# Scala algorithm: Leaky Bucket Rate Limiter

Published

## 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 Â© 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-")
``````

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)
``````
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")
``````
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)
``````
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)
``````

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