一、前述
Spark中默认有两大类算子，Transformation（转换算子）,懒执行。action算子，立即执行，有一个action算子 ，就有一个job。
通俗些来说由RDD变成RDD就是Transformation算子，由RDD转换成其他的格式就是Action算子。
二、常用Transformation算子
假设数据集为此：
1、filter
过滤符合条件的记录数，true保留，false过滤掉。
Java版：
package com.spark.spark.transformations; import org.apache.spark.SparkConf;
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.VoidFunction;
/**
* filter
* 过滤符合符合条件的记录数，true的保留，false的过滤掉。
*
*/
public class Operator_filter {
public static void main(String[] args) {
/**
* SparkConf对象中主要设置Spark运行的环境参数。
* 1.运行模式
* 2.设置Application name
* 3.运行的资源需求
*/
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("filter");
/**
* JavaSparkContext对象是spark运行的上下文，是通往集群的唯一通道。
*/
JavaSparkContext jsc = new JavaSparkContext(conf);
JavaRDD<String> lines = jsc.textFile("./words.txt");
JavaRDD<String> resultRDD = lines.filter(new Function<String, Boolean>() { /**
*
*/
private static final long serialVersionUID = 1L; @Override
public Boolean call(String line) throws Exception {
return !line.contains("hadoop");//这里是不等于
} }); resultRDD.foreach(new VoidFunction<String>() { /**
*
*/
private static final long serialVersionUID = 1L; @Override
public void call(String line) throws Exception {
System.out.println(line);
}
});
jsc.stop();
}
}
scala版：
函数解释：
进来一个String，出去一个Booean.
结果：
2、map
将一个RDD中的每个数据项，通过map中的函数映射变为一个新的元素。
特点：输入一条，输出一条数据。
/**
* map
* 通过传入的函数处理每个元素，返回新的数据集。
* 特点：输入一条，输出一条。
*
*
* @author root
*
*/
public class Operator_map {
public static void main(String[] args) {
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("map");
JavaSparkContext jsc = new JavaSparkContext(conf);
JavaRDD<String> line = jsc.textFile("./words.txt");
JavaRDD<String> mapResult = line.map(new Function<String, String>() { /**
*
*/
private static final long serialVersionUID = 1L; @Override
public String call(String s) throws Exception {
return s+"~";
}
}); mapResult.foreach(new VoidFunction<String>() { /**
*
*/
private static final long serialVersionUID = 1L; @Override
public void call(String t) throws Exception {
System.out.println(t);
}
}); jsc.stop();
}
}
函数解释：
进来一个String，出去一个String。
函数结果：
3、flatMap（压扁输出，输入一条，输出零到多条）
先map后flat。与map类似，每个输入项可以映射为0到多个输出项。
package com.spark.spark.transformations; import java.util.Arrays; import org.apache.spark.SparkConf;
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.VoidFunction; /**
* flatMap
* 输入一条数据，输出0到多条数据。
* @author root
*
*/
public class Operator_flatMap {
public static void main(String[] args) {
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("flatMap"); JavaSparkContext jsc = new JavaSparkContext(conf);
JavaRDD<String> lines = jsc.textFile("./words.txt");
JavaRDD<String> flatMapResult = lines.flatMap(new FlatMapFunction<String, String>() { /**
*
*/
private static final long serialVersionUID = 1L; @Override
public Iterable<String> call(String s) throws Exception { return Arrays.asList(s.split(" "));
} });
flatMapResult.foreach(new VoidFunction<String>() { /**
*
*/
private static final long serialVersionUID = 1L; @Override
public void call(String t) throws Exception {
System.out.println(t);
}
}); jsc.stop();
}
}
函数解释：
进来一个String，出去一个集合。
Iterater 集合
iterator 遍历元素
函数结果：
4、sample（随机抽样）
随机抽样算子，根据传进去的小数按比例进行又放回或者无放回的抽样。（True，fraction，long）
True 抽样放回
Fraction 一个比例 float 大致 数据越大 越准确
第三个参数：随机种子，抽到的样本一样 方便测试
package com.spark.spark.transformations; import java.util.ArrayList;
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.PairFlatMapFunction;
import org.apache.spark.api.java.function.VoidFunction; import scala.Tuple2; public class Operator_sample {
public static void main(String[] args) {
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("sample"); JavaSparkContext jsc = new JavaSparkContext(conf);
JavaRDD<String> lines = jsc.textFile("./words.txt");
JavaPairRDD<String, Integer> flatMapToPair = lines.flatMapToPair(new PairFlatMapFunction<String, String, Integer>() { /**
*
*/
private static final long serialVersionUID = 1L; @Override
public Iterable<Tuple2<String, Integer>> call(String t)
throws Exception {
List<Tuple2<String,Integer>> tupleList = new ArrayList<Tuple2<String,Integer>>();
tupleList.add(new Tuple2<String,Integer>(t,1));
return tupleList;
}
});
JavaPairRDD<String, Integer> sampleResult = flatMapToPair.sample(true,0.3,4);//样本有7个所以大致抽样为1-2个
sampleResult.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);
}
}); jsc.stop();
}
}
函数结果：
5.reduceByKey
将相同的Key根据相应的逻辑进行处理。
package com.spark.spark.transformations; 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 Operator_reduceByKey {
public static void main(String[] args) {
SparkConf conf = new SparkConf();
conf.setMaster("local").setAppName("reduceByKey");
JavaSparkContext jsc = new JavaSparkContext(conf);
JavaRDD<String> lines = jsc.textFile("./words.txt");
JavaRDD<String> flatMap = 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> mapToPair = flatMap.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> reduceByKey = mapToPair.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;
} },10);
reduceByKey.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);
}
}); jsc.stop();
}
}
函数解释：
函数结果：
6、sortByKey/sortBy
作用在K,V格式的RDD上，对key进行升序或者降序排序。
Sortby在java中没有
package com.spark.spark.transformations; 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 Operator_sortByKey {
public static void main(String[] args) {
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("sortByKey");
JavaSparkContext jsc = new JavaSparkContext(conf);
JavaRDD<String> lines = jsc.textFile("./words.txt");
JavaRDD<String> flatMap = 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> mapToPair = flatMap.mapToPair(new PairFunction<String, String, Integer>() { /**
*
*/
private static final long serialVersionUID = 1L; @Override
public Tuple2<String, Integer> call(String s) throws Exception {
return new Tuple2<String, Integer>(s, 1);
}
}); JavaPairRDD<String, Integer> reduceByKey = mapToPair.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;
}
});
reduceByKey.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);
}
}).sortByKey(false).mapToPair(new PairFunction<Tuple2<Integer,String>, String, Integer>() {//先把key.value对调，然后排完序后再对调回来 false是降序，True是升序 /**
*
*/
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);
}
}).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);
}
});
}
}
代码解释：先对调，排完序，在对调过来
代码结果：