Reading data with multiple data formats

With Scala QL, it's really easy to run a Query on data of different kind and format.

Start by importing:

import scalaql._

// Docs classes
import scalaql.docs.DocUtils._
import scalaql.docs.Hogwarts._

// Used in examples
import java.nio.file.Paths
import io.circe.generic.auto._

Assume you have the following aggregation Query:

case class FacultyStats(faculty: String, avgGrade: Double)

val query =
  select[Student]
    .where(_.age >= 18)
    .join(select[Faculty])
    .on(_.faculty == _.name)
    .groupBy { case (_, faculty) => faculty.name }
    .aggregate { (faculty, studentsWithFaculties) =>
      studentsWithFaculties
        .avgBy { case (student, _) => student.grade }
        .map(avgGrade => FacultyStats(faculty, avgGrade))
    }
// query: Query[From[Student] with From[Faculty], FacultyStats] = FROM(Hogwarts::Student) -> WHERE INNER JOIN FROM(Hogwarts::Faculty) -> GROUP BY(String) -> AGGREGATE(MdocSession::App::FacultyStats)

You could run this Query through CSV and JSON files at the same time!
Let's start by defining input files paths:

val studentsPath  = Paths.get("docs/src/main/resources/students.csv")
// studentsPath: java.nio.file.Path = docs/src/main/resources/students.csv
val facultiesPath = Paths.get("docs/src/main/resources/faculties.json")
// facultiesPath: java.nio.file.Path = docs/src/main/resources/faculties.json
  
val outPath = Paths.get("target/faculty_stats_complex.csv")
// outPath: java.nio.file.Path = target/faculty_stats_complex.csv

Then you only need to just provide the correct input:

query
  .foreach(
    csv.write[FacultyStats].file(outPath)
  )
  .run(
    from(
      csv.read[Student].file(studentsPath)
    ) & from(
      json.read[Faculty].file(facultiesPath)
    )
  )

It will produce the following CSV file:

printFile(outPath)
// faculty,avgGrade
// Slytherin,85.1
// Gryffindor,83.63333333333334
//