一、基于排序机制的wordcount程序
1、要求
1、对文本文件内的每个单词都统计出其出现的次数。 2、按照每个单词出现次数的数量，降序排序。
2、代码实现
------java实现------- package cn.spark.study.core; import java.util.Arrays; 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 org.apache.spark.api.java.function.VoidFunction; import scala.Tuple2; public class SortWordCount {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("SortWordCount").setMaster("local");
JavaSparkContext sc = new JavaSparkContext(conf); JavaRDD<String> lines = sc.textFile("D:\\test-file\\spark.txt"); JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() { private static final long serialVersionUID = 1L; @Override
public Iterable<String> call(String t) throws Exception {
return Arrays.asList(t.split(" "));
}
}); JavaPairRDD<String, Integer> pairs = words.mapToPair(new PairFunction<String, String, Integer>() { private static final long serialVersionUID = 1L; @Override
public Tuple2<String, Integer> call(String t) throws Exception {
return new Tuple2<String, Integer>(t, 1);
}
}); JavaPairRDD<String, Integer> wordCounts = pairs.reduceByKey(new Function2<Integer, Integer, Integer>() { private static final long serialVersionUID = 1L; @Override
public Integer call(Integer v1, Integer v2) throws Exception {
return v1 + v2;
}
}); // 到这里为止，就得到了每个单词出现的次数
// 但是，问题是，我们的新需求，是要按照每个单词出现次数的顺序，降序排序
// wordCounts RDD内的元素是什么？应该是这种格式的吧：(hello, 3) (you, 2)
// 我们需要将RDD转换成(3, hello) (2, you)的这种格式，才能根据单词出现次数进行排序把！ // 进行key-value的反转映射
JavaPairRDD<Integer, String> countWords = wordCounts.mapToPair(new PairFunction<Tuple2<String,Integer>, Integer, String>() { private static final long serialVersionUID = 1L; @Override
public Tuple2<Integer, String> call(Tuple2<String, Integer> t) throws Exception {
return new Tuple2<Integer, String>(t._2, t._1);
}
}); //按照key进行排序
JavaPairRDD<Integer, String> sortedCountWords = countWords.sortByKey(false); //再次将value-key进行反转映射
JavaPairRDD<String, Integer> sortedWordCounts = sortedCountWords.mapToPair(new PairFunction<Tuple2<Integer,String>, String, Integer>() { private static final long serialVersionUID = 1L; @Override
public Tuple2<String, Integer> call(Tuple2<Integer, String> t) throws Exception {
return new Tuple2<String, Integer>(t._2, t._1);
}
}); // 到此为止，我们获得了按照单词出现次数排序后的单词计数
// 打印出来
sortedWordCounts.foreach(new VoidFunction<Tuple2<String,Integer>>() { private static final long serialVersionUID = 1L; @Override
public void call(Tuple2<String, Integer> t) throws Exception {
System.out.println(t._1 + " appears " + t._2 + " times.");
}
}); sc.close();
} } ---------scala实现--------- package cn.spark.study.core import org.apache.spark.SparkConf
import org.apache.spark.SparkContext /**
* @author Administrator
*/
object SortWordCount { def main(args: Array[String]) {
val conf = new SparkConf()
.setAppName("SortWordCount")
.setMaster("local")
val sc = new SparkContext(conf) val lines = sc.textFile("D:\\test-file\\spark.txt", 1)
val words = lines.flatMap { line => line.split(" ") }
val pairs = words.map { word => (word, 1) }
val wordCounts = pairs.reduceByKey(_ + _) val countWords = wordCounts.map(wordCount => (wordCount._2, wordCount._1))
val sortedCountWords = countWords.sortByKey(false)
val sortedWordCounts = sortedCountWords.map(sortedCountWord => (sortedCountWord._2, sortedCountWord._1)) sortedWordCounts.foreach(sortedWordCount => println(
sortedWordCount._1 + " appear " + sortedWordCount._2 + " times."))
} }
二、二次排序
1、要求
1、按照文件中的第一列排序。 2、如果第一列相同，则按照第二列排序。
2、java代码
###SecondarySortKey package cn.spark.study.core; import java.io.Serializable; import scala.math.Ordered; /**
* 自定义的二次排序key
* @author Administrator
*
*/
public class SecondarySortKey implements Ordered<SecondarySortKey>, Serializable { private static final long serialVersionUID = -2366006422945129991L; // 首先在自定义key里面，定义需要进行排序的列
private int first;
private int second; public SecondarySortKey(int first, int second) {
this.first = first;
this.second = second;
} @Override
public boolean $greater(SecondarySortKey other) {
if(this.first > other.getFirst()) {
return true;
} else if(this.first == other.getFirst() &&
this.second > other.getSecond()) {
return true;
}
return false;
} @Override
public boolean $greater$eq(SecondarySortKey other) {
if(this.$greater(other)) {
return true;
} else if(this.first == other.getFirst() &&
this.second == other.getSecond()) {
return true;
}
return false;
} @Override
public boolean $less(SecondarySortKey other) {
if(this.first < other.getFirst()) {
return true;
} else if(this.first == other.getFirst() &&
this.second < other.getSecond()) {
return true;
}
return false;
} @Override
public boolean $less$eq(SecondarySortKey other) {
if(this.$less(other)) {
return true;
} else if(this.first == other.getFirst() &&
this.second == other.getSecond()) {
return true;
}
return false;
} @Override
public int compare(SecondarySortKey other) {
if(this.first - other.getFirst() != 0) {
return this.first - other.getFirst();
} else {
return this.second - other.getSecond();
}
} @Override
public int compareTo(SecondarySortKey other) {
if(this.first - other.getFirst() != 0) {
return this.first - other.getFirst();
} else {
return this.second - other.getSecond();
}
} // 为要进行排序的多个列，提供getter和setter方法，以及hashcode和equals方法
public int getFirst() {
return first;
} public void setFirst(int first) {
this.first = first;
} public int getSecond() {
return second;
} public void setSecond(int second) {
this.second = second;
} @Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + first;
result = prime * result + second;
return result;
} @Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (getClass() != obj.getClass())
return false;
SecondarySortKey other = (SecondarySortKey) obj;
if (first != other.first)
return false;
if (second != other.second)
return false;
return true;
} } ###SecondarySort package cn.spark.study.core; 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.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.api.java.function.VoidFunction; import scala.Tuple2; /**
* 二次排序
* 1、实现自定义的key，要实现Ordered接口和Serializable接口，在key中实现自己对多个列的排序算法
* 2、将包含文本的RDD，映射成key为自定义key，value为文本的JavaPairRDD
* 3、使用sortByKey算子按照自定义的key进行排序
* 4、再次映射，剔除自定义的key，只保留文本行
* @author Administrator
*
*/
public class SecondarySort { public static void main(String[] args) {
SparkConf conf = new SparkConf()
.setAppName("SecondarySort")
.setMaster("local");
JavaSparkContext sc = new JavaSparkContext(conf); JavaRDD<String> lines = sc.textFile("D:\\test-file\\sort.txt"); JavaPairRDD<SecondarySortKey, String> pairs = lines.mapToPair( new PairFunction<String, SecondarySortKey, String>() { private static final long serialVersionUID = 1L; @Override
public Tuple2<SecondarySortKey, String> call(String line) throws Exception {
String[] lineSplited = line.split(" ");
SecondarySortKey key = new SecondarySortKey(
Integer.valueOf(lineSplited[0]),
Integer.valueOf(lineSplited[1]));
return new Tuple2<SecondarySortKey, String>(key, line);
} }); JavaPairRDD<SecondarySortKey, String> sortedPairs = pairs.sortByKey(); JavaRDD<String> sortedLines = sortedPairs.map( new Function<Tuple2<SecondarySortKey,String>, String>() { private static final long serialVersionUID = 1L; @Override
public String call(Tuple2<SecondarySortKey, String> v1) throws Exception {
return v1._2;
} }); sortedLines.foreach(new VoidFunction<String>() { private static final long serialVersionUID = 1L; @Override
public void call(String t) throws Exception {
System.out.println(t);
} }); sc.close();
} }
3、scala代码
###SecondSortKey package cn.spark.study.core /**
* @author Administrator
*/
class SecondSortKey(val first: Int, val second: Int)
extends Ordered[SecondSortKey] with Serializable { def compare(that: SecondSortKey): Int = {
if(this.first - that.first != 0) {
this.first - that.first
} else {
this.second - that.second
}
} } ###SecondSort package cn.spark.study.core import org.apache.spark.SparkConf
import org.apache.spark.SparkContext /**
* @author Administrator
*/
object SecondSort { def main(args: Array[String]): Unit = {
val conf = new SparkConf()
.setAppName("SecondSort")
.setMaster("local")
val sc = new SparkContext(conf) val lines = sc.textFile("D:\\test-file\\sort.txt", 1)
val pairs = lines.map { line => (
new SecondSortKey(line.split(" ")(0).toInt, line.split(" ")(1).toInt),
line)}
val sortedPairs = pairs.sortByKey()
val sortedLines = sortedPairs.map(sortedPair => sortedPair._2) sortedLines.foreach { sortedLine => println(sortedLine) }
} }
三、topn
1、要求
1、对文本文件内的数字，取最大的前3个。 2、对每个班级内的学生成绩，取出前3名。（分组取topn） 3、课后作业：用Scala来实现分组取topn。
2、获取文本内最大的前三个数
---------java实现---------- package cn.spark.study.core; import java.util.List; 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.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.api.java.function.VoidFunction; import scala.Tuple2; public class Top3 {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("Top3Java").setMaster("local");
JavaSparkContext sc = new JavaSparkContext(conf); JavaRDD<String> lines = sc.textFile("D:\\test-file\\top.txt"); JavaPairRDD<Integer, String> pairs = lines.mapToPair(new PairFunction<String, Integer, String>() { private static final long serialVersionUID = 1L; @Override
public Tuple2<Integer, String> call(String t) throws Exception {
return new Tuple2<Integer, String>(Integer.valueOf(t), t);
}
}); JavaPairRDD<Integer, String> sortedPairs = pairs.sortByKey(false);
JavaRDD<Integer> sortedNumbers = sortedPairs.map(new Function<Tuple2<Integer,String>, Integer>() { private static final long serialVersionUID = 1L; @Override
public Integer call(Tuple2<Integer, String> v1) throws Exception {
return v1._1;
}
}); List<Integer> sortedNumberList = sortedNumbers.take(3); //此时sortedNumberList是： [9, 7, 6]
for(Integer num : sortedNumberList) {
System.out.println(num);
} sc.close(); }
} ---------scala实现---------- package cn.spark.study.core import org.apache.spark.SparkConf
import org.apache.spark.SparkContext /**
* @author Administrator
*/
object Top3 { def main(args: Array[String]): Unit = {
val conf = new SparkConf()
.setAppName("Top3")
.setMaster("local")
val sc = new SparkContext(conf) val lines = sc.textFile("D:\\test-file\\top.txt", 1)
val pairs = lines.map { line => (line.toInt, line) }
val sortedPairs = pairs.sortByKey(false)
val sortedNumbers = sortedPairs.map(sortedPair => sortedPair._1)
val top3Number = sortedNumbers.take(3) for(num <- top3Number) {
println(num)
}
} }
3、对每个班级内的学生成绩，取出前3名。（分组取topn）
----java实现----- package cn.spark.study.core; import java.util.Arrays;
import java.util.Iterator; 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.PairFunction;
import org.apache.spark.api.java.function.VoidFunction; import scala.Tuple2; /**
* 分组取top3
* @author Administrator
*
*/
public class GroupTop3 { public static void main(String[] args) {
SparkConf conf = new SparkConf()
.setAppName("Top3")
.setMaster("local");
JavaSparkContext sc = new JavaSparkContext(conf); JavaRDD<String> lines = sc.textFile("D:\\test-file\\score.txt"); JavaPairRDD<String, Integer> pairs = lines.mapToPair( new PairFunction<String, String, Integer>() { private static final long serialVersionUID = 1L; @Override
public Tuple2<String, Integer> call(String line) throws Exception {
String[] lineSplited = line.split(" ");
return new Tuple2<String, Integer>(lineSplited[0],
//Integer.valueOf()可以将基本类型int转换为包装类型Integer，或者将String转换成Integer，String如果为Null或“”都会报错;
Integer.valueOf(lineSplited[1]));
} }); JavaPairRDD<String, Iterable<Integer>> groupedPairs = pairs.groupByKey(); JavaPairRDD<String, Iterable<Integer>> top3Score = groupedPairs.mapToPair( new PairFunction<Tuple2<String,Iterable<Integer>>, String, Iterable<Integer>>() { private static final long serialVersionUID = 1L; @Override
public Tuple2<String, Iterable<Integer>> call(
Tuple2<String, Iterable<Integer>> classScores)
throws Exception {
Integer[] top3 = new Integer[3]; String className = classScores._1;
Iterator<Integer> scores = classScores._2.iterator(); while(scores.hasNext()) {
Integer score = scores.next(); for(int i = 0; i < 3; i++) {
if(top3[i] == null) {
top3[i] = score;
break;
} else if(score > top3[i]) {
for(int j = 2; j > i; j--) {
top3[j] = top3[j - 1];
} top3[i] = score; break;
}
}
} return new Tuple2<String,
Iterable<Integer>>(className, Arrays.asList(top3));
} }); top3Score.foreach(new VoidFunction<Tuple2<String,Iterable<Integer>>>() { private static final long serialVersionUID = 1L; @Override
public void call(Tuple2<String, Iterable<Integer>> t) throws Exception {
System.out.println("class: " + t._1);
Iterator<Integer> scoreIterator = t._2.iterator();
while(scoreIterator.hasNext()) {
Integer score = scoreIterator.next();
System.out.println(score);
}
System.out.println("=======================================");
} }); sc.close();
} } -----scala实现------ package cn.spark.study.core import org.apache.spark.SparkConf
import org.apache.spark.SparkContext object GroupTop3 {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("GroupTop3Scala").setMaster("local")
val context = new SparkContext(conf)
val linesRDD = context.textFile("D:\\test-file\\score.txt")
val studentScores = linesRDD.map(line => (line.split(" ")(0), line.split(" ")(1).toInt))
val groupStudentScores = studentScores.groupByKey()
val result = groupStudentScores.map(student => {
val maxScore = new Array[Int](3)
val scores = student._2
for(score <- scores) {
var flag = true
for(i <- 0 until maxScore.length if flag) {
if(maxScore(i) == Nil) {
maxScore(i) = score
flag = false
}else{
if(maxScore(i) < score) {
for(j <- (i + 1 to maxScore.length - 1).reverse){
maxScore(j) = maxScore(j - 1)
}
maxScore(i) = score
flag = false
}
}
}
}
(student._1, maxScore)
}) result.foreach(result =>{
print(result._1 + "班级前三明成绩为：")
for(i <- 0 until result._2.length) {
if(i == 0) print(result._2(i))
else print("," + result._2(i))
}
println()
})
}
}