Kth Largest Element in a Stream
A easy-tier problem at 60% community acceptance, tagged with Tree, Design, Binary Search Tree. Reported in interviews at Tinder and 4 others.
You're in an online assessment and you see Kth Largest Element in a Stream. It's marked Easy, which usually means you should knock it out fast. But the acceptance rate hovers around 60%, and companies like Tinder, Box, and Wells Fargo have asked it repeatedly. The trap is that a naive sorted list approach will time out on large streams. The real solution lives in heaps, and if you freeze during the live OA, StealthCoder runs invisible to the proctor and surfaces the working code in seconds.
Companies that ask "Kth Largest Element in a Stream"
Kth Largest Element in a Stream is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know.
Get StealthCoderThe trick here is that you're not sorting the entire stream once. You're maintaining the kth largest as data arrives continuously. A min-heap of size k does this elegantly: keep only the k largest elements, with the smallest of those k at the root. When a new element arrives, compare it to the root. If it's larger, pop the root and insert the new element. If it's smaller or equal, ignore it. Most candidates default to sorting the whole list after each insertion, which is O(n log n) per call. The heap approach is O(log k) per insertion. On a large stream, that difference explodes. If you hit this problem cold in an assessment and the heap pattern doesn't click, StealthCoder is your safety net.
Pattern tags
You know the problem.
Make sure you actually pass it.
Kth Largest Element in a Stream recycles across companies for a reason. It's easy-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Kth Largest Element in a Stream interview FAQ
Why does the naive sorted-list approach fail?+
Sorting the entire stream after each insertion is O(n log n) per call. With hundreds or thousands of insertions, you'll time out. A min-heap of size k maintains the kth largest in O(log k) time per insertion, which scales much better on large data streams.
Is this still asked at major companies?+
Yes. Tinder, Box, Wells Fargo, Arista Networks, and Atlassian have all reported asking it. The 60% acceptance rate suggests many candidates either miss the heap insight or implement it sloppily under time pressure.
What's the core insight I need to remember?+
Use a min-heap of exactly k elements. The root is always the kth largest. When a new element arrives, if it's larger than the root, evict the root and insert the new element. This keeps the heap size constant and the operation time logarithmic.
How does this relate to the other topics listed?+
It touches Tree, Binary Search Tree, and Binary Tree concepts because heaps are tree structures. The Heap (Priority Queue) topic is the core. Design matters because you're architecting a data structure that answers queries efficiently as data streams in.
What's the most common mistake during the live assessment?+
Candidates either forget to initialize the heap with the first k elements before processing the stream, or they compare new elements to the wrong value. Read the problem carefully: some variants ask for the kth largest immediately, others only after k elements arrive.
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