Optiver·Software Engineer·Onsite - System Design / Architecture·Intermediate

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Jul 2026

Summary

Optiver system design round for a software engineering role. The question was about designing a queue abstraction from scratch and then comparing different internal implementations, which sounds straightforward until they start pushing on trade-offs and thread safety.

Questions Asked (2)

Design an object-oriented Queue abstraction with a clean interface, then describe and compare at least two or three different internal implementations (linked list, circular array, two stacks), including trade-offs around time complexity, memory behavior, and when you'd choose each.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with the interface, which was fine.

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AI HintsAI Generated

Suggested Approach

Start by defining a clean Queue interface with core operations (enqueue, dequeue, peek, isEmpty, size). Then present three implementations—linked list, circular array, and two stacks—comparing their time and space complexities, memory behavior, and practical use cases. Conclude with guidance on when to choose each based on constraints like performance, memory, and concurrency.

Pro tip: Mention that in latency-sensitive systems like trading, a circular array often wins due to cache locality and predictable O(1) operations, but a linked list can be better if the queue size is highly variable and memory fragmentation is a concern.

1. Define the Queue Interface

Specify the abstract data type with methods like enqueue, dequeue, peek, isEmpty, and size. Discuss generic typing and exception handling for empty queue operations.

2. Describe Linked List Implementation

Explain using a singly linked list with head and tail pointers. Enqueue at tail, dequeue from head, both O(1). Memory overhead per node, but dynamic size and no resizing.

3. Describe Circular Array Implementation

Use a fixed-size array with head and tail indices, wrapping around. Enqueue and dequeue are O(1) amortized (with resizing). Better cache locality, but resizing can cause occasional O(n) cost.

4. Describe Two-Stack Implementation

Use two stacks: one for enqueue, one for dequeue. Enqueue is O(1), dequeue is amortized O(1) (worst-case O(n) when transferring). Simple but higher memory overhead and less predictable performance.

5. Compare and Choose

Contrast time complexity, memory behavior, and use cases. Recommend linked list for unbounded queues, circular array for performance-critical bounded queues, and two stacks for simplicity or when using existing stack implementations.

Key Points to Mention

Time complexity: all implementations provide O(1) amortized enqueue and dequeue, but two-stack has O(n) worst-case dequeue.
Memory behavior: linked list has per-node overhead and poor cache locality; circular array is contiguous and cache-friendly but may waste space or require resizing; two stacks use extra memory for the second stack.
When to choose: linked list for dynamic size and no resizing; circular array for high-throughput, low-latency scenarios; two stacks for simplicity or when stacks are already available.
Thread safety: discuss how to make the queue thread-safe (e.g., locks, lock-free) and the impact on performance.
Edge cases: handling empty queue, full queue (for bounded), and resizing strategies.
Real-world examples: mention that Java's ArrayDeque uses a circular array, while LinkedList implements Queue; two-stack queues are common in functional programming.

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

How would you extend the queue design to support bounded versus unbounded queues, and thread-safe versus non-thread-safe variants, while keeping the same interface?

System DesignTechnical Trade-offs

Author's notes

This came as a follow-up and I wasn't fully prepared for the interface design angle.

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AI HintsAI Generated

Suggested Approach

Start by defining a minimal queue interface with core operations like enqueue, dequeue, and size. Then explain how to use the strategy pattern to inject different behaviors for bounded/unbounded and thread-safe/non-thread-safe variants, keeping the interface unchanged. Emphasize trade-offs in performance, complexity, and use cases.

Pro tip: Mention that thread-safety can be achieved via locks or lock-free algorithms, but always measure contention; sometimes a non-thread-safe queue with external synchronization is faster. Also, bounded queues require backpressure or blocking policies, which should be configurable.

1. Define the core interface

Specify a generic Queue interface with methods like enqueue, dequeue, peek, and size, ensuring it abstracts away implementation details.

2. Separate concerns with composition

Use composition to delegate storage and synchronization to separate components, allowing independent variation of boundedness and thread-safety.

3. Implement bounded vs unbounded

For bounded, enforce capacity with blocking or rejection policies; for unbounded, allow dynamic growth, possibly with memory limits.

4. Implement thread-safe vs non-thread-safe

For thread-safe, add synchronization (locks, atomics) or use lock-free structures; for non-thread-safe, omit synchronization for performance.

5. Discuss trade-offs and use cases

Compare performance, complexity, and suitability for different scenarios, such as low-latency trading systems vs batch processing.

Key Points to Mention

Strategy pattern or dependency injection to swap behaviors without changing the interface.
Bounded queues: blocking, backpressure, or rejection policies; unbounded queues: dynamic resizing and memory concerns.
Thread-safety mechanisms: mutexes, read-write locks, atomics, lock-free queues (e.g., Michael-Scott queue).
Performance implications: contention, cache coherence, and overhead of synchronization.
Use cases: bounded for producer-consumer with limited buffers, unbounded for event queues; thread-safe for concurrent access, non-thread-safe for single-threaded performance.
Testing and validation: ensure interface consistency across variants and stress-test thread-safety.

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