While I have a preference for Scala ( not surprising as a Typesafe person), the DataFrame API gives feature and performance parity for Python. The RDD API gives feature parity.
So, use what makes you most successful for other reasons ;) Sent from my rotary phone. > On Oct 6, 2015, at 4:14 PM, dant <dan.tr...@gmail.com> wrote: > > Hi, > I'm hearing a common theme running that I should only do serious programming > in Scala on Spark (1.5.1). Real power users use Scala. It is said that > Python is great for analytics but in the end the code should be written to > Scala to finalise. There are a number of reasons I'm hearing: > > 1. Spark is written in Scala so will always be faster than any other > language implementation on top of it. > 2. Spark releases always favour more features being visible and enabled for > Scala API than Python API. > > Are there any truth's to the above? I'm a little sceptical. > > Thanks > Dan > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Does-feature-parity-exist-between-Scala-and-Python-on-Spark-tp24961.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 > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
