Question

我正在使用单节点Kafka代理（0.10.2）和单节点zookeeper代理（3.4.9）。我有一个消费者服务器（单核和1.5 GB RAM ）。每当我运行一个包含5个或更多线程的进程时，我的消费者线程在抛出这些异常后就会被杀死

例外1

java.lang.OutOfMemoryError：Java堆空间在java.nio.HeapByteBuffer。（HeapByteBuffer.java:57） at java.nio.ByteBuffer.allocate（ByteBuffer.java:335）在org.apache.kafka.common.network.NetworkReceive.readFromReadableChannel（NetworkReceive.java:93）在org.apache.kafka.common.network.NetworkReceive.readFrom（NetworkReceive.java:71）在org.apache.kafka.common.network.KafkaChannel.receive（KafkaChannel.java:169）在org.apache.kafka.common.network.KafkaChannel.read（KafkaChannel.java:150）在org.apache.kafka.common.network.Selector.pollSelectionKeys（Selector.java:355）在org.apache.kafka.common.network.Selector.poll（Selector.java:303）在org.apache.kafka.clients.NetworkClient.poll（NetworkClient.java:349）在org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll（ConsumerNetworkClient.java:226）在org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.pollNoWakeup（ConsumerNetworkClient.java:263）在org.apache.kafka.clients.consumer.internals.AbstractCoordinator $ HeartbeatThread.run（AbstractCoordinator.java:887）

例外2

kafka-coordinator-heartbeat-thread中的未捕获异常TOPIC1： java.lang.OutOfMemoryError：直接缓冲内存 at java.nio.Bits.reserveMemory（Bits.java:693）在java.nio.DirectByteBuffer。（DirectByteBuffer.java:123） at java.nio.ByteBuffer.allocateDirect（ByteBuffer.java:311） at sun.nio.ch.Util.getTemporaryDirectBuffer（Util.java:241）在sun.nio.ch.IOUtil.read（IOUtil.java:195） at sun.nio.ch.SocketChannelImpl.read（SocketChannelImpl.java:380）在org.apache.kafka.common.network.PlaintextTransportLayer.read（PlaintextTransportLayer.java:110）在org.apache.kafka.common.network.NetworkReceive.readFromReadableChannel（NetworkReceive.java:97）在org.apache.kafka.common.network.NetworkReceive.readFrom（NetworkReceive.java:71）在org.apache.kafka.common.network.KafkaChannel.receive（KafkaChannel.java:169）在org.apache.kafka.common.network.KafkaChannel.read（KafkaChannel.java:150）在org.apache.kafka.common.network.Selector.pollSelectionKeys（Selector.java:355）在org.apache.kafka.common.network.Selector.poll（Selector.java:303）在org.apache.kafka.clients.NetworkClient.poll（NetworkClient.java:349）在org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll（ConsumerNetworkClient.java:226）在org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.pollNoWakeup（ConsumerNetworkClient.java:263）在org.apache.kafka.clients.consumer.internals.AbstractCoordinator $ HeartbeatThread.run（AbstractCoordinator.java:887）

我用Google搜索并使用了下面提到的JVM参数，但仍然发生了相同的异常

-XX：MaxDirectMemorySize =768米

-Xms512m

如何解决此问题？是否需要进行任何其他javm参数调整？

我的Kafka消费者代码是

import com.mongodb.DBObject
import org.apache.kafka.clients.consumer.ConsumerRebalanceListener
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.kafka.clients.consumer.ConsumerRecords
import org.apache.kafka.clients.consumer.KafkaConsumer
import org.apache.kafka.clients.consumer.OffsetAndMetadata
import org.apache.kafka.clients.consumer.OffsetCommitCallback
import org.apache.kafka.common.TopicPartition
import org.apache.kafka.common.errors.InterruptException
import org.apache.kafka.common.errors.WakeupException
import org.slf4j.Logger
import org.slf4j.LoggerFactory
import java.util.regex.Pattern

class KafkaPollingConsumer implements Runnable {
private static final Logger logger = LoggerFactory.getLogger(KafkaPollingConsumer.class)
private static final String TAG = "[KafkaPollingConsumer]"
private final KafkaConsumer<String, byte []> kafkaConsumer
private Map<TopicPartition,OffsetAndMetadata> currentOffsetsMap = new HashMap<>()
List topicNameList
Map kafkaTopicConfigMap = new HashMap<String,Object>()
Map kafkaTopicMessageListMap = new HashMap<String,List>()
Boolean isRebalancingTriggered = false
private final Long REBALANCING_SLEEP_TIME = 1000

public KafkaPollingConsumer(String serverType, String groupName, String topicNameRegex, Integer batchSize, Integer maxPollTime, Integer requestTime){
    logger.debug("{} [Constructor] [Enter] Thread Name {} serverType group Name TopicNameRegex",TAG,Thread.currentThread().getName(),serverType,groupName,topicNameRegex)
    logger.debug("Populating Property for kafak consumer")
    logger.debug("BatchSize {}",batchSize)
    Properties kafkaConsumerProperties = new Properties()
    kafkaConsumerProperties.put("group.id", groupName)
    kafkaConsumerProperties.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
    kafkaConsumerProperties.put("value.deserializer", "com.custom.kafkaconsumerv2.deserializer.CustomObjectDeserializer")
    switch(serverType){
        case KafkaTopicConfigEntity.KAFKA_NODE_TYPE_ENUM.Priority.toString() :
            kafkaConsumerProperties.put("bootstrap.servers",ConfigLoader.conf.kafkaServer.priority.kafkaNode)
            kafkaConsumerProperties.put("enable.auto.commit",ConfigLoader.conf.kafkaServer.priority.consumer.enable.auto.commit)
            kafkaConsumerProperties.put("auto.offset.reset",ConfigLoader.conf.kafkaServer.priority.consumer.auto.offset.reset)
            break
        case KafkaTopicConfigEntity.KAFKA_NODE_TYPE_ENUM.Bulk.toString() :
            kafkaConsumerProperties.put("bootstrap.servers",ConfigLoader.conf.kafkaServer.bulk.kafkaNode)
            kafkaConsumerProperties.put("enable.auto.commit",ConfigLoader.conf.kafkaServer.bulk.consumer.enable.auto.commit)
            kafkaConsumerProperties.put("auto.offset.reset",ConfigLoader.conf.kafkaServer.bulk.consumer.auto.offset.reset)
            kafkaConsumerProperties.put("max.poll.records",1)
            kafkaConsumerProperties.put("max.poll.interval.ms",600000)
            kafkaConsumerProperties.put("request.timeout.ms",600005)
            break
        default :
            throw "Invalid server type"
            break
    }
    logger.debug("{} [Constructor] KafkaConsumer Property Populated {}",properties.toString())
    kafkaConsumer = new KafkaConsumer<String, byte []>(kafkaConsumerProperties)
    topicNameList = topicNameRegex.split(Pattern.quote('|'))
    logger.debug("{} [Constructor] Kafkatopic List {}",topicNameList.toString())
    logger.debug("{} [Constructor] Exit",TAG)
}

private class HandleRebalance implements ConsumerRebalanceListener {
    public void onPartitionsAssigned(Collection<TopicPartition> partitions) {
        logger.error('{} In onPartitionAssigned setting isRebalancingTriggered to false',TAG)
        isRebalancingTriggered = false
    }

    public void onPartitionsRevoked(Collection<TopicPartition> partitions) {
        logger.error("{} In onPartitionsRevoked setting osRebalancingTriggered to true",TAG)
        isRebalancingTriggered = true
        publishAllKafkaTopicBatchMessages()
        commitOffset()

    }
}

private class AsyncCommitCallBack implements OffsetCommitCallback{

    @Override
    void onComplete(Map<TopicPartition, OffsetAndMetadata> map, Exception e) {

    }
}

@Override
void run() {
    logger.debug("{} Starting Thread ThreadName {}",TAG,Thread.currentThread().getName())
    populateKafkaConfigMap()
    initializeKafkaTopicMessageListMap()
    String topicName
    String consumerClassName
    String consumerMethodName
    Boolean isBatchJob
    Integer batchSize = 0
    final Thread mainThread = Thread.currentThread()
    Runtime.getRuntime().addShutdownHook(new Thread() {
        public void run() {
            logger.error("{},gracefully shutdowning thread {}",TAG,mainThread.getName())
            kafkaConsumer.wakeup()
            try {
                mainThread.join()
            } catch (InterruptedException exception) {
                logger.error("{} Error : {}",TAG,exception.getStackTrace().join("\n"))
            }
        }
    })
    kafkaConsumer.subscribe(topicNameList , new HandleRebalance())
    try{
        while(true){
            logger.debug("{} Starting Consumer with polling time in ms 100",TAG)
            ConsumerRecords kafkaRecords
            if(isRebalancingTriggered == false) {
                kafkaRecords = kafkaConsumer.poll(100)
            }
            else{
                logger.error("{} in rebalancing going to sleep",TAG)
                Thread.sleep(REBALANCING_SLEEP_TIME)
                continue
            }
            for(ConsumerRecord record: kafkaRecords){
                if(isRebalancingTriggered == true){
                    break
                }
                currentOffsetsMap.put(new TopicPartition(record.topic(), record.partition()),new OffsetAndMetadata(record.offset() +1))
                topicName = record.topic()
                DBObject kafkaTopicConfigDBObject = kafkaTopicConfigMap.get(topicName)
                consumerClassName = kafkaTopicConfigDBObject.get(KafkaTopicConfigEntity.CLASS_NAME_KEY)
                consumerMethodName = kafkaTopicConfigDBObject.get(KafkaTopicConfigEntity.METHOD_NAME_KEY)
                isBatchJob = kafkaTopicConfigDBObject.get(KafkaTopicConfigEntity.IS_BATCH_JOB_KEY)
                logger.debug("Details about Message")
                logger.debug("Thread {}",mainThread.getName())
                logger.debug("Topic {}",topicName)
                logger.debug("Partition {}",record.partition().toString())
                logger.debug("Offset {}",record.offset().toString())
                logger.debug("clasName {}",consumerClassName)
                logger.debug("methodName {}",consumerMethodName)
                logger.debug("isBatchJob {}",isBatchJob.toString())
                Object message = record.value()
                logger.debug("message {}",message.toString())
                if(isBatchJob == true){
                    prepareMessagesBatch(topicName,message)
                    //batchSize = Integer.parseInt(kafkaTopicConfigDBObject.get(KafkaTopicConfigEntity.BATCH_SIZE_KEY).toString())
                    //logger.debug("batchSize {}",batchSize.toString())
                }
                else{
                    publishMessageToNonBatchConsumer(consumerClassName,consumerMethodName,message)
                }
                //publishMessageToConsumers(consumerClassName,consumerMethodName,isBatchJob,batchSize,message,topicName)
                //try {
                //  kafkaConsumer.commitAsync(currentOffsetsMap,new AsyncCommitCallBack())
                logger.debug("{} Commiting Messages to Kafka",TAG)
                //}
                /*catch(Exception exception){
                    kafkaConsumer.commitSync(currentOffsetsMap)
                    currentOffsetsMap.clear()
                    logger.error("{} Error while commiting async so commiting in sync {}",TAG,exception.getStackTrace().join("\n"))
                }*/
            }
            commitOffset()
            publishAllKafkaTopicBatchMessages()
        }
    }
    catch(InterruptException exception){
        logger.error("{} In InterruptException",TAG)
        logger.error("{} In Exception exception message {}",TAG,exception.getMessage())
        logger.error("{} Exception {}",TAG,exception.getStackTrace().join("\n"))
    }
    catch (WakeupException exception) {
        logger.error("{} In WakeUp Exception",TAG)
        logger.error("{} In Exception exception message {}",TAG,exception.getMessage())
        logger.error("{} Exception {}",TAG,exception.getStackTrace().join("\n"))
    }
    catch(Exception exception){
        exception.getMessage()
        logger.error("{} In Exception",TAG)
        logger.error("{} In Exception exception message {}",TAG,exception.getMessage())
        logger.error("{} Exception {}",TAG,exception.getStackTrace().join("\n"))
    }
    finally {
        logger.error("{} In finally commiting remaining offset ",TAG)
        publishAllKafkaTopicBatchMessages()
        //kafkaConsumer.commitSync(currentOffsetsMap)
        kafkaConsumer.close()
        logger.error("{} Exiting Consumer",TAG)
    }
}

private void commitOffset(){
    logger.debug("{} [commitOffset] Enter")
    logger.debug("{} currentOffsetMap {}",currentOffsetsMap.toString())
    if(currentOffsetsMap.size() > 0) {
        kafkaConsumer.commitSync(currentOffsetsMap)
        currentOffsetsMap.clear()
    }
    logger.debug("{} [commitOffset] Exit")

}

private void publishMessageToConsumers(String consumerClassName,String consumerMethodName,Boolean isBatchJob,Integer batchSize,Object message, String topicName){
    logger.debug("{} [publishMessageToConsumer] Enter",TAG)
    if(isBatchJob == true){
        publishMessageToBatchConsumer(consumerClassName, consumerMethodName,batchSize, message, topicName)
    }
    else{
        publishMessageToNonBatchConsumer(consumerClassName, consumerMethodName, message)
    }
    logger.debug("{} [publishMessageToConsumer] Exit",TAG)
}

private void publishMessageToNonBatchConsumer(String consumerClassName, String consumerMethodName, message){
    logger.debug("{} [publishMessageToNonBatchConsumer] Enter",TAG)
    executeConsumerMethod(consumerClassName,consumerMethodName,message)
    logger.debug("{} [publishMessageToNonBatchConsumer] Exit",TAG)
}

private void publishMessageToBatchConsumer(String consumerClassName, String consumerMethodName, Integer batchSize, Object message, String topicName){
    logger.debug("{} [publishMessageToBatchConsumer] Enter",TAG)
    List consumerMessageList = kafkaTopicMessageListMap.get(topicName)
    consumerMessageList.add(message)
    if(consumerMessageList.size() == batchSize){
        logger.debug("{} [publishMessageToBatchConsumer] Pushing Messages In Batches",TAG)
        executeConsumerMethod(consumerClassName, consumerMethodName, consumerMessageList)
        consumerMessageList.clear()
    }
    kafkaTopicMessageListMap.put(topicName,consumerMessageList)
    logger.debug("{} [publishMessageToBatchConsumer] Exit",TAG)
}

private void populateKafkaConfigMap(){
    logger.debug("{} [populateKafkaConfigMap] Enter",TAG)
    KafkaTopicConfigDBService kafkaTopicConfigDBService = KafkaTopicConfigDBService.getInstance()
    topicNameList.each { topicName ->
        DBObject kafkaTopicDBObject = kafkaTopicConfigDBService.findByTopicName(topicName)
        kafkaTopicConfigMap.put(topicName,kafkaTopicDBObject)
    }
    logger.debug("{} [populateKafkaConfigMap] kafkaConfigMap {}",TAG,kafkaTopicConfigMap.toString())
    logger.debug("{} [populateKafkaConfigMap] Exit",TAG)
}

private void initializeKafkaTopicMessageListMap(){
    logger.debug("{} [initializeKafkaTopicMessageListMap] Enter",TAG)
    topicNameList.each { topicName ->
        kafkaTopicMessageListMap.put(topicName,[])
    }
    logger.debug("{} [populateKafkaConfigMap] kafkaTopicMessageListMap {}",TAG,kafkaTopicMessageListMap.toString())
    logger.debug("{} [initializeKafkaTopicMessageListMap] Exit",TAG)
}

private void executeConsumerMethod(String className, String methodName, def messages){
    try{
        logger.debug("{} [executeConsumerMethod] Enter",TAG)
        logger.debug("{} [executeConsumerMethod] className  {} methodName {} messages {}",TAG,className,methodName,messages.toString())
        Class.forName(className)."$methodName"(messages)
    } catch (Exception exception){
        logger.error("{} [{}] Error while executing method : {} of class: {} with params : {} - {}", TAG, Thread.currentThread().getName(), methodName,
                className, messages.toString(), exception.getStackTrace().join("\n"))
    }
    logger.debug("{} [executeConsumerMethod] Exit",TAG)
}

private void publishAllKafkaTopicBatchMessages(){
    logger.debug("{} [publishAllKafkaTopicBatchMessages] Enter",TAG)
    String consumerClassName = null
    String consumerMethodName = null
    kafkaTopicMessageListMap.each { topicName, messageList ->
        if (messageList != null && messageList.size() > 0) {
            DBObject kafkaTopicDBObject = kafkaTopicConfigMap.get(topicName)
            consumerClassName = kafkaTopicDBObject.get(KafkaTopicConfigEntity.CLASS_NAME_KEY)
            consumerMethodName = kafkaTopicDBObject.get(KafkaTopicConfigEntity.METHOD_NAME_KEY)
            logger.debug("{} Pushing message in topic {} className {} methodName {} ", TAG, topicName, consumerClassName, consumerMethodName)
            if (messageList != null && messageList.size() > 0) {
                executeConsumerMethod(consumerClassName, consumerMethodName, messageList)
                messageList.clear()
                kafkaTopicMessageListMap.put(topicName, messageList)

            }
        }
    }
    logger.debug("{} [publishAllKafkaTopicBatchMessages] Exit",TAG)
}

private void prepareMessagesBatch(String topicName,Object message){
    logger.debug("{} [prepareMessagesBatch] Enter",TAG)
    logger.debug("{} [prepareMessagesBatch] preparing batch for topic {}",TAG,topicName)
    logger.debug("{} [prepareMessagesBatch] preparting batch for message {}",TAG,message.toString())
    List consumerMessageList = kafkaTopicMessageListMap.get(topicName)
    consumerMessageList.add(message)
    kafkaTopicMessageListMap.put(topicName,consumerMessageList)

}

}

Answer 1

Kafka Consumers通过以下两个参数

处理数据积压

max.poll.interval.ms
使用消费者组管理时poll（）调用之间的最大延迟。这为消费者在获取更多记录之前可以闲置的时间量设置了上限。如果在此超时到期之前未调用poll（），则认为使用者失败，并且该组将重新平衡以便将分区重新分配给另一个成员。
默认值为300000。

max.poll.records
一次调用poll（）时返回的最大记录数。
默认值为500。

忽略根据要求设置上述两个参数可能导致轮询消费者可能无法利用可用资源处理的最大数据，从而导致OutOfMemory或有时无法提交消费者偏移。因此，始终建议使用 max.poll.records 和 max.poll.interval.ms 参数。

在您的代码中，案例 KafkaTopicConfigEntity.KAFKA_NODE_TYPE_ENUM.Priority.toString（）缺少这两个参数，这些参数可能是轮询期间OutOfMemory问题的原因。

Kafka使用者抛出java.lang.OutOfMemoryError：直接缓冲内存

1 个答案: