You are looking to perform an *argmax*, which you can do with a single
aggregation. Here is an example
<https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/1023043053387187/3170497669323442/2840265927289860/latest.html>
.
On Thu, Nov 3, 2016 at 4:53 AM, Rabin Banerjee <dev.rabin.baner...@gmail.com
> wrote:
> Hi All ,
>
> I want to do a dataframe operation to find the rows having the latest
> timestamp in each group using the below operation
>
> df.orderBy(desc("transaction_date")).groupBy("mobileno").agg(first("customername").as("customername"),first("service_type").as("service_type"),first("cust_addr").as("cust_abbr"))
> .select("customername","service_type","mobileno","cust_addr")
>
>
> *Spark Version :: 1.6.x*
>
> My Question is *"Will Spark guarantee the Order while doing the groupBy , if
> DF is ordered using OrderBy previously in Spark 1.6.x"??*
>
>
> *I referred a blog here ::
> **https://bzhangusc.wordpress.com/2015/05/28/groupby-on-dataframe-is-not-the-groupby-on-rdd/
>
> <https://bzhangusc.wordpress.com/2015/05/28/groupby-on-dataframe-is-not-the-groupby-on-rdd/>*
>
> *Which claims it will work except in Spark 1.5.1 and 1.5.2 .*
>
>
> *I need a bit elaboration of how internally spark handles it ? also is it
> more efficient than using a Window function ?*
>
>
> *Thanks in Advance ,*
>
> *Rabin Banerjee*
>
>
>
>
