Spark read CSV - Not showing corroupt Records - apache-spark

Question

Spark has a Permissive mode for reading CSV files which stores the corroupt records into a separate column named _corroupt_record.
permissive -
Sets all fields to null when it encounters a corrupted record and places all corrupted records in a string column
called _corrupt_record
However, when I am trying following example, I don't see any column named _corroupt_record. the reocords which doesn't match with schema appears to be null
data.csv
data
10.00
11.00
$12.00
$13
gaurang
code
import org.apache.spark.sql.types.{StructField, StructType, StringType, LongType, DecimalType}
val schema = new StructType(Array(
new StructField("value", DecimalType(25,10), false)
))
val df = spark.read.format("csv")
.option("header", "true")
.option("mode", "PERMISSIVE")
.schema(schema)
.load("../test.csv")
schema
scala> df.printSchema()
root
|-- value: decimal(25,10) (nullable = true)
scala> df.show()
+-------------+
| value|
+-------------+
|10.0000000000|
|11.0000000000|
| null|
| null|
| null|
+-------------+
If I change the mode to FAILFAST I am getting error when I try to see data.

Answer 1

Adding the _corroup_record as suggested by Andrew and Prateek resolved the issue.
import org.apache.spark.sql.types.{StructField, StructType, StringType, LongType, DecimalType}
val schema = new StructType(Array(
new StructField("value", DecimalType(25,10), true),
new StructField("_corrupt_record", StringType, true)
))
val df = spark.read.format("csv")
.option("header", "true")
.option("mode", "PERMISSIVE")
.schema(schema)
.load("../test.csv")
querying Data
scala> df.show()
+-------------+---------------+
| value|_corrupt_record|
+-------------+---------------+
|10.0000000000| null|
|11.0000000000| null|
| null| $12.00|
| null| $13|
| null| gaurang|
+-------------+---------------+