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afro-americanQ: How do I know what base to use with Spark’s apply? I have a simple code in scala that looks like this: for (x <- 0 until Data.length) { val correct = Data(x)(3).toString() dataFrame.select("Data").where("Data == " + correct).select("L1.L2.L3.L4.L5").count() } This code is not producing the results I expect. The Data frame is defined like so: import spark.implicits._ import org.apache.spark.sql.Row import org.apache.spark.sql.functions._ case class Data(x: Int) case class L1(v1: Int, v2: Int, v3: Int, v4: Int, v5: Int) case class L2(v1: Int, v2: Int) case class L3(v1: Int, v2: Int) case class L4(v1: Int, v2: Int, v3: Int, v4: Int, v5: Int, v6: Int) case class L5(v1: Int, v2: Int, v3: Int, v4: Int, v5: Int, v6: Int, v7: Int) val dataFrame = spark.createDataFrame( Array( Data(3), Data(2), Data(1), Data(0), Data(1), Data(2), Data(3), Data(4), Data(0), Data(5), Data(6) ) ) Since Data(x)(3) is never used anywhere (I don't even know why it was included), I decided to drop it and changed the code to: for (x <- 0 until Data.length) { val correct = Data(x)(3).toString() dataFrame.select("Data").where("Data == " + correct).select("L1.L2.L3.L4.L5").count() } But now my results aren't what I expect. What is the base and what data types are valid to use in spark's apply function? A: You must specify the data type when using this method. for (x <- 0 until Data.length) { val correct: Array[Any] = Data(x)(3).toString() dataFrame.select("Data").where("Data == " + correct).select("L1.L2.L3.L4.L5").count() } It is also possible to use the over function for this: for (x <- 0 until Data.length) { val correct = x dataFrame.select("Data").where("Data == " + correct).select("L1.L2.L3.L4.L5").count() } this is from http://www.scala-lang.org/files/archive/nightly/docs/api/index.
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