AkunaCapital·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Akuna Capital coding screen focused entirely on a rate limiter design problem for a trading engine context. Pretty applied, less algorithmic than I expected, more about getting the sliding window logic right and writing clean test cases.

Questions Asked (1)

Design and implement a class that a trading engine can use to check how many order messages can be sent at a given timestamp without exceeding a configurable rate limit, where the time window is a continuous sliding window and batching is supported.

System DesignAlgorithms & Data StructuresAPI & Integrations

Author's notes

The example they gave made it look deceptively simple.

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AI HintsAI Generated

Suggested Approach

Clarify requirements first: rate limit (N messages per T seconds), continuous sliding window, batching support, and thread-safety. Then design a class using a timestamp queue (e.g., deque) to track message timestamps, and for batching, consider storing counts per timestamp or using a bucketed approach with fine granularity. Implement methods to check available capacity and to record sent messages, ensuring O(1) amortized operations.

Pro tip: Mention that a naive queue of individual timestamps can be memory-heavy under high throughput; propose a bucketed or aggregated approach (e.g., store counts per millisecond) to balance precision and efficiency, and discuss trade-offs.

1. Clarify Requirements and Constraints

Ask about the exact rate limit semantics (e.g., N per T seconds), whether the window is truly continuous or can be approximated, expected message rates, and thread-safety needs.

2. Design Data Structures

Choose a data structure to track recent message timestamps, such as a deque of timestamps or a bucketed counter (e.g., per millisecond). For batching, consider storing counts per timestamp to reduce memory.

3. Implement Core Methods

Implement methods like `canSend(timestamp)` to check available capacity, and `recordSend(timestamp, count)` to update the tracker. Ensure the sliding window evicts expired entries efficiently.

4. Handle Batching and Concurrency

For batching, allow checking and recording multiple messages atomically. Add thread-safety using locks or atomic operations if needed, and discuss performance implications.

5. Analyze Complexity and Trade-offs

Analyze time and space complexity (e.g., O(1) amortized per operation). Discuss trade-offs between precision (exact timestamps vs. bucketed) and memory/performance.

Key Points to Mention

Sliding window rate limiting algorithm using a timestamp queue or bucketed counters.
Batching support: how to check and record multiple messages atomically, and how batching affects the window.
Thread-safety considerations for concurrent access in a trading engine.
Time and space complexity: O(1) amortized operations, memory usage proportional to rate limit.
Edge cases: window boundaries, clock skew, and burst handling.
Trade-offs between exact timestamp tracking and bucketed approximation for performance.

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