So you're still having a problem getting partitions or offsets from kafka when creating the stream. You can try each of those kafka operations individually (getPartitions / getLatestLeaderOffsets)
checkErrors should be dealing with an arraybuffer of throwables, not just a single one. Is that the only error you're seeing, or are there more? You can also modify it to call printStackTrace or whatever on each individual error, instead of only printing the message. On Thu, Sep 24, 2015 at 5:00 PM, Sourabh Chandak <sourabh3...@gmail.com> wrote: > I was able to get pass this issue. I was pointing the SSL port whereas > SimpleConsumer should point to the PLAINTEXT port. But after fixing that I > am getting the following error: > > Exception in thread "main" org.apache.spark.SparkException: > java.nio.BufferUnderflowException > at > org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$checkErrors$1.apply(KafkaCluster.scala:366) > at > org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$checkErrors$1.apply(KafkaCluster.scala:366) > at scala.util.Either.fold(Either.scala:97) > at > org.apache.spark.streaming.kafka.KafkaCluster$.checkErrors(KafkaCluster.scala:365) > at > org.apache.spark.streaming.kafka.KafkaUtils$.createDirectStream(KafkaUtils.scala:309) > at > org.ofe.weve.test.KafkaTest$.setupProcessingContext(KafkaTest.scala:36) > at org.ofe.weve.test.KafkaTest$.main(KafkaTest.scala:59) > at org.ofe.weve.test.KafkaTest.main(KafkaTest.scala) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:497) > at > org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:358) > at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75) > at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) > > Thanks, > Sourabh > > On Thu, Sep 24, 2015 at 2:04 PM, Cody Koeninger <c...@koeninger.org> > wrote: > >> That looks like the OOM is in the driver, when getting partition metadata >> to create the direct stream. In that case, executor memory allocation >> doesn't matter. >> >> Allocate more driver memory, or put a profiler on it to see what's taking >> up heap. >> >> >> >> On Thu, Sep 24, 2015 at 3:51 PM, Sourabh Chandak <sourabh3...@gmail.com> >> wrote: >> >>> Adding Cody and Sriharsha >>> >>> On Thu, Sep 24, 2015 at 1:25 PM, Sourabh Chandak <sourabh3...@gmail.com> >>> wrote: >>> >>>> Hi, >>>> >>>> I have ported receiver less spark streaming for kafka to Spark 1.2 and >>>> am trying to run a spark streaming job to consume data form my broker, but >>>> I am getting the following error: >>>> >>>> 15/09/24 20:17:45 ERROR BoundedByteBufferReceive: OOME with size >>>> 352518400 >>>> java.lang.OutOfMemoryError: Java heap space >>>> at java.nio.HeapByteBuffer.<init>(HeapByteBuffer.java:57) >>>> at java.nio.ByteBuffer.allocate(ByteBuffer.java:335) >>>> at >>>> kafka.network.BoundedByteBufferReceive.byteBufferAllocate(BoundedByteBufferReceive.scala:80) >>>> at >>>> kafka.network.BoundedByteBufferReceive.readFrom(BoundedByteBufferReceive.scala:63) >>>> at >>>> kafka.network.Receive$class.readCompletely(Transmission.scala:56) >>>> at >>>> kafka.network.BoundedByteBufferReceive.readCompletely(BoundedByteBufferReceive.scala:29) >>>> at >>>> kafka.network.BlockingChannel.receive(BlockingChannel.scala:111) >>>> at >>>> kafka.consumer.SimpleConsumer.liftedTree1$1(SimpleConsumer.scala:83) >>>> at >>>> kafka.consumer.SimpleConsumer.kafka$consumer$SimpleConsumer$$sendRequest(SimpleConsumer.scala:80) >>>> at kafka.consumer.SimpleConsumer.send(SimpleConsumer.scala:103) >>>> at >>>> org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$getPartitionMetadata$1.apply(KafkaCluster.scala:126) >>>> at >>>> org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$getPartitionMetadata$1.apply(KafkaCluster.scala:125) >>>> at >>>> org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$org$apache$spark$streaming$kafka$KafkaCluster$$withBrokers$1.apply(KafkaCluster.scala:346) >>>> at >>>> org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$org$apache$spark$streaming$kafka$KafkaCluster$$withBrokers$1.apply(KafkaCluster.scala:342) >>>> at >>>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) >>>> at >>>> scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:34) >>>> at org.apache.spark.streaming.kafka.KafkaCluster.org >>>> $apache$spark$streaming$kafka$KafkaCluster$$withBrokers(KafkaCluster.scala:342) >>>> at >>>> org.apache.spark.streaming.kafka.KafkaCluster.getPartitionMetadata(KafkaCluster.scala:125) >>>> at >>>> org.apache.spark.streaming.kafka.KafkaCluster.getPartitions(KafkaCluster.scala:112) >>>> at >>>> org.apache.spark.streaming.kafka.KafkaUtils$.createDirectStream(KafkaUtils.scala:296) >>>> at >>>> org.ofe.weve.test.KafkaTest$.setupProcessingContext(KafkaTest.scala:35) >>>> at org.ofe.weve.test.KafkaTest$.main(KafkaTest.scala:58) >>>> at org.ofe.weve.test.KafkaTest.main(KafkaTest.scala) >>>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >>>> at >>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) >>>> at >>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>>> at java.lang.reflect.Method.invoke(Method.java:497) >>>> at >>>> org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:358) >>>> at >>>> org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75) >>>> at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) >>>> >>>> >>>> >>>> I have tried allocating 100G of memory with 1 executor but it is still >>>> failing. >>>> >>>> Spark version: 1.2.2 >>>> Kafka version ported: 0.8.2 >>>> Kafka server version: trunk version with SSL enabled >>>> >>>> Can someone please help me debug this. >>>> >>>> Thanks, >>>> Sourabh >>>> >>> >>> >> >
