Hi! I am actually working to get some more docs out there, there is a lack right now, agreed.
Concerning your questions: (1) Batch programs basically recover from the data sources right now. Checkpointing as in the streaming case does not happen for batch programs. We have branches that materialize the intermediate streams and apply backtracking logic for batch programs, but they are not merged into the master at this point. (2) Streaming operators and user functions are long lived. They are started once and live to the end of the stream, or the machine failure. Greetings, Stephan On Thu, Jul 2, 2015 at 11:48 AM, tambunanw <if05...@gmail.com> wrote: > Hi All, > > I see that the way batch processing works in Flink is quite different with > Spark. It's all about using streaming engine in Flink. > > I have a couple of question > > 1. Is there any support on Checkpointing on batch processing also ? Or > that's only for streaming > > 2. I want to ask about operator lifecyle ? is that short live or long live > ? > Any docs where i can read about this more ? > > > Cheers > > > > -- > View this message in context: > http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Batch-Processing-as-Streaming-tp1909.html > Sent from the Apache Flink User Mailing List archive. mailing list archive > at Nabble.com. >
