Hi Jon, I am looking for an answer for a similar question in the doc now, so far no clue.
I would need to know what is spark behaviour in a situation like the example you provided, but taking into account also that there are multiple partitions/workers. I could imagine it's possible that different spark workers are not synchronized in terms of waiting for each other to progress to the next step/stage for the partitions of data they get assigned, while I believe in streaming they would wait for the current batch to complete before they start working on a new one. In the code I am working on, I need to make sure a particular step is completed (in all workers, for all partitions) before next transformation is applied. Would be great if someone could clarify or point to these issues in the doc! :-) -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Does-Spark-automatically-run-different-stages-concurrently-when-possible-tp21075p21227.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
