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From Bill Schwanitz <bil...@bilsch.org>
Subject Re: question on transforms for spark 2.0 dataset
Date Wed, 01 Mar 2017 18:28:16 GMT
Subhash,

Yea that did the trick thanks!

On Wed, Mar 1, 2017 at 12:20 PM, Subhash Sriram <subhash.sriram@gmail.com>
wrote:

> If I am understanding your problem correctly, I think you can just create
> a new DataFrame that is a transformation of sample_data by first
> registering sample_data as a temp table.
>
> //Register temp table
> sample_data.createOrReplaceTempView("sql_sample_data")
>
> //Create new DataSet with transformed values
> val transformed = spark.sql("select trim(field1) as field1, trim(field2)
> as field2...... from sql_sample_data")
>
> //Test
> transformed.show(10)
>
> I hope that helps!
> Subhash
>
>
> On Wed, Mar 1, 2017 at 12:04 PM, Marco Mistroni <mmistroni@gmail.com>
> wrote:
>
>> Hi I think u need an UDF if u want to transform a column....
>> Hth
>>
>> On 1 Mar 2017 4:22 pm, "Bill Schwanitz" <bilsch@bilsch.org> wrote:
>>
>>> Hi all,
>>>
>>> I'm fairly new to spark and scala so bear with me.
>>>
>>> I'm working with a dataset containing a set of column / fields. The data
>>> is stored in hdfs as parquet and is sourced from a postgres box so fields
>>> and values are reasonably well formed. We are in the process of trying out
>>> a switch from pentaho and various sql databases to pulling data into hdfs
>>> and applying transforms / new datasets with processing being done in spark
>>> ( and other tools - evaluation )
>>>
>>> A rough version of the code I'm running so far:
>>>
>>> val sample_data = spark.read.parquet("my_data_input")
>>>
>>> val example_row = spark.sql("select * from parquet.my_data_input where
>>> id = 123").head
>>>
>>> I want to apply a trim operation on a set of fields - lets call them
>>> field1, field2, field3 and field4.
>>>
>>> What is the best way to go about applying those trims and creating a new
>>> dataset? Can I apply the trip to all fields in a single map? or do I need
>>> to apply multiple map functions?
>>>
>>> When I try the map ( even with a single )
>>>
>>> scala> val transformed_data = sample_data.map(
>>>      |   _.trim(col("field1"))
>>>      |   .trim(col("field2"))
>>>      |   .trim(col("field3"))
>>>      |   .trim(col("field4"))
>>>      | )
>>>
>>> I end up with the following error:
>>>
>>> <console>:26: error: value trim is not a member of
>>> org.apache.spark.sql.Row
>>>          _.trim(col("field1"))
>>>            ^
>>>
>>> Any ideas / guidance would be appreciated!
>>>
>>
>

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