Scala algorithm: Fixed Window Rate Limiter

Published

Algorithm goal

The fixed window rate limiter re-sets the available tokens when the next window is reached, which is also the same length.

Tokens are taken from the window and once exhausted, taking is no longer possible.

This algorithm could be used when dealing with APIs that rate-limit you, and you don't want to exceed their rate limits

Test cases in Scala

assert(
  sampleFixedWindow.take1.nonEmpty,
  "One last remaining item can be taken"
)
assert(
  sampleFixedWindow.take1.flatMap(_.take1).isEmpty,
  "Cannot take 2 before a refresh"
)
assert(
  sampleFixedWindow.updateTime(sampleInstant.plusSeconds(4)).remaining ==
    sampleFixedWindow.remaining,
  "4 second refresh has no effect"
)
assert(
  sampleFixedWindow.updateTime(sampleInstant.plusSeconds(5)).remaining ==
    sampleFixedWindow.remaining,
  "5 second refresh, does not yet re-set it"
)
assert(
  sampleFixedWindow
    .updateTime(sampleInstant.plusSeconds(5).plusMillis(1))
    .remaining == 5,
  "5 second & a bit refresh, resets it back to 5"
)
assert(
  sampleFixedWindow.updateTime(sampleInstant.plusSeconds(500)).remaining ==
    5,
  "500 second refresh, resets it back to 5"
)

Algorithm in Scala

28 lines of Scala (compatible versions 2.13 & 3.0), showing how concise Scala can be!

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Explanation

The key part here is the 'updateTime' function, which checks for whether we are after the end of the current window. If we are, then we re-set the 'remaining' token counter.

We also want to make sure that the last window is updated, which is why we update the 'startOfWindow' value as well. (this is © from www.scala-algorithms.com)

Scala concepts & Hints

  1. Option Type

    The 'Option' type is used to describe a computation that either has a result or does not. In Scala, you can 'chain' Option processing, combine with lists and other data structures. For example, you can also turn a pattern-match into a function that return an Option, and vice-versa!

    assert(Option(1).flatMap(x => Option(x + 2)) == Option(3))

assert(Option(1).flatMap(x => None) == None)

  2. Stack Safety

    Stack safety is present where a function cannot crash due to overflowing the limit of number of recursive calls.

    This function will work for n = 5, but will not work for n = 2000 (crash with java.lang.StackOverflowError) - however there is a way to fix it :-)

    In Scala Algorithms, we try to write the algorithms in a stack-safe way, where possible, so that when you use the algorithms, they will not crash on large inputs. However, stack-safe implementations are often more complex, and in some cases, overly complex, for the task at hand.

    def sum(from: Int, until: Int): Int =
  if (from == until) until else from + sum(from + 1, until)

def thisWillSucceed: Int = sum(1, 5)

def thisWillFail: Int = sum(1, 300)

  3. Tail Recursion

    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)

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Explore the 22 most useful Scala concepts

To save you going through various tutorials, we cherry-picked the most useful Scala concepts in a consistent form.

  1. Class Inside Class
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  8. Lazy List
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  10. Ordering
  11. Partial Function
  12. Pattern Matching
  13. Range
  14. scanLeft and scanRight
  15. Sliding / Sliding Window
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  18. Tail Recursion
  19. Type Class
  20. Variance
  21. View
  22. Zip

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