spark程序解析——WordCount

本篇解析spark的词频统计源程序代码。

java源码如下：

</pre><pre name="code" class="java">package sparkTest;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 scala.Tuple2;public class WordCount {public static void main(String[] args) {String logFile = "file:///home/hadoop/workspace/sparkTest/input/README.md"; // Should be some file on your systemSparkConf conf = new SparkConf().setAppName("Simple Application").setMaster("local");JavaSparkContext sc = new JavaSparkContext(conf);JavaRDD<String> textFile = sc.textFile(logFile);//构建String型RDDJavaRDD<String> words = textFile.flatMap(new FlatMapFunction<String, String>() {//flatMap相对map，多了flattening环节：即将所有行返回的结果合并很一个对象public Iterable<String> call(String s) {return Arrays.asList(s.split(" "));}});JavaPairRDD<String, Integer> pairs = words.mapToPair(new PairFunction<String, String, Integer>() {//执行PairFunction，返回keyValue值对public Tuple2<String, Integer> call(String s) {return new Tuple2<String, Integer>(s, 1);}});JavaPairRDD<String, Integer> counts = pairs.reduceByKey(new Function2<Integer, Integer, Integer>() {//合并相同的Keypublic Integer call(Integer a, Integer b) {return a + b;}});counts.saveAsTextFile("file:///home/hadoop/workspace/sparkTest/output");//System.out.println("Lines with a: " + numAs + ", lines with b: " + numBs);}}

源码解析步骤：

1、textFile()之前，构建JavaRDD，String型的。

2、flatMap()对RDD元素进行操作并合并。不同于map()，flatMap的函数参数返回的必须是list等序列。官方文档解释如下：

flatMap(func)Similar to map, but each input item can be mapped to 0 or more output items (so func should return a Seq rather than a single item).

3、words.mapToPair(new PairFunction<String, String, Integer>())将flatMap结果转化成keyValue对。

4、pairs.reduceByKey(new Function2<Integer, Integer, Integer>())将mapToPair结果合并成最终结果。

5、saveAsTextFile(path)把最终结果存入path对应的文件中，可以是local file system、hadoop支持的其它文件系统、HDFS等。

注意：这里除了saveAsTextFile()是action操作，其他都属于Transformation.

输入文件内容如下：

# Apache SparkSpark is a fast and general cluster computing system for Big Data. It provides<http://spark.apache.org/>

输出文件内容如下：

(Spark,2)(provides,1)(is,1)(general,1)(a,1)(Big,1)(fast,1)(Apache,1)(#,1)(,2)(cluster,1)(Data.,1)(It,1)(for,1)(computing,1)(and,1)(<http://spark.apache.org/>,1)(system,1)

观察可知，实现了词频统计功能。