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
