Would there be a way to chunk up/batch up the contents of the checkpointing directories as they're being processed by Spark Streaming? Is it mandatory to load the whole thing in one go?
On Mon, Aug 10, 2015 at 12:42 PM, Ted Yu <yuzhih...@gmail.com> wrote: > I wonder during recovery from a checkpoint whether we can estimate the > size of the checkpoint and compare with Runtime.getRuntime().freeMemory(). > > If the size of checkpoint is much bigger than free memory, log warning, etc > > Cheers > > On Mon, Aug 10, 2015 at 9:34 AM, Dmitry Goldenberg < > dgoldenberg...@gmail.com> wrote: > >> Thanks, Cody, will try that. Unfortunately due to a reinstall I don't >> have the original checkpointing directory :( Thanks for the clarification >> on spark.driver.memory, I'll keep testing (at 2g things seem OK for now). >> >> On Mon, Aug 10, 2015 at 12:10 PM, Cody Koeninger <c...@koeninger.org> >> wrote: >> >>> That looks like it's during recovery from a checkpoint, so it'd be >>> driver memory not executor memory. >>> >>> How big is the checkpoint directory that you're trying to restore from? >>> >>> On Mon, Aug 10, 2015 at 10:57 AM, Dmitry Goldenberg < >>> dgoldenberg...@gmail.com> wrote: >>> >>>> We're getting the below error. Tried increasing spark.executor.memory >>>> e.g. from 1g to 2g but the below error still happens. >>>> >>>> Any recommendations? Something to do with specifying -Xmx in the submit >>>> job scripts? >>>> >>>> Thanks. >>>> >>>> Exception in thread "main" java.lang.OutOfMemoryError: GC overhead >>>> limit exceeded >>>> at java.util.Arrays.copyOf(Arrays.java:3332) >>>> at >>>> java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:137) >>>> at >>>> java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:121) >>>> at >>>> java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:421) >>>> at java.lang.StringBuilder.append(StringBuilder.java:136) >>>> at java.lang.StackTraceElement.toString(StackTraceElement.java:173) >>>> at >>>> org.apache.spark.util.Utils$$anonfun$getCallSite$1.apply(Utils.scala:1212) >>>> at >>>> org.apache.spark.util.Utils$$anonfun$getCallSite$1.apply(Utils.scala:1190) >>>> at >>>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) >>>> at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) >>>> at org.apache.spark.util.Utils$.getCallSite(Utils.scala:1190) >>>> at >>>> org.apache.spark.SparkContext$$anonfun$getCallSite$2.apply(SparkContext.scala:1441) >>>> at >>>> org.apache.spark.SparkContext$$anonfun$getCallSite$2.apply(SparkContext.scala:1441) >>>> at scala.Option.getOrElse(Option.scala:120) >>>> at org.apache.spark.SparkContext.getCallSite(SparkContext.scala:1441) >>>> at org.apache.spark.rdd.RDD.<init>(RDD.scala:1365) >>>> at org.apache.spark.streaming.kafka.KafkaRDD.<init>(KafkaRDD.scala:46) >>>> at >>>> org.apache.spark.streaming.kafka.DirectKafkaInputDStream$DirectKafkaInputDStreamCheckpointData$$anonfun$restore$2.apply(DirectKafkaInputDStream.scala:155) >>>> at >>>> org.apache.spark.streaming.kafka.DirectKafkaInputDStream$DirectKafkaInputDStreamCheckpointData$$anonfun$restore$2.apply(DirectKafkaInputDStream.scala:153) >>>> at >>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >>>> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) >>>> at >>>> org.apache.spark.streaming.kafka.DirectKafkaInputDStream$DirectKafkaInputDStreamCheckpointData.restore(DirectKafkaInputDStream.scala:153) >>>> at >>>> org.apache.spark.streaming.dstream.DStream.restoreCheckpointData(DStream.scala:402) >>>> at >>>> org.apache.spark.streaming.dstream.DStream$$anonfun$restoreCheckpointData$2.apply(DStream.scala:403) >>>> at >>>> org.apache.spark.streaming.dstream.DStream$$anonfun$restoreCheckpointData$2.apply(DStream.scala:403) >>>> at scala.collection.immutable.List.foreach(List.scala:318) >>>> at >>>> org.apache.spark.streaming.dstream.DStream.restoreCheckpointData(DStream.scala:403) >>>> at >>>> org.apache.spark.streaming.dstream.DStream$$anonfun$restoreCheckpointData$2.apply(DStream.scala:403) >>>> at >>>> org.apache.spark.streaming.dstream.DStream$$anonfun$restoreCheckpointData$2.apply(DStream.scala:403) >>>> at scala.collection.immutable.List.foreach(List.scala:318) >>>> at >>>> org.apache.spark.streaming.dstream.DStream.restoreCheckpointData(DStream.scala:403) >>>> at >>>> org.apache.spark.streaming.DStreamGraph$$anonfun$restoreCheckpointData$2.apply(DStreamGraph.scala:149) >>>> >>>> >>>> >>>> >>> >> >
