I'm exploring features of Spark 2.0, and am trying to load a simple csv file
into a dataset.
These are the contents of my file named people.csv:
name,age,occupation
John,21,student
Mark,33,analyst
Susan,27,scientist
Below is my code:
import org.apache.spark.sql._
val spark =
SparkSession.builder().appName("test").master("local").getOrCreate
val data = spark.read.option("header", true).csv("people.csv")
import spark.implicits._
case class People(name: String, age: Int, occupation: String)
val people = train.as[People]
Running the above gives me the following error:
org.apache.spark.sql.AnalysisException: Cannot up cast `age` from bigint to
int as it may truncate
The type path of the target object is:
- field (class: "scala.Int", name: "age")
- root class: "People"
You can either add an explicit cast to the input data or choose a higher
precision type of the field in the target object;
How do I fix that? Is this not the right way to directly load data from csv
into a dataset using the desired case class?
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