Tail Recursion, a Scala language concept

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In Scala, tail recursion enables you to rewrite a mutable structure such as a while-loop, into an immutable algorithm.

def fibonacci(n: Int): Int = {
  @scala.annotation.tailrec
  def go(i: Int, previous: Int, beforePrevious: Int): Int =
    if (i >= n) previous else go(i + 1, previous + beforePrevious, previous)

  go(i = 1, previous = 1, beforePrevious = 0)
}

assert(fibonacci(8) == 21)

Tail recursion always has a recursive call in a "final" position, ie you can only either return a result (exit the function), or return another call to self-function

In canonical form, the immutable form gets compiled down to the mutable form,

def evaluateGeneralImmutable[State, Result](initialParams: State)(
    iterate: State => State,
    terminate: State => Boolean,
    extractResult: State => Result
): Result = {
  @scala.annotation.tailrec
  def go(currentParams: State): Result =
    if (terminate(currentParams)) extractResult(currentParams)
    else go(currentParams = iterate(currentParams))

  go(initialParams)
}

becomes (after a stage of compilation):

def evaluateGeneralMutable[State, Result](initialParams: State)(
    iterate: State => State,
    terminate: State => Boolean,
    extractResult: State => Result
): Result = {
  var currentParams: State = initialParams
  while (!terminate(currentParams)) {
    currentParams = iterate(currentParams)
  }
  extractResult(currentParams)
}

This transformation can also be performed the other way round, as to give you a pure immutable solution

What are the benefits of tail recursion?

Tail recursion in Scala utilises a principle known as tail-call optimisation. It allows one to write iterative algorithms (that would otherwise would be complicated while-loops) in immutable form.

What are the benefits of immutability?

It becomes easier to reason about your code, and you always know that you can re-run a function as manytimes as you wish without causing unexpected side effects.

But really, can anything be written in this shape?

Anything that is iterative in nature can, so long as it can be represented in the canonical form.

Let's look at two versions of List#drop(n) - mutable and immutable (Drop, Take, dropRight, takeRight):

def dropMutable[T](list: List[T], n: Int): List[T] = {
  var remaining = n
  var returnList = list
  while (remaining > 0 && returnList.nonEmpty) {
    remaining = remaining - 1
    returnList = returnList.tail
  }
  returnList
}

def dropImmutable[T](list: List[T], n: Int): List[T] = {
  @scala.annotation.tailrec
  def go(remaining: Int, returnList: List[T]): List[T] = {
    if (remaining == 0) returnList
    else
      returnList match {
        case _ :: rest => go(remaining - 1, rest)
        case Nil       => Nil
      }
  }
  go(remaining = n, list)
}

assert(dropMutable(List(1, 2, 3), 2) == List(3))

assert(dropImmutable(List(1, 2, 3), 2) == List(3))

The key thing to notice really is that you move all the `var`s to arguments of the `go` function.

Very detailed advanced example:

Let's try to implement List#foldLeft (foldLeft and foldRight):

Example how to turn a function from mutable to immutable. Warning: a lot of boilerplate code.
def foldLeftMutable[T, S](list: List[T])(initial: S)(f: (S, T) => S): S = {
  var currentResult: S = initial
  var remaining: List[T] = list
  while (remaining.nonEmpty) {
    currentResult = f(currentResult, remaining.head)
    remaining = remaining.tail
  }
  currentResult
}

final case class CurrentState[S, T](
    currentResult: S,
    remainingItems: List[T]
)

def foldLeftMutableSimplified[T, S](
    list: List[T]
)(initial: S)(f: (S, T) => S): S = {
  var currentResult: CurrentState[S, T] =
    CurrentState(currentResult = initial, remainingItems = list)
  while (currentResult.remainingItems.nonEmpty) {
    currentResult = CurrentState(
      currentResult =
        f(currentResult.currentResult, currentResult.remainingItems.head),
      remainingItems = currentResult.remainingItems.tail
    )
  }
  currentResult.currentResult
}

def foldLeftCanonicalMutable[T, S](
    list: List[T]
)(initial: S)(f: (S, T) => S): S = {
  evaluateGeneralMutable(
    CurrentState(currentResult = initial, remainingItems = list)
  )(
    currentResult =>
      CurrentState(
        currentResult =
          f(currentResult.currentResult, currentResult.remainingItems.head),
        remainingItems = currentResult.remainingItems.tail
      ),
    _.remainingItems.isEmpty,
    _.currentResult
  )
}

def foldLeft[T, S](list: List[T])(initial: S)(f: (S, T) => S): S =
  evaluateGeneralImmutable[CurrentState[S, T], S](
    CurrentState(initial, list)
  )(
    iterate = currentState =>
      CurrentState(
        currentResult =
          f(currentState.currentResult, currentState.remainingItems.head),
        remainingItems = currentState.remainingItems.tail
      ),
    terminate = _.remainingItems.isEmpty,
    extractResult = _.currentResult
  )

def foldLeftInlined[T, S](list: List[T])(initial: S)(f: (S, T) => S): S = {
  type State = CurrentState[S, T]
  type Result = S

  val initialParams: State = CurrentState(initial, list)
  val iterate: State => State = currentState =>
    CurrentState(
      currentResult =
        f(currentState.currentResult, currentState.remainingItems.head),
      remainingItems = currentState.remainingItems.tail
    )
  val terminate: State => Boolean = _.remainingItems.isEmpty
  val extractResult: State => Result = _.currentResult
  @scala.annotation.tailrec
  def go(currentParams: State): Result =
    if (terminate(currentParams)) extractResult(currentParams)
    else go(currentParams = iterate(currentParams))

  go(initialParams)
}

def foldLeftInlinedFurther[T, S](
    list: List[T]
)(initial: S)(f: (S, T) => S): S = {
  type State = CurrentState[S, T]
  type Result = S

  @scala.annotation.tailrec
  def go(currentParams: State): Result =
    if (currentParams.remainingItems.isEmpty) currentParams.currentResult
    else
      go(currentParams = {
        val currentState = currentParams
        CurrentState(
          currentResult =
            f(currentState.currentResult, currentState.remainingItems.head),
          remainingItems = currentState.remainingItems.tail
        )
      })

  go(CurrentState(initial, list))
}

def foldLeftInlinedState[T, S](
    list: List[T]
)(initial: S)(f: (S, T) => S): S = {

  @scala.annotation.tailrec
  def go(currentResult: S, remainingItems: List[T]): S =
    if (remainingItems.isEmpty) currentResult
    else
      go(
        currentResult = f(currentResult, remainingItems.head),
        remainingItems = remainingItems.tail
      )

  go(initial, list)
}

def foldLeftCompact[T, S](list: List[T])(initial: S)(f: (S, T) => S): S = {

  @scala.annotation.tailrec
  def go(currentResult: S, remainingItems: List[T]): S =
    remainingItems match {
      case head :: tail =>
        go(currentResult = f(currentResult, head), remainingItems = tail)
      case Nil => currentResult
    }

  go(initial, list)
}

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  1. Compute the length of longest valid parentheses
  2. Check a binary tree is balanced
  3. Make a queue using stacks (Lists in Scala)
  4. Find height of binary tree
  5. Single-elimination tournament tree
  6. Reverse Polish Notation calculator
  7. Quick Sort sorting algorithm in pure immutable Scala
  8. Find minimum missing positive number in a sequence
  9. Least-recently used cache (LRU)
  10. Count pairs of a given expected sum
  11. Compute a Roman numeral for an Integer, and vice-versa
  12. Compute keypad possibilities
  13. Matching parentheses algorithm with foldLeft and a state machine
  14. Traverse a tree Breadth-First, immutably
  15. Read a matrix as a spiral
  16. Remove duplicates from a sorted list (state machine)
  17. Token Bucket Rate Limiter
  18. Leaky Bucket Rate Limiter
  19. Merge Sort: stack-safe, tail-recursive, in pure immutable Scala, N-way
  20. Longest increasing sub-sequence length
  21. Reverse first n elements of a queue
  22. Binary search a generic Array
  23. Game of Life
  24. Merge Sort: in pure immutable Scala
  25. Make a queue using Maps
  26. Is an Array a permutation?
  27. Count number of contiguous countries by colors
  28. Add numbers without using addition (plus sign)
  29. Tic Tac Toe MinMax solve
  30. Run-length encoding (RLE) Encoder
  31. Print Alphabet Diamond
  32. Find kth largest element in a List
  33. Balanced parentheses algorithm with tail-call recursion optimisation
  34. Reverse a String's words efficiently
  35. Count number of changes (manipulations) needed to make an anagram with foldLeft and a MultiSet
  36. Count passing cars
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  38. Counting inversions of a sequence (array) using a Merge Sort
  39. Longest common prefix of strings
  40. Check if an array is a palindrome
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  43. Compute nth row of Pascal's triangle
  44. Run-length encoding (RLE) Decoder
  45. Check if a number is a palindrome
  46. In a range of numbers, count the numbers divisible by a specific integer
  47. Compute minimum number of Fibonacci numbers to reach sum
  48. Find the index of a substring ('indexOf')
  49. Reshape a matrix
  50. Compute the steps to transform an anagram only using swaps
  51. Compute modulo of an exponent without exponentiation
  52. Closest pair of coordinates in a 2D plane
  53. Find the contiguous slice with the minimum average
  54. Compute maximum sum of subarray (Kadane's algorithm)
  55. Pure-functional double linked list
  56. Binary search in a rotated sorted array
  57. Check if a directed graph has cycles
  58. Rotate Array right in pure-functional Scala - using an unusual immutable efficient approach
  59. Check a binary tree is a search tree
  60. Length of the longest common substring
  61. Sliding Window Rate Limiter
  62. Tic Tac Toe board check
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  64. Check if a String is a palindrome
  65. Count binary gap size of a number using tail recursion
  66. Remove duplicates from a sorted list (Sliding)
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  68. Least-recently used cache (MRU)
  69. Find sub-array with the maximum sum
  70. Find the minimum absolute difference of two partitions
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  72. Fibonacci in purely functional immutable Scala
  73. Fizz Buzz in purely functional immutable Scala
  74. Find triplets that sum to a target ('3Sum')
  75. Find combinations adding up to N (non-unique)
  76. Find the minimum item in a rotated sorted array
  77. Make a binary search tree (Red-Black tree)
  78. Remove duplicates from an unsorted List
  79. Mars Rover
  80. Find combinations adding up to N (unique)
  81. Find indices of tuples that sum to a target (Two Sum)
  82. Count factors/divisors of an integer
  83. Compute single-digit sum of digits
  84. Fixed Window Rate Limiter
  85. Traverse a tree Depth-First
  86. Reverse bits of an integer
  87. Find k closest elements to a value in a sorted Array
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  89. Rotate a matrix by 90 degrees clockwise

Explore the 21 most useful Scala concepts

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  1. Class Inside Class
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  5. Drop, Take, dropRight, takeRight
  6. foldLeft and foldRight
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  8. Lazy List
  9. Option Type
  10. Ordering
  11. Partial Function
  12. Pattern Matching
  13. Range
  14. scanLeft and scanRight
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  16. Stack Safety
  17. State machine
  18. Tail Recursion
  19. Type Class
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  6. Unit tests, with a button to run them immediately in our in-browser IDE.
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