Smallest Range Covering Elements from K Lists
A hard-tier problem at 70% community acceptance, tagged with Array, Hash Table, Greedy. Reported in interviews at Flipkart and 5 others.
You're given K sorted lists and need to find the smallest range that includes at least one number from each list. Flipkart, Lyft, Databricks, and Pinterest have all asked this. The brute force, checking every possible range, times out instantly. The trick is recognizing that you're really doing a constrained sliding window across K pointers, one in each list. Most candidates either miss the greedy insight or build the heap wrong and blow through time limits. This is the kind of problem where the obvious approach feels right but fails at scale. If you hit this live and blank on the heap-pointer strategy, StealthCoder surfaces the solution in seconds, invisible to the proctor.
Companies that ask "Smallest Range Covering Elements from K Lists"
Smallest Range Covering Elements from K Lists 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. Built because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround.
Get StealthCoderThe core pattern: maintain one pointer in each of the K lists, track the min and max values across those K pointers, and shrink from the side with the minimum to hunt for a tighter range. A min-heap tells you which list to advance next. Each time you advance a pointer, you've got a new range candidate. The gotcha is that you can't just greedily advance the max pointer; you advance from the min-heap position, always trying to shrink the gap. Most candidates either implement a naive K-pointer scan (too slow) or forget to handle the heap updates correctly. The acceptance rate sits near 70 percent, which means plenty of people get the general idea but fumble the execution. This is exactly the wall StealthCoder is built for: you understand the problem, you're under time pressure in the OA, and the heap bookkeeping trips you up.
Pattern tags
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Smallest Range Covering Elements from K Lists recycles across companies for a reason. It's hard-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. Built because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Smallest Range Covering Elements from K Lists interview FAQ
Why does a min-heap work here and not just track pointers manually?+
A min-heap tells you which list's pointer is holding back the overall range (the bottleneck). Advancing that pointer is the only move that can shrink your current range. Manual pointer tracking forces you to scan all K lists each iteration, ballooning time complexity. The heap makes each 'next best move' O(log K) instead of O(K).
Is this still asked at Flipkart, Lyft, and Databricks?+
Yes. Six companies in the dataset ask it, including Flipkart, Lyft, Databricks, and Pinterest. It's a standard high-difficulty screen for roles where multi-source data merging and optimization matter. Lower frequency than two-pointer classics, but absolutely live.
What's the trick I'm missing if I time out?+
You're probably iterating over all K lists to find the min on each step, or rebuilding the heap repeatedly. The real move: heap the pointers themselves, not the values. Pop the min-value entry, advance that list's pointer, re-heap. Total work per step is O(log K). Do that N times across all lists, you're at O(N log K).
How does this connect to Sliding Window and Greedy?+
The greedy choice is always advancing from the list whose current value is smallest; that's how you shrink. The sliding window is your range of values. Unlike typical sliding windows on one array, you're here managing windows across K lists simultaneously, which is why the heap is critical.
Why is this rated Hard and not Medium?+
The heap-pointer bookkeeping is non-obvious, and off-by-one bugs are rampant. The 70 percent acceptance rate reflects that many people understand the concept but ship buggy code under interview pressure. Getting the range update, the pointer advance, and the termination condition all right in one go is genuinely tricky.
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