Algorithm goal

The leaky bucket algorithm provides a constant output rate based on an input, and where the maximum capacity is exceeded, inputs are ignored.

Goal: Implement a leaky bucket algorithm, where you have:

• Maximum capacity (an integer)
• Leak rate as a parameter

Test cases in Scala

assert(sampleLeakRate.canLeak(java.time.Duration.ofMillis(199)) == 0)
assert(sampleLeakRate.canLeak(java.time.Duration.ofMillis(200)) == 1)
assert(sampleLeakRate.canLeak(java.time.Duration.ofMillis(201)) == 1)
assert(sampleLeakRate.canLeak(java.time.Duration.ofMillis(1200)) == 6)
assert(sampleBucket[Int].accept(1).nonEmpty)
assert(
  sampleBucket[Int]
    .accept(1)
    .map(_.newTime(sampleInstant.plusMillis(199)))
    .toVector
    .flatMap(_.emitted) == Vector.empty,
  "Adding an item less than 200ms before does not emit it"
)
assert(
  sampleBucket[Int]
    .accept(1)
    .map(_.newTime(sampleInstant.plusMillis(201)))
    .toVector
    .flatMap(_.emitted) == Vector(1),
  "Adding an item at 200ms emits it"
)
assert(
  sampleBucket[Int]
    .accept(1)
    .map(_.newTime(sampleInstant.plusMillis(201)))
    .exists(_.queue.isEmpty),
  "Queue becomes empty after 200ms"
)
assert(
  sampleBucket[Int]
    .accept(1)
    .flatMap(_.accept(2))
    .flatMap(_.accept(3))
    .flatMap(_.accept(4))
    .flatMap(_.accept(5))
    .nonEmpty,
  "5 items can be accepted"
)
assert(
  sampleBucket[Int]
    .accept(1)
    .flatMap(_.accept(2))
    .flatMap(_.accept(3))
    .flatMap(_.accept(4))
    .flatMap(_.accept(5))
    .flatMap(_.accept(6))
    .isEmpty,
  "6th item cannot be accepted"
)
assert(
  sampleBucket[Int]
    .accept(1)
    .flatMap(_.accept(2))
    .flatMap(_.accept(3))
    .flatMap(_.accept(4))
    .flatMap(_.accept(5))
    .map(_.newTime(sampleInstant.plusSeconds(1)))
    .flatMap(_.accept(6))
    .nonEmpty,
  "After 1 second, and dequeuing, we can enqueue again"
)

Algorithm in Scala

78 lines of Scala (compatible versions 2.13 & 3.0).

Explanation

We implement several aspects: a LeakyBucket class to contain our state (immutable), as well as a class to describe the leak rate. As a result of the algorithm we also need to implement an extension function `dequeueUpToN`, which allows to dequeue up to `n` values from a Queue (and return 'None' if there are no values at all to dequeue). (this is © from www.scala-algorithms.com)

Scala concepts & Hints

1. Def Inside Def

   A great aspect of Scala is being able to declare functions inside functions, making it possible to reduce repetition.

   def exampleDef(input: String): String = {
     def surroundInputWith(char: Char): String = s"$char$input$char"
     surroundInputWith('-')
   }
   
   assert(exampleDef("test") == "-test-")
   

   ▶ Run code in the IDE

   It is also frequently used in combination with Tail Recursion.

2. 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)
   

   ▶ Run code in the IDE

3. Pattern Matching

   Pattern matching in Scala lets you quickly identify what you are looking for in a data, and also extract it.

   assert("Hello World".collect {
     case character if Character.isUpperCase(character) => character.toLower
   } == "hw")
   

   ▶ Run code in the IDE

4. 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)
   

   ▶ Run code in the IDE

5. 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)
   

   ▶ Run code in the IDE