Hi Richard,
If I understand the question correctly it sounds like you could probably do
this using mapValues (I'm assuming that you want two pieces of information
out of all rows, the states as individual items, and the number of states
in the row)
val separatedInputStrings = input:RDD[(Int, String).mapValues{
val inputsString = "TX,NV,WY"
val stringList = inputString.split(",")
(stringList, stringList.size)
}
If you then wanted to find out how many state columns you should have in
your table you could use a normal reduce (with a filter beforehand to
reduce how much data you are shuffling)
val numColumns = separatedInputStrings.filter(_._2).reduce(math.max)
I hope this helps!
On Tue, Jan 19, 2016 at 8:05 AM Richard Siebeling <rsiebel...@gmail.com>
wrote:
> that's true and that's the way we're doing it now but then we're only
> using the first row to determine the number of splitted columns.
> It could be that in the second (or last) row there are 10 new columns and
> we'd like to know that too.
>
> Probably a reduceby operator can be used to do that, but I'm hoping that
> there is a better or another way,
>
> thanks,
> Richard
>
> On Tue, Jan 19, 2016 at 4:22 PM, Sabarish Sasidharan <
> sabarish.sasidha...@manthan.com> wrote:
>
>> The most efficient to determine the number of columns would be to do a
>> take(1) and split in the driver.
>>
>> Regards
>> Sab
>> On 19-Jan-2016 8:48 pm, "Richard Siebeling" <rsiebel...@gmail.com> wrote:
>>
>>> Hi,
>>>
>>> what is the most efficient way to split columns and know how many
>>> columns are created.
>>>
>>> Here is the current RDD
>>> -----------------
>>> ID STATE
>>> -----------------
>>> 1 TX, NY, FL
>>> 2 CA, OH
>>> -----------------
>>>
>>> This is the preferred output:
>>> -------------------------
>>> ID STATE_1 STATE_2 STATE_3
>>> -------------------------
>>> 1 TX NY FL
>>> 2 CA OH
>>> -------------------------
>>>
>>> With a separated with the new columns STATE_1, STATE_2, STATE_3
>>>
>>>
>>> It looks like the following output is feasible using a ReduceBy operator
>>> -------------------------
>>> ID STATE_1 STATE_2 STATE_3 NEW_COLUMNS
>>> -------------------------
>>> 1 TX NY FL STATE_1, STATE_2,
>>> STATE_3
>>> 2 CA OH STATE_1, STATE_2
>>> -------------------------
>>>
>>> Then in the reduce step, the distinct new columns can be calculated.
>>> Is it possible to get the second output where next to the RDD the
>>> new_columns are saved somewhere?
>>> Or is the required to use the second approach?
>>>
>>> thanks in advance,
>>> Richard
>>>
>>>
>
