Sorry but I didn't fully understand the grouping. This line: >> The group must only take the closest previous trigger. The first one hence shows alone.
Can you please explain further? On Wed, Apr 29, 2015 at 4:42 PM, bipin <bipin....@gmail.com> wrote: > Hi, I have a ddf with schema (CustomerID, SupplierID, ProductID, Event, > CreatedOn), the first 3 are Long ints and event can only be 1,2,3 and > CreatedOn is a timestamp. How can I make a group triplet/doublet/singlet > out > of them such that I can infer that Customer registered event from 1to 2 and > if present to 3 timewise and preserving the number of entries. For e.g. > > Before processing: > 10001, 132, 2002, 1, 2012-11-23 > 10001, 132, 2002, 1, 2012-11-24 > 10031, 102, 223, 2, 2012-11-24 > 10001, 132, 2002, 2, 2012-11-25 > 10001, 132, 2002, 3, 2012-11-26 > (total 5 rows) > > After processing: > 10001, 132, 2002, 2012-11-23, "1" > 10031, 102, 223, 2012-11-24, "2" > 10001, 132, 2002, 2012-11-24, "1,2,3" > (total 5 in last field - comma separated!) > > The group must only take the closest previous trigger. The first one hence > shows alone. Can this be done using spark sql ? If it needs to processed in > functionally in scala, how to do this. I can't wrap my head around this. > Can > anyone help. > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/How-to-group-multiple-row-data-tp22701.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 > >
