I use extractors:
sql("SELECT name, age FROM people").map {
Row(name: String, age: Int) =>
...
}
On Tue, Jan 20, 2015 at 6:48 AM, Sunita Arvind <sunitarv...@gmail.com>
wrote:
> The below is not exactly a solution to your question but this is what we
> are doing. For the first time we do end up doing row.getstring() and we
> immediately parse it through a map function which aligns it to either a
> case class or a structType. Then we register it as a table and use just
> column names. The spark sql wiki has good examples for this. Looks more
> easy to manage to me than your solution below.
>
> Agree with you on the fact that when there are lot of columns,
> row.getString() even once is not convenient
>
> Regards
>
> Sunita
>
>
> On Tuesday, January 20, 2015, Night Wolf <nightwolf...@gmail.com> wrote:
>
>> In Spark SQL we have Row objects which contain a list of fields that
>> make up a row. A Rowhas ordinal accessors such as .getInt(0) or
>> getString(2).
>>
>> Say ordinal 0 = ID and ordinal 1 = Name. It becomes hard to remember what
>> ordinal is what, making the code confusing.
>>
>> Say for example I have the following code
>>
>> def doStuff(row: Row) = {
>> //extract some items from the row into a tuple;
>> (row.getInt(0), row.getString(1)) //tuple of ID, Name}
>>
>> The question becomes how could I create aliases for these fields in a Row
>> object?
>>
>> I was thinking I could create methods which take a implicit Row object;
>>
>> def id(implicit row: Row) = row.getInt(0)def name(implicit row: Row) =
>> row.getString(1)
>>
>> I could then rewrite the above as;
>>
>> def doStuff(implicit row: Row) = {
>> //extract some items from the row into a tuple;
>> (id, name) //tuple of ID, Name}
>>
>> Is there a better/neater approach?
>>
>>
>> Cheers,
>>
>> ~NW
>>
>
