Spark'中的java.lang.InterruptedException；s rdd。收集方法

我在Spark的

rdd.collect（）

方法中得到了一个异常。堆栈跟踪是：

java.lang.Error: java.lang.InterruptedException
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1155)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
        at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.InterruptedException
        at java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedInterruptibly(AbstractQueuedSynchronizer.java:998)
        at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireSharedInterruptibly(AbstractQueuedSynchronizer.java:1304)
        at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:202)
        at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
        at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:153)
        at org.apache.spark.util.ThreadUtils$.awaitReady(ThreadUtils.scala:222)
        at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:633)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2027)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2048)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2067)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2092)
        at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:939)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
        at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
        at org.apache.spark.rdd.RDD.collect(RDD.scala:938)
        at org.apache.spark.api.java.JavaRDDLike$class.collect(JavaRDDLike.scala:361)
        at org.apache.spark.api.java.AbstractJavaRDDLike.collect(JavaRDDLike.scala:45)
        at com.cybernetix.kaafka.EnrichEventSparkConsumer.lambda$startEnrichEventConsumer$ef798c17$1(EnrichEventSparkConsumer.java:76)
        at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:272)
        at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:272)
        at org.apache.spark.streaming.dstream.DStream$$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)
        ... 2 more

我的代码是：

mapper = new ObjectMapper();
Collection<String> topics = Arrays.asList(sparkConfiguration.getEnrichEventTopic());
Map<String, Object> kafkaParams = new HashedMap();
kafkaParams.put("bootstrap.servers", sparkConfiguration.getBootStrapServers());
kafkaParams.put("key.deserializer", StringDeserializer.class);
kafkaParams.put("value.deserializer", StringDeserializer.class);
kafkaParams.put("group.id", "group1");
kafkaParams.put("auto.offset.reset", "latest");
kafkaParams.put("enable.auto.commit", true);

JavaInputDStream<ConsumerRecord<String, String>> enrichEventRDD =
    KafkaUtils.createDirectStream(javaStreamingContext, LocationStrategies.PreferConsistent(),
        ConsumerStrategies.<String, String>Subscribe(topics, kafkaParams));

JavaDStream<String> enrichEventDStream = enrichEventRDD.map((x) -> x.value());

JavaDStream<List<Map<String, Object>>> enrichDataModelDStream = enrichEventDStream.map(convertIntoMapList);

// JavaDStream<EnrichEventDataModel> enrichDataModelDStream = enrichEventDStream.map(convertIntoEnrichModel);

enrichDataModelDStream.foreachRDD(enrichDataModelRdd -> {
    if (enrichDataModelRdd.count() > 0) {
        List<List<Map<String, Object>>> enrichEventDataModelList = enrichDataModelRdd.collect();
         saveDataToElasticSearch(enrichEventDataModelList);

writeToLogsFile("EnrichEvent consumer SparkStreaming job started.", Constants.INFO);

mapper=newObjectMapper（）；
集合主题=Arrays.asList（sparkConfiguration.getEnrichEventTopic（））；
Map kafkaParams=new HashedMap（）；
kafkaParams.put（“bootstrap.servers”，sparkConfiguration.getbootstrapserver（））；
kafkaParams.put（“key.deserializer”，StringDeserializer.class）；
kafkaParams.put（“value.deserializer”，StringDeserializer.class）；
kafkaParams.put（“group.id”，“group1”）；
kafkaParams.put（“自动偏移重置”、“最新”）；
kafkaParams.put（“enable.auto.commit”，true）；
JavaInputDStream Enricheventdd=
KafkaUtils.createDirectStream（javaStreamingContext，LocationStrategies.PreferConsistent（），
订阅（主题，卡夫卡帕兰））；
JavaDStream enrichEventDStream=enricheventdd.map（（x）->x.value（））；
JavaDStream EnricDataModeldStream=enrichEventDStream.map（convertIntoMapList）；
//JavaDStream enrichDataModelDStream=EnrichEventStream.map（ConvertingOnEnrichModel）；
EnricDataModeldStream.foreachRDD（EnricDataModelRDD->{
如果（EnricDataModelRDD.count（）>0）{
List enrichEventDataModelList=enrichDataModelRdd.collect（）；
保存数据到LasticSearch（enrichEventDataModelList）；
writeToLogsFile（“EnrichEvent consumer SparkStreaming作业已启动。”，Constants.INFO）；