Thanks, I'll check it out.
On Mon, Aug 28, 2017 at 10:22 PM Praneeth Gayam <praneeth.ga...@gmail.com>
wrote:
> You can create a UDF which will invoke your java lib
>
> def calculateExpense: UserDefinedFunction = udf((pexpense: String, cexpense:
> String) => new MyJava().calculateExpense(pexpense.toDouble,
> cexpense.toDouble))
>
>
>
>
>
> On Tue, Aug 29, 2017 at 6:53 AM, purna pradeep <purna2prad...@gmail.com>
> wrote:
>
>> I have data in a DataFrame with below columns
>>
>> 1)Fileformat is csv
>> 2)All below column datatypes are String
>>
>> employeeid,pexpense,cexpense
>>
>> Now I need to create a new DataFrame which has new column called
>> `expense`, which is calculated based on columns `pexpense`, `cexpense`.
>>
>> The tricky part is the calculation algorithm is not an **UDF** function
>> which I created, but it's an external function that needs to be imported
>> from a Java library which takes primitive types as arguments - in this case
>> `pexpense`, `cexpense` - to calculate the value required for new column.
>>
>> The external function signature
>>
>> public class MyJava
>>
>> {
>>
>> public Double calculateExpense(Double pexpense, Double cexpense) {
>> // calculation
>> }
>>
>> }
>>
>> So how can I invoke that external function to create a new calculated
>> column. Can I register that external function as UDF in my Spark
>> application?
>>
>> Stackoverflow reference
>>
>>
>> https://stackoverflow.com/questions/45928007/use-withcolumn-with-external-function
>>
>>
>>
>>
>>
>>
>
