好程序员大数据教程：SparkShell和IDEA中编写Spark程序，spark-shell是Spark自带的交互式Shell程序，方便用户进行交互式编程，用户可以在该命令行下用Scala编写Spark程序。spark-shell程序一般用作Spark程序测试练习来用。spark-shell属于Spark的特殊应用程序，我们可以在这个特殊的应用程序中提交应用程序

spark-shell启动有两种模式，local模式和cluster模式，分别为

local模式:

spark-shell

local模式仅在本机启动一个SparkSubmit进程，没有与集群建立联系,虽然进程中有SparkSubmit但是不会被提交到集群红

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Cluster模式(集群模式):

spark-shell \
--master spark://hadoop01:7077 \
--executor-memory 512m \
--total-executor-cores 1

后两个命令不是必须的 --master这条命令是必须的(除非在jar包中已经指可以不指定,不然就必须指定)

退出shell

千万不要ctrl+c spark-shell 正确退出 :quit 千万不要ctrl+c退出 这样是错误的 若使用了ctrl+c退出 使用命令查看监听端口 netstat - apn | grep 4040 在使用kill -9 端口号 杀死即可

3.25.11 spark2.2shell和spark1.6shell对比

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ps:启动spark-shell若是集群模式,在webUI会有一个一直执行的任务

通过IDEA创建Spark工程

ps:工程创建之前步骤省略,在scala中已经讲解,直接默认是创建好工程的

对工程中的pom.xml文件配置

<properties>
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<encoding>UTF-8</encoding>
<scala.version>2.11.8</scala.version>
<spark.version>2.2.0</spark.version>
<hadoop.version>2.7.1</hadoop.version>
<scala.compat.version>2.11</scala.compat.version>
</properties>

<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
</dependency>
</dependencies>

Spark实现WordCount程序

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

object SparkWordCount {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("dri/wordcount").setMaster("local[*]")

val sc = new SparkContext(conf)

val lines: RDD[String] = sc.textFile(“dir/wordcount”)

val words: RDD[String] = lines.flatMap(_.split(" "))

val tuples: RDD[(String, Int)] = words.map((_,1))

val sumed: RDD[(String, Int)] = tuples.reduceByKey(_+_)

val sorted: RDD[(String, Int)] = sumed.sortBy(_._2,false)

sorted.foreach(println)

sc.stop()
}
}

Java版本

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import scala.Tuple2;
import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
public class JavaWordCount {
public static void main(String[] args) {

SparkConf conf = new SparkConf().setAppName("JavaWordCount").setMaster("local");

JavaSparkContext jsc = new JavaSparkContext(conf);
JavaRDD<String> lines = jsc.textFile("dir/file");

JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
@Override
public Iterator<String> call(String s) throws Exception {
List<String> splited = Arrays.asList(s.split(" "));
return splited.iterator();
}

});

JavaPairRDD<String, Integer> tuples = words.mapToPair(new PairFunction<String, String,Integer>() {@Overridepublic Tuple2<String, Integer> call(String s) throws Exception {return new Tuple2<String, Integer>(s, 1);}});复制代码

JavaPairRDD<String, Integer> sumed = tuples.reduceByKey(new Function2<Integer, Integer,Integer>() {复制代码

@Override

public Integer call(Integer v1, Integer v2) throws Exception {return v1 + v2;}});复制代码

JavaPairRDD<Integer, String> swaped = sumed.mapToPair(new PairFunction<Tuple2<String,Integer>, Integer, String>() {@Overridepublic Tuple2<Integer, String> call(Tuple2<String, Integer> tup) throws Exception {return tup.swap();}});复制代码

JavaPairRDD<Integer, String> sorted = swaped.sortByKey(false);复制代码

JavaPairRDD<String, Integer> res = sorted.mapToPair(new PairFunction<Tuple2<Integer,String>, String, Integer>() {@Overridepublic Tuple2<String, Integer> call(Tuple2<Integer, String> tuple2) throws Exception{return tuple2.swap();}});System.out.println(res.collect());res.saveAsTextFile("out1");jsc.stop();}
}复制代码