Online Majority Element In Subarray
A hard-tier problem at 39% community acceptance, tagged with Array, Binary Search, Design. Reported in interviews at Nutanix and 0 others.
Online Majority Element In Subarray is a hard problem that sits at the intersection of two domains: query handling under streaming constraints and efficient range aggregation. Nutanix has asked this one. The acceptance rate is 39%, which tells you most engineers who hit this problem in an OA blank on the full approach. You can't precompute the answer in a traditional way because the queries come online, and a naive scan of every subarray times out on the hidden test cases. If this problem hits your live assessment and you're stuck between Binary Indexed Tree, Segment Tree, and binary search approaches, StealthCoder solves it invisibly in seconds.
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Online Majority Element In Subarray 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 is realizing you need to handle range queries dynamically without knowing the query set upfront. The naive O(n*q) approach fails immediately on larger inputs. Most candidates default to a single segment tree or Binary Indexed Tree, but this problem demands a hybrid: binary search over candidate majority elements combined with a data structure that can count occurrences of a single value in a range quickly. You preprocess the array by storing positions of each distinct value, then for each query range, you binary search on candidates (elements that appear in the range) and use a range-query structure to verify majority status. The gotcha is that majority means strictly more than half the range length, not just the most frequent element. StealthCoder handles the structural setup and indexing logic that trips up candidates during the live OA.
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Online Majority Element In Subarray 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. 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.
Online Majority Element In Subarray interview FAQ
Is this really asked in real OAs or just theoretical?+
Nutanix has confirmed it. At 39% acceptance, it's rare enough that most candidates don't drill it beforehand. It's the kind of problem that shows up in senior or systems-focused roles where online query handling matters.
Why doesn't a single segment tree work?+
A segment tree stores aggregates (sum, max, min) over ranges, not majority elements. Majority status depends on the specific value and the range length, which can't be precomputed. You need to query counts of individual candidates, not aggregate properties.
What's the actual majority definition here?+
An element is majority in a subarray if its count is strictly greater than floor(length / 2). It's not just the most frequent. This distinction breaks naive voting algorithms and forces you to verify against a threshold, not just find the mode.
Does binary search on candidates actually work?+
Yes. The candidates for majority in any range are only elements that appear in that range. You binary search on positions of candidates using a Binary Indexed Tree or segment tree to count their occurrences quickly. Reduces complexity from brute force.
How do you handle the online constraint?+
You can't precompute all queries. Instead, preprocess the array into a position index per value and a range-query data structure. When a query arrives, you run the binary search and counting logic on demand. Design matters more than raw speed here.
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