combineByKey&groupByKey&sortedByKey
分组操做
在分布式程序中，通讯的代价是很大的，所以控制数据分布以得到最少的网络传输能够极大地提高总体性能。和单节点的程序须要为记录集合选择合适的数据结构同样，Spark 程序能够经过控制RDD 分区方式来减小通讯开销。java
分区并非对全部应用都有好处的——好比，若是给定RDD 只须要被扫描一次，咱们彻底没有必要对其预先进行分区处理。只有当数据集屡次在诸如链接这种基于键的操做中使用时，分区才会有帮助。apache
好比， sortByKey() 和 groupByKey()会分别生成范围分区的 RDD 和哈希分区的 RDD。而另外一方面，诸如 map() 这样的操做会致使新的 RDD 失去父 RDD 的分区信息，由于这样的操做理论上可能会修改每条记录的键。api
从分区中获益的操做缓存
Spark 的许多操做都引入了将数据根据键跨节点进行混洗的过程。全部这些操做都会从数据分区中获益。就 Spark 1.0 而言，可以从数据分区中获益的操做有 cogroup() 、groupWith() 、 join() 、 leftOuterJoin() 、 rightOuterJoin() 、 groupByKey() 、 reduceByKey() 、combineByKey() 以及 lookup() 。网络
而对于诸如 cogroup() 和join() 这样的二元操做，预先进行数据分区会致使其中至少一个 RDD（使用已知分区器的那个 RDD）不发生数据混洗。若是两个 RDD 使用一样的分区方式，而且它们还缓存在一样的机器上（好比一个 RDD 是经过 mapValues() 从另外一个 RDD 中建立出来的，这两个RDD 就会拥有相同的键和分区方式），或者其中一个 RDD 尚未被计算出来，那么跨节点的数据混洗就不会发生了。数据结构
影响分区方式的操做分布式
全部会为生成的结果 RDD 设好分区方式的操做： cogroup() 、 groupWith() 、join() 、 leftOuterJoin() 、 rightOuterJoin() 、 groupByKey() 、 reduceByKey() 、combineByKey() 、 partitionBy() 、 sort() 、 mapValues() （若是父 RDD 有分区方式的话）、flatMapValues() （若是父 RDD 有分区方式的话），以及 filter() （若是父 RDD 有分区方式的话）。其余全部的操做生成的结果都不会存在特定的分区方式。ide
最后，对于二元操做，输出数据的分区方式取决于父 RDD 的分区方式。默认状况下，结果会采用哈希分区，分区的数量和操做的并行度同样。不过，若是其中的一个父 RDD 已性能
经设置过度区方式，那么结果就会采用那种分区方式；若是两个父 RDD 都设置过度区方式，结果 RDD 会采用第一个父 RDD 的分区方式。this
1. combineByKey
test1
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
import java.util.Scanner;
import org.apache.hive.com.esotericsoftware.kryo.serializers.JavaSerializer;
import org.apache.spark.HashPartitioner;
import org.apache.spark.Partitioner;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.AbstractJavaRDDLike;
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.storage.StorageLevel;
import scala.Tuple2;
//JavaPairRDD<String, Integer> results = mapRdd.reduceByKey((x, y)->x+y);
public class CombineByKeyTest3 {
public static void main(String[] xx){
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("WordCounter");
conf.set("spark.testing.memory", "2147480000");
conf.set("spark.default.parallelism", "4");
JavaSparkContext ctx = new JavaSparkContext(conf);
//建立RDD：1）经过读取外部存储 ----- 集群环境使用 2）经过内存中的集合
List<Tuple2<String, Integer>> data = new ArrayList<Tuple2<String, Integer>>();
data.add(new Tuple2<>("Cake", 2));
data.add(new Tuple2<>("Bread", 3));
data.add(new Tuple2<>("Cheese", 4));
data.add(new Tuple2<>("Milk", 1));
data.add(new Tuple2<>("Toast", 2));
data.add(new Tuple2<>("Bread", 2));
data.add(new Tuple2<>("Egg", 6));
JavaRDD<Tuple2<String, Integer>> rdd1 = ctx.parallelize(data, 2);
JavaPairRDD<String, Integer> mapRdd = rdd1.mapToPair(new PairFunction<Tuple2<String, Integer>, String, Integer>() {
@Override
public Tuple2<String, Integer> call(Tuple2<String, Integer> t) throws Exception {
return t;
}
}).partitionBy(new HashPartitioner(2)).persist(StorageLevel.MEMORY_ONLY());
JavaPairRDD<String, Tuple2<Integer, Integer>> mapRdd1 = mapRdd.mapToPair(new PairFunction<Tuple2<String,Integer>, String, Tuple2<Integer, Integer>>() {
@Override
public Tuple2<String, Tuple2<Integer, Integer>> call(Tuple2<String, Integer> t) throws Exception {
return new Tuple2<String, Tuple2<Integer, Integer>>(t._1(), new Tuple2<Integer, Integer>(t._2() , 1));
}
});
mapRdd1.foreach(x->System.out.println(x));
/*
* 所有使用List或者Iterable都能实现
*/
// JavaPairRDD<String, List<Tuple2<Integer, Integer>>> results = mapRdd1.groupByKey();
// JavaPairRDD<String, Iterable<Tuple2<Integer, Integer>>> results = mapRdd1.groupByKey();
JavaPairRDD<String, List<Tuple2<Integer, Integer>>> results = mapRdd1.combineByKey(
new Function<Tuple2<Integer,Integer>, List<Tuple2<Integer, Integer>>>() {
@Override
public List<Tuple2<Integer, Integer>> call(Tuple2<Integer, Integer> value) throws Exception {
List<Tuple2<Integer, Integer>> list = new ArrayList<Tuple2<Integer, Integer>>();
list.add(value);
return list;
}
},
new Function2<List<Tuple2<Integer, Integer>>, Tuple2<Integer, Integer>, List<Tuple2<Integer, Integer>>>() {
@Override
public List<Tuple2<Integer, Integer>> call(
List<Tuple2<Integer, Integer>> it,
Tuple2<Integer, Integer> value) throws Exception {
// List<Tuple2<Integer, Integer>> list = new ArrayList<Tuple2<Integer, Integer>>();
// it.forEach(list::add);
// list.add(value);
((List<Tuple2<Integer, Integer>>)it).add(value);
return it;
}
},
new Function2<List<Tuple2<Integer, Integer>>, List<Tuple2<Integer, Integer>>, List<Tuple2<Integer, Integer>>>() {
@Override
public List<Tuple2<Integer, Integer>> call(
List<Tuple2<Integer, Integer>> it1,
List<Tuple2<Integer, Integer>> it2) throws Exception {
// List<Tuple2<Integer, Integer>> list = new ArrayList<Tuple2<Integer, Integer>>();
// it1.forEach(list::add);
// it2.forEach(list::add);
// return list;
((List)it1).addAll((List)it2);
return it1;
}
});
results.foreach(x->System.out.println(x));
//其实，distinct 基于 reduceByKey实现
// mapRdd1.distinct();
ctx.stop();
}
}
test2
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
import java.util.Scanner;
import org.apache.hive.com.esotericsoftware.kryo.serializers.JavaSerializer;
import org.apache.spark.HashPartitioner;
import org.apache.spark.Partitioner;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.AbstractJavaRDDLike;
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.storage.StorageLevel;
import scala.Tuple2;
//JavaPairRDD<String, Integer> results = mapRdd.reduceByKey((x, y)->x+y);
public class CombineByKeyTest2 {
public static void main(String[] xx){
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.set("spark.testing.memory", "2147480000");
conf.setAppName("WordCounter");
conf.set("spark.default.parallelism", "4");
JavaSparkContext ctx = new JavaSparkContext(conf);
//建立RDD：1）经过读取外部存储 ----- 集群环境使用 2）经过内存中的集合
List<Tuple2<String, Integer>> data = new ArrayList<Tuple2<String, Integer>>();
data.add(new Tuple2<>("Cake", 2));
data.add(new Tuple2<>("Bread", 3));
data.add(new Tuple2<>("Cheese", 4));
data.add(new Tuple2<>("Milk", 1));
data.add(new Tuple2<>("Toast", 2));
data.add(new Tuple2<>("Bread", 2));
data.add(new Tuple2<>("Egg", 6));
int index="Code".hashCode() % 4;
JavaRDD<Tuple2<String, Integer>> rdd1 = ctx.parallelize(data, 2);
JavaPairRDD<String, Integer> mapRdd = rdd1.mapToPair(new PairFunction<Tuple2<String, Integer>, String, Integer>() {
@Override
public Tuple2<String, Integer> call(Tuple2<String, Integer> t) throws Exception {
return t;
}
}).partitionBy(new HashPartitioner(4)).persist(StorageLevel.MEMORY_ONLY());
// JavaPairRDD<String, Tuple2<Integer, Integer>> results = mapRdd.combineByKey(
// (value) -> new Tuple2<Integer, Integer>(value,1),
// (acc, value) -> new Tuple2<Integer, Integer>(acc._1() + value, acc._2() + 1),
// (acc1, acc2) -> new Tuple2<Integer, Integer>(acc1._1() + acc2._1(), acc1._2() + acc2._2()),
// new HashPartitioner(2),
// false,
// null
// );
// JavaPairRDD<String, Tuple2<Integer, Integer>> results = mapRdd.aggregateByKey(
// new Tuple2<Integer, Integer>(0,0),
// (acc, value) -> new Tuple2<Integer, Integer>(acc._1() + value, acc._2() + 1),
// (acc1, acc2) -> new Tuple2<Integer, Integer>(acc1._1() + acc2._1(), acc1._2() + acc2._2())
// );
// JavaPairRDD<String, Tuple2<Integer, Integer>> mapRdd1 = mapRdd.mapToPair(new PairFunction<Tuple2<String,Integer>, String, Tuple2<Integer, Integer>>() {
// @Override
// public Tuple2<String, Tuple2<Integer, Integer>> call(Tuple2<String, Integer> t) throws Exception {
// return new Tuple2<String, Tuple2<Integer, Integer>>(t._1(), new Tuple2<Integer, Integer>(t._2() , 1));
// }
// });
// mapRdd1.foreach(System.out::println);
// JavaPairRDD<String, Tuple2<Integer, Integer>> results = mapRdd1.reduceByKey(new Function2<Tuple2<Integer, Integer>, Tuple2<Integer, Integer>, Tuple2<Integer, Integer>>() {
// @Override
// public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> acc1, Tuple2<Integer, Integer> acc2) throws Exception {
// return new Tuple2<Integer, Integer>(acc1._1() + acc2._1(), acc1._2() + acc2._2());
// }
// });
//results.foreach(System.out::println);
// results = mapRdd1.foldByKey(new Tuple2<Integer, Integer>(0, 0), new Function2<Tuple2<Integer,Integer>, Tuple2<Integer,Integer>, Tuple2<Integer,Integer>>() {
// @Override
// public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> acc1, Tuple2<Integer, Integer> acc2) throws Exception {
// return new Tuple2<Integer, Integer>(acc1._1() + acc2._1(), acc1._2() + acc2._2());
// }
// });
//results.foreach(System.out::println);
//思考：如何用combineByKey实现groupByKey
// mapRdd.groupByKey().foreach(System.out::println);
Function<Integer, List<Integer>> createCombiner=new Function<Integer, List<Integer>>() {
@Override
public List<Integer> call(Integer arg0) throws Exception {
List<Integer>list=new ArrayList<Integer>();
list.add(arg0);
return list;
}
};
Function2<List<Integer>, Integer, List<Integer>> mergeValue=new Function2<List<Integer>, Integer, List<Integer>>() {
@Override
public List<Integer> call(List<Integer> list, Integer value) throws Exception {
list.add(value);
return list;
}
};
Function2< List<Integer>,List<Integer> ,List<Integer> > mergeCombiners=new Function2<List<Integer>, List<Integer>, List<Integer>>() {
@Override
public List<Integer> call(List<Integer> list1, List<Integer> list2) throws Exception {
List<Integer> list=new ArrayList<Integer>();
// list.addAll(list1);
// list.addAll(list2);
list1.forEach(list::add);
list2.forEach(list::add);
return list;
}
};
JavaPairRDD<String, List<Integer>> results=mapRdd.combineByKey(createCombiner, mergeValue, mergeCombiners);
results.foreach(x->System.out.println(x));
JavaPairRDD<String, Integer> re=mapRdd.partitionBy(new HashPartitioner(2));
System.out.println(re.glom().collect());
//第四个参数是分区数，glom()打印分区状态
JavaPairRDD<String, List<Integer>> results2=mapRdd.combineByKey(createCombiner, mergeValue, mergeCombiners, 2);
System.out.println(results2.glom().collect());
System.out.println(results2.getNumPartitions());
//第四个参数自定义分区器
JavaPairRDD<String, List<Integer>> results3=mapRdd.combineByKey(createCombiner, mergeValue, mergeCombiners,new HashPartitioner(3));
System.out.println(results3.glom().collect());
System.out.println(results3.getNumPartitions());
//第四个参数自定义分区器，第五个参数Boolean类型（map短是否merge），第六个参数定义序列化规则，null为默认序列化规则
JavaPairRDD<String, List<Integer>> results4=mapRdd.combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(3), true, null);
System.out.println(results4.glom().collect());
System.out.println(results4.getNumPartitions());
// mapRdd1.combineByKey(
// new Function<Tuple2<Integer,Integer>, Tuple2<Integer,Integer>>() {
// @Override
// public Tuple2<Integer,Integer> call(Tuple2<Integer, Integer> arg0) throws Exception {
// return arg0;
// }
// },
//
// new Function2<Tuple2<Integer, Integer>, Integer, Tuple2<Integer, Integer>[]>() {
// @Override
// public Tuple2<Integer, Integer>[] call(Tuple2<Integer, Integer> arg0, Integer arg1) throws Exception {
// return null;
// }
// },
// mergeCombiners);
//其实，distinct 基于 reduceByKey实现
// mapRdd1.distinct();
ctx.stop();
}
}
2.group&join
groupByKey
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
import java.util.Scanner;
import org.apache.hive.com.esotericsoftware.kryo.serializers.JavaSerializer;
import org.apache.spark.HashPartitioner;
import org.apache.spark.Partitioner;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.AbstractJavaRDDLike;
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.storage.StorageLevel;
import scala.Tuple2;
//JavaPairRDD<String, Integer> results = mapRdd.reduceByKey((x, y)->x+y);
public class CombineByKeyTest {
@SuppressWarnings("serial")
public static void main(String[] xx){
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("WordCounter");
conf.set("spark.testing.memory", "5435657567560");
conf.set("spark.default.parallelism", "4");
JavaSparkContext ctx = new JavaSparkContext(conf);
//建立RDD：1）经过读取外部存储 ----- 集群环境使用 2）经过内存中的集合
List<Tuple2<String, Integer>> data = new ArrayList<Tuple2<String, Integer>>();
data.add(new Tuple2<>("Cake", 2));
data.add(new Tuple2<>("Bread", 3)); //<"Bread", <3,1>>
data.add(new Tuple2<>("Cheese", 4));
data.add(new Tuple2<>("Milk", 1));
data.add(new Tuple2<>("Toast", 2));
data.add(new Tuple2<>("Bread", 2));
data.add(new Tuple2<>("Egg", 6));
JavaRDD<Tuple2<String, Integer>> rdd1 = ctx.parallelize(data, 2);
JavaPairRDD<String, Integer> mapRdd = rdd1.mapToPair(
new PairFunction<Tuple2<String, Integer>, String, Integer>() {
@Override
public Tuple2<String, Integer> call(Tuple2<String, Integer> t) throws Exception {
return t;
}
});
// JavaPairRDD<String, Integer> mapRdd=ctx.parallelizePairs(data,2);
mapRdd.groupByKey().foreach(x->System.out.println(x));
// JavaPairRDD<String, Tuple2<Integer, Integer>> results = mapRdd.combineByKey(
// new Function<Integer, Tuple2<Integer, Integer>>() {
// @Override
// public Tuple2<Integer, Integer> call(Integer v1) throws Exception {
// return new Tuple2<Integer, Integer>(v1 ,1);
// }
// }, new Function2<Tuple2<Integer, Integer>, Integer, Tuple2<Integer, Integer>>() {
// @Override
// public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> v1, Integer v2) throws Exception {
// return new Tuple2<Integer, Integer>(v1._1() + v2, v1._2() + 1);
// }
// }, new Function2<Tuple2<Integer, Integer>, Tuple2<Integer, Integer>, Tuple2<Integer, Integer>>() {
// @Override
// public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> v1, Tuple2<Integer, Integer> v2) throws Exception {
// return new Tuple2<Integer, Integer>(v1._1() + v2._1(), v1._2() + v2._2());
// }
// });
JavaPairRDD<String, Tuple2<Integer, Integer>> result2s = mapRdd.combineByKey(
(Integer value) -> new Tuple2<Integer, Integer>(value,1),
(Tuple2<Integer, Integer> acc, Integer value) -> new Tuple2<Integer, Integer>(acc._1() + value, acc._2() + 1),
(Tuple2<Integer, Integer> acc1, Tuple2<Integer, Integer> acc2) -> new Tuple2<Integer, Integer>(acc1._1() + acc2._1(), acc1._2() + acc2._2()),
new HashPartitioner(3),
true,
null
);
result2s.foreach(x->System.out.println(x));
JavaPairRDD<String, Tuple2<Integer, Integer>> results3 = mapRdd.aggregateByKey(
new Tuple2<Integer, Integer>(0,0),
(acc, value) -> new Tuple2<Integer, Integer>(acc._1() + value, acc._2() + 1),
(acc1, acc2) -> new Tuple2<Integer, Integer>(acc1._1() + acc2._1(), acc1._2() + acc2._2())
);
results3.foreach(x->System.out.println(x));
JavaPairRDD<String, Tuple2<Integer, Integer>> mapRdd1 = mapRdd.mapToPair(new PairFunction<Tuple2<String,Integer>, String, Tuple2<Integer, Integer>>() {
@Override
public Tuple2<String, Tuple2<Integer, Integer>> call(Tuple2<String, Integer> t) throws Exception {
return new Tuple2<String, Tuple2<Integer, Integer>>(t._1(), new Tuple2<Integer, Integer>(t._2() , 1));
}
});
JavaPairRDD<String, Tuple2<Integer, Integer>> results = mapRdd1.reduceByKey(new Function2<Tuple2<Integer, Integer>, Tuple2<Integer, Integer>, Tuple2<Integer, Integer>>() {
@Override
public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> acc1, Tuple2<Integer, Integer> acc2) throws Exception {
return new Tuple2<Integer, Integer>(acc1._1() + acc2._1(), acc1._2() + acc2._2());
}
});
// results.foreach(System.out::println);
results.foreach(x->System.out.println(x));
ctx.stop();
}
}
join
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
import java.util.Scanner;
import org.apache.hive.com.esotericsoftware.kryo.serializers.JavaSerializer;
import org.apache.spark.HashPartitioner;
import org.apache.spark.Partitioner;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.AbstractJavaRDDLike;
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.Optional;
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.storage.StorageLevel;
import scala.Tuple2;
//JavaPairRDD<String, Integer> results = mapRdd.reduceByKey((x, y)->x+y);
public class CogroupApiTest {
public static void main(String[] xx){
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.set("spark.testing.memory", "2147480000");
conf.setAppName("WordCounter");
conf.set("spark.default.parallelism", "4");
JavaSparkContext ctx = new JavaSparkContext(conf);
//建立RDD：1）经过读取外部存储 ----- 集群环境使用 2）经过内存中的集合
List<Tuple2<String, Integer>> data1 = new ArrayList<Tuple2<String, Integer>>();
data1.add(new Tuple2<>("Cake", 2));
data1.add(new Tuple2<>("Bread", 3));
data1.add(new Tuple2<>("Cheese", 4));
data1.add(new Tuple2<>("Milk", 1));
data1.add(new Tuple2<>("Toast", 2));
data1.add(new Tuple2<>("Bread", 2));
data1.add(new Tuple2<>("Egg", 6));
// JavaPairRDD<String, Integer> mapRdd1=ctx.parallelizePairs(data1);
JavaRDD<Tuple2<String, Integer>> rdd1 = ctx.parallelize(data1, 2);
JavaPairRDD<String, Integer> mapRdd1 = rdd1.mapToPair(new PairFunction<Tuple2<String, Integer>, String, Integer>() {
@Override
public Tuple2<String, Integer> call(Tuple2<String, Integer> t) throws Exception {
return t;
}
}).partitionBy(new HashPartitioner(2)).persist(StorageLevel.MEMORY_ONLY());
List<Tuple2<String, Integer>> data2 = new ArrayList<Tuple2<String, Integer>>();
data2.add(new Tuple2<>("Cake", 2));
data2.add(new Tuple2<>("Bread", 3));
data2.add(new Tuple2<>("Cheese", 4));
data2.add(new Tuple2<>("Milk", 1));
data2.add(new Tuple2<>("Toast", 2));
JavaRDD<Tuple2<String, Integer>> rdd2 = ctx.parallelize(data2, 2);
JavaPairRDD<String, Integer> mapRdd2 = rdd2.mapToPair(new PairFunction<Tuple2<String, Integer>, String, Integer>() {
@Override
public Tuple2<String, Integer> call(Tuple2<String, Integer> t) throws Exception {
return t;
}
}).partitionBy(new HashPartitioner(2)).persist(StorageLevel.MEMORY_ONLY());
//groupWith，和cogroup是同样的效果 (Bread,([3, 2],[3]))
JavaPairRDD<String, Tuple2<Iterable<Integer>, Iterable<Integer>>> mapRdd3 = mapRdd1.cogroup(mapRdd2);
mapRdd3.foreach(x->System.out.println(x));
//(Bread,(3,3)),(Bread,(2,3)),(Cake,(2,2)) 聚合操做
// JavaPairRDD<String, Tuple2<Integer, Integer>> mapRdd3 = mapRdd1.join(mapRdd2);
// mapRdd3.foreach(x->System.out.println(x));
//(Bread,(Optional[3],3)), (Bread,(Optional[3],2)),(Cake,(Optional[2],2)) 聚合操做，主集合能够为optional.empty
// JavaPairRDD<String, Tuple2<Optional<Integer>, Integer>> mapRdd3 = mapRdd2.rightOuterJoin(mapRdd1);
// mapRdd3.foreach(x->System.out.println(x));
//(Cheese,(4,Optional[4])), (Toast,(2,Optional[2])), (Egg,(6,Optional.empty))
// JavaPairRDD<String, Tuple2<Integer, Optional<Integer>>> mapRdd4 = mapRdd1.leftOuterJoin(mapRdd2);
// mapRdd4.foreach(x->System.out.println(x));
//两边都能为空
// JavaPairRDD<String, Tuple2<Optional<Integer>, Optional<Integer>>> mapRdd5 = mapRdd1.fullOuterJoin(mapRdd2);
// mapRdd5.foreach(x->System.out.println(x));
//groupWith，和cogroup是同样的效果 (Bread,([3, 2],[3]))
// JavaPairRDD<String, Tuple2<Iterable<Integer>, Iterable<Integer>>> mapRdd6 = mapRdd1.groupWith(mapRdd2);
// mapRdd6.foreach(x->System.out.println(x));
//(Bread,(3,3)),(Bread,(2,3)),(Cake,(2,2)) 聚合操做
// JavaPairRDD<String, Tuple2<Integer, Integer>> mapRdd7=mapRdd1.join(mapRdd2);
// mapRdd7.foreach(x->System.out.println(x));
//聚合操做，将两个maprdd并集，重复元素不会被删掉
// JavaPairRDD<String,Integer> mapRdd8=mapRdd1.union(mapRdd2);
// mapRdd8.foreach(x->System.out.println(x));
//删除key相同的元素
// JavaPairRDD<String, Integer> mapRdd9=mapRdd1.subtractByKey(mapRdd2);
// mapRdd9.foreach(x->System.out.println(x));
//求交集，只返回key，value相同的tuple
// JavaPairRDD<String, Integer> mapRdd10=mapRdd1.intersection(mapRdd2);
// mapRdd10.foreach(x->System.out.println(x));
}
}
2.sortBykey
test1
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Comparator;
import java.util.List;
import org.apache.spark.HashPartitioner;
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.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.storage.StorageLevel;
import scala.Tuple2;
public class SortByKeyApi {
public static void main(String[] xx){
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("WordCounter");
conf.set("spark.testing.memory", "2147480000");
conf.set("spark.default.parallelism", "4");
JavaSparkContext ctx = new JavaSparkContext(conf);
//建立RDD：1）经过读取外部存储 ----- 集群环境使用 2）经过内存中的集合
List<Tuple2<String, Integer>> data = new ArrayList<Tuple2<String, Integer>>();
data.add(new Tuple2<>("Cake", 2));
data.add(new Tuple2<>("Bread", 3));
data.add(new Tuple2<>("Cheese", 4));
data.add(new Tuple2<>("Milk", 1));
data.add(new Tuple2<>("Toast", 2));
data.add(new Tuple2<>("Bread", 2));
data.add(new Tuple2<>("Egg", 6));
JavaRDD<Tuple2<String, Integer>> rdd1 = ctx.parallelize(data, 2);
JavaPairRDD<String, Integer> mapRdd = rdd1.mapToPair(new PairFunction<Tuple2<String, Integer>, String, Integer>() {
@Override
public Tuple2<String, Integer> call(Tuple2<String, Integer> t) throws Exception {
return t;
}
}).partitionBy(new HashPartitioner(2)).persist(StorageLevel.MEMORY_ONLY());
//mapRdd.sortByKey().foreach(System.out::println);
mapRdd.sortByKey(false).foreach(x->System.out.println(x));
// mapRdd.sortByKey(new Comparator<Tuple2<String, Integer>>() {
// @Override
// public int compare(Tuple2<String, Integer> o1, Tuple2<String, Integer> o2) {
// return 0;
// }
// });
// mapRdd.f
// mapRdd.mapValues(x->x+1).foreach(x->System.out.println(x));
// mapRdd.flatMapValues(()->Arrays.asList(1,1,1));
ctx.stop();
}
}
test2
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import scala.Tuple2;
public class SortByKeyApiTest {
public static void main(String[] xx){
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("WordCounter");
conf.set("spark.default.parallelism", "4");
conf.set("spark.testing.memory", "2147480000");
JavaSparkContext ctx = new JavaSparkContext(conf);
//建立RDD：1）经过读取外部存储 ----- 集群环境使用 2）经过内存中的集合
List<Person> data1 =
new ArrayList<Person>();
data1.add(new Person("Cake",32));
data1.add(new Person("Bread",21));
data1.add(new Person("Smith",32));
data1.add(new Person("Hourse",21));
data1.add(new Person("Mary",32));
data1.add(new Person("Greey",21));
data1.add(new Person("Greey",21));
data1.add(new Person("Tom",32));
data1.add(new Person("Gao",21));
System.out.println(ctx.parallelize(data1).distinct().count());
// .sortBy(x->x, true, 2).foreach(x->System.out.println(x));
List<Tuple2<Person, Integer>> data =
new ArrayList<Tuple2<Person, Integer>>();
data.add(new Tuple2<Person, Integer>(new Person("Cake",32), 2));
data.add(new Tuple2<Person, Integer>(new Person("Bread",21), 3));
data.add(new Tuple2<Person, Integer>(new Person("Smith",32), 2));
data.add(new Tuple2<Person, Integer>(new Person("Hourse",21), 3));
data.add(new Tuple2<Person, Integer>(new Person("Mary",32), 2));
data.add(new Tuple2<Person, Integer>(new Person("Greey",21), 3));
data.add(new Tuple2<Person, Integer>(new Person("Greey",11), 3));
data.add(new Tuple2<Person, Integer>(new Person("Tom",32), 2));
data.add(new Tuple2<Person, Integer>(new Person("Gao",21), 3));
JavaPairRDD<Person, Integer> dataRdd = ctx.parallelizePairs(data);
dataRdd.sortByKey().foreach(x->System.out.println(x));
dataRdd.sortByKey(new Comparator<Person>() {
@Override
public int compare(Person o1, Person o2) {
int res = o1.name.compareTo(o2.name);
if(res == 0){
res = o1.age - o2.age;
}
return res;
}
});
ctx.close();
ctx.stop();
}
}
class Person implements Serializable, Comparable<Person>{
private static final long serialVersionUID = 1L;
public Person(String name, int age) {
super();
this.name = name;
this.age = age;
}
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + age;
result = prime * result + ((name == null) ? 0 : name.hashCode());
return result;
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (getClass() != obj.getClass())
return false;
Person other = (Person) obj;
if (age != other.age)
return false;
if (name == null) {
if (other.name != null)
return false;
} else if (!name.equals(other.name))
return false;
return true;
}
String name;
int age;
@Override
public int compareTo(Person p) {
int res = this.name.compareTo(p.name);
if(res == 0){
res = this.age - p.age;
}
return res;
}
@Override
public String toString() {
return "Person [name=" + name + ", age=" + age + "]";
}
}
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