Copy schema from one dataframe to another dataframe

Question

I'm trying to change the schema of an existing dataframe to the schema of another dataframe.

DataFrame 1:

Column A | Column B | Column C | Column D
   "a"   |    1     |   2.0    |   300
   "b"   |    2     |   3.0    |   400
   "c"   |    3     |   4.0    |   500

DataFrame 2:

Column K | Column B | Column F
   "c"   |    4     |   5.0
   "b"   |    5     |   6.0
   "f"   |    6     |   7.0

So I want to apply the schema of the first dataframe on the second. So all the columns which are the same remain. The columns in dataframe 2 that are not in 1 get deleted. The others become "NULL".

Output

Column A | Column B | Column C | Column D
 "NULL"  |    4     |   "NULL" |  "NULL"
 "NULL"  |    5     |   "NULL" |  "NULL"
 "NULL"  |    6     |   "NULL" |  "NULL"

So I came with a possible solution:

val schema = df1.schema
val newRows: RDD[Row] = df2.map(row => {
  val values = row.schema.fields.map(s => {
    if(schema.fields.contains(s)){
      row.getAs(s.name).toString
    }else{
      "NULL"
    }
  })
  Row.fromSeq(values)
})
sqlContext.createDataFrame(newRows, schema)}

Now as you can see this will not work because the schema contains String, Int and Double. And all my rows have String values.

This is where I'm stuck, is there a way to automatically convert the type of my values to the schema?

Answer 1

If schema is flat I would use simply map over per-existing schema and select required columns:

val exprs = df1.schema.fields.map { f => 
  if (df2.schema.fields.contains(f)) col(f.name)
  else lit(null).cast(f.dataType).alias(f.name) 
}

df2.select(exprs: _*).printSchema

// root
//  |-- A: string (nullable = true)
//  |-- B: integer (nullable = false)
//  |-- C: double (nullable = true)
//  |-- D: integer (nullable = true)

Answer 2

Working in 2018 (Spark 2.3) reading a .sas7bdat

Scala

val sasFile = "file.sas7bdat"
val dfSas = spark.sqlContext.sasFile(sasFile)
val myManualSchema = dfSas.schema //getting the schema from another dataframe
val df = spark.read.format("csv").option("header","true").schema(myManualSchema).load(csvFile)

PD: spark.sqlContext.sasFile use saurfang library, you could skip that part of code and get the schema from another dataframe.