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
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 9999 a hello world a hello world hello hw^Hello word a a a a a a a
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 println
package org.apache.spark.Streaming.learning
import org.apache.spark.SparkConf
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
/**
* 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