【Spark篇】---Spark中Transformations转换算子

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：