Palo·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Apr 2026

Summary

System design round at Palo for a software engineer role. The whole session was basically one big rate limiter question broken into layers, starting simple and getting progressively harder until we were talking distributed consensus and failure modes.

Questions Asked (1)

Design a rate limiter for a backend service that enforces per-identity limits (per user ID, IP, or API key), handles high concurrency, scales to millions of requests per second, and works across a distributed deployment. Walk through algorithm choice, data structures, rejection behavior, and how you'd handle distributed state.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This was the whole interview basically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (identity types, limits, accuracy, latency, scale) and then propose a layered architecture: a local in-memory rate limiter per node backed by a distributed store like Redis for global coordination. Compare token bucket vs. sliding window vs. fixed window, justify your choice based on trade-offs, and explain how you handle rejection, synchronization, and failure modes.

Pro tip: Emphasize that perfect accuracy at millions of RPS is impractical; instead, propose approximate algorithms with bounded error (e.g., sliding window with local counters synced periodically) and discuss how to degrade gracefully under load or partition.

1. Clarify Requirements and Constraints

Ask about identity types (user ID, IP, API key), limit granularity (per second/minute), accuracy needs, latency budget, and expected scale. Confirm whether strict global enforcement is required or if eventual consistency is acceptable.

2. Choose an Algorithm and Data Structures

Compare token bucket (smooth bursts, memory efficient), sliding window log (accurate but memory heavy), sliding window counter (approximate, low memory), and fixed window (simple but bursty). Select one and detail the data structures (e.g., hash map of counters, sorted sets for timestamps).

3. Design Distributed State Management

Propose a hybrid approach: local in-memory counters per node for fast decisions, with periodic synchronization to a distributed store (e.g., Redis) for global limits. Discuss sharding by identity, using consistent hashing, and handling hot keys.

4. Define Rejection Behavior and Client Feedback

Specify HTTP 429 responses with Retry-After headers, and consider returning remaining quota and reset time. Discuss whether to reject immediately or queue requests, and how to handle retries and idempotency.

5. Address Scalability, Fault Tolerance, and Trade-offs

Explain how to scale to millions of RPS (e.g., local decisions, async sync, sharded Redis), handle failures (fallback to local limits, circuit breakers), and monitor. Acknowledge trade-offs between accuracy, latency, and complexity.

Key Points to Mention

Token bucket algorithm for its balance of burst handling and memory efficiency, with lazy refill to avoid timers.
Sliding window counter as an approximate but scalable alternative, using two fixed windows and weighted average.
Local in-memory rate limiting per node with periodic synchronization to Redis to reduce latency and load on the distributed store.
Sharding the distributed store by identity (e.g., user ID) using consistent hashing to distribute load and avoid hot spots.
Graceful degradation: if Redis is unavailable, fall back to local limits or allow requests with logging to avoid cascading failures.
Rejection behavior: return 429 with Retry-After and X-RateLimit headers, and consider differentiating between hard and soft limits.

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