# 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

### Def Inside Def

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

It is also frequently used in combination with Tail Recursion.

### 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!

### Pattern Matching

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

### 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.

### Tail Recursion

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