check data size spark dataframes - scala

Question

I have the following question :
Actually I am working with the following csv file:
""job"";""marital"""
""management"";""married"""
""technician"";""single"""
I loaded it into a spark dataframe as follows:
My aim is to check the length and type of each field in the dataframe following the set od rules below :
col type
job char10
marital char7
I started implementing the check of the length of each field but I am getting a compilation error :
val data = spark.read.option("inferSchema", "true").option("header", "true").csv("file:////home/user/Desktop/user/file.csv")
data.map(line => {
val fields = line.toString.split(";")
fields(0).size
fields(1).size
})
The expected output should be:
List(10,10)
As for the check of the types I don't have any idea about how to implement it as we are using dataframes. Any idea about a function verifying the data format ?
Thanks a lot in advance for your replies.
ata

Answer 1

I see you are trying to use Dataframe, But if there are multiple double quotes then you can read as a textFile and remove them and convert to Dataframe as below
import org.apache.spark.sql.functions._
import spark.implicits._
val raw = spark.read.textFile("path to file ")
.map(_.replaceAll("\"", ""))
val header = raw.first
val data = raw.filter(row => row != header)
.map { r => val x = r.split(";"); (x(0), x(1)) }
.toDF(header.split(";"): _ *)
You get with data.show(false)
+----------+-------+
|job |marital|
+----------+-------+
|management|married|
|technician|single |
+----------+-------+
To calculate the size you can use withColumn and length function and play around as you need.
data.withColumn("jobSize", length($"job"))
.withColumn("martialSize", length($"marital"))
.show(false)
Output:
+----------+-------+-------+-----------+
|job |marital|jobSize|martialSize|
+----------+-------+-------+-----------+
|management|married|10 |7 |
|technician|single |10 |6 |
+----------+-------+-------+-----------+
All the column type are String.
Hope this helps!

Answer 2

You are using a dataframe. So when you use the map method, you are processing Row in your lambda.
so line is a Row.
Row.toString will return a string representing the Row, so in your case 2 structfields typed as String.
If you want to use map and process your Row, you have to get the vlaue inside the fields manually. with getAsString and getAsString.
Usually when you use Dataframes, you have to work in column's logic as in SQL using select, where... or directly the SQL syntax.