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  • Reducing memory usage 10 times with High-Performance Primitive Collections

    Kotlin basic types such as Int or Double correspond to high-performance Java primitive types such as int or double. But nullable (Int?) and generic (<Int>) versions of those types are mapped to boxed Java types such as Integer or Double.

    Boxed types are memory heavy. Let’s make a simple comparison.

    Kotlin
    @Test
fun `memory occupied by primitive int`() {
    data class A(
        val x: Int
    )

    val N = 100_000_000

    val mem1 = calculateOccupiedMemoryMB()

    val list = List(N) { A(it) }

    val mem2 = calculateOccupiedMemoryMB()

    println("Occupied memory: ${mem2 - mem1} MB")

    list
}

> Occupied memory: 1910 MB
    Kotlin
    @Test
fun `memory occupied by boxed Int`() {
    data class A(
        val x: Int?
    )

    val N = 100_000_000

    val mem1 = calculateOccupiedMemoryMB()

    val list = List(N) { A(it) }

    val mem2 = calculateOccupiedMemoryMB()

    println("Occupied memory: ${mem2 - mem1} MB")

    list
}

> Occupied memory: 3436 MB

    We already see almost 2x difference, but actually it’s more serious as our test is not accurate enough.

    Code explained

    calculateOccupiedMemoryMB measures the diff between total and occupied memory running garbage collection for at least 3 seconds in advance to reduce the garbage footprint.

    Kotlin
    fun calculateOccupiedMemoryMB(): Int {
    getRuntime().gc()
    Thread.sleep(3000)
    return ((getRuntime().totalMemory() - getRuntime().freeMemory()) / (1024 * 1024)).toInt()
}

    list reference at the end of the block is a trick to avoid JVM optimization. If JVM sees an object is not used it might wipe it off the RAM.

    What if we need a huge Set of Int‘s or a huge Map of Int to Object? Unfortunately standard Java Collections are based on generics which means all of the objects will be autoboxed.

    Here HPPC: High Performance Primitive Collections comes to the rescue. This library has predefined collection for all the primitive types.

    Let’s compare memory footprints of a normal Java HashSet<Int> and a corresponding HPPC IntHashSet.

    Kotlin
    @Test
fun `memory occupied by HashSet`() {
    val N = 100_000_000

    val mem1 = calculateOccupiedMemoryMB()

    val set = hashSetOf<Int>()
    repeat(N) { set.add(it) }

    val mem2 = calculateOccupiedMemoryMB()

    println("Occupied memory: ${mem2 - mem1} MB")

    1 in set
}

> Occupied memory: 5098 MB
    Kotlin
    @Test
fun `memory occupied by HashSet`() {
    val N = 100_000_000

    val mem1 = calculateOccupiedMemoryMB()

    val set = IntHashSet()
    repeat(N) { set.add(it) }

    val mem2 = calculateOccupiedMemoryMB()

    println("Occupied memory: ${mem2 - mem1} MB")

    1 in set
}

> Occupied memory: 518 MB

    10 times less memory used!