Spark Streaming + Kafka direct 从Zookeeper中恢复offset

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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> item:topicOffsets.entrySet()){//            item.setValue(0l);//        }      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();    }}

2、准备测试环境，并记录目前consumer中的信息，如下图：
这里写图片描述
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：
这里写图片描述
打印出了，从zookeeper中读取到的offset。

这里写图片描述
打印出了，从Kafka的kafka_direct中消费的消息的结果数据。

这里写图片描述
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：
这里写图片描述
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。

说明：源代码中，由于时间紧，没有写注释，等后面有时间了再补上。

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