groupByKey,reduceByKey,sortByKey算子
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1、groupByKey
groupByKey是对每个key进行合并操作，但只生成一个sequence，groupByKey本身不能自定义操作函数。
java：
package com.bean.spark.trans; import java.util.Arrays;
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 TraGroupByKey {
public static void main(String[] args) {
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("union");
System.setProperty("hadoop.home.dir", "D:/tools/spark-2.0.0-bin-hadoop2.6");
JavaSparkContext sc = new JavaSparkContext(conf);
List<Tuple2<String, Integer>> list = Arrays.asList(new Tuple2<String, Integer>("cl1", 90),
new Tuple2<String, Integer>("cl2", 91),new Tuple2<String, Integer>("cl3", 97),
new Tuple2<String, Integer>("cl1", 96),new Tuple2<String, Integer>("cl1", 89),
new Tuple2<String, Integer>("cl3", 90),new Tuple2<String, Integer>("cl2", 60));
JavaPairRDD<String, Integer> listRDD = sc.parallelizePairs(list);
JavaPairRDD<String, Iterable<Integer>> results = listRDD.groupByKey();
System.out.println(results.collect());
sc.close();
}
}
python：
# -*- coding:utf-8 -*- from pyspark import SparkConf
from pyspark import SparkContext
import os if __name__ == '__main__':
os.environ["SPARK_HOME"] = "D:/tools/spark-2.0.0-bin-hadoop2.6"
conf = SparkConf().setMaster('local').setAppName('group')
sc = SparkContext(conf=conf)
data = [('tom',90),('jerry',97),('luck',92),('tom',78),('luck',64),('jerry',50)]
rdd = sc.parallelize(data)
print rdd.groupByKey().map(lambda x: (x[0],list(x[1]))).collect()
注意：当采用groupByKey时，由于它不接收函数，spark只能先将所有的键值对都移动，这样的后果是集群节点之间的开销很大，导致传输延时。
整个过程如下：
因此，在对大数据进行复杂计算时，reduceByKey优于groupByKey。
另外，如果仅仅是group处理，那么以下函数应该优先于 groupByKey ：
（1）、combineByKey 组合数据，但是组合之后的数据类型与输入时值的类型不一样。
（2）、foldByKey合并每一个 key 的所有值，在级联函数和“零值”中使用。
2、reduceByKey
对数据集key相同的值，都被使用指定的reduce函数聚合到一起。
java：
package com.bean.spark.trans; import java.util.Arrays;
import java.util.List; import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function2; import scala.Tuple2; public class TraReduceByKey {
public static void main(String[] args) {
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("reduce");
System.setProperty("hadoop.home.dir", "D:/tools/spark-2.0.0-bin-hadoop2.6");
JavaSparkContext sc = new JavaSparkContext(conf);
List<Tuple2<String, Integer>> list = Arrays.asList(new Tuple2<String, Integer>("cl1", 90),
new Tuple2<String, Integer>("cl2", 91),new Tuple2<String, Integer>("cl3", 97),
new Tuple2<String, Integer>("cl1", 96),new Tuple2<String, Integer>("cl1", 89),
new Tuple2<String, Integer>("cl3", 90),new Tuple2<String, Integer>("cl2", 60));
JavaPairRDD<String, Integer> listRDD = sc.parallelizePairs(list);
JavaPairRDD<String, Integer> results = listRDD.reduceByKey(new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer s1, Integer s2) throws Exception {
// TODO Auto-generated method stub
return s1 + s2;
}
});
System.out.println(results.collect());
sc.close();
}
}
python：
# -*- coding:utf-8 -*- from pyspark import SparkConf
from pyspark import SparkContext
import os
from operator import add
if __name__ == '__main__':
os.environ["SPARK_HOME"] = "D:/tools/spark-2.0.0-bin-hadoop2.6"
conf = SparkConf().setMaster('local').setAppName('reduce')
sc = SparkContext(conf=conf)
data = [('tom',90),('jerry',97),('luck',92),('tom',78),('luck',64),('jerry',50)]
rdd = sc.parallelize(data)
print rdd.reduceByKey(add).collect()
sc.close()
当采用reduceByKey时，Spark可以在每个分区移动数据之前将待输出数据与一个共用的key结合。 注意在数据对被搬移前同一机器上同样的key是怎样被组合的。
3、sortByKey
通过key进行排序。
java:
package com.bean.spark.trans; import java.util.Arrays;
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 TraSortByKey {
public static void main(String[] args) {
SparkConf conf = new SparkConf();
conf.setMaster("local");
conf.setAppName("sort");
System.setProperty("hadoop.home.dir", "D:/tools/spark-2.0.0-bin-hadoop2.6");
JavaSparkContext sc = new JavaSparkContext(conf);
List<Tuple2<Integer, String>> list = Arrays.asList(new Tuple2<Integer,String>(3,"Tom"),
new Tuple2<Integer,String>(2,"Jerry"),new Tuple2<Integer,String>(5,"Luck")
,new Tuple2<Integer,String>(1,"Spark"),new Tuple2<Integer,String>(4,"Storm"));
JavaPairRDD<Integer,String> rdd = sc.parallelizePairs(list);
JavaPairRDD<Integer, String> results = rdd.sortByKey(false);
System.out.println(results.collect());
sc.close()
}
}
python:
#-*- coding:utf-8 -*-
if __name__ == '__main__':
os.environ["SPARK_HOME"] = "D:/tools/spark-2.0.0-bin-hadoop2.6"
conf = SparkConf().setMaster('local').setAppName('reduce')
sc = SparkContext(conf=conf)
data = [(5,90),(1,92),(3,50)]
rdd = sc.parallelize(data)
print rdd.sortByKey(False).collect()
sc.close()