How do you find the top-K elements from a large stream efficiently?
Reviewed by Gurusankar M.
⚡ Short Answer
Keep a min-heap (PriorityQueue) of size K: push each element, and when size exceeds K pop the smallest. You retain the K largest in O(n log K) time and O(K) space — far better than sorting everything (O(n log n)).
☕Coffee Chat Question
Concept Made Simple
“How do you find the top-K elements from a large stream efficiently?”
🧠Mind Map Answer
Remember It Faster
For top-K largest, use a min-heap of size K: the smallest of your current best-K sits at the top, ready to be evicted the moment something larger arrives.
⌨️Hands-on Keyboard
Learn by Doing
PriorityQueue<Integer> heap = new PriorityQueue<>(); // min-heap
for (int x : stream) {
heap.offer(x);
if (heap.size() > k) heap.poll(); // drop smallest
}
// heap now holds the k largestk largest elements
🔥What If?
Think Beyond the Expected
Why a min-heap for top-K largest, not a max-heap?
A size-K min-heap keeps the smallest of your current top-K at the root, so you can evict it in O(log K) when a larger element arrives — using only O(K) memory. A max-heap of all n elements needs O(n) space and gains nothing.
😂Real World
Top-N dashboards (highest-value orders, slowest endpoints, top customers) over huge datasets use a bounded heap so you never load or sort the whole dataset in memory.
🎯Interviewer's Expectation
Keywords they're listening for:
⚠️Common Mistakes
- ✗Sorting the entire dataset to take K
- ✗Using a max-heap of all n elements (O(n) space)
- ✗Wrong heap direction (min vs max)
✅Best Practices
- ✓Bound the heap to K for streaming top-K
- ✓Use a Comparator for domain objects
- ✓Consider QuickSelect for one-shot in-memory top-K
🔁Follow-up Questions
- 1How does the heap-based approach compare to full sort or QuickSelect?
- 2How would you parallelize top-K across shards?
- 3What Comparator do you use for top-K by a custom field?
🧩Related Technologies
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Plain-language foundations
I'm preparing for a software engineering interview and want to understand this from scratch, as a beginner. Topic: Performance (Java Collections) Interview question: "How do you find the top-K elements from a large stream efficiently?" Please: 1. Explain the core idea in simple, plain language, using an everyday analogy. 2. Define any technical terms you use. 3. Walk through one small, concrete example. 4. Finish with a single sentence I can easily remember. Keep the tone friendly and assume I'm new to this topic.
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