Spark DataFrame not respecting schema and considering everything as String

Question

I am facing a problem which I have failed to get over for ages now.

I am on Spark 1.4 and Scala 2.10. I cannot upgrade at this moment (big distributed infrastructure)
I have a file with few hundred columns, only 2 of which are string and rest all are Long. I want to convert this data into a Label/Features dataframe.
I have been able to get it into LibSVM format.
I just cannot get it into a Label/Features format.

The reason being

I am not being able to use the toDF() as shown here https://spark.apache.org/docs/1.5.1/ml-ensembles.html
```
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
```
it it not supported in 1.4

So I first converted the txtFile into a DataFrame where I used something like this

def getColumnDType(columnName:String):StructField = {

        if((columnName== "strcol1") || (columnName== "strcol2")) 
            return StructField(columnName, StringType, false)
        else
            return StructField(columnName, LongType, false)
    }
def getDataFrameFromTxtFile(sc: SparkContext,staticfeatures_filepath: String,schemaConf: String) : DataFrame = {
        val sfRDD = sc.textFile(staticfeatures_filepath)//
        val sqlContext = new org.apache.spark.sql.SQLContext(sc)
         // reads a space delimited string from application.properties file
        val schemaString = readConf(Array(schemaConf)).get(schemaConf).getOrElse("")

        // Generate the schema based on the string of schema
        val schema =
          StructType(
            schemaString.split(" ").map(fieldName => getSFColumnDType(fieldName)))

        val data = sfRDD
        .map(line => line.split(","))
        .map(p => Row.fromSeq(p.toSeq))

        var df = sqlContext.createDataFrame(data, schema)

        //schemaString.split(" ").drop(4)
        //.map(s => df = convertColumn(df, s, "int"))

        return df
    }

When I do a df.na.drop() df.printSchema() with this returned dataframe I get perfect Schema Like this

root
 |-- rand_entry: long (nullable = false)
 |-- strcol1: string (nullable = false)
 |-- label: long (nullable = false)
 |-- strcol2: string (nullable = false)
 |-- f1: long (nullable = false)
 |-- f2: long (nullable = false)
 |-- f3: long (nullable = false)
and so on till around f300

But - the sad part is whatever I try to do (see below) with the df, I am always getting a ClassCastException (java.lang.String cannot be cast to java.lang.Long)

val featureColumns = Array("f1","f2",....."f300")
assertEquals(-99,df.select("f1").head().getLong(0))
assertEquals(-99,df.first().get(4))
val transformeddf = new VectorAssembler()
        .setInputCols(featureColumns)
        .setOutputCol("features")
        .transform(df)

So - the bad is - even though the schema says Long - the df is still internally considering everything as String.

Edit

Adding a simple example

Say I have a file like this

1,A,20,P,-99,1,0,0,8,1,1,1,1,131153
1,B,23,P,-99,0,1,0,7,1,1,0,1,65543
1,C,24,P,-99,0,1,0,9,1,1,1,1,262149
1,D,7,P,-99,0,0,0,8,1,1,1,1,458759

and

sf-schema=f0 strCol1 f1 strCol2 f2 f3 f4 f5 f6 f7 f8 f9 f10 f11

(column names really do not matter so you can disregard this details)

All I am trying to do is create a Label/Features kind of dataframe where my 3rd column becomes a label and the 5th to 11th columns become a feature [Vector] column. Such that I can follow the rest of the steps in https://spark.apache.org/docs/latest/ml-classification-regression.html#tree-ensembles.

I have cast the columns too like suggested by zero323

val types = Map("strCol1" -> "string", "strCol2" -> "string")
        .withDefault(_ => "bigint")
df = df.select(df.columns.map(c => df.col(c).cast(types(c)).alias(c)): _*)
df = df.drop("f0")
df = df.drop("strCol1")
df = df.drop("strCol2")

But the assert and VectorAssembler still fails. featureColumns = Array("f2","f3",....."f11") This is whole sequence I want to do after I have my df

    var transformeddf = new VectorAssembler()
    .setInputCols(featureColumns)
    .setOutputCol("features")
    .transform(df)

    transformeddf.show(2)

    transformeddf = new StringIndexer()
    .setInputCol("f1")
    .setOutputCol("indexedF1")
    .fit(transformeddf)
    .transform(transformeddf)

    transformeddf.show(2)

    transformeddf = new VectorIndexer()
    .setInputCol("features")
    .setOutputCol("indexedFeatures")
    .setMaxCategories(5)
    .fit(transformeddf)
    .transform(transformeddf)

The exception trace from ScalaIDE - just when it hits the VectorAssembler is as below

java.lang.ClassCastException: java.lang.String cannot be cast to java.lang.Long
    at scala.runtime.BoxesRunTime.unboxToLong(BoxesRunTime.java:110)
    at scala.math.Numeric$LongIsIntegral$.toDouble(Numeric.scala:117)
    at org.apache.spark.sql.catalyst.expressions.Cast$$anonfun$castToDouble$5.apply(Cast.scala:364)
    at org.apache.spark.sql.catalyst.expressions.Cast$$anonfun$castToDouble$5.apply(Cast.scala:364)
    at org.apache.spark.sql.catalyst.expressions.Cast.eval(Cast.scala:436)
    at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:118)
    at org.apache.spark.sql.catalyst.expressions.CreateStruct$$anonfun$eval$2.apply(complexTypes.scala:75)
    at org.apache.spark.sql.catalyst.expressions.CreateStruct$$anonfun$eval$2.apply(complexTypes.scala:75)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
    at scala.collection.AbstractTraversable.map(Traversable.scala:105)
    at org.apache.spark.sql.catalyst.expressions.CreateStruct.eval(complexTypes.scala:75)
    at org.apache.spark.sql.catalyst.expressions.CreateStruct.eval(complexTypes.scala:56)
    at org.apache.spark.sql.catalyst.expressions.ScalaUdf$$anonfun$2.apply(ScalaUdf.scala:72)
    at org.apache.spark.sql.catalyst.expressions.ScalaUdf$$anonfun$2.apply(ScalaUdf.scala:70)
    at org.apache.spark.sql.catalyst.expressions.ScalaUdf.eval(ScalaUdf.scala:960)
    at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:118)
    at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:68)
    at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:52)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
    at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
    at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
    at scala.collection.AbstractIterator.to(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
    at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
    at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$3.apply(SparkPlan.scala:143)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$3.apply(SparkPlan.scala:143)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1767)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1767)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
    at org.apache.spark.scheduler.Task.run(Task.scala:70)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:745)

Spark DataFrame not respecting schema and considering everything as String

1 Answers