一、window滑动窗口
1、概述
Spark Streaming提供了滑动窗口操作的支持，从而让我们可以对一个滑动窗口内的数据执行计算操作。每次掉落在窗口内的RDD的数据，
会被聚合起来执行计算操作，然后生成的RDD，会作为window DStream的一个RDD。比如下图中，就是对每三秒钟的数据执行一次滑动窗口计算，
这3秒内的3个RDD会被聚合起来进行处理，然后过了两秒钟，又会对最近三秒内的数据执行滑动窗口计算。所以每个滑动窗口操作，都必须指定
两个参数，窗口长度以及滑动间隔，而且这两个参数值都必须是batch间隔的整数倍。（Spark Streaming对滑动窗口的支持，是比Storm更加完善和强大的）
2、window滑动窗口操作
案例：热点搜索词滑动统计，每隔10秒钟，统计最近60秒钟的搜索词的搜索频次，并打印出排名最靠前的3个搜索词以及出现次数
2、java案例
package cn.spark.study.streaming; import java.util.List; import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaPairRDD; 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.streaming.Durations; import org.apache.spark.streaming.api.java.JavaDStream; import org.apache.spark.streaming.api.java.JavaPairDStream; import org.apache.spark.streaming.api.java.JavaReceiverInputDStream; import org.apache.spark.streaming.api.java.JavaStreamingContext; import scala.Tuple2; /** * 基于滑动窗口的热点搜索词实时统计 * @author Administrator * */ public class WindowHotWord { public static void main(String[] args) { SparkConf conf = new SparkConf() .setMaster("local[2]") .setAppName("WindowHotWord"); JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(1)); // 说明一下，这里的搜索日志的格式 // leo hello // tom world JavaReceiverInputDStream<String> searchLogsDStream = jssc.socketTextStream("spark1", 9999); // 将搜索日志给转换成，只有一个搜索词，即可 JavaDStream<String> searchWordsDStream = searchLogsDStream.map(new Function<String, String>() { private static final long serialVersionUID = 1L; @Override public String call(String searchLog) throws Exception { return searchLog.split(" ")[1]; } }); // 将搜索词映射为(searchWord, 1)的tuple格式 JavaPairDStream<String, Integer> searchWordPairDStream = searchWordsDStream.mapToPair( new PairFunction<String, String, Integer>() { private static final long serialVersionUID = 1L; @Override public Tuple2<String, Integer> call(String searchWord) throws Exception { return new Tuple2<String, Integer>(searchWord, 1); } }); // 针对(searchWord, 1)的tuple格式的DStream，执行reduceByKeyAndWindow，滑动窗口操作 // 第二个参数，是窗口长度，这里是60秒 // 第三个参数，是滑动间隔，这里是10秒 // 也就是说，每隔10秒钟，将最近60秒的数据，作为一个窗口，进行内部的RDD的聚合，然后统一对一个RDD进行后续 // 计算 // 所以说，这里的意思，就是，之前的searchWordPairDStream为止，其实，都是不会立即进行计算的 // 而是只是放在那里 // 然后，等待我们的滑动间隔到了以后，10秒钟到了，会将之前60秒的RDD，因为一个batch间隔是，5秒，所以之前 // 60秒，就有12个RDD，给聚合起来，然后，统一执行redcueByKey操作 // 所以这里的reduceByKeyAndWindow，是针对每个窗口执行计算的，而不是针对某个DStream中的RDD JavaPairDStream<String, Integer> searchWordCountsDStream = //Function2<T1, T2, R>：一个双参数函数，它接受类型为T1和T2的参数并返回一个R searchWordPairDStream.reduceByKeyAndWindow(new Function2<Integer, Integer, Integer>() { private static final long serialVersionUID = 1L; @Override public Integer call(Integer v1, Integer v2) throws Exception { return v1 + v2; } }, Durations.seconds(60), Durations.seconds(10)); // 到这里为止，就已经可以做到，每隔10秒钟，出来，之前60秒的收集到的单词的统计次数 // 执行transform操作，因为，一个窗口，就是一个60秒钟的数据，会变成一个RDD，然后，对这一个RDD // 根据每个搜索词出现的频率进行排序，然后获取排名前3的热点搜索词 JavaPairDStream<String, Integer> finalDStream = searchWordCountsDStream.transformToPair( new Function<JavaPairRDD<String,Integer>, JavaPairRDD<String,Integer>>() { private static final long serialVersionUID = 1L; @Override public JavaPairRDD<String, Integer> call( JavaPairRDD<String, Integer> searchWordCountsRDD) throws Exception { // 执行搜索词和出现频率的反转 JavaPairRDD<Integer, String> countSearchWordsRDD = searchWordCountsRDD .mapToPair(new PairFunction<Tuple2<String,Integer>, Integer, String>() { private static final long serialVersionUID = 1L; @Override public Tuple2<Integer, String> call( Tuple2<String, Integer> tuple) throws Exception { return new Tuple2<Integer, String>(tuple._2, tuple._1); } }); // 然后执行降序排序 JavaPairRDD<Integer, String> sortedCountSearchWordsRDD = countSearchWordsRDD .sortByKey(false); // 然后再次执行反转，变成(searchWord, count)的这种格式 JavaPairRDD<String, Integer> sortedSearchWordCountsRDD = sortedCountSearchWordsRDD .mapToPair(new PairFunction<Tuple2<Integer,String>, String, Integer>() { private static final long serialVersionUID = 1L; @Override public Tuple2<String, Integer> call( Tuple2<Integer, String> tuple) throws Exception { return new Tuple2<String, Integer>(tuple._2, tuple._1); } }); // 然后用take()，获取排名前3的热点搜索词 List<Tuple2<String, Integer>> hogSearchWordCounts = sortedSearchWordCountsRDD.take(3); for(Tuple2<String, Integer> wordCount : hogSearchWordCounts) { System.out.println(wordCount._1 + ": " + wordCount._2); } return searchWordCountsRDD; } }); // 这个无关紧要，只是为了触发job的执行，所以必须有output操作 finalDStream.print(); jssc.start(); jssc.awaitTermination(); jssc.close(); } } ##在eclipse中启动程序 ##服务器上启动nc，并输入内容 [root@spark1 ~]# nc -lk 9999 leo hello tom word leo hello jack you leo you ##统计结果 (hello,2) (word,1) (you,2)
3、scala案例
package cn.spark.study.streaming import org.apache.spark.SparkConf import org.apache.spark.streaming.StreamingContext import org.apache.spark.streaming.Seconds /** * @author Administrator */ object WindowHotWord { def main(args: Array[String]): Unit = { val conf = new SparkConf() .setMaster("local[2]") .setAppName("WindowHotWord") val ssc = new StreamingContext(conf, Seconds(1)) val searchLogsDStream = ssc.socketTextStream("spark1", 9999) val searchWordsDStream = searchLogsDStream.map { _.split(" ")(1) } val searchWordPairsDStream = searchWordsDStream.map { searchWord => (searchWord, 1) } val searchWordCountsDSteram = searchWordPairsDStream.reduceByKeyAndWindow( (v1: Int, v2: Int) => v1 + v2, Seconds(60), Seconds(10)) val finalDStream = searchWordCountsDSteram.transform(searchWordCountsRDD => { val countSearchWordsRDD = searchWordCountsRDD.map(tuple => (tuple._2, tuple._1)) val sortedCountSearchWordsRDD = countSearchWordsRDD.sortByKey(false) val sortedSearchWordCountsRDD = sortedCountSearchWordsRDD.map(tuple => (tuple._1, tuple._2)) val top3SearchWordCounts = sortedSearchWordCountsRDD.take(3) for(tuple <- top3SearchWordCounts) { println(tuple) } searchWordCountsRDD }) finalDStream.print() ssc.start() ssc.awaitTermination() } } ##在eclipse中启动程序 ##服务器上启动nc，并输入内容 [root@spark1 ~]# nc -lk 9999 leo hello leo hello leo hello leo word leo word leo word leo hello leo you leo you ##统计结果 (hello,4) (word,3) (you,2)