Apache spark 使用java.lang.AbstractMethodError在cloudera上执行spark驱动失败

下面是我的POM文件。我正在用aws动觉写一篇火花流

<dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming_2.10</artifactId>
        <version>1.6.0</version>
    </dependency>

    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.10</artifactId>
        <version>1.6.0</version>
    </dependency>
     <dependency>
        <groupId>com.amazonaws</groupId>
        <artifactId>amazon-kinesis-client</artifactId>
        <version>1.6.1</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming-kinesis-asl_2.10</artifactId>
        <version>1.6.0</version>
    </dependency>

org.apache.spark
spark-2.10
1.6.0
org.apache.spark
spark-core_2.10
1.6.0
亚马逊网站
亚马逊运动客户端
1.6.1
org.apache.spark
spark-streaming-kinesis-asl_2.10
1.6.0

在Cloudera 5.10上运行spark of spark程序期间，我面临以下异常

17/04/27 05:34:04 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 58.0 (TID 179, hadoop1.local, executor 5): java.lang.AbstractMethodError
at org.apache.spark.Logging$class.log(Logging.scala:50)
at org.apache.spark.streaming.kinesis.KinesisCheckpointer.log(KinesisCheckpointer.scala:39)
at org.apache.spark.Logging$class.logDebug(Logging.scala:62)
at org.apache.spark.streaming.kinesis.KinesisCheckpointer.logDebug(KinesisCheckpointer.scala:39)
at org.apache.spark.streaming.kinesis.KinesisCheckpointer.startCheckpointerThread(KinesisCheckpointer.scala:119)
at org.apache.spark.streaming.kinesis.KinesisCheckpointer.<init>(KinesisCheckpointer.scala:50)
at org.apache.spark.streaming.kinesis.KinesisReceiver.onStart(KinesisReceiver.scala:149)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.startReceiver(ReceiverSupervisor.scala:148)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.start(ReceiverSupervisor.scala:130)
at org.apache.spark.streaming.scheduler.ReceiverTracker$ReceiverTrackerEndpoint$$anonfun$9.apply(ReceiverTracker.scala:575)
at org.apache.spark.streaming.scheduler.ReceiverTracker$ReceiverTrackerEndpoint$$anonfun$9.apply(ReceiverTracker.scala:565)
at org.apache.spark.SparkContext$$anonfun$38.apply(SparkContext.scala:2000)
at org.apache.spark.SparkContext$$anonfun$38.apply(SparkContext.scala:2000)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:242)
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)

17/04/27 05:34:04警告调度程序.TaskSetManager:在58.0阶段丢失任务0.0（TID 179，hadoop1.local，executor 5）：java.lang.AbstractMethodError
位于org.apache.spark.Logging$class.log（Logging.scala:50）
在org.apache.spark.streaming.kinesis.kinescheckpointer.log（kinescheckpointer.scala:39）上
位于org.apache.spark.Logging$class.logDebug（Logging.scala:62）
位于org.apache.spark.streaming.kinesis.kinescheckpointer.logDebug（kinescheckpointer.scala:39）
位于org.apache.spark.streaming.kinesis.kinescheckpointer.startCheckpointerThread（kinescheckpointer.scala:119）
在org.apache.spark.streaming.kinesis.KinesisCheckpointer上（kinescheckpointer.scala:50）
位于org.apache.spark.streaming.kinesis.kinesReceiver.onStart（kinesReceiver.scala:149）
位于org.apache.spark.streaming.receiver.ReceiverSupervisor.startReceiver（ReceiverSupervisor.scala:148）
位于org.apache.spark.streaming.receiver.ReceiverSupervisor.start（ReceiverSupervisor.scala:130）
在org.apache.spark.streaming.scheduler.ReceiverTracker$receivertrackerdpoint$$anonfun$9.apply（ReceiverTracker.scala:575）
在org.apache.spark.streaming.scheduler.ReceiverTracker$receivertrackerdpoint$$anonfun$9.apply（ReceiverTracker.scala:565）
位于org.apache.spark.SparkContext$$anonfun$38.apply（SparkContext.scala:2000）
位于org.apache.spark.SparkContext$$anonfun$38.apply（SparkContext.scala:2000）
位于org.apache.spark.scheduler.ResultTask.runTask（ResultTask.scala:66）
位于org.apache.spark.scheduler.Task.run（Task.scala:89）
位于org.apache.spark.executor.executor$TaskRunner.run（executor.scala:242）
位于java.util.concurrent.ThreadPoolExecutor.runWorker（ThreadPoolExecutor.java:1142）
位于java.util.concurrent.ThreadPoolExecutor$Worker.run（ThreadPoolExecutor.java:617）
运行（Thread.java:745）

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这在EMR4.4上运行得非常好，但是CDH失败了。任何建议

根本问题似乎是使用org.apache.spark.Logging：

注意：不要在Spark之外使用该等级。 它是一个内部实用程序。 这可能会在将来的版本中更改或删除

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如中所述，这在2.0.0中得到了修复。

似乎也存在类似的问题。然而，这有助于卡桑德拉联系，而不是运动