1、自定义Receiver分析
2、自定义Receiver实战
package com.tom.spark.SparkApps.sparkstreaming;import java.util.Arrays;import java.util.List;import org.apache.hadoop.hive.ql.parse.HiveParser.ifExists_return;import org.apache.spark.Accumulator;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.broadcast.Broadcast;import org.apache.spark.streaming.Durations;import org.apache.spark.streaming.Time;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;public class SparkStreamingBroadcastAccumulator { private static volatile Broadcast<List<String>> broadcastList = null; private static volatile Accumulator<Integer> accumulator = null; /** * @param args */ public static void main(String[] args) { // TODO Auto-generated method stub //好处：1、checkpoint 2、工厂 SparkConf conf = new SparkConf().setAppName("SparkStreamingBroadcastAccumulator").setMaster("hdfs://Master:7077/"); JavaStreamingContext javassc = new JavaStreamingContext(conf, Durations.seconds(15)); //没有action广播不会发出 //使用Broadcast广播黑名单到每个Executor中 broadcastList = javassc.sparkContext().broadcast(Arrays.asList("Hadoop","Mahout","Hive")); //全局计数器，用于统计在线过滤了多少个黑名单 accumulator = javassc.sparkContext().accumulator(0, "OnlineBlacklistCounter"); //创建Kafka元数据来让Spark Streaming这个Kafka Consumer利用 JavaReceiverInputDStream<String> lines = javassc.socketTextStream("Master", 9999); JavaPairDStream<String, Integer> pairs = lines.mapToPair(new PairFunction<String, String, Integer>() { public Tuple2<String, Integer> call(String t) throws Exception { // TODO Auto-generated method stub return new Tuple2<String, Integer>(t, 1); } }); JavaPairDStream<String, Integer> wordsCount = pairs.reduceByKey(new Function2<Integer, Integer, Integer>(){ //对相同的key，进行Value的累加（包括Local和Reducer级别同时Reduce） public Integer call(Integer v1, Integer v2) throws Exception { // TODO Auto-generated method stub return v1 + v2; } }); wordsCount.foreachRDD(new Function2<JavaPairRDD<String, Integer>, Time, Void>() { public Void call(JavaPairRDD<String, Integer> rdd, Time time) throws Exception { // TODO Auto-generated method stub rdd.filter(new Function<Tuple2<String,Integer>, Boolean>() { public Boolean call(Tuple2<String, Integer> wordPair) throws Exception { if(broadcastList.value().contains(wordPair._1)) { accumulator.add(wordPair._2); return false; } else { return true; } } }).collect(); System.out.println(broadcastList.value().toString() + " : " + accumulator.value()); return null; } }); wordsCount.print(); /** * Spark Streaming 执行引擎也就是Driver开始运行，Driver启动的时候是位于一条新的线程中的，当然其内部有消息循环体，用于 * 接收应用程序本身或者Executor中的消息， */ javassc.start(); javassc.awaitTermination(); javassc.close(); }}
大数据IMF传奇行动绝密课程第103课：动手实战Spark Streaming Broadcast Accumulator实现在线黑名单过滤和计数
