Hi 2 cents
1. It should not be true anymore if data frames are used. The reason is regardless of the language DF uses same optimization engine behind the scene. 2. This is generally true in the sense Python APIs are typically little behind of scala/java ones. Best Ayan On Wed, Oct 7, 2015 at 9:15 AM, 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. > > Apologies for the duplication, my previous message was held up due to > subscription issue. Reposting now. > > Thanks > Dan > > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Does-feature-parity-exist-between-Spark-and-PySpark-tp24963.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 > > -- Best Regards, Ayan Guha
