Spark WordCount的两种方式。
语言:Java
工具:Idea
项目:Java Maven
pom.xml如下:
<properties>
<spark.version>1.2.0</spark.version>
</properties> <dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
</dependencies>
第一种方式,比较常规的按部就班的
package pairs; 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;
import java.util.Arrays; public class WordCount1 {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setMaster("local").setAppName("wordcount1");
JavaSparkContext sc = new JavaSparkContext(conf); String filename = "D:\\tmp\\words.txt";
JavaRDD<String> input = sc.textFile(filename);
JavaRDD<String> lines = input.flatMap(new FlatMapFunction<String, String>() {
public Iterable<String> call(String s) throws Exception {
return Arrays.asList(s.split(" "));
}
}); //pairs
JavaPairRDD<String,Integer> pairs = lines.mapToPair(new PairFunction<String, String, Integer>() {
public Tuple2<String, Integer> call(String s) throws Exception {
return new Tuple2<String, Integer>(s,1);
}
}); //reduce
JavaPairRDD<String,Integer> counts = pairs.reduceByKey(new Function2<Integer, Integer, Integer>() {
public Integer call(Integer x, Integer y) throws Exception {
return x+y;
}
}); //output
counts.foreach(new VoidFunction<Tuple2<String, Integer>>() {
public void call(Tuple2<String, Integer> tuple2) throws Exception {
System.out.println(tuple2);
}
}); sc.stop(); }
}
代码输出:
(rose,2)
(jack,3)
第二种更为简洁
package pairs; 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 java.util.Arrays;
import java.util.Map; public class WordCount2 { public static void main(String[] args) {
SparkConf conf = new SparkConf().setMaster("local").setAppName("wordcount2");
JavaSparkContext sc = new JavaSparkContext(conf); String filename = "D:\\tmp\\words.txt";
JavaRDD<String> input = sc.textFile(filename);
JavaRDD<String> lines = input.flatMap(new FlatMapFunction<String, String>() {
public Iterable<String> call(String s) throws Exception {
return Arrays.asList(s.split(" "));
}
}); Map<String,Long> result = lines.countByValue();
System.out.println(result); sc.stop();
}
}
代码输出:
{rose=2, jack=3}
通过对比可以发现,第一种方式一直都是转化操作,最后打印的是Tuple2;而第二种方式变成了行动操作,直接输出Map<String,Long>。
具体有什么区别,或者效率上有啥不同,待后续深入学习。
参考资料:
《Spark快速大数据分析》