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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: write and call UDF in spark dataframe
Date Thu, 21 Jul 2016 03:53:05 GMT
something similar

def ChangeToDate (word : String) : Date = {
  //return
TO_DATE(FROM_UNIXTIME(UNIX_TIMESTAMP(word,"dd/MM/yyyy"),"yyyy-MM-dd"))
  val d1 = Date.valueOf(ReverseDate(word))
  return d1
}
sqlContext.udf.register("ChangeToDate", ChangeToDate(_:String))

Dr Mich Talebzadeh



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On 21 July 2016 at 03:53, Divya Gehlot <divya.htconex@gmail.com> wrote:

> Hi ,
> To be very specific I am looking for UDFs syntax for example which takes
> String as parameter and returns integer .. how do we define the return type
> .
>
>
> Thanks,
>
> Divya
>
> On 21 July 2016 at 00:24, Andy Davidson <Andy@santacruzintegration.com>
> wrote:
>
>> Hi Divya
>>
>> In general you will get better performance if you can minimize your use
>> of UDFs. Spark 2.0/ tungsten does a lot of code generation. It will have to
>> treat your UDF as a block box.
>>
>> Andy
>>
>> From: Rishabh Bhardwaj <rbnext29@gmail.com>
>> Date: Wednesday, July 20, 2016 at 4:22 AM
>> To: Rabin Banerjee <dev.rabin.banerjee@gmail.com>
>> Cc: Divya Gehlot <divya.htconex@gmail.com>, "user @spark" <
>> user@spark.apache.org>
>> Subject: Re: write and call UDF in spark dataframe
>>
>> Hi Divya,
>>
>> There is already "from_unixtime" exists in
>> org.apache.spark.sql.frunctions,
>> Rabin has used that in the sql query,if you want to use it in
>> dataframe DSL you can try like this,
>>
>> val new_df = df.select(from_unixtime($"time").as("newtime"))
>>
>>
>> Thanks,
>> Rishabh.
>>
>> On Wed, Jul 20, 2016 at 4:21 PM, Rabin Banerjee <
>> dev.rabin.banerjee@gmail.com> wrote:
>>
>>> Hi Divya ,
>>>
>>> Try,
>>>
>>> val df = sqlContext.sql("select from_unixtime(ts,'YYYY-MM-dd') as `ts` from mr")
>>>
>>> Regards,
>>> Rabin
>>>
>>> On Wed, Jul 20, 2016 at 12:44 PM, Divya Gehlot <divya.htconex@gmail.com>
>>> wrote:
>>>
>>>> Hi,
>>>> Could somebody share example of writing and calling udf which converts
>>>> unix tme stamp to date tiime .
>>>>
>>>>
>>>> Thanks,
>>>> Divya
>>>>
>>>
>>>
>>
>

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