Meta·Software Engineer·Onsite - Coding / Algorithms·Senior

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Jun 2026

Summary

Meta SWE coding round, one problem the whole time: a memory allocator simulation. Felt more like a systems design-lite question dressed up as a coding problem, which I wasn't totally prepared for.

Questions Asked (1)

You're given a memory bitmap of 0s and 1s. Implement an alloc(x) function that finds the leftmost contiguous run of x free cells starting at an 8-byte aligned index, marks them as occupied, and returns a unique allocation ID. Also implement erase(id) that frees the previously allocated block. Discuss fragmentation behavior under interleaved calls.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The bitmap part felt manageable at first but the alignment constraint tripped me up.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a data structure that efficiently finds the leftmost aligned free run and supports O(1) allocation ID mapping. Implement alloc and erase with careful handling of alignment and fragmentation, and discuss trade-offs between time and space complexity.

Pro tip: Mention that alignment constraints can be handled by scanning only aligned indices, and that a free-list or interval tree can reduce search time; also note that fragmentation is inherent but can be mitigated with coalescing or best-fit strategies.

1. Clarify Requirements and Constraints

Ask about bitmap size, expected allocation sizes, alignment requirements, and whether allocations can be freed out of order. Confirm that 'leftmost' means smallest index and that alignment is 8-byte (64-bit) aligned.

2. Design Data Structures

Propose a bitmap for occupancy and a hash map from allocation ID to start index and size. Consider auxiliary structures like a free list or interval tree to speed up finding contiguous free runs.

3. Implement alloc(x)

Scan the bitmap for a run of x consecutive 0s starting at an 8-byte aligned index. Mark them as 1, generate a unique ID, store the mapping, and return the ID. Optimize by skipping occupied regions.

4. Implement erase(id)

Look up the allocation by ID, validate it exists, then set the corresponding bits back to 0. Remove the mapping and optionally coalesce adjacent free blocks to reduce fragmentation.

5. Analyze Fragmentation and Trade-offs

Discuss how interleaved alloc/erase leads to external fragmentation, affecting future allocations. Compare strategies like first-fit, best-fit, and buddy system, and their impact on performance and memory utilization.

Key Points to Mention

Alignment handling: only consider indices that are multiples of 8 bytes (or 64 bits) when searching for free runs.
Time complexity: naive scan is O(n) per allocation; using a free list or interval tree can improve to O(log n) or O(1) for certain patterns.
Space complexity: bitmap is O(n) bits; additional structures add overhead but can be justified for performance.
Unique allocation ID: can be a simple counter, but must handle wrap-around or reuse safely.
Fragmentation: interleaved allocations of varying sizes cause external fragmentation; coalescing adjacent free blocks on erase helps.
Edge cases: allocation size larger than available free space, invalid ID in erase, and alignment when x is not a multiple of 8.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.