Top K Elements Pattern

When to use Top K Elements

• The problem asks for the k largest, smallest, or most frequent elements in a collection.
• You need the k points closest to the origin (or any reference point) in a list of coordinates.
• You need the kth largest element in a stream where elements arrive one at a time.
• You want to avoid the full O(n log n) sort cost when k is much smaller than n.
• You need to reorganize or rearrange characters / tasks based on top frequencies.

Template

function topKLargest(nums, k) {
  const minHeap = new MinHeap();
  for (const num of nums) {
    minHeap.push(num);
    if (minHeap.size > k) {
      minHeap.pop();
    }
  }
  return minHeap.toArray();
}

Common mistakes

• Using a max-heap of size n when a min-heap of size k would suffice; the larger heap costs O(n log n) and defeats the pattern's purpose.
• Pushing first and then conditionally popping vs comparing the heap top before pushing — both work, but mixing the two creates off-by-one heap sizes.
• Forgetting to bound the heap; if you never pop, you end up with a fully-sorted output and lose the k-bound on memory.
• On ties in Top K Frequent Words, ignoring the secondary lexicographic sort and emitting the wrong order.
• Using `Array.prototype.sort` as a shortcut and writing O(n log n) when the interviewer explicitly asks for better than full-sort complexity.