单机版本:
package com.itheima.java_wordcount;
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;
/**
* Date:2019/4/24
* Author:Lynn.cn.Li
* Desc:
*/
public class WordCountJava {
public static void main(String[] args) {
// 1.创建sparkConf对象。设置appName和master地址
SparkConf sparkConf = new SparkConf().setAppName("LocalJavaWordCount").setMaster("local[2]");
// 2.创建sparkContext对象
JavaSparkContext jsc = new JavaSparkContext(sparkConf);
// 3.读取数据文件
JavaRDD<String> textFileRDD = jsc.textFile("g://input/1.txt");
// 4.切分每一行,得到每一个单词
JavaRDD<String> flatMapRDD = textFileRDD.flatMap(new FlatMapFunction<String, String>() {
public Iterator<String> call(String s) throws Exception {
// 按照空格切分单词
String[] arr = s.split(" ");
return Arrays.asList(arr).iterator();
}
});
// 5.每个单词计数为1
JavaPairRDD<String, Integer> javaPairRDD = flatMapRDD.mapToPair(new PairFunction<String, String, Integer>() {
public Tuple2<String, Integer> call(String s) throws Exception {
return new Tuple2<String, Integer>(s, 1);
}
});
// 6.相同单词出现的次数累加
JavaPairRDD<String, Integer> resultRDD = javaPairRDD.reduceByKey(new Function2<Integer, Integer, Integer>() {
public Integer call(Integer v1, Integer v2) throws Exception {
return v1 + v2;
}
});
/**
* 细节:实现排序
* 1.先将(单词,次数)进行位置对调成(次数,单词),进行排序
* 2.排序后再将(次数,单词)进行位置对调成(单词,次数)
*/
JavaPairRDD<Integer, String> sortRDD = resultRDD.mapToPair(new PairFunction<Tuple2<String, Integer>, Integer, String>() {
public Tuple2<Integer, String> call(Tuple2<String, Integer> tuple2) throws Exception {
return new Tuple2<Integer, String>(tuple2._2, tuple2._1);
}
});
// 排序
JavaPairRDD<Integer, String> sortDescRDD = sortRDD.sortByKey(false);
// 再对调位置
JavaPairRDD<String, Integer> finalResultRDD = sortDescRDD.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
public Tuple2<String, Integer> call(Tuple2<Integer, String> tuple2) throws Exception {
return new Tuple2<String, Integer>(tuple2._2, tuple2._1);
}
});
// 7.收集结果数据
List<Tuple2<String, Integer>> wordData = finalResultRDD.collect();
// 8.循环打印结果数据
for (Tuple2<String, Integer> tuple2 : wordData) {
System.out.println("单词:"+tuple2._1+"出现了"+tuple2._2+"次");
}
// 9.关闭SparkCount
jsc.stop();
}
}
提交集群版本:
package com.itheima.java_wordcount;
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;
/**
* Date:2019/4/24
* Author:Lynn.cn.Li
* Desc:
*/
public class WordCountJavaOnline {
public static void main(String[] args) {
// 1.创建sparkConf对象。设置appName和master地址
SparkConf sparkConf = new SparkConf().setAppName("OnlineJavaWordCount");
// 2.创建sparkContext对象
JavaSparkContext jsc = new JavaSparkContext(sparkConf);
// 3.读取数据文件
JavaRDD<String> textFileRDD = jsc.textFile(args[0]);//动态参数传入
// 4.切分每一行,得到每一个单词
JavaRDD<String> flatMapRDD = textFileRDD.flatMap(new FlatMapFunction<String, String>() {
public Iterator<String> call(String s) throws Exception {
// 按照空格切分单词
String[] arr = s.split(" ");
return Arrays.asList(arr).iterator();
}
});
// 5.每个单词计数为1
JavaPairRDD<String, Integer> javaPairRDD = flatMapRDD.mapToPair(new PairFunction<String, String, Integer>() {
public Tuple2<String, Integer> call(String s) throws Exception {
return new Tuple2<String, Integer>(s, 1);
}
});
// 6.相同单词出现的次数累加
JavaPairRDD<String, Integer> resultRDD = javaPairRDD.reduceByKey(new Function2<Integer, Integer, Integer>() {
public Integer call(Integer v1, Integer v2) throws Exception {
return v1 + v2;
}
});
resultRDD.saveAsTextFile(args[1]);//动态参数传入
// 9.关闭SparkCount
jsc.stop();
}
}
spark执行脚本:
spark-submit --class com.itheima.java_wordcount.WordCountJavaOnline \
--master spark://node01:7077,node02:7077 \
--executor-memory 512m \
--total-executor-cores 2 \
/export/servers/sparkTestData/wordcount_java.jar \
/spark/wordcount/input2/1.txt \
/spark/wordcount/output4