Field data validation using spark dataframe - apache-spark

Question

I have a bunch of columns, sample like my data displayed as show below.
I need to check the columns for errors and will have to generate two output files.
I'm using Apache Spark 2.0 and I would like to do this in a efficient way.
Schema Details
---------------
EMPID - (NUMBER)
ENAME - (STRING,SIZE(50))
GENDER - (STRING,SIZE(1))
Data
----
EMPID,ENAME,GENDER
1001,RIO,M
1010,RICK,MM
1015,123MYA,F
My excepected output files should be as shown below:
1.
EMPID,ENAME,GENDER
1001,RIO,M
1010,RICK,NULL
1015,NULL,F
2.
EMPID,ERROR_COLUMN,ERROR_VALUE,ERROR_DESCRIPTION
1010,GENDER,"MM","OVERSIZED"
1010,GENDER,"MM","VALUE INVALID FOR GENDER"
1015,ENAME,"123MYA","NAME SHOULD BE A STRING"
Thanks

Answer 1

I have not really worked with Spark 2.0, so I'll try answering your question with a solution in Spark 1.6.
// Load you base data
val input = <<you input dataframe>>
//Extract the schema of your base data
val originalSchema = input.schema
// Modify you existing schema with you additional metadata fields
val modifiedSchema= originalSchema.add("ERROR_COLUMN", StringType, true)
.add("ERROR_VALUE", StringType, true)
.add("ERROR_DESCRIPTION", StringType, true)
// write a custom validation function
def validateColumns(row: Row): Row = {
var err_col: String = null
var err_val: String = null
var err_desc: String = null
val empId = row.getAs[String]("EMPID")
val ename = row.getAs[String]("ENAME")
val gender = row.getAs[String]("GENDER")
// do checking here and populate (err_col,err_val,err_desc) with values if applicable
Row.merge(row, Row(err_col),Row(err_val),Row(err_desc))
}
// Call you custom validation function
val validateDF = input.map { row => validateColumns(row) }
// Reconstruct the DataFrame with additional columns
val checkedDf = sqlContext.createDataFrame(validateDF, newSchema)
// Filter out row having errors
val errorDf = checkedDf.filter($"ERROR_COLUMN".isNotNull && $"ERROR_VALUE".isNotNull && $"ERROR_DESCRIPTION".isNotNull)
// Filter our row having no errors
val errorFreeDf = checkedDf.filter($"ERROR_COLUMN".isNull && !$"ERROR_VALUE".isNull && !$"ERROR_DESCRIPTION".isNull)
I have used this approach personally and it works for me. I hope it points you in the right direction.