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Data Engineering with Scala and Spark

You're reading from  Data Engineering with Scala and Spark

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781804612583
Pages 300 pages
Edition 1st Edition
Languages
Authors (3):
Eric Tome Eric Tome
Profile icon Eric Tome
Rupam Bhattacharjee Rupam Bhattacharjee
Profile icon Rupam Bhattacharjee
David Radford David Radford
Profile icon David Radford
View More author details

Table of Contents (21) Chapters

Preface 1. Part 1 – Introduction to Data Engineering, Scala, and an Environment Setup
2. Chapter 1: Scala Essentials for Data Engineers 3. Chapter 2: Environment Setup 4. Part 2 – Data Ingestion, Transformation, Cleansing, and Profiling Using Scala and Spark
5. Chapter 3: An Introduction to Apache Spark and Its APIs – DataFrame, Dataset, and Spark SQL 6. Chapter 4: Working with Databases 7. Chapter 5: Object Stores and Data Lakes 8. Chapter 6: Understanding Data Transformation 9. Chapter 7: Data Profiling and Data Quality 10. Part 3 – Software Engineering Best Practices for Data Engineering in Scala
11. Chapter 8: Test-Driven Development, Code Health, and Maintainability 12. Chapter 9: CI/CD with GitHub 13. Part 4 – Productionalizing Data Engineering Pipelines – Orchestration and Tuning
14. Chapter 10: Data Pipeline Orchestration 15. Chapter 11: Performance Tuning 16. Part 5 – End-to-End Data Pipelines
17. Chapter 12: Building Batch Pipelines Using Spark and Scala 18. Chapter 13: Building Streaming Pipelines Using Spark and Scala 19. Index 20. Other Books You May Enjoy

Variance

As mentioned earlier, functions are first-class objects in Scala. Scala automatically converts function literals into objects of the FunctionN type (N = 0 to 22). For example, consider the following anonymous function:

val f: Int => Any = (x: Int) => x

Example 1.45

This function will be converted automatically to the following:

val f = new Function1[Int, Any] {def apply(x: Int) = x}

Example 1.46

Please note that the preceding syntax represents an object of an anonymous class that extends Function1[Int, Any] and implements its abstract apply method. In other words, it is equivalent to the following:

class AnonymousClass extends Function1[Int, Any] {
  def apply(x: Int): Any = x
}
val f = new AnonymousClass

Example 1.47

If we refer to the type signature of the Function1 trait, we would see the following:

Function1[-T1, +T2]

Example 1.48

T1 represents the argument type and T2 represents the return type. The type variance of T1 is contravariant and that of T2 is covariant. In general, covariance designed by + means if a class or trait is covariant in its type parameter T, that is, C[+T], then C[T1] and C[T2] will adhere to the subtyping relationship between T1 and T2. For example, since Any is a supertype of Int, C[Any] will be a supertype of C[Int].

The order is reversed for contravariance. So, if we have C[-T], then C[Int] will be a supertype of C[Any].

Since we have Function1[-T1, +R], that would then mean type Function1[Int, Any] will be a supertype of, say, Function1[Any, String].

To see it in action, let’s define a method that takes a function of type Int => Any and returns Unit:

def caller(op: Int => Any): Unit = List
  .tabulate(5)(i => i + 1)
  .foreach(i => print(s"$i "))

Example 1.49

Let’s now define two functions:

scala> val f1: Int => Any = (x: Int) => x
f1: Int => Any = $Lambda$9151/1234201645@34f561c8
scala> val f2 : Any => String = (x: Any) => x.toString
f2: Any => String = $Lambda$9152/1734317897@699fe6f6

Example 1.50

A function (or method) with a parameter of type T can be invoked with an argument that is either of type T or its subtype. And since Int => Any is a supertype of Any => String, we should be able to pass both of these functions as arguments. As can be seen, both of them indeed work:

scala> caller(f1)
1 2 3 4 5
scala> caller(f2)
1 2 3 4 5

Example 1.51

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Data Engineering with Scala and Spark
Published in: Jan 2024 Publisher: Packt ISBN-13: 9781804612583
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