Looks like workaround is to reduce *window length.* *Cheers*
On Mon, Aug 10, 2015 at 10:07 AM, Cody Koeninger <c...@koeninger.org> wrote: > You need to keep a certain number of rdds around for checkpointing, based > on e.g. the window size. Those would all need to be loaded at once. > > On Mon, Aug 10, 2015 at 11:49 AM, Dmitry Goldenberg < > dgoldenberg...@gmail.com> wrote: > >> 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) >>>>>> >>>>>> >>>>>> >>>>>> >>>>> >>>> >>> >> >
