← Openai Interview Insights

Openai·Backend Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
Jun 2026

Summary

System design round at OpenAI for a backend role. The whole session was basically one deep problem about building a memory allocator from scratch, and they kept pushing on the data structure choices until I ran out of confident answers.

Questions Asked (2)

Q1

Design a memory allocator that supports allocate(size) and free(ptr). Walk through the data structures you'd use and how you'd achieve O(log n) per operation.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

I started with a free list, which felt safe, but they pushed back almost immediately asking how I'd find a block of the right size efficiently.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., alignment, thread safety, fragmentation) and then propose a segregated free list with size classes, using a balanced BST (e.g., red-black tree) per size class to track free blocks. Explain how allocate finds the smallest suitable block in O(log n) and free inserts the block back into the appropriate tree, also in O(log n).

Pro tip: Mention that real allocators like jemalloc and tcmalloc use size-class segregation and per-thread caches to reduce lock contention, and that O(log n) is often achieved with intrusive data structures to avoid extra memory overhead.

1. Clarify requirements and constraints

Ask about alignment, thread safety, fragmentation tolerance, and performance goals to tailor the design. This shows you consider real-world trade-offs.

2. Choose a data structure for free blocks

Propose segregated free lists by size class, each implemented as a balanced BST (e.g., red-black tree) keyed by block size. This allows O(log n) search, insert, and delete.

3. Design allocate(size)

Round up size to the next size class, find the smallest free block >= requested size in the corresponding tree (or next larger class), remove it, and split if necessary. Splitting may require inserting the remainder into a smaller size class.

4. Design free(ptr)

Locate the block's metadata (e.g., via a header before the pointer), determine its size class, and insert it into the appropriate free tree. Optionally coalesce with adjacent free blocks, which may involve removing neighbors from their trees and inserting the merged block.

5. Analyze complexity and trade-offs

Explain that each operation is O(log n) due to tree operations, and discuss trade-offs like memory overhead of trees, fragmentation, and potential improvements (e.g., per-thread caches, lock-free structures).

Key Points to Mention

  • Segregated free lists by size class to reduce search space and fragmentation.
  • Balanced BST (e.g., red-black tree) per size class for O(log n) operations.
  • Block metadata (size, free/used flag) stored in a header before the pointer.
  • Splitting and coalescing to manage fragmentation, with complexity implications.
  • Thread safety considerations: per-thread caches or locks, and their impact on performance.
  • Comparison to real-world allocators (jemalloc, tcmalloc) and alternative approaches (e.g., buddy system, slab allocation).

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

Q2

How would you handle fragmentation in your allocator design, and how does coalescing adjacent free blocks work?

System DesignTechnical Trade-offs
Author's notes

This came naturally after the main design question.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by defining fragmentation and its two types (internal and external), then discuss strategies to mitigate each, such as segregated free lists, size classes, and compaction. Explain coalescing as a technique to merge adjacent free blocks, and describe how boundary tags and free list management enable efficient coalescing.

Pro tip: Mention that coalescing can be done immediately or deferred, and that immediate coalescing reduces external fragmentation but may increase allocation latency; deferred coalescing can improve throughput at the cost of more fragmentation. Also, note that modern allocators like jemalloc and tcmalloc use size-class-based segregation to minimize fragmentation.

1. Define fragmentation and its impact

Briefly explain internal and external fragmentation, and why they matter for allocator performance and memory utilization.

2. Discuss strategies to handle fragmentation

Describe techniques like segregated free lists, size classes, slab allocation, and compaction to reduce fragmentation.

3. Explain coalescing of free blocks

Detail how adjacent free blocks are merged using boundary tags and free list pointers, and the role of immediate vs. deferred coalescing.

4. Analyze trade-offs

Compare immediate vs. deferred coalescing in terms of latency, throughput, and fragmentation, and mention real-world allocator examples.

5. Conclude with practical considerations

Summarize how you would choose an approach based on workload characteristics and performance goals.

Key Points to Mention

  • Internal vs. external fragmentation
  • Segregated free lists and size classes
  • Boundary tags for coalescing
  • Immediate vs. deferred coalescing
  • Trade-offs: latency vs. throughput vs. memory utilization
  • Real-world allocators (jemalloc, tcmalloc) and their fragmentation strategies

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