Is that happening only at startup, or during processing? If that's happening during normal operation of the stream, you don't have enough resources to process the stream in time.
There's not a clean way to deal with that situation, because it's a violation of preconditions. If you want to modify the code to do what makes sense for you, start looking at handleFetchErr in KafkaRDD.scala Recompiling that package isn't a big deal, because it's not a part of the core spark deployment, so you'll only have to change your job, not the deployed version of spark. On Fri, Mar 18, 2016 at 6:16 AM, Ramkumar Venkataraman <ram.the.m...@gmail.com> wrote: > I am using Spark streaming and reading data from Kafka using > KafkaUtils.createDirectStream. I have the "auto.offset.reset" set to > smallest. > > But in some Kafka partitions, I get kafka.common.OffsetOutOfRangeException > and my spark job crashes. > > I want to understand if there is a graceful way to handle this failure and > not kill the job. I want to keep ignoring these exceptions, as some other > partitions are fine and I am okay with data loss. > > Is there any way to handle this and not have my spark job crash? I have no > option of increasing the kafka retention period. > > I tried to have the DStream returned by createDirectStream() wrapped in a > Try construct, but since the exception happens in the executor, the Try > construct didn't take effect. Do you have any ideas of how to handle this? > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/How-to-gracefully-handle-Kafka-OffsetOutOfRangeException-tp26534.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
