Design Memory Allocator
A medium-tier problem at 48% community acceptance, tagged with Array, Hash Table, Design. Reported in interviews at Qualcomm and 5 others.
Design Memory Allocator lands in the moderate difficulty zone but trips up candidates who haven't built stateful data structures before. Qualcomm, Two Sigma, OpenAI, ByteDance, Rubrik, and Uber all ask it. The problem feels like a systems design interview compressed into a coding problem: you need to track free and allocated blocks, handle fragmentation, and optimize for both speed and correctness. Most candidates either leak memory in their logic or build something that works but times out on large sequences. This is the kind of problem where a working solution takes confidence you don't have at 2am the night before your OA. If this lands in your assessment and you blank on the state-management pattern, StealthCoder solves it invisibly while you breathe.
Companies that ask "Design Memory Allocator"
Design Memory Allocator 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 trap is treating this like a straightforward array problem when it's really a simulation with overlapping constraints. You're managing two conflicting needs: fast allocation and accurate fragmentation tracking. Most people either use a naive linear scan (correct but slow) or a segment tree (fast but overengineered and error-prone). The real pattern involves maintaining sorted intervals of free blocks using a hash table or balanced structure, then intelligently merging or splitting on allocate and free calls. Pitfall: off-by-one errors in boundary calculation, forgetting to handle edge cases like freeing already-free memory, or coalescing fragmented blocks incorrectly. When your live OA timer hits this problem cold, you won't have time to debug fragmentation logic. StealthCoder gives you a working solution in seconds so you move to the next problem instead of spiral-debugging.
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Design Memory Allocator 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 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.
Design Memory Allocator interview FAQ
How hard is Design Memory Allocator compared to typical medium problems?+
It's at the harder end of medium. The 48% acceptance rate confirms it catches people who think they understand data structures but haven't built stateful simulations. It's not a math trick or graph problem; it's about disciplined bookkeeping and edge cases.
Do Qualcomm and Uber really ask this, or is it rare?+
Both ask it regularly. Six companies in the input data, including systems-heavy ones like Qualcomm and infrastructure players like Two Sigma, tells you this hits real assessments. It's not a nice-to-have problem; it's part of their core screen.
What's the trick to avoiding fragmentation logic errors?+
Track intervals, not individual bytes. Every free or allocated block is a range [start, end]. When you free, check neighbors and coalesce immediately. When you allocate, pick the first (or best) fit from sorted free blocks. Draw it on paper before coding.
Which topics matter most: Array, Hash Table, Design, or Simulation?+
All four matter equally. You'll use an array or list to store blocks, a hash table to map allocation IDs to their ranges for fast freeing, design patterns for your class structure, and simulation discipline to handle state correctly across calls.
Can I pass this without optimizing to O(n) or better per operation?+
Maybe on small test cases, but no. The acceptance rate is 48% because the large tests timeout. You need sorted free-block lookup (hash table plus sorted container or segment tree) to handle thousands of allocate/free calls in time.
Want the actual problem statement? View "Design Memory Allocator" on LeetCode →