Top-K with a Heap - Swift DSA

Keep only the largest k values by maintaining a small min-heap.

Algorithm

@steps

Store the heap in an array.
Compare parent and child indexes instead of building explicit tree nodes.
Swap only when the heap order is violated.
Print the deterministic final heap state for replay comparison. @end @complexity

Time: O(n log k)
Space: O(k) @end

bounded heap For top-k largest values, a min-heap of size k keeps the current cutoff at the root.

Swift DSA Implementation

basic.swift

func listString(_ values: [Int]) -> String { "[" + values.map(String.init).joined(separator: ", ") + "]" }
func heapInsert(_ heap: inout [Int], _ value: Int) {
    heap.append(value)
    var child = heap.count - 1
    while child > 0 {
        let parent = (child - 1) / 2
        if heap[parent] <= heap[child] { break }
        heap.swapAt(parent, child)
        child = parent
    }
}
func heapPop(_ heap: inout [Int]) -> Int {
    let smallest = heap[0]
    heap[0] = heap.removeLast()
    var parent = 0
    while true {
        let left = parent * 2 + 1
        let right = left + 1
        if left >= heap.count { break }
        var child = left
        if right < heap.count && heap[right] < heap[left] { child = right }
        if heap[parent] <= heap[child] { break }
        heap.swapAt(parent, child)
        parent = child
    }
    return smallest
}
var heap: [Int] = []
for value in [5, 1, 9, 3, 7, 2] { heapInsert(&heap, value); if heap.count > 3 { _ = heapPop(&heap) } }
print(listString(heap.sorted(by: >)))

@end @output [9, 7, 5] @end