KafkaConsumer 抛出KafkaConsumer is not safe for multi-threaded

KafkaConsumer 抛出KafkaConsumer is not safe for multi-threaded access异常

环境：spark-2.x kafka_0.10.x

异常：

java.util.ConcurrentModificationException: KafkaConsumer is not safe for multi-threaded access    at org.apache.kafka.clients.consumer.KafkaConsumer.acquire(KafkaConsumer.java:1431)    at org.apache.kafka.clients.consumer.KafkaConsumer.seek(KafkaConsumer.java:1132)    at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.seek(CachedKafkaConsumer.scala:95)    at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.get(CachedKafkaConsumer.scala:69)    at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:227)    at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:193)    at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)at org.apache.spark.storage.memory.MemoryStore.putIteratorAsBytes(MemoryStore.scala:364)at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1021)    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:996)    at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:936)    at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:996)    at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:700)    at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:285)    at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:105)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)    at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:105)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)    at org.apache.spark.scheduler.Task.run(Task.scala:99)    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)    at java.lang.Thread.run(Thread.java:745)

异常分析：

当kafka执行rebalance时，kafka可能抛出此异常。confluent存在两种worker：    1.负责数据读写的source／sink worker；    2.负责协调source/sink worker的herder worker；当rebalance发生时，herder会去主动close sink worker线程。如果sink worker正在操作就会抛出异常。当代码中的slideDuration比batchDuration大很多时也会抛出异常：java.util.ConcurrentModificationException: KafkaConsumer is not safe for multi-threaded access

解决思路：

1.对使用window操作的DStream在调用window之前先调用checkpoint方法，可以截断lineage，从而避免这个问题。2.修改slideDuration和batchDuration，使得两个的值很相近

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