Some guy's blog
I had a great time at DataStax Accelerate and got asked a lot of great questions about Cassandra and Spark. I’ll post some the most common ones and their answers here for posterity. —
One of the greatest features of Spark is the Dataframe API. It’s amazing and available in a variety of languages: R, Python, Java, Scala, and there is even work being done in .net! One of the drawbacks of RDD’s was that depending on the language you choose, your performance could be dramatically different (Scala and Java = Fast; Python = Slow) but in DataFrames every language will perform nearly identically (with some caveats.)
The reason that Dataframes provide such consistent and excellent performance is because they only describe what the application has to do at a very high level. This description is then, regardless of language, transformed into highly optimized Java code. This means that if we provide the same description of the problem it does not matter what language we use. The end result of our operation will yield the same optimized Java code!
The caveats are:
2.Initial paralleization of datasets can be different depending on the native language. For example if you parallelize a python collection it will need to be converted into Java objects before it can be turned into a DataFrame. Usually you will not be parallelizing a large data set so this shouldn’t be a huge perf difference, but the schema may end up being different based on the language. For example a Python numerical type doesn’t have a direct analogue to a Java type, so parallelizing a Java/Scala collection of numbers will probably result in “Integer” type in Spark but Python numbers will end up as “BigIntger/Long” type.