Spark-Kafka kafka.common.OffsetOutOfRangeException

问题描述

国庆前启动spark streaming任务去消费了kafka，后来由于其他原因停止了，放假回来后，重启spark任务后，报kafka.common.OffsetOutOfRangeException，期初我以为是ZK重启造成的就换了个group.id正常了，今天看到一篇文章才知道真实的原因

kafka会定时清理日志

当我们的任务开始的时候，如果之前消费过某个topic，那么这个topic会在zk上设置offset,我们一般会去获取这个offset来继续从上次结束的地方继续消费，但是kafka定时清理日志的功能，比如定时一天一清理，那么如果你的offset是前天消费的offset，那么这个时候你再去消费，自然而然的你的offset肯定已经不在有效范围内，所以就报OffsetOutOfRangeException了

解决方法

也很简单，就是去判断一下zk中的offset是否小于topic最小的offset，如果小于的话，就把最小的offset设置到zk中

代码

def setOrUpdateOffsets(implicit topics: Set[String], kc: KafkaCluster): Unit = 

{
    topics.foreach(topic => 

{
      println("current topic:" + topic)
      val groupId = Config.kafkaConf.getOrElse("group.id", "")

 var hasConsumed = true
      val kafkaPartitionsE = kc.getPartitions(Set(topic)) 

if (kafkaPartitionsE.isLeft) throw new SparkException("get kafka partition failed:") 

 val kafkaPartitions = kafkaPartitionsE.right.get 

 val consumerOffsetsE = kc.getConsumerOffsets(groupId, kafkaPartitions)

if (consumerOffsetsE.isLeft) hasConsumed = false
      if (hasConsumed) { 

//如果有消费过，有两种可能，如果streaming程序执行的时候出现kafka.common.OffsetOutOfRangeException，

说明zk上保存的offsets已经过时了，即kafka的定时清理策略已经将包含该offsets的文件删除。 

//针对这种情况，只要判断一下zk上的consumerOffsets和leaderEarliestOffsets的大小，

如果consumerOffsets比leaderEarliestOffsets还小的话，说明是过时的offsets,这时把leaderEarliestOffsets更新为consumerOffsets

        val leaderEarliestOffsets = kc.getEarliestLeaderOffsets(kafkaPartitions).right.get
        println(leaderEarliestOffsets) 

 val consumerOffsets = consumerOffsetsE.right.get 

 val flag = consumerOffsets.forall {
          case (tp, n) => n < leaderEarliestOffsets(tp).offset
        } 

if (flag) {
          println("consumer group:" + groupId + " offsets已经过时，更新为leaderEarliestOffsets")
          v

al offsets = leaderEarliestOffsets.map {
            case (tp, offset) => (tp, offset.offset)
          } 

 kc.setConsumerOffsets(groupId, offsets)
        }
        else {
          println("consumer group:" + groupId + " offsets正常，无需更新")
        }
      }
      else { 

//如果没有被消费过，则从最新的offset开始消费。 

 val leaderLatestOffsets = kc.getLatestLeaderOffsets(kafkaPartitions).right.get
        println(leaderLatestOffsets)

 println("consumer group:" + groupId + " 还未消费过，更新为leaderLatestOffsets") 

 val offsets = leaderLatestOffsets.map {
          case (tp, offset) => (tp, offset.offset)
        }
        kc.setConsumerOffsets(groupId, offsets)
      }
    })
  }

错误：

18/02/19 12:31:39 ERROR Executor: Exception in task 0.0 in stage 3.0 (TID 39)
org.apache.kafka.clients.consumer.OffsetOutOfRangeException: Offsets out of range with no configured reset policy for partitions: {prensa4-0=744}
        at org.apache.kafka.clients.consumer.internals.Fetcher.parseFetchedData(Fetcher.java:588)
        at org.apache.kafka.clients.consumer.internals.Fetcher.fetchedRecords(Fetcher.java:354)
        at org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(KafkaConsumer.java:1000)
        at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:938)
        at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.poll(CachedKafkaConsumer.scala:99)
        at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.get(CachedKafkaConsumer.scala:70)
        at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:223)
        at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:189)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
        at scala.collection.Iterator$$anon$10.next(Iterator.scala:393)
        at scala.collection.Iterator$class.foreach(Iterator.scala:893)
        at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
        at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
        at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
        at scala.collection.AbstractIterator.to(Iterator.scala:1336)
        at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
        at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
        at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
        at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
        at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
        at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2069)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2069)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:108)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
        at java.lang.Thread.run(Thread.java:748)
18/02/19 12:31:39 ERROR TaskSetManager: Task 0 in stage 3.0 failed 1 times; aborting job
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 3.0 failed 1 times, most recent failure: Lost task 0.0 in stage 3.0 (TID 39, localhost, executor driver): org.apache.kafka.clients.consumer.OffsetOutOfRangeException: Offsets out of range with no configured reset policy for partitions: {prensa4-0=744}
        at org.apache.kafka.clients.consumer.internals.Fetcher.parseFetchedData(Fetcher.java:588)
        at org.apache.kafka.clients.consumer.internals.Fetcher.fetchedRecords(Fetcher.java:354)
        at org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(KafkaConsumer.java:1000)
        at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:938)
        at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.poll(CachedKafkaConsumer.scala:99)
        at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.get(CachedKafkaConsumer.scala:70)
        at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:223)
        at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:189)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
        at scala.collection.Iterator$$anon$10.next(Iterator.scala:393)
        at scala.collection.Iterator$class.foreach(Iterator.scala:893)
        at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
        at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
        at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
        at scala.collection.AbstractIterator.to(Iterator.scala:1336)
        at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
        at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
        at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
        at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
        at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
        at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2069)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2069)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:108)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
        at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
        at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1517)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1505)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1504)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1504)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
        at scala.Option.foreach(Option.scala:257)
        at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1732)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1687)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1676)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2050)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2069)
        at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1354)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
        at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
        at org.apache.spark.rdd.RDD.take(RDD.scala:1327)
        at org.apache.spark.rdd.RDD$$anonfun$isEmpty$1.apply$mcZ$sp(RDD.scala:1462)
        at org.apache.spark.rdd.RDD$$anonfun$isEmpty$1.apply(RDD.scala:1462)
        at org.apache.spark.rdd.RDD$$anonfun$isEmpty$1.apply(RDD.scala:1462)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
        at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
        at org.apache.spark.rdd.RDD.isEmpty(RDD.scala:1461)
        at itcl.adquisicionDatos$$anonfun$main$3.apply(adquisicionDatos-gonvarri.scala:212)
        at itcl.adquisicionDatos$$anonfun$main$3.apply(adquisicionDatos-gonvarri.scala:200)
        at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:628)
        at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:628)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
        at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
        at scala.util.Try$.apply(Try.scala:192)
        at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
        at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:257)
        at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257)
        at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257)
        at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
        at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:256)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
        at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.kafka.clients.consumer.OffsetOutOfRangeException: Offsets out of range with no configured reset policy for partitions: {prensa4-0=744}
        at org.apache.kafka.clients.consumer.internals.Fetcher.parseFetchedData(Fetcher.java:588)
        at org.apache.kafka.clients.consumer.internals.Fetcher.fetchedRecords(Fetcher.java:354)
        at org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(KafkaConsumer.java:1000)
        at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:938)
        at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.poll(CachedKafkaConsumer.scala:99)
        at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.get(CachedKafkaConsumer.scala:70)
        at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:223)
        at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:189)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
        at scala.collection.Iterator$$anon$10.next(Iterator.scala:393)
        at scala.collection.Iterator$class.foreach(Iterator.scala:893)
        at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
        at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
        at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
        at scala.collection.AbstractIterator.to(Iterator.scala:1336)
        at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
        at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
        at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
        at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
        at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
        at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2069)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2069)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:108)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
        ... 3 more：

解决方案

We had the same issue. For us the problem was:

1. master creates tasks like "read from that topic-partition form offset X to offset Y" and passes that tasks to executors.

2. executor receives tasks and start consuming data form topic-partition. At that time, due to topic configuration (time or size retention) offset X become unavailable. KafkaConsumer that is created for consuming messages doesn't have offset reset policy.

3. result is exception you see

solution: you need to tune

• your kafka topic config
• speed of your spark job (i.e. decrease batch time, increase number of executors).

两种解决方案供参考

https://issues.apache.org/jira/browse/SPARK-19680

下面这个链接解释比较详细：

https://blog.csdn.net/xueba207/article/details/51174818

代码解决方案：

https://blog.csdn.net/zyj_2012/article/details/78665709