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  • 删除了原有的offset之后再次启动会报错park Streaming from Kafka has error numRecords must not ...

     
     
     
    笔者使用Spark streaming读取Kakfa中的数据,做进一步处理,用到了KafkaUtil的createDirectStream()方法;该方法不会自动保存topic partition的offset到zk,需要在代码中编写提交逻辑,此处介绍了保存offset的方法。 
    当删除已经使用过的kafka topic,然后新建同名topic,使用该方式时出现了"numRecords must not be negative"异常 
    详细信息如下图: 

    是不合法的参数异常,RDD的记录数目必须不能是负数。 
    下文详细分析该问题的出现的场景,以及解决方法。
    异常分析
    numRecords确定
    首先,定位出异常出现的问题,和大致原因。异常中打印出了出现的位置org.apache.spark.streaming.scheduler.StreamInputInfo.InputInfoTracker的第38行,此处代码:

    代码38行,判断了numRecords是否大于等于0,当不满足条件时抛出异常,可判断此时numRecords<0。 
    numRecords的解释: 
    numRecords: the number of records in a batch 
    应该是当前rdd中records 数目计算出了问题。 
    numRecords 构造StreamInputInfo时的参数,结合异常中的信息,找到了DirectKafkaInputDStream中的构造InputInfo的位置: 
    可知 numRecords是rdd.count()的值。
    rdd.count的计算
    根据以上分析可知rdd.count()值为负值,因此需要分析rdd的是如何生成的。 
    同样在DirectKafkaInputDStream中找到rdd的生成代码:
    从此处一路跟踪代码,可在KafkaRDD.Scala中找到rdd.count的赋值逻辑:

    offsetRanges的计算逻辑
    offsetRanges的定义
    offsetRanges: offset ranges that define the Kafka data belonging to this RDD
    在KafkaRDDPartition 40行找到kafka partition offsetRange的计算逻辑:
    def count(): Long = untilOffset - fromOffset 
    fromOffset: per-topic/partition Kafka offset defining the (inclusive) starting point of the batch 
    untilOffset: per-topic/partition Kafka offset defining the (inclusive) ending point of the batch
    fromOffset来自zk中保存; 
    untilOffset通过DirectKafkaInputDStream第145行:
    val untilOffsets = clamp(latestLeaderOffsets(maxRetries))
    计算得到,计算过程得到最新的offset,然后使用spark.streaming.kafka.maxRatePerPartition做clamp,得到允许的最大untilOffsets,##而此时新建的topic,如果topic中没有数据,untilOffsets应该为0##
    原因总结
    当删除一个topic时,zk中的offset信息并没有被清除,因此KafkaDirectStreaming再次启动时仍会得到旧的topic offset为old_offset,作为fromOffset。 
    当新建了topic后,使用untiloffset计算逻辑,得到untilOffset为0(如果topic已有数据则>0); 
    再次被启动的KafkaDirectStreaming Job通过异常的计算逻辑得到的rdd numRecords值为可计算为: 
    numRecords = untilOffset - fromOffset(old_offset) 
    当untilOffset < old_offset时,此异常会出现,对于新建的topic这种情况的可能性很大
    解决方法
    思路
    根据以上分析,可在确定KafkaDirectStreaming 的fromOffsets时判断fromOffset与untiloffset的大小关系,当untilOffset < fromOffset时,矫正fromOffset为offset初始值0。
    流程
    • 从zk获取topic/partition 的fromOffset(获取方法链接
    • 利用SimpleConsumer获取每个partiton的lastOffset(untilOffset )
    • 判断每个partition lastOffset与fromOffset的关系
    • 当lastOffset < fromOffset时,将fromOffset赋值为0 
    通过以上步骤完成fromOffset的值矫正。

    核心代码

    获取kafka topic partition lastoffset代码:
    package org.frey.example.utils.kafka;
    
    import com.google.common.collect.Lists;
    import com.google.common.collect.Maps;
    import kafka.api.PartitionOffsetRequestInfo;
    import kafka.cluster.Broker;
    import kafka.common.TopicAndPartition;
    import kafka.javaapi.*;
    import kafka.javaapi.consumer.SimpleConsumer;
    
    import java.util.Date;
    import java.util.HashMap;
    import java.util.List;
    import java.util.Map;
    
    /**
     * KafkaOffsetTool
     *
     * @author angel
     * @date 2016/4/11
     */
    public class KafkaOffsetTool {
    
      private static KafkaOffsetTool instance;
      final int TIMEOUT = 100000;
      final int BUFFERSIZE = 64 * 1024;
    
      private KafkaOffsetTool() {
      }
    
      public static synchronized KafkaOffsetTool getInstance() {
        if (instance == null) {
          instance = new KafkaOffsetTool();
        }
        return instance;
      }
    
      public Map<TopicAndPartition, Long> getLastOffset(String brokerList, List<String> topics,
          String groupId) {
    
        Map<TopicAndPartition, Long> topicAndPartitionLongMap = Maps.newHashMap();
    
        Map<TopicAndPartition, Broker> topicAndPartitionBrokerMap =
            KafkaOffsetTool.getInstance().findLeader(brokerList, topics);
    
        for (Map.Entry<TopicAndPartition, Broker> topicAndPartitionBrokerEntry : topicAndPartitionBrokerMap
            .entrySet()) {
          // get leader broker
          Broker leaderBroker = topicAndPartitionBrokerEntry.getValue();
    
          SimpleConsumer simpleConsumer = new SimpleConsumer(leaderBroker.host(), leaderBroker.port(),
              TIMEOUT, BUFFERSIZE, groupId);
    
          long readOffset = getTopicAndPartitionLastOffset(simpleConsumer,
              topicAndPartitionBrokerEntry.getKey(), groupId);
    
          topicAndPartitionLongMap.put(topicAndPartitionBrokerEntry.getKey(), readOffset);
    
        }
    
        return topicAndPartitionLongMap;
    
      }
    
      /**
       * 得到所有的 TopicAndPartition
       *
       * @param brokerList
       * @param topics
       * @return topicAndPartitions
       */
      private Map<TopicAndPartition, Broker> findLeader(String brokerList, List<String> topics) {
        // get broker's url array
        String[] brokerUrlArray = getBorkerUrlFromBrokerList(brokerList);
        // get broker's port map
        Map<String, Integer> brokerPortMap = getPortFromBrokerList(brokerList);
    
        // create array list of TopicAndPartition
        Map<TopicAndPartition, Broker> topicAndPartitionBrokerMap = Maps.newHashMap();
    
        for (String broker : brokerUrlArray) {
    
          SimpleConsumer consumer = null;
          try {
            // new instance of simple Consumer
            consumer = new SimpleConsumer(broker, brokerPortMap.get(broker), TIMEOUT, BUFFERSIZE,
                "leaderLookup" + new Date().getTime());
    
            TopicMetadataRequest req = new TopicMetadataRequest(topics);
    
            TopicMetadataResponse resp = consumer.send(req);
    
            List<TopicMetadata> metaData = resp.topicsMetadata();
    
            for (TopicMetadata item : metaData) {
              for (PartitionMetadata part : item.partitionsMetadata()) {
                TopicAndPartition topicAndPartition =
                    new TopicAndPartition(item.topic(), part.partitionId());
                topicAndPartitionBrokerMap.put(topicAndPartition, part.leader());
              }
            }
          } catch (Exception e) {
            e.printStackTrace();
          } finally {
            if (consumer != null)
              consumer.close();
          }
        }
        return topicAndPartitionBrokerMap;
      }
    
      /**
       * get last offset
       * @param consumer
       * @param topicAndPartition
       * @param clientName
       * @return
       */
      private long getTopicAndPartitionLastOffset(SimpleConsumer consumer,
          TopicAndPartition topicAndPartition, String clientName) {
        Map<TopicAndPartition, PartitionOffsetRequestInfo> requestInfo =
            new HashMap<TopicAndPartition, PartitionOffsetRequestInfo>();
    
        requestInfo.put(topicAndPartition, new PartitionOffsetRequestInfo(
            kafka.api.OffsetRequest.LatestTime(), 1));
    
        OffsetRequest request = new OffsetRequest(
            requestInfo, kafka.api.OffsetRequest.CurrentVersion(),
            clientName);
    
        OffsetResponse response = consumer.getOffsetsBefore(request);
    
        if (response.hasError()) {
          System.out
              .println("Error fetching data Offset Data the Broker. Reason: "
                  + response.errorCode(topicAndPartition.topic(), topicAndPartition.partition()));
          return 0;
        }
        long[] offsets = response.offsets(topicAndPartition.topic(), topicAndPartition.partition());
        return offsets[0];
      }
      /**
       * 得到所有的broker url
       *
       * @param brokerlist
       * @return
       */
      private String[] getBorkerUrlFromBrokerList(String brokerlist) {
        String[] brokers = brokerlist.split(",");
        for (int i = 0; i < brokers.length; i++) {
          brokers[i] = brokers[i].split(":")[0];
        }
        return brokers;
      }
    
      /**
       * 得到broker url 与 其port 的映射关系
       *
       * @param brokerlist
       * @return
       */
      private Map<String, Integer> getPortFromBrokerList(String brokerlist) {
        Map<String, Integer> map = new HashMap<String, Integer>();
        String[] brokers = brokerlist.split(",");
        for (String item : brokers) {
          String[] itemArr = item.split(":");
          if (itemArr.length > 1) {
            map.put(itemArr[0], Integer.parseInt(itemArr[1]));
          }
        }
        return map;
      }
    
      public static void main(String[] args) {
        List<String> topics = Lists.newArrayList();
        topics.add("ys");
        topics.add("bugfix");
        Map<TopicAndPartition, Long> topicAndPartitionLongMap =
            KafkaOffsetTool.getInstance().getLastOffset("broker001:9092,broker002:9092", topics, "my.group.id");
    
        for (Map.Entry<TopicAndPartition, Long> entry : topicAndPartitionLongMap.entrySet()) {
         System.out.println(entry.getKey().topic() + "-"+ entry.getKey().partition() + ":" + entry.getValue());
        }
      }
    }
    
    矫正offset核心代码:
        /** 以下 矫正 offset */
        // 得到Topic/partition 的lastOffsets
        Map<TopicAndPartition, Long> topicAndPartitionLongMap =
            KafkaOffsetTool.getInstance().getLastOffset(kafkaParams.get("metadata.broker.list"),
                topicList, "my.group.id");
    
        // 遍历每个Topic.partition
        for (Map.Entry<TopicAndPartition, Long> topicAndPartitionLongEntry : fromOffsets.entrySet()) {
          // fromOffset > lastOffset时
          if (topicAndPartitionLongEntry.getValue() >
              topicAndPartitionLongMap.get(topicAndPartitionLongEntry.getKey())) {
             //矫正fromoffset为offset初始值0
            topicAndPartitionLongEntry.setValue(0L);
          }
        }
        /** 以上 矫正 offset */

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  • 原文地址:https://www.cnblogs.com/niutao/p/10547854.html
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