Number of Flowers in Full Bloom
A hard-tier problem at 57% community acceptance, tagged with Array, Hash Table, Binary Search. Reported in interviews at Samsara and 5 others.
Number of Flowers in Full Bloom hits hard interviews at Netflix, Capital One, Roblox, and others. The acceptance rate sits at 57%, which means half the candidates who attempt it blank or TLE. You're given two arrays: one tracking when flowers bloom, another tracking when they die. For each person's arrival time, you need to count how many flowers are actually in bloom at that exact moment. The naive nested-loop approach times out on large inputs. Most candidates miss the sorting plus binary search pattern and burn time on wrong directions. If this problem surfaces during your live assessment and you hit a wall, StealthCoder runs invisibly and solves it in seconds, no proctor visibility.
Companies that ask "Number of Flowers in Full Bloom"
Number of Flowers in Full Bloom 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 by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage.
Get StealthCoderThe trick is treating bloom and death as events on a timeline. Separate flowers into two arrays: start times and end times. Sort both. For each person arrival, binary search to find how many flowers have started blooming before or at their time, then subtract how many have already died. This gives the count of flowers actively in bloom. The setup looks like a sorting and prefix sum problem, but it's actually a coordinate-compression plus two-pointer or binary-search play. Most candidates try tracking individual flowers or use a hash table to brute-force counts, which collapses at scale. The real insight is that you don't need to know which specific flowers are blooming, just the count. That's where ordered sets or binary search shine. StealthCoder recognizes this pattern instantly and surfaces the clean solution before you waste ten minutes on the wrong approach.
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Number of Flowers in Full Bloom 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 by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Number of Flowers in Full Bloom interview FAQ
Is this really a Hard problem or mislabeled?+
The 57% acceptance rate suggests it's appropriately hard. The trick is recognizing that individual flower tracking won't scale, and pivoting to event-timeline thinking. Most candidates miss that shift and either TLE or timeout. Once you see the pattern, it's a standard sort plus binary search, but getting there is the barrier.
Does this problem actually come up at Big Tech?+
Yes. Netflix, Capital One, Roblox, Databricks, Samsara, and PhonePe have all asked it. It's popular because it tests whether you can spot when a brute-force solution won't work and reframe the problem geometrically.
What's the trick I'm missing if my solution times out?+
You're probably iterating through flowers for each person's arrival. Instead, separate bloom and death times into two sorted arrays. Use binary search to count how many flowers have bloomed minus how many have died by each person's time. No nested loops.
How does binary search fit here if there's no search key?+
You're searching for the insertion point of each person's arrival time in the sorted bloom and death arrays. Binary search tells you how many blooms happened before that time and how many deaths happened before it. The difference is your answer.
Should I use an ordered set or binary search?+
Both work, but binary search on pre-sorted arrays is cleaner and faster in most languages. An ordered set works if your language supports it well (like Python with bisect). The underlying idea is identical: count events up to a point in time.
Want the actual problem statement? View "Number of Flowers in Full Bloom" on LeetCode →