
* Usage: StatefulNetworkWordCount <hostname> <port>
* <hostname> and <port> 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]): Unit = {
if (args.length < 2) {
System.err.println("Usage: StatefulNetworkWordCount <hostname> <port>")
System.exit(1)
}
StreamingExamples.setStreamingLogLevels()
val sparkConf = new SparkConf().setAppName("StatefulNetworkWordCount")
// Create the context with a 1 second batch size
val ssc = new StreamingContext(sparkConf, Seconds(1))
ssc.checkpoint(".")
// Initial state RDD for mapWithState operation
val initialRDD = ssc.sparkContext.parallelize(List(("hello", 1), ("world", 1)))
// Create a ReceiverInputDStream on target ip:port and count the
// words in input stream of \n delimited test (e.g. 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 mapWithState
// This will give a DStream made of state (which is the cumulative count of the words)
val mappingFunc = (word: String, one: Option[Int], state: State[Int]) => {
val sum = one.getOrElse(0) + state.getOption.getOrElse(0)
val output = (word, sum)
state.update(sum)
output
}
val stateDstream = wordDstream.mapWithState(
StateSpec.function(mappingFunc).initialState(initialRDD))
stateDstream.print()
ssc.start()
ssc.awaitTermination()
}
}
// scalastyle:on println
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