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 <sunitarvind@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



On Tuesday, January 20, 2015, Night Wolf <nightwolfzor@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?