java编写WordCound的Spark程序，Scala编写wordCound程序

1、创建一个maven项目，项目的相关信息如下：

<groupId>cn.toto.spark</groupId>

<artifactId>bigdata</artifactId>

<version>1.0-SNAPSHOT</version>

2、修改Maven仓库的位置配置：

java编写WordCound的Spark程序，Scala编写wordCound程序

3、首先要编写Maven的Pom文件

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>

<groupId>cn.toto.spark</groupId>
<artifactId>bigdata</artifactId>
<version>1.0-SNAPSHOT</version>

<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_2.10</artifactId>
<version>${spark.version}</version>
</dependency>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
</dependency>
</dependencies>

<build>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
<configuration>
<args>
<arg>-make:transitive</arg>
<arg>-dependencyfile</arg>
<arg>${project.build.directory}/.scala_dependencies</arg>
</args>
</configuration>
</execution>
</executions>
</plugin>

<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.4.3</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>

</project>

4、编写Java代码

package cn.toto.spark;

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;

/**
* Created by toto on 2017/7/6.
*/
public class JavaWordCount {

public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("JavaWordCount");
//创建java sparkcontext
JavaSparkContext jsc = new JavaSparkContext(conf);
//读取数据
JavaRDD<String> lines = jsc.textFile(args[0]);
//切分
JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
@Override
public Iterable<String> call(String line) throws Exception {
return Arrays.asList(line.split(" "));
}
});
//遇见一个单词就记作一个1
JavaPairRDD<String, Integer> wordAndOne = words.mapToPair(new PairFunction<String, String, Integer>() {
@Override
public Tuple2<String, Integer> call(String word) throws Exception {
return new Tuple2<String, Integer>(word, 1);
}
});
//分组聚合
JavaPairRDD<String, Integer> result = wordAndOne.reduceByKey(new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer i1, Integer i2) throws Exception {
return i1 + i2;
}
});
//反转顺序
JavaPairRDD<Integer, String> swapedPair = result.mapToPair(new PairFunction<Tuple2<String, Integer>, Integer, String>() {
@Override
public Tuple2<Integer, String> call(Tuple2<String, Integer> tp) throws Exception {
return new Tuple2<Integer, String>(tp._2, tp._1);
}
});
//排序并调换顺序
JavaPairRDD<String, Integer> finalResult = swapedPair.sortByKey(false).mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
@Override
public Tuple2<String, Integer> call(Tuple2<Integer, String> tp) throws Exception {
return tp.swap();
}
});

//保存
finalResult.saveAsTextFile(args[1]);
jsc.stop();
}
}

5、准备数据

数据放置在E:\wordcount\input中：
java编写WordCound的Spark程序，Scala编写wordCound程序

里面的文件内容是：
java编写WordCound的Spark程序，Scala编写wordCound程序

6、通过工具传递参数：

java编写WordCound的Spark程序，Scala编写wordCound程序

7、运行结果：

java编写WordCound的Spark程序，Scala编写wordCound程序

8、scala编写wordCount

单词统计的代码如下：

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

/**
* Created by ZhaoXing on 2016/6/30.
*/
object ScalaWordCount {

def main(args: Array[String]) {

val conf = new SparkConf().setAppName("ScalaWordCount")
//非常重要的一个对象SparkContext
val sc = new SparkContext(conf)

//textFile方法生成了两个RDD： HadoopRDD[LongWritable, Text] -> MapPartitionRDD[String]
val lines: RDD[String] = sc.textFile(args(0))
//flatMap方法生成了一个MapPartitionRDD[String]
val words: RDD[String] = lines.flatMap(_.split(" "))

//Map方法生成了一个MapPartitionRDD[(String, Int)]
val wordAndOne: RDD[(String, Int)] = words.map((_, 1))

val counts: RDD[(String, Int)] = wordAndOne.reduceByKey(_+_)

val sortedCounts: RDD[(String, Int)] = counts.sortBy(_._2, false)
//保存的HDFS
//sortedCounts.saveAsTextFile(args(1))
counts.saveAsTextFile(args(1))
//释放SparkContext
sc.stop()

}
}

java编写WordCound的Spark程序，Scala编写wordCound程序

1、创建一个maven项目，项目的相关信息如下：

2、修改Maven仓库的位置配置：

3、首先要编写Maven的Pom文件

4、编写Java代码

5、准备数据

6、通过工具传递参数：

7、运行结果：

8、scala编写wordCount

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