Spent the first few minutes just talking through the token bucket model before touching code, which I think was the right call.
Start by clarifying requirements (rate, burst capacity, fairness, blocking vs non-blocking) and then design a token bucket with a mutex or lock-free approach. Implement a class with a method to atomically consume tokens, using a background refill thread or lazy refill on each request. Discuss trade-offs between precision, performance, and complexity.
Pro tip: Mention that you would use a monotonic clock for refill timing to avoid issues with system clock adjustments, and consider using a lock-free atomic approach for high contention scenarios.
Ask about expected rate, burst size, number of threads, whether blocking is allowed, and if fairness (FIFO) is needed. This shapes the design.
Define the token bucket with capacity, current tokens, refill rate, and last refill timestamp. Choose synchronization primitives (mutex, atomic, condition variable).
Write a method that locks, refills tokens based on elapsed time, checks if enough tokens, deducts, and returns success/failure. Ensure atomicity.
If blocking is required, use condition variables to wait for tokens. Otherwise, return immediately. Discuss fairness and potential starvation.
Compare mutex vs lock-free, lazy vs background refill, and discuss performance under high contention. Mention possible optimizations like sharding or using atomic CAS.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the conversation got interesting.
Start by framing the problem: under high concurrency, a rate limiter's lock becomes a bottleneck, so we need to reduce contention while preserving correctness. Then compare the three approaches—global lock, lock-free CAS, and sharding—by analyzing their trade-offs in throughput, latency, complexity, and accuracy, and conclude with a recommendation (e.g., sharding with per-shard locks or CAS) tailored to Uber's scale.
Pro tip: Mention that sharding can be combined with CAS per shard to get the best of both worlds, and that the choice depends on the required accuracy (e.g., strict global limit vs. approximate per-shard limit) and the cost of coordination.
Ask about the required accuracy (strict global limit vs. approximate), expected QPS, latency SLA, and whether the limiter is per-user, per-IP, or global. This sets the context for trade-offs.
Explain that a single mutex serializes all requests, causing contention and limiting throughput to the lock's critical section. It's simple and accurate but doesn't scale under high concurrency.
Describe using atomic compare-and-swap on a shared counter. It avoids blocking but can suffer from CAS retry storms under high contention, wasting CPU and increasing latency.
Propose partitioning the key space (e.g., by user ID) into N shards, each with its own lock or CAS. This reduces contention by a factor of N but may allow up to N times the limit if not coordinated.
Summarize trade-offs: global lock (simple, accurate, poor scalability), CAS (better scalability but retry overhead), sharding (high scalability, approximate). Recommend a hybrid (e.g., sharded CAS) and mention techniques like token bucket with lazy refill to reduce writes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went straight to a concurrent hash map keyed by user ID.
Start by clarifying the current design and requirements (e.g., rate limit granularity, accuracy, scale). Then propose a distributed rate limiting architecture using a key-value store like Redis with appropriate data structures and sharding, and discuss memory management techniques such as sharding, TTLs, and probabilistic data structures for sparse keys.
Pro tip: Mention that for per-key rate limiting, you can use a hash-based approach where each key maps to a counter with a TTL, and for sparse keys, consider using a probabilistic data structure like a count-min sketch to reduce memory, but be transparent about the trade-off in accuracy.
Ask about the expected scale (number of users/keys, request rate), accuracy requirements, and latency constraints. This will guide the choice of data store and algorithm.
Propose using a distributed counter store (e.g., Redis) with keys like `rate_limit:{user_id}:{window}`. Use atomic operations (INCR, EXPIRE) to implement sliding window or token bucket algorithms.
Discuss sharding the key space across multiple Redis instances (e.g., consistent hashing) to distribute load and avoid hotspots. Mention using a proxy or client-side sharding.
For large, sparse key spaces, use TTLs to expire inactive keys, and consider probabilistic data structures (e.g., count-min sketch) or approximate counting to reduce memory footprint, accepting some inaccuracy.
Discuss trade-offs between consistency and availability (e.g., using Redis with replication, or a eventually consistent store). Mention fallback strategies like local rate limiting if the central store is unavailable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining each algorithm's core mechanism and then compare them across dimensions like burst handling, memory usage, precision, and implementation complexity. Use a concrete example (e.g., API rate limiting at Uber) to illustrate trade-offs and conclude with when to choose each.
Pro tip: Mention that token bucket and leaky bucket are duals: token bucket allows bursts up to bucket size, while leaky bucket smooths output at a constant rate. This shows deep understanding and helps you stand out.
Briefly explain how token bucket, sliding window counter, and leaky bucket work, focusing on their core mechanics.
Analyze trade-offs in terms of burst handling, memory footprint, accuracy, and implementation complexity.
Relate each algorithm to real-world scenarios, such as API rate limiting, DDoS protection, or traffic shaping.
Conclude with guidance on when to use each algorithm, considering factors like need for burst tolerance, precision, and resource constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.