Copy schema from one dataframe to another dataframe - scala

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.

Answer 3

Below are simple PYSPARK steps to achieve same:
df = <dataframe whose schema needs to be copied>
df_tmp = <dataframe with result with fewer fields>
#Note: field names from df_tmp must match with field names from df
df_tmp_cols = [colmn.lower() for colmn in df_tmp.columns]
for col_dtls in df.dtypes:
col_name, dtype = col_dtls
if col_name.lower() in df_tmp_cols:
df_tmp = df_tmp.withColumn(col_name,f.col(col_name).cast(dtype))
else:
df_tmp = df_tmp.withColumn(col_name,f.lit(None).cast(dtype))
df_fin = df_tmp.select(df.columns) #Final dataframe

Answer 4

You could simply do Left Join on your dataframes with query like this:-
SELECT Column A, Column B, Column C, Column D FROM foo LEFT JOIN BAR ON Column C = Column C
Please checkout the answer by #zero323 in this post:-
Spark specify multiple column conditions for dataframe join
Thanks,
Charles.