Hello,
I was reading the spark docs about spark structured streaming, since we are
thinking about updating our code base that today uses Dstreams, hence spark
streaming. Also, one main reason for this change that we want to realize is
that reading headers in kafka messages is only supported in spark
structured streaming and not in Dstreams.
I was surprised to not see an obvious way to handle manually the offsets by
committing the offsets to kafka. In spark streaming we used to do it with
something similar to these lines of code:
stream.foreachRDD { rdd =>
val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
// some time later, after outputs have completed
stream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)}
And this works perfectly ! Especially, this works very nice in case of job
failure/restart... I am wondering how this can be achieved in spark
structured streaming ?
I read about checkpoints, and this reminds me the old way of doing things
in spark 1.5/kafka0.8 and is not perfect since we are not deciding when to
commit offsets by ourselves.
Did I miss anything ? What would be the best way of committing offsets to
kafka with spark structured streaming to the concerned consumer group ?
Best regards,
Ali Gouta.
