Hi Jacob.

My understanding of Dataset is that it is basically an RDD with some optimization gone into it. RDD is meant to deal with unstructured data?

Now DataFrame is the tabular format of RDD designed for tabular work, csv, SQL stuff etc.

When you mention DataFrame is just an alias for Dataset[Row] does that mean  that it converts an RDD to DataSet thus producing a tabular format?


Dr Mich Talebzadeh


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On 1 September 2016 at 22:49, Jakob Odersky <jakob@odersky.com> wrote:
> However, what really worries me is not having Dataset APIs at all in Python. I think thats a deal breaker.

What is the functionality you are missing? In Spark 2.0 a DataFrame is just an alias for Dataset[Row] ("type DataFrame = Dataset[Row]" in core/.../o/a/s/sql/package.scala).
Since python is dynamically typed, you wouldn't really gain anything by using Datasets anyway.

On Thu, Sep 1, 2016 at 2:20 PM, ayan guha <guha.ayan@gmail.com> wrote:
Thanks All for your replies.

Feature Parity: 

MLLib, RDD and dataframes features are totally comparable. Streaming is now at par in functionality too, I believe. However, what really worries me is not having Dataset APIs at all in Python. I think thats a deal breaker. 

I do  get this bit when RDDs are involved, but not when Data frame is the only construct I am operating on.  Dataframe supposed to be language-agnostic in terms of performance.  So why people think python is slower? is it because of using UDF? Any other reason?

Is there any kind of benchmarking/stats around Python UDF vs Scala UDF comparison? like the one out there  b/w RDDs.

@Kant:  I am not comparing ANY applications. I am comparing SPARK applications only. I would be glad to hear your opinion on why pyspark applications will not work, if you have any benchmarks please share if possible. 

On Fri, Sep 2, 2016 at 12:57 AM, kant kodali <kanth909@gmail.com> wrote:
c'mon man this is no Brainer..Dynamic Typed Languages for Large Code Bases or Large Scale Distributed Systems makes absolutely no sense. I can write a 10 page essay on why that wouldn't work so great. you might be wondering why would spark have it then? well probably because its ease of use for ML (that would be my best guess). 

On Wed, Aug 31, 2016 11:45 PM, AssafMendelson assaf.mendelson@rsa.com wrote:

I believe this would greatly depend on your use case and your familiarity with the languages.


In general, scala would have a much better performance than python and not all interfaces are available in python.

That said, if you are planning to use dataframes without any UDF then the performance hit is practically nonexistent.

Even if you need UDF, it is possible to write those in scala and wrap them for python and still get away without the performance hit.

Python does not have interfaces for UDAFs.


I believe that if you have large structured data and do not generally need UDF/UDAF you can certainly work in python without losing too much.



From: ayan guha [mailto:[hidden email]]
Sent: Thursday, September 01, 2016 5:03 AM
To: user
Subject: Scala Vs Python


Hi Users


Thought to ask (again and again) the question: While I am building any production application, should I use Scala or Python? 


I have read many if not most articles but all seems pre-Spark 2. Anything changed with Spark 2? Either pro-scala way or pro-python way? 


I am thinking performance, feature parity and future direction, not so much in terms of skillset or ease of use. 


Or, if you think it is a moot point, please say so as well. 


Any real life example, production experience, anecdotes, personal taste, profanity all are welcome :)



Best Regards,
Ayan Guha

View this message in context: RE: Scala Vs Python
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Best Regards,
Ayan Guha