Like `RDD.map`, you can throw whatever exceptions and they will be propagated to the driver side and fail the Spark job.
On Mon, Apr 8, 2019 at 3:10 PM Andrew Melo <andrew.m...@gmail.com> wrote: > Hello, > > I'm developing a (java) DataSourceV2 to read a columnar fileformat > popular in a number of physical sciences (https://root.cern.ch/). (I > also understand that the API isn't fixed and subject to change). > > My question is -- what is the expected way to transmit exceptions from > the DataSource up to Spark? The DSV2 interface (unless I'm misreading > it) doesn't specify any caught exceptions that can be thrown in the > DS, so should I instead catch/rethrow any exceptions as uncaught > exceptions? If so, is there a recommended hierarchy to throw from? > > thanks! > Andrew > > --------------------------------------------------------------------- > To unsubscribe e-mail: dev-unsubscr...@spark.apache.org > >
