K Closest Points to Origin
A medium-tier problem at 68% community acceptance, tagged with Array, Math, Divide and Conquer. Reported in interviews at Wix and 8 others.
K Closest Points to Origin is a medium-difficulty problem that appears regularly in assessments at Meta, Amazon, and LinkedIn. You're given a list of points and need to return exactly K of them that are nearest to the origin. The acceptance rate sits around 68%, which sounds high until you realize most solutions are naive sorts that barely pass. The trick isn't sorting all points. It's knowing which algorithm to deploy based on what K looks like relative to the total. If you hit this live and blank on the optimization, StealthCoder runs invisibly during screen share and surfaces a working solution in seconds.
Companies that ask "K Closest Points to Origin"
K Closest Points to Origin 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 Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE.
Get StealthCoderThe naive approach sorts all points by Euclidean distance, then slices the first K. It works but wastes time sorting data you don't need. The real patterns are Quickselect, which partitions in O(n) average time without full sorting, and a min-heap approach that's elegant when K is much smaller than n. A max-heap of size K is another solid choice for small K. The gotcha: candidates often implement Quickselect correctly but mess up the distance comparison or forget that you're comparing squared distances to avoid sqrt overhead. Companies like Wix and Asana ask this to test whether you know the difference between 'sort everything' and 'select K'. If Quickselect or heap logic escapes you mid-OA, StealthCoder surfaces a production-ready solution that handles edge cases and passes all test suites.
Pattern tags
You know the problem.
Make sure you actually pass it.
K Closest Points to Origin recycles across companies for a reason. It's medium-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 Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE. Works on HackerRank, CodeSignal, CoderPad, and Karat.
K Closest Points to Origin interview FAQ
Is sorting all points and slicing K actually wrong?+
Technically no, it passes. But it's O(n log n) when you can do O(n) average with Quickselect. Interviewers at Meta and Amazon often ask you to optimize after your first pass. If you lead with naive sort, be ready to explain Quickselect or a heap-based approach unprompted.
Why do people use a max-heap instead of sorting?+
A max-heap of size K keeps only K closest points in memory and runs in O(n log K) time. When K is much smaller than n (like K=3 from 10,000 points), this beats sorting. It's space-efficient and avoids full iteration of unsorted data.
Does the distance formula matter, or can I skip sqrt?+
Always compare squared distances. Since sqrt is monotonically increasing, sqrt(a) < sqrt(b) if and only if a < b. Skipping sqrt saves CPU and avoids floating-point precision issues. This is a detail Axon and Visa interviewers sometimes probe.
When do I use Quickselect over a heap?+
Quickselect is faster O(n) average time, but harder to implement correctly under pressure. A heap is O(n log K) and more forgiving. If K is small relative to n, heap is safer. If you know Quickselect cold, it's the flex.
Will this problem come up if I interview at Snap or LinkedIn?+
Yes. Both companies have asked it. It's a classic top-K selection problem. The medium difficulty and variety of valid approaches (sort, Quickselect, heap) make it a reliable filter question across tech companies.
Want the actual problem statement? View "K Closest Points to Origin" on LeetCode →