Sam D Sam D - 3 months ago 41
Scala Question

Spark extracting values from a Row

I have the following dataframe

val transactions_with_counts = sqlContext.sql(
"""SELECT user_id AS user_id, category_id AS category_id,
COUNT(category_id) FROM transactions GROUP BY user_id, category_id""")


I'm trying to convert the rows to Rating objects but since x(0) returns an array this fails

val ratings = transactions_with_counts
.map(x => Rating(x(0).toInt, x(1).toInt, x(2).toInt))



error: value toInt is not a member of Any

Answer

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)]