spring-sparkstreaming-kafka10集成实现和疑难杂症解决

一.前期准备

1.开发环境

window7

eclipse

jdk1.8

2.linux环境

zookeeper-3.4.8

hadoop-2.6.4

spark-1.6.0

scala-2.10.6

kafka_2.10-0.10.1.0

各环境的安装和部署请自行准备。

二.疑难杂症

1. spark+scala+kafka版本要一致

2. org.apache.spark.SparkException: A master URL must be set in your configuration

原因：

SparkConf中未设置master。由于Master为cluster程序管理中心，负责接收Client提交的作业，管理Worker，并命令Worker启动Driver和Executor。

解决：

在SparkConf中设置master，本处设置为“local[2]”.

local(default)在本地而非集群跑Spark作业，并且只有一个worker thread（所以，并事实上没有并行）
local[k] 在本地跑Spark Application，有k个worker thread
spark://HOST:PORT连接到指定URL的standalone集群
mesos://HOST:PORT连接到指定的Mesos集群
yarn 连接到默认的YARN集群。yarn集群在SPARK_HOME/conf/yarn-site.xml中指定

3. object not serializable (class: org.apache.kafka.clients.consumer.ConsumerRecord)

原因：

ConsumerRecord是Kafka10版本自带的类。该类为接受kafka消息的key-value类，而异常时该类未序列化。而程序中调用JavaRdd的collect方法，而该方法需要将数据加载到内存，需要进行序列化。

解决：

方法1：将获取数据集合的形式转化单个数据获取。

方法2：通过kryo序列化ConsumerRecord类

三.代码实现

1. sparkStreaming+kafka处理

package com.lm.spark;import java.io.Serializable;import java.util.Arrays;import java.util.Collection;import java.util.HashMap;import java.util.List;import java.util.Map;import org.apache.kafka.clients.consumer.ConsumerRecord;import org.apache.kafka.common.serialization.StringDeserializer;import org.apache.spark.SparkConf;import org.apache.spark.api.java.JavaRDD;import org.apache.spark.api.java.function.VoidFunction;import org.apache.spark.api.java.function.VoidFunction2;import org.apache.spark.streaming.Durations;import org.apache.spark.streaming.Time;import org.apache.spark.streaming.api.java.JavaInputDStream;import org.apache.spark.streaming.api.java.JavaStreamingContext;import org.apache.spark.streaming.kafka010.ConsumerStrategies;import org.apache.spark.streaming.kafka010.KafkaUtils;import org.apache.spark.streaming.kafka010.LocationStrategies;import org.slf4j.Logger;import org.slf4j.LoggerFactory;import org.springframework.beans.factory.annotation.Value;import org.springframework.stereotype.Component;@Componentpublic class SparkStreamingKafka implements Serializable {/** *  */private static final long serialVersionUID = 1L;public static Logger LOGGER = LoggerFactory.getLogger(SparkStreamingKafka.class);@Value("${spark.appname}")private String appName;@Value("${spark.master}")private String master;@Value("${spark.seconds}")private long second;@Value("${kafka.metadata.broker.list}")private String metadataBrokerList;@Value("${kafka.auto.offset.reset}")private String autoOffsetReset;@Value("${kafka.topics}")private String kafkaTopics;@Value("${kafka.group.id}")private String kafkaGroupId;public void processSparkStreaming() throws InterruptedException {// 1.配置sparkconf,必须要配置masterSparkConf conf = new SparkConf().setAppName(appName).setMaster(master);conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");conf.set("spark.kryo.registrator", "com.lm.kryo.MyRegistrator");// 2.根据sparkconf 创建JavaStreamingContextJavaStreamingContext jsc = new JavaStreamingContext(conf, Durations.seconds(second));// 3.配置kafkaMap<String, Object> kafkaParams = new HashMap<>();kafkaParams.put("bootstrap.servers", metadataBrokerList);kafkaParams.put("key.deserializer", StringDeserializer.class);kafkaParams.put("value.deserializer", StringDeserializer.class);kafkaParams.put("group.id", kafkaGroupId);kafkaParams.put("auto.offset.reset", autoOffsetReset);kafkaParams.put("enable.auto.commit", false);// 4.kafka主题Collection<String> topics = Arrays.asList(kafkaTopics.split(","));// 5.创建SparkStreaming输入数据来源input Streamfinal JavaInputDStream<ConsumerRecord<String, String>> stream =KafkaUtils.createDirectStream(jsc, LocationStrategies.PreferConsistent(),ConsumerStrategies.<String, String> Subscribe(topics, kafkaParams));// 6.spark rdd转化和行动处理stream.foreachRDD(new VoidFunction2<JavaRDD<ConsumerRecord<String, String>>, Time>() {private static final long serialVersionUID = 1L;@Overridepublic void call(JavaRDD<ConsumerRecord<String, String>> v1, Time v2) throws Exception {List<ConsumerRecord<String, String>> consumerRecords = v1.collect();System.out.println("获取消息:" + consumerRecords.size());}});// 6. 启动执行jsc.start();// 7. 等待执行停止，如有异常直接抛出并关闭jsc.awaitTermination();}}

2. kryo序列化接收器

package com.lm.kryo;import org.apache.kafka.clients.consumer.ConsumerRecord;import org.apache.spark.serializer.KryoRegistrator;import com.esotericsoftware.kryo.Kryo;public class MyRegistrator implements KryoRegistrator {@Overridepublic void registerClasses(Kryo arg0) {arg0.register(ConsumerRecord.class);}}

四.代码地址

github:https://github.com/a123demi/spring-sparkstreaming-kafka-10

oschina:http://git.oschina.net/a123demi/spring-sparkstreaming-kafka-10