在上一遍《将 Spark Streaming + Kafka direct 的 offset 保存进入Zookeeper》中，我们已经成功的将 topic 的 partition 的 offset 保存到了 Zookeeper中，使监控工具发挥了其监控效果。那现在是时候来处理《“Spark Streaming + Kafka direct + checkpoints + 代码改变” 引发的问题》中提到的问题了。
解决的方法是：分别从Kafka中获得某个Topic当前每个partition的offset，再从Zookeeper中获得某个consumer消费当前Topic中每个partition的offset，最后再这两个根据项目情况进行合并，就可以了。
一、具体实现
1、程序实现，如下：
public class SparkStreamingOnKafkaDirect{ public static JavaStreamingContext createContext(){ SparkConf conf = new SparkConf().setMaster("local[4]").setAppName("SparkStreamingOnKafkaDirect"); JavaStreamingContext jsc = new JavaStreamingContext(conf, Durations.seconds(30)); jsc.checkpoint("/checkpoint"); Map<String, String> kafkaParams = new HashMap<String, String>(); kafkaParams.put("metadata.broker.list","192.168.1.151:1234,192.168.1.151:1235,192.168.1.151:1236"); Map<TopicAndPartition, Long> topicOffsets = getTopicOffsets("192.168.1.151:1234,192.168.1.151:1235,192.168.1.151:1236", "kafka_direct"); Map<TopicAndPartition, Long> consumerOffsets = getConsumerOffsets("192.168.1.151:2181", "spark-group", "kafka_direct"); if(null!=consumerOffsets && consumerOffsets.size()>0){ topicOffsets.putAll(consumerOffsets); } for(Map.Entry<TopicAndPartition,Long> entry:topicOffsets.entrySet()){ System.out.println(entry.getKey().topic()+"\t"+entry.getKey().partition()+"\t"+entry.getValue()); } JavaInputDStream<String> lines = KafkaUtils.createDirectStream(jsc, String.class, String.class, StringDecoder.class, StringDecoder.class, String.class, kafkaParams, topicOffsets, new Function<MessageAndMetadata<String,String>,String>() { public String call(MessageAndMetadata<String, String> v1) throws Exception { return v1.message(); } }); final AtomicReference<OffsetRange[]> offsetRanges = new AtomicReference<>(); JavaDStream<String> words = lines.transform( new Function<JavaRDD<String>, JavaRDD<String>>() { @Override public JavaRDD<String> call(JavaRDD<String> rdd) throws Exception { OffsetRange[] offsets = ((HasOffsetRanges) rdd.rdd()).offsetRanges(); offsetRanges.set(offsets); return rdd; } } ).flatMap(new FlatMapFunction<String, String>() { public Iterable<String> call( String event) throws Exception { return Arrays.asList(event); } }); JavaPairDStream<String, Integer> pairs = words .mapToPair(new PairFunction<String, String, Integer>() { public Tuple2<String, Integer> call( String word) throws Exception { return new Tuple2<String, Integer>( word, 1); } }); JavaPairDStream<String, Integer> wordsCount = pairs .reduceByKey(new Function2<Integer, Integer, Integer>() { public Integer call(Integer v1, Integer v2) throws Exception { return v1 + v2; } }); lines.foreachRDD(new VoidFunction<JavaRDD<String>>(){ @Override public void call(JavaRDD<String> t) throws Exception { ObjectMapper objectMapper = new ObjectMapper(); CuratorFramework curatorFramework = CuratorFrameworkFactory.builder() .connectString("192.168.1.151:2181").connectionTimeoutMs(1000) .sessionTimeoutMs(10000).retryPolicy(new RetryUntilElapsed(1000, 1000)).build(); curatorFramework.start(); for (OffsetRange offsetRange : offsetRanges.get()) { final byte[] offsetBytes = objectMapper.writeValueAsBytes(offsetRange.untilOffset()); String nodePath = "/consumers/spark-group/offsets/" + offsetRange.topic()+ "/" + offsetRange.partition(); if(curatorFramework.checkExists().forPath(nodePath)!=null){ curatorFramework.setData().forPath(nodePath,offsetBytes); }else{ curatorFramework.create().creatingParentsIfNeeded().forPath(nodePath, offsetBytes); } } curatorFramework.close(); } }); wordsCount.print(); return jsc; } public static Map<TopicAndPartition,Long> getConsumerOffsets(String zkServers, String groupID, String topic) { Map<TopicAndPartition,Long> retVals = new HashMap<TopicAndPartition,Long>(); ObjectMapper objectMapper = new ObjectMapper(); CuratorFramework curatorFramework = CuratorFrameworkFactory.builder() .connectString(zkServers).connectionTimeoutMs(1000) .sessionTimeoutMs(10000).retryPolicy(new RetryUntilElapsed(1000, 1000)).build(); curatorFramework.start(); try{ String nodePath = "/consumers/"+groupID+"/offsets/" + topic; if(curatorFramework.checkExists().forPath(nodePath)!=null){ List<String> partitions=curatorFramework.getChildren().forPath(nodePath); for(String partiton:partitions){ int partitionL=Integer.valueOf(partiton); Long offset=objectMapper.readValue(curatorFramework.getData().forPath(nodePath+"/"+partiton),Long.class); TopicAndPartition topicAndPartition=new TopicAndPartition(topic,partitionL); retVals.put(topicAndPartition, offset); } } }catch(Exception e){ e.printStackTrace(); } curatorFramework.close(); return retVals; } public static Map<TopicAndPartition,Long> getTopicOffsets(String zkServers, String topic){ Map<TopicAndPartition,Long> retVals = new HashMap<TopicAndPartition,Long>(); for(String zkServer:zkServers.split(",")){ SimpleConsumer simpleConsumer = new SimpleConsumer(zkServer.split(":")[0], Integer.valueOf(zkServer.split(":")[1]), 10000, 1024, "consumer"); TopicMetadataRequest topicMetadataRequest = new TopicMetadataRequest(Arrays.asList(topic)); TopicMetadataResponse topicMetadataResponse = simpleConsumer.send(topicMetadataRequest); for (TopicMetadata metadata : topicMetadataResponse.topicsMetadata()) { for (PartitionMetadata part : metadata.partitionsMetadata()) { Broker leader = part.leader(); if (leader != null) { TopicAndPartition topicAndPartition = new TopicAndPartition(topic, part.partitionId()); PartitionOffsetRequestInfo partitionOffsetRequestInfo = new PartitionOffsetRequestInfo(kafka.api.OffsetRequest.LatestTime(), 10000); OffsetRequest offsetRequest = new OffsetRequest(ImmutableMap.of(topicAndPartition, partitionOffsetRequestInfo), kafka.api.OffsetRequest.CurrentVersion(), simpleConsumer.clientId()); OffsetResponse offsetResponse = simpleConsumer.getOffsetsBefore(offsetRequest); if (!offsetResponse.hasError()) { long[] offsets = offsetResponse.offsets(topic, part.partitionId()); retVals.put(topicAndPartition, offsets[0]); } } } } simpleConsumer.close(); } return retVals; } public static void main(String[] args) throws Exception{ JavaStreamingContextFactory factory = new JavaStreamingContextFactory() { public JavaStreamingContext create() { return createContext(); } }; JavaStreamingContext jsc = JavaStreamingContext.getOrCreate("/checkpoint", factory); jsc.start(); jsc.awaitTermination(); jsc.close(); }}
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2、准备测试环境，并记录目前consumer中的信息，如下图：
从界面上可以看到，目前所有的消息都已经被处理过了。
现在向kafka_direct中新增一个消息，如下图：
3、运行Spark Streaming 程序(注意：要先清空 checkpoint 目录下的内容)，观察命令行输出情况，及kafka manager中关于 spark-group的变化情况：
命令行输出：
打印出了，从zookeeper中读取到的offset。
打印出了，从Kafka的kafka_direct中消费的消息的结果数据。
从图片中，可以看到consumer offset 和 logSize 是一样的。
4、下面我们人为的将topic的partition的offset的值设置为0，看其是否会打印出所有消息的结果数据。
（取消上面程序中注释的部分即可）
5、再次运行Spark Streaming 程序(注意：要先清空 checkpoint 目录下的内容)，看命令行输出效果：
从图中，可以看出Spark Streaming 程序将之前所有的测试消息都重新处理了一次。
至此，《“Spark Streaming + Kafka direct + checkpoints + 代码改变” 引发的问题》的整个解决的过程都已经结束了。
原文链接：http://blog.csdn.net/sun_qiangwei/article/details/52098917
