Spark组件之Spark Streaming学习1--NetworkWordCount学习

更多代码请见：https://github.com/xubo245/SparkLearning

NetworkWordCount：每个1秒将接收的数据进行wordCount，不累加

使用

1.方法1：在集群的examples中启动

一个terminal：

nc -lk 9999

可以在这个terminal发送数据，前面一个terminal就会统计信息

另一个terminal：

./bin/run-example streaming.NetworkWordCount localhost 9999

2.运行方法2：打成jar包上传运行：

运行脚本：

    #!/usr/bin/env bash      spark-submit --name WordCountSpark  \--class org.apache.spark.Streaming.learning.NetworkWordCount \--master spark://<strong>Master</strong>:7077 \--executor-memory 512M \--total-executor-cores 10 Streaming.jar localhost 9999

然后一个ternimal运行nc，一个运行这个脚本，同上

输入数据：

hadoop@Master:~$ sudo nc -lk 9999ahelloworldahelloworldhellohw^Hellowordaaaaaaa

结果输出：

hadoop@Master:~/cloud/testByXubo/spark/Streaming$ ./submitJob.sh -------------------------------------------                                     Time: 1461661853000 ms--------------------------------------------------------------------------------------Time: 1461661854000 ms-------------------------------------------(,1)(hello,1)(world,1)(a,1)-------------------------------------------Time: 1461661855000 ms-------------------------------------------(a,1)-------------------------------------------Time: 1461661856000 ms--------------------------------------------------------------------------------------Time: 1461661857000 ms-------------------------------------------(hello,1)-------------------------------------------Time: 1461661858000 ms-------------------------------------------(world,1)-------------------------------------------Time: 1461661859000 ms--------------------------------------------------------------------------------------Time: 1461661860000 ms-------------------------------------------(hello,1)-------------------------------------------Time: 1461661861000 ms--------------------------------------------------------------------------------------Time: 1461661862000 ms-------------------------------------------(hello,1)-------------------------------------------Time: 1461661863000 ms-------------------------------------------(word,1)-------------------------------------------                                     Time: 1461661864000 ms-------------------------------------------(a,5)-------------------------------------------                                     Time: 1461661865000 ms-------------------------------------------

代码：

/* * 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 printlnpackage org.apache.spark.Streaming.learningimport org.apache.spark.SparkConfimport org.apache.spark.storage.StorageLevelimport org.apache.spark.streaming.Secondsimport org.apache.spark.streaming.StreamingContextimport org.apache.spark.streaming.dstream.DStream.toPairDStreamFunctions/** * Counts words in UTF8 encoded, '\n' delimited text received from the network every second. * * Usage: NetworkWordCount <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.NetworkWordCount localhost 9999` */object NetworkWordCount {  def main(args: Array[String]) {    if (args.length < 2) {      System.err.println("Usage: NetworkWordCount <hostname> <port>")      System.exit(1)    }    StreamingExamples.setStreamingLogLevels()    // Create the context with a 1 second batch size    val sparkConf = new SparkConf().setAppName("NetworkWordCount")    val ssc = new StreamingContext(sparkConf, Seconds(1))    // Create a socket stream on target ip:port and count the    // words in input stream of \n delimited text (eg. generated by 'nc')    // Note that no duplication in storage level only for running locally.    // Replication necessary in distributed scenario for fault tolerance.    val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER)    val words = lines.flatMap(_.split(" "))    val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)    wordCounts.print()    ssc.start()    ssc.awaitTermination()  }}// scalastyle:on println

参考：

【1】 http://spark.apache.org/docs/1.5.2/streaming-programming-guide.html