Hey Gerard, Spark Streaming should just queue the processing and not delete the block data. There are reports of this error and I am still unable to reproduce the problem. One workaround you can try the configuration "spark.streaming.unpersist = false" . This stops Spark Streaming from cleaning up old blocks. See the spark configuration page for more details.
TD On Thu, Sep 4, 2014 at 6:33 AM, Gerard Maas <gerard.m...@gmail.com> wrote: > Hello Sparkers, > > I'm currently running load tests on a Spark Streaming job. When the task > duration increases beyond the batchDuration the job become unstable. In the > logs I see tasks failed with the following message: > > Job aborted due to stage failure: Task 266.0:1 failed 4 times, most recent > failure: Exception failure in TID 19929 on host dnode-0.hdfs.private: > java.lang.Exception: Could not compute split, block input-2-1409835930000 > not found org.apache.spark.rdd.BlockRDD.compute(BlockRDD.scala:51) > > I understand it's not healthy that the task execution duration is longer > than the batchDuration, but I guess we should be able to support peaks. > I'm wondering whether this is this spark streaming 'graceful degradation' > or is data being lost that that moment? What is the reason for the block > lost and what is the recommended approach to deal with this? > > Thanks in advance, > > Gerard. >
