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sparkRDD算子的创建和使用-创新互联
spark是大数据领域近几年比较火的编程开发语言。有众多的好处，比如速度快，基于内存式计算框架。
不多说直接讲 spark的RDD 算子的使用。
如果有spark环境搭建等问题，请自行查找资料。本文不做讲述。
spark rdd的创建有两种方式:
1>从集合创建。也就是从父rdd继承过来
2>从外部创建。
import java.util.Arrays;
import java.util.Iterator;
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.FlatMapFunction;
import org.apache.spark.api.java.function.Function;
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 com.google.common.base.Optional;
import scala.Tuple2;
public class Demo01 {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("Demo01").setMaster("local");
JavaSparkContext jsc = new JavaSparkContext(conf);
//map(jsc);
//filter(jsc);
// flatMap(jsc);
//groupByKey(jsc);
//reduceByKey(jsc);
//sortByKey(jsc);
//join(jsc);
leftOutJoin(jsc);
jsc.stop();
}
//每一条元素 都乘以2，并且打印
private static void map(JavaSparkContext jsc) {
//数据源
List lst = Arrays.asList(1,2,3,4,5,6,7,8);
JavaRDD numRDD = jsc.parallelize(lst);
JavaRDD resultRDD = numRDD.map(new Function() {
private static final long serialVersionUID = 1L;
@Override
public Integer call(Integer num) throws Exception {
return num * 2;
}
});
resultRDD.foreach(new VoidFunction() {
private static final long serialVersionUID = 1L;
@Override
public void call(Integer num) throws Exception {
System.out.println(num);
}
});
}
// 把集合中的偶数过滤出来
private static void filter(JavaSparkContext jsc) {
//数据源
List lst = Arrays.asList(1,2,3,4,5,6,7,8);
JavaRDD numRDD = jsc.parallelize(lst);
System.out.println(numRDD.filter(new Function() {
private static final long serialVersionUID = 1L;
@Override
public Boolean call(Integer num) throws Exception {
return num % 2 ==0;
}
}).collect());
}
//将一行行数据的单词拆分为一个个单词
private static void flatMap(JavaSparkContext jsc) {
List lst = Arrays.asList("hi tim ","hello girl","hello spark");
JavaRDD lines = jsc.parallelize(lst);
JavaRDD resultRDD = lines.flatMap(new FlatMapFunction() {
private static final long serialVersionUID = 1L;
@Override
public Iterable call(String line) throws Exception {
return Arrays.asList(line.split(" "));
}
});
System.out.println(resultRDD.collect());
}
// 根据班级进行分组
private static void groupByKey(JavaSparkContext jsc) {
// int ,Integer
// scala 里面的类型，没有像Java这样分为基本类型和包装类，因为scala是一种更加强的面向对象语言，
//一切皆对象，里面的类型，也有对应的方法可以调用,隐式转换
// 模拟数据
@SuppressWarnings("unchecked")
List> lst = Arrays.asList(
new Tuple2("class01", 100),
new Tuple2("class02",101),
new Tuple2("class01",199),
new Tuple2("class02",121),
new Tuple2("class02",120));
JavaPairRDD cla***DD = jsc.parallelizePairs(lst);
JavaPairRDD> groupedRDD = cla***DD.groupByKey();
groupedRDD.foreach(new VoidFunction>>() {
private static final long serialVersionUID = 1L;
@Override
public void call(Tuple2> tuple)
throws Exception {
String classKey = tuple._1;
Iterator values = tuple._2.iterator();
while (values.hasNext()) {
Integer value = values.next();
System.out.println("key:" + classKey + "\t" + "value:" + value);
}
}
});
}
private static void reduceByKey(JavaSparkContext jsc) {
@SuppressWarnings("unchecked")
List> lst = Arrays.asList(
new Tuple2("class01", 100),
new Tuple2("class02",101),
new Tuple2("class01",199),
new Tuple2("class02",121),
new Tuple2("class02",120));
JavaPairRDD cla***DD = jsc.parallelizePairs(lst);
JavaPairRDD resultRDD = cla***DD.reduceByKey(new Function2() {
private static final long serialVersionUID = 1L;
@Override
public Integer call(Integer v1, Integer v2) throws Exception {
return v1 + v2;
}
});
resultRDD.foreach(new VoidFunction>() {
private static final long serialVersionUID = 1L;
@Override
public void call(Tuple2 tuple) throws Exception {
System.out.println("key:" + tuple._1 + "\t" + "value:" + tuple._2);
}
});
}
// 把学生的成绩前3名取出来，并打印
// 1.先排序sortByKey，然后take（3），再foreach
private static void sortByKey(JavaSparkContext jsc) {
@SuppressWarnings("unchecked")
List> lst = Arrays.asList(
new Tuple2("tom", 60),
new Tuple2("kate",80),
new Tuple2("kobe",100),
new Tuple2("马蓉",4),
new Tuple2("宋哲",2),
new Tuple2("白百合",3),
new Tuple2("隔壁老王",1));
JavaPairRDD cla***DD = jsc.parallelizePairs(lst);
JavaPairRDD pairRDD = cla***DD.mapToPair(new PairFunction,Integer , String>() {
private static final long serialVersionUID = 1L;
@Override
public Tuple2 call(Tuple2 tuple)
throws Exception {
return new Tuple2(tuple._2, tuple._1);
}
});
//do no
JavaPairRDD sortedRDD = pairRDD.sortByKey();
JavaPairRDD sortedRDD01 = sortedRDD.mapToPair(new PairFunction, String, Integer>() {
private static final long serialVersionUID = 1L;
@Override
public Tuple2 call(Tuple2 tuple)
throws Exception {
return new Tuple2(tuple._2, tuple._1);
}
} );
// take 也是一个action操作
List> result = sortedRDD01.take(3);
System.out.println(result);
}
private static void join(JavaSparkContext jsc) {
// 模拟数据
@SuppressWarnings("unchecked")
List> names =Arrays.asList(
new Tuple2(1,"jack"),
new Tuple2(2,"rose"),
new Tuple2(3,"tom"),
new Tuple2(4,"赵丽颖"));
JavaPairRDD num2NamesRDD = jsc.parallelizePairs(names);
List> scores = Arrays.asList(
new Tuple2(1,60),
new Tuple2(4,100),
new Tuple2(2,30));
JavaPairRDD num2scoresRDD = jsc.parallelizePairs(scores);
JavaPairRDD> joinedRDD = num2scoresRDD.join(num2NamesRDD);
//姓名成绩排序，取前2名
JavaPairRDD score2NameRDD = joinedRDD.mapToPair(new PairFunction>,Integer, String>() {
private static final long serialVersionUID = 1L;
@Override
public Tuple2 call(
Tuple2> tuple)
throws Exception {
Integer score = tuple._2._1;
String name = tuple._2._2;
return new Tuple2(score,name);
}
});
// sortByKey之后，你可以执行一个maptoPair的操作，转换为
System.out.println(score2NameRDD.sortByKey(false).take(2));
}
// 学生成绩改良版
private static void leftOutJoin(JavaSparkContext jsc) {
// 模拟数据
@SuppressWarnings("unchecked")
List> names =Arrays.asList(
new Tuple2(1,"jack"),
new Tuple2(2,"rose"),
new Tuple2(3,"tom"),
new Tuple2(4,"赵丽颖"));
JavaPairRDD num2NamesRDD = jsc.parallelizePairs(names);
List> scores = Arrays.asList(
new Tuple2(1,60),
new Tuple2(4,100),
new Tuple2(2,30));
JavaPairRDD num2scoresRDD = jsc.parallelizePairs(scores);
// num2scoresRDD num2NamesRDD
//JavaPairRDD>> joinedRDD = num2NamesRDD.leftOuterJoin(num2scoresRDD);
// 注意join，谁join谁，没区别，但是leftoutjoin 是有顺序的
JavaPairRDD>> joinedRDD = num2NamesRDD.leftOuterJoin(num2scoresRDD);
JavaPairRDD pairRDD = joinedRDD.mapToPair(new PairFunction>>, Integer, String>() {
private static final long serialVersionUID = 1L;
@Override
public Tuple2 call(
Tuple2>> tuple)
throws Exception {
String name = tuple._2._1;
Optional scoreOptional = tuple._2._2;
Integer score = null;
if(scoreOptional.isPresent()){
score= scoreOptional.get();
}else {
score = 0;
}
return new Tuple2(score, name);
}
});
JavaPairRDD sortedRDD = pairRDD.sortByKey(false);
sortedRDD.foreach(new VoidFunction>() {
private static final long serialVersionUID = 1L;
@Override
public void call(Tuple2 tuple)
throws Exception {
if(tuple._1 == 0){
System.out.println("name:" + tuple._2 + "\t" + "要努力了，你的成绩0分" );
}else{
System.out.println("姓名:" + tuple._2 + "\t" + "分数：" + tuple._1);
}
}
});
}
} 如有疑问可跟帖讨论。欢迎拍砖
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文章名称：sparkRDD算子的创建和使用-创新互联
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