Familiarising yourself with lambdas in Kotlin

Kotlin is a language that was built on top of the JVM and what really sets it apart from Java is the emphasis on functions being a first-class construct, meaning many fancy things like lambdas are directly available to Kotlin developers without having to include bulky constructs like the concept of functional interfaces.

What are lambdas?

In my article on Diving Into Streams, I explained how lambdas were this miraculous constructs in Java that removed a lot of the boilerplate required when passing behavior from one method to another.

However, this definition was really under the assumption of pure Java. Lambdas, as a whole, are a mathematical concept. In programming, a lambda can be seen as a “portable” function or a piece of behavior that can be passed around and used accordingly.

Then, what are functions?

Functions are simply pieces of behavior that take in some input (or none at all) and return some output (or none at all) , this is very much the same as the mathematical concept it is based off of.

A case for lambdas

Scenario:

Say you were working in a performance-critical situation and you just discovered that there is a major memory issue with a function and it is causing some major performance drawbacks and you want to see how long it takes to benchmark and fix this issue.

“No biggie, I can take the time before and after the execution and check how long that took to benchmark the function” would be something you are thinking to yourself and that would be true in a simple case like ours.

You begin to write some code to test this function:

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fun foo(upper: Int): Int {
  var sum = 0
  for(i in 1..upper) sum += i
  return sum
}

fun main(main: Array<String>) {
  val before = System.currentTimeMillis()
  println("foo returns: ${foo(10000)}")
  val after = System.currentTimeMillis()
  println("foo took ${after - before}ms to run")
}

Awesome! It worked, you can now go on your merry way and fix the bug. Then you manager comes around and tells you yet another performance issue was found and you think “Eh, this is kind of tedious to redo so I guess I could copy-paste the code I made earlier”.

Now, would you be able to continually repeat this if you suddenly had a whole class of functions that needed to be tested?

Lambdas to the rescue!

Going back to how I mentioned how a lambda is a "portable" function or a piece of behavior that can be passed around and used accordingly.

In this scenario, we ideally, would want to have a function that can time any function with a method signature like foo() and this timing function would not need to know anything about the function it receives, all it needs to know it that it should calculate the time before and after this function took to run and print that out, nicely formatted.

Some basic pseudo-code would look like this:

Defining structure

In Kotlin, in order to declare a lambda, you have to declare a function type, or some kind of contract for the functions to be passed to follow. To simplify things, all the memory leaks that have surfaced all take in exactly 1 Int input and return an Int, so the function type for this case would look something like:

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func: (Int) -> Int

Note how the function type is literally just the method signature of a potential function without the parameter names. As mentioned previously, these function types are merely just contracts, it defines what a function to be passed in should look like, it does not dictate what the inputs will be used for (generally what the parameter names represent) nor does it dictate the specific behaviors of this function.

Creating the timing function

Now that we have the general structure of the function we want to be able to time, we can then proceed with declaring the function body of this timer.

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fun functionTimer(name: String, upper: Int, func: (Int) -> Int) {
  val before = System.currentTimeMillis();
  println("$name returned ${func(upper)}")
  val after = System.currentTimeMillis();
  println("$name took ${after - before}ms to run")
}

As you can see, this function’s body is very much the same as the original method of timing the function in the main method. However, whilst there were some similarities, the benefits of creating a specialized function for timing is as follows:

1. Modularity - your code is now modular and can be reused if needed
2. Flexible - because this is a function, you can apply consistent changes to the timing and the changes made will be reflected in any function timed by this function made
3. Abstraction - a new user of your codebase does not need to think about how a function is timed, all they need to know is how to call this timing function and they know that this function will help to time and do everything for them
4. Consistency - we can remove the upper argument and apply a consistent argument to be passed to func so that the timing is not skewed by changes in the upper, thus ensuring a degree of consistency

Timing things

With the newly made timing function, we can then begin to time how long each function took in our class. Let’s begin with the first function we tested, which simply added a bunch of numbers from 1 up till an upper value.

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fun main(args: Array<String>) {
  functionTimer("add", 10000) {
    var sum = 0
    for (i in 1..it) sum += i
    sum
  }
}

What we have used is the lambda syntax Kotlin provides. We declare the arguments of the functionTimer function as per usual, but we then use {} which houses the body of the function. We also use the it keyword, to refer to the one and only input this lambda receives, which we can assume would be the upper limit (in this case, 10000).

Another thing to note is we omit the return keyword from the lambda body even though the function type specifies that each function has to return an Int. We simply put the value to be returned on a line of its own and Kotlin is clever enough to know to return this value.

Now, we can extend this function to work with any other function that takes in a single Int input and return an Int…

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functionTimer("multiply", 100) {
  var result = 1
  for (i in 2..it) result *= i
  result
}

functionTimer("wacky", 1000) { (1..it).map { num -> num * 3 }.sum() }

Sample output:

add returned 50005000
add took 1ms to run
multiply returned 0
multiply took 0ms to run
wacky returned 1501500
wacky took 58ms to run

You can see how flexible we can make this timer be, taking in all sorts of functions and timing them.

Timing existing functions

Sometimes, you might not want to rewrite a long function just to time it, and that is perfectly fine, you can pass a reference to an existing method for it to be run as such:

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functionTimer("add", 10000, ::add)

Conclusion

Lambdas are far more than just useful for timing stuff, it is extremely versatile and Kotlin uses them extensively throughout their language, in Collections to APIs that leverage off the design of lambdas.

Some resources for further reading:

• Kotlin documentation
• Baeldung