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From Ajay Chander <itsche...@gmail.com>
Subject Re: UseCase_Design_Help
Date Wed, 05 Oct 2016 14:44:40 GMT
Hi Ayan,

My Schema for DF2 is fixed but it has around 420 columns (70 Animal type
columns and 350 other columns).

Thanks,
Ajay

On Wed, Oct 5, 2016 at 10:37 AM, ayan guha <guha.ayan@gmail.com> wrote:

> Is your schema for df2 is fixed? ie do you have 70 category columns?
>
> On Thu, Oct 6, 2016 at 12:50 AM, Daniel Siegmann <
> dsiegmann@securityscorecard.io> wrote:
>
>> I think it's fine to read animal types locally because there are only 70
>> of them. It's just that you want to execute the Spark actions in parallel.
>> The easiest way to do that is to have only a single action.
>>
>> Instead of grabbing the result right away, I would just add a column for
>> the animal type and union the datasets for the animal types. Something like
>> this (not sure if the syntax is correct):
>>
>> val animalCounts: DataFrame = animalTypes.map { anmtyp =>
>>     sqlContext.sql("select lit("+anmtyp+") as animal_type,
>> count(distinct("+anmtyp+")) from TEST1 ")
>> }.reduce(_.union(_))
>>
>> animalCounts.foreach( /* print the output */ )
>>
>> On Wed, Oct 5, 2016 at 12:42 AM, Daniel <daniel.tizon@gmail.com> wrote:
>>
>>> First of all, if you want to read a txt file in Spark, you should use
>>> sc.textFile, because you are using "Source.fromFile", so you are reading it
>>> with Scala standard api, so it will be read sequentially.
>>>
>>> Furthermore you are going to need create a schema if you want to use
>>> dataframes.
>>>
>>> El 5/10/2016 1:53, "Ajay Chander" <itschevva@gmail.com> escribió:
>>>
>>>> Right now, I am doing it like below,
>>>>
>>>> import scala.io.Source
>>>>
>>>> val animalsFile = "/home/ajay/dataset/animal_types.txt"
>>>> val animalTypes = Source.fromFile(animalsFile).getLines.toArray
>>>>
>>>> for ( anmtyp <- animalTypes ) {
>>>>       val distinctAnmTypCount = sqlContext.sql("select
>>>> count(distinct("+anmtyp+")) from TEST1 ")
>>>>       println("Calculating Metrics for Animal Type: "+anmtyp)
>>>>       if( distinctAnmTypCount.head().getAs[Long](0) <= 10 ){
>>>>         println("Animal Type: "+anmtyp+" has <= 10 distinct values")
>>>>       } else {
>>>>         println("Animal Type: "+anmtyp+" has > 10 distinct values")
>>>>       }
>>>>     }
>>>>
>>>> But the problem is it is running sequentially.
>>>>
>>>> Any inputs are appreciated. Thank you.
>>>>
>>>>
>>>> Regards,
>>>> Ajay
>>>>
>>>>
>>>> On Tue, Oct 4, 2016 at 7:44 PM, Ajay Chander <itschevva@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi Everyone,
>>>>>
>>>>> I have a use-case where I have two Dataframes like below,
>>>>>
>>>>> 1) First Dataframe(DF1) contains,
>>>>>
>>>>> *    ANIMALS    *
>>>>> Mammals
>>>>> Birds
>>>>> Fish
>>>>> Reptiles
>>>>> Amphibians
>>>>>
>>>>> 2) Second Dataframe(DF2) contains,
>>>>>
>>>>> *    ID, Mammals, Birds, Fish, Reptiles, Amphibians    *
>>>>> 1,      Dogs,      Eagle,      Goldfish,      NULL,      Frog
>>>>> 2,      Cats,      Peacock,      Guppy,     Turtle,      Salamander
>>>>> 3,      Dolphins,      Eagle,      Zander,      NULL,      Frog
>>>>> 4,      Whales,      Parrot,      Guppy,      Snake,      Frog
>>>>> 5,      Horses,      Owl,      Guppy,      Snake,      Frog
>>>>> 6,      Dolphins,      Kingfisher,      Zander,      Turtle,      Frog
>>>>> 7,      Dogs,      Sparrow,      Goldfish,      NULL,      Salamander
>>>>>
>>>>> Now I want to take each row from DF1 and find out its distinct count
>>>>> in DF2. Example, pick Mammals from DF1 then find out
>>>>> count(distinct(Mammals)) from DF2 i.e. 5
>>>>>
>>>>> DF1 has 70 distinct rows/Animal types
>>>>> DF2 has some million rows
>>>>>
>>>>> Whats the best way to achieve this efficiently using parallelism ?
>>>>>
>>>>> Any inputs are helpful. Thank you.
>>>>>
>>>>> Regards,
>>>>> Ajay
>>>>>
>>>>>
>>>>
>>
>
>
> --
> Best Regards,
> Ayan Guha
>

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