multilevel sorting of column data(string datatype) in spark SQL - scala

Question

for example lets consider there are three columns namely empid,year of joining and designation in employee table. initially the table is sorted based on the joining date in order. if in case two people have joined on same date then the table must have higher priority designation on top and down the lesser ones.
how to assign priority for the designation for the already sorted data in spark sql dataframe.
example if ceo and a project manager has joined an company on same date then the ceo details must be on top of projectmanager viewed in dataframe schema.

Answer 1

Let say you have a Dataframe with 3 columns - empid, joiningYear, designation. Then you can do something like this to sort on multiple columns:
import org.apache.spark.sql.functions._
val sorted = df.sort(asc("joiningYear"), asc("designation"))
In this case, data will firstly be sorted on joiningYear and for people with same joiningYear, it will be sorted by designation.

Answer 2

Considering there are three columns namely empid,year_of_joining and designation in employee table. You can sort or orderBy both columns but how will you order based on designation if it has e.g. "CEO", "projectManager". Using designation within sort() will sort it in its alphabetical order. So you must have some increasing numbers denoting the designation based on seniority then simply use below code.
import org.apache.spark.sql.functions._
val sortedEmp = df.sort(asc("year_of_joining"), desc("designation "))
Since you want higher priority designation on top and down the lesser ones so you should use desc for designation.
Since there wont be many designations, you can assign increasing numbers to these designations based on seniority.