In this OpenAI technical interview, the candidate was asked to implement a simplified memory allocator — essentially the core logic of malloc and free. The problem statement was presented as follows:

Given N free bytes in memory, implement the following two functions.

• malloc(k): allocates a block of k bytes of memory and returns a pointer to the beginning of the block. The assigned memory should be one contiguous space in the memory.
• free(ptr): frees the memory that is pointed to by the input pointer.

Restrictions:

• The assigned memory block has to be a contiguous sequence of bytes.
• Once a memory block is assigned, you cannot move it (no fragmentation cleanup after malloc).

Example:

m = MemoryManager(1000)
a = m.malloc(100)   # -> [0..99]
b = m.malloc(500)   # -> [100..599]
m.malloc(950)       # -> error
m.free(b); m.free(a)
m.free(a)           # -> error
m.malloc(950)       # -> success

What the interviewer really wanted to see was not just working code, but whether the candidate could abstract memory allocation into the right data structures, reason clearly about edge cases, and balance algorithmic complexity with practical trade-offs.

With CSOahelp’s support, the candidate began by clarifying important constraints: is N always positive, can malloc requests be invalid (zero or negative sizes), and should free detect double frees or invalid pointers? These clarifications, while subtle, establish a foundation of rigor and prevent messy edge cases later on.

Two design paths were then presented. The first was the simplest to explain: maintain an ordered list of allocated intervals, sorted by starting address, and add sentinel markers at the beginning and end. When malloc(k) is called, scan adjacent pairs of intervals to detect gaps; if a gap of size ≥ k exists, allocate from the left edge and record the mapping. free(ptr) simply checks the mapping and removes the corresponding interval. This approach is easy to reason about and communicates the essential points clearly, but its limitation is obvious: as allocations grow, malloc requires linear scans, making it less efficient.

The second design was closer to what a real system might use, and also what interviewers usually probe for. Instead of focusing on allocated blocks, it manages free blocks sorted by size. This allows malloc(k) to quickly locate the smallest available block that fits (best-fit), splitting if necessary. When free(ptr) is called, the released block is merged with adjacent free blocks (coalescing), preventing fragmentation from spiraling out of control. Compared to the first solution, this achieves O(log n) behavior for both allocation and deallocation, and scales better under heavy load, though it requires more bookkeeping.

As expected, the interviewer pushed on edge cases. What if a pointer is freed twice? The answer: check the mapping; if it’s missing, raise an error. What if no contiguous space exists? Either return an error code or throw an exception — never silently fail. What about fragmentation? Since blocks cannot be moved once allocated, compaction isn’t allowed, so the only remedy is coalescing during free.

Throughout the conversation, CSOahelp’s role was not to drop tiny hints but to provide a ready-to-reproduce narrative: begin with the simple solution to show correctness, then expand into the optimized version to demonstrate depth; cover the main logic, then walk systematically through the failure cases and complexity analysis. This let the candidate speak with confidence and clarity, without gaps or hesitation.

By the end, the candidate had clearly explained the trade-offs between the two designs and delivered a solid complexity analysis: the gap-scanning method is straightforward but linear, while the free-list method scales to O(log n) at the cost of complexity. This structured, layered explanation was exactly what the OpenAI interviewers were looking for.

And this is where CSOahelp proves its unique value. Instead of scrambling for ideas under pressure, the candidate had a full, annotated solution to articulate — not just code, but the reasoning, the trade-offs, and the edge cases. With this support, what might have been a stressful coding exercise became an opportunity to demonstrate structured thinking and technical maturity.

If you’re preparing for algorithm and system design interviews at Apple, Meta, TikTok, etc., but aren’t sure how to break down problems and handle edge cases, get North American interview secrets. We also offer interview proxy, interview assistance, OA writing, and more to help you land your dream job sooner~

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