about the stackoverflow question, do this:

def validateAndTransform(df: DataFrame) : DataFrame = {

  import df.sparkSession.implicits._

  ...
}



On Fri, Oct 14, 2016 at 5:51 PM, Koert Kuipers <ko...@tresata.com> wrote:

> b
> ‚Äčasically the implicit conversiosn that need it are rdd => dataset and seq
> => dataset‚Äč
>
> On Fri, Oct 14, 2016 at 5:47 PM, Koert Kuipers <ko...@tresata.com> wrote:
>
>> for example when do you Seq(1,2,3).toDF("a") it needs to get the
>> SparkSession from somewhere. by importing the implicits from
>> spark.implicits._ they have access to a SparkSession for operations like
>> this.
>>
>> On Fri, Oct 14, 2016 at 4:42 PM, Jakub Dubovsky <
>> spark.dubovsky.ja...@gmail.com> wrote:
>>
>>> Hey community,
>>>
>>> I would like to *educate* myself about why all *sql implicits* (most
>>> notably conversion to Dataset API) are imported from *instance* of
>>> SparkSession and not using static imports.
>>>
>>> Having this design one runs into problems like this
>>> <http://stackoverflow.com/questions/32453886/spark-sql-dataframe-import-sqlcontext-implicits>.
>>> It requires the presence of SparkSession instance (the only one we have) in
>>> many parts of code. This makes code structuring harder.
>>>
>>> I assume that there is a *reason* why this design was *chosen*. Can
>>> somebody please point me to a resource or explain why is this?
>>> What is an advantage of this approach?
>>> Or why it is not possible to implement it with static imports?
>>>
>>> Thanks a lot!
>>>
>>> Jakub
>>>
>>>
>>
>

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