How to throw casting exception in Spark Dataset

I'm loading csv file via Spark (java)

Dataset<Row> dataset = sparkSession.read().option("header", "true").csv("/test.csv");

This is the schema of the file :

dataset.printSchema();
root
|-- eid: string (nullable = true)
|-- name: string (nullable = true)
|-- salary: string (nullable = true)
|-- designation: string (nullable = true)

This is the sample data :

dataset.show();
+-----+------------------+------------+-----------+
|  eid|              name|      salary|designation|
+-----+------------------+------------+-----------+
|    1|            "John"|     "10000"|       "SE"|
|    2|             "Dan"|    "100000"|       "SE"|
|    3|         "ironman"|  "10000000"|     "King"|
|    4|          "Batman"| "100000000"|  "Fighter"|
|awqwq| "captain america"|    "300000"|  "Captain"|
+-----+------------------+------------+-----------+

Casting to integer type

dataset = dataset.withColumn("eid", dataset.col("eid").cast(DataTypes.IntegerType));
dataset.show();
+----+------------------+------------+-----------+
| eid|              name|      salary|designation|
+----+------------------+------------+-----------+
|   1|            "John"|     "10000"|       "SE"|
|   2|             "Dan"|    "100000"|       "SE"|
|   3|         "ironman"|  "10000000"|     "King"|
|   4|          "Batman"| "100000000"|  "Fighter"|
|null| "captain america"|    "300000"|  "Captain"|
+----+------------------+------------+-----------+

But after casting values in eid columns are becoming null (String values). it's not throwing any casting exception.

Is there any way exception can be thrown. I have huge number of columns and throwing exceptions are required

Probably the most fluent way is to avoid casting and using failfast read mode with a pre-defined schema:

spark.read
.schema("eid INT, name STRING, salary STRING, designation STRING")
.option("mode", "FAILFAST")
.option("header", true)
.csv("/test.csv")
.show()

This throws:

org.apache.spark.SparkException: Malformed records are detected in record parsing. Parse Mode: FAILFAST. To process malformed records as null result, try setting the option 'mode' as 'PERMISSIVE'.

If this approach can't be used for some reason, arbitrary casting and other operations can be done using the dataset API. This example is in Scala but it could be written in Java as well:

spark.read
.option("header", true)
.csv("/test.csv")
.as[(String, String, String, String)]
.map {
case (eid, name, salary, designation) => (eid.toInt, name, salary, designation)
}
.show()

Throws

java.lang.NumberFormatException: For input string: "awqwq"

Alternatively, a UDF could be used as well.