java编写WordCound的Spark程序,Scala编写wordCound程序
2017-07-06 17:05
411 查看
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:
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>
<properties>
<maven.compiler.source>1.7</maven.compiler.source>
<maven.compiler.target>1.7</maven.compiler.target>
<encoding>UTF-8</encoding>
<scala.version>2.10.6</scala.version>
<spark.version>1.6.2</spark.version>
<hadoop.version>2.6.4</hadoop.version>
</properties>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>${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:里面的文件内容是:
6、通过工具传递参数:
7、运行结果:
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() } }
相关文章推荐
- 分别用Java、Scala、spark-shell开发wordcount程序及测试代码
- scala-eclipse 编写spark简单程序 WordCount
- 用java编写spark程序,简单示例及运行
- Spark Streaming开发入门——WordCount(Java&Scala)
- Spark官方文档——本地编写并运行scala程序
- scala java+spring boot写spark程序骨架
- scala编写的Spark程序远程提交到服务器集群上运行
- Spark基于排序机制的wordcount程序(Java版)
- Spark中的wordcount以及TopK的程序编写
- Spark wordcount - Python, Scala, Java
- 启动Spark Shell,在Spark Shell中编写WordCount程序,在IDEA中编写WordCount的Maven程序,spark-submit使用spark的jar来做单词统计
- Intellij idea使用java编写并执行spark程序
- Spark:用Scala和Java实现WordCount
- 编写Java程序访问Spark环境
- 将java开发的wordcount程序部署到spark集群上运行
- scala本地wordcount的程序编写
- 利用Scala编写Wordcount并在spark框架下运行
- 将java开发的wordcount程序部署到spark集群上运行
- sbt的安装以及用sbt编译打包scala编写的spark程序
- spark shell中编写WordCount程序