Spark extracting values from a Row

Lets start with some dummy data:

val transactions = Seq((1, 2), (1, 4), (2, 3)).toDF("user_id", "category_id")

val transactions_with_counts = transactions
  .groupBy($"user_id", $"category_id")
  .count

transactions_with_counts.printSchema

// root
// |-- user_id: integer (nullable = false)
// |-- category_id: integer (nullable = false)
// |-- count: long (nullable = false)

There are a few ways to access Row values and keep expected types:

1. Pattern matching

   import org.apache.spark.sql.Row
   
   transactions_with_counts.map{
     case Row(user_id: Int, category_id: Int, rating: Long) =>
       Rating(user_id, category_id, rating)
   } 
   

2. Typed get* methods like getInt, getLong:

   transactions_with_counts.map(
     r => Rating(r.getInt(0), r.getInt(1), r.getLong(2))
   )
   

3. getAs method which can use both names and indices:

   transactions_with_counts.map(r => Rating(
     r.getAs[Int]("user_id"), r.getAs[Int]("category_id"), r.getAs[Long](2)
   ))
   

   It can be used to properly extract user defined types, including mllib.linalg.Vector. Obviously accessing by name requires a schema.

4. Converting to statically typed Dataset (Spark 1.6+ / 2.0+):

   transactions_with_counts.as[(Int, Int, Long)]