Yes, precisely! Also, for other folks who may read this, could reply back with the trusted conversion that worked for you (for a clear solution)?
TD On Mon, Oct 19, 2015 at 3:08 PM, Jason White <jason.wh...@shopify.com> wrote: > Ah, that makes sense then, thanks TD. > > The conversion from RDD -> DF involves a `.take(10)` in PySpark, even if > you provide the schema, so I was avoiding back-and-forth conversions. I’ll > see if I can create a ‘trusted’ conversion that doesn’t involve the `take`. > > -- > Jason > > On October 19, 2015 at 5:23:59 PM, Tathagata Das (t...@databricks.com) > wrote: > > RDD and DF are not compatible data types. So you cannot return a DF when > you have to return an RDD. What rather you can do is return the underlying > RDD of the dataframe by dataframe.rdd(). > > > On Fri, Oct 16, 2015 at 12:07 PM, Jason White <jason.wh...@shopify.com> > wrote: > >> Hi Ken, thanks for replying. >> >> Unless I'm misunderstanding something, I don't believe that's correct. >> Dstream.transform() accepts a single argument, func. func should be a >> function that accepts a single RDD, and returns a single RDD. That's what >> transform_to_df does, except the RDD it returns is a DF. >> >> I've used Dstream.transform() successfully in the past when transforming >> RDDs, so I don't think my problem is there. >> >> I haven't tried this in Scala yet, and all of the examples I've seen on >> the >> website seem to use foreach instead of transform. Does this approach work >> in >> Scala? >> >> >> >> >> -- >> View this message in context: >> http://apache-spark-user-list.1001560.n3.nabble.com/PySpark-Streaming-DataFrames-tp25095p25099.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 >> >> >
