← Robinhood Interview Insights
I've done this problem enough times that the base implementation felt automatic.
Start by clarifying the requirements (capacity, eviction policy, thread-safety) and then explain the design: a hash map for O(1) access to nodes and a doubly-linked list to track recency order. Walk through how get and put update the list and map, and discuss edge cases like updating an existing key and evicting the least recently used item.
Pro tip: Mention that you would use dummy head and tail nodes to simplify edge cases in the linked list, and explicitly state the time and space complexity. Also, briefly note how you would handle concurrency if needed, showing awareness beyond the basic algorithm.
Ask about capacity limits, expected operations, thread-safety, and whether the cache needs to be generic. Confirm that O(1) get and put are required and that the eviction policy is LRU.
Explain that a hash map provides O(1) access to cache entries, and a doubly-linked list maintains the recency order. Emphasize that combining them allows O(1) updates for both get and put.
Describe the node structure (key, value, prev, next) and how to add/remove nodes from the list. Mention using dummy head and tail nodes to avoid null checks and simplify insertion/removal at boundaries.
For get: if key exists, move node to the front (most recently used) and return value; else return -1. For put: if key exists, update value and move to front; else create new node, add to front, and if capacity exceeded, remove the least recently used node (tail) and delete from map.
State that both get and put are O(1) time and O(capacity) space. Discuss edge cases: updating an existing key, cache full eviction, and handling capacity of 0 or 1.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the concurrency requirements and access patterns, then explain the trade-offs between coarse-grained and fine-grained locking strategies. Walk through a concrete design (e.g., sharded LRU with per-shard locks) and discuss how it handles contention, consistency, and eviction under load.
Pro tip: Mention that you'd measure contention and consider lock-free or optimistic approaches only if profiling shows the lock is the bottleneck—showing you avoid premature optimization while still knowing advanced options.
Ask about read/write ratio, latency SLOs, cache size, and consistency needs (e.g., is stale data acceptable?). This frames the locking choice.
Explain that a single global lock serializes all operations, causing high contention and poor scalability. Highlight that LRU updates (move-to-front) make reads writes, worsening the problem.
Describe sharding the cache into N partitions, each with its own lock (e.g., mutex or RWMutex). Discuss trade-offs: reduced contention vs. approximate LRU and potential hot shards.
Explain how to handle eviction across shards, avoid deadlocks (e.g., lock ordering), and ensure thread safety for operations like get-and-update. Mention alternatives like lock-free structures or read-copy-update if appropriate.
Emphasize measuring performance under realistic load, monitoring lock contention, and adjusting shard count or strategy based on metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the attack scenario and cache characteristics, then propose a layered defense combining admission control, rate limiting, and cache partitioning. Emphasize trade-offs between security, performance, and complexity, and tie your answer to Robinhood's low-latency, high-throughput environment.
Pro tip: Mention that you'd first measure the impact (e.g., hit ratio drop, latency increase) to avoid over-engineering, and propose a canary rollout of defenses to monitor false positives.
Ask questions to understand the cache type (e.g., LRU, LFU), key space, and how unique keys are generated. Identify the goal: prevent cache thrashing while minimizing impact on legitimate traffic.
Use techniques like TinyLFU or Bloom filters to probabilistically admit only keys likely to be reused, blocking one-hit wonders. Consider a small probationary cache for new keys to detect abuse.
Enforce per-client rate limits on cache insertions or unique key generation, using token buckets or sliding windows. Combine with anomaly detection to flag and throttle suspicious clients.
Segment the cache by client or request type to limit blast radius, or use a dedicated cache for untrusted traffic. Consider consistent hashing to distribute load and prevent hotspots.
Instrument hit ratio, eviction rates, and latency to measure defense effectiveness. Balance security with performance overhead and false positives, and plan for gradual rollout.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Scoping cache keys by user or role was my first instinct, something like prefixing every key with a user ID or tenant ID.
Start by acknowledging the risk of cache poisoning and data leakage, then propose a defense-in-depth strategy: include authorization context in cache keys, enforce access checks at read time, and scope caches per user or tenant. Emphasize that caching should never bypass authorization, and discuss trade-offs between cache hit rate and security.
Pro tip: Mention that even with user-scoped keys, you should validate authorization on every cache hit to guard against key collisions or misconfiguration—this shows you think about defense in depth and failure modes.
Explain how shared caches can leak data if cache keys don't include user identity or authorization context, leading to one user receiving another's cached response.
Propose including a user identifier (e.g., user ID, tenant ID) and relevant authorization attributes (e.g., roles, permissions) in the cache key to ensure isolation.
Describe validating the requester's authorization before serving cached data, even if the key matches, to prevent bypasses due to key collisions or misconfiguration.
Discuss options like per-user caches, per-tenant caches, or partitioned caches, and trade-offs between isolation, hit rate, and operational complexity.
Explain how to invalidate cached data when permissions change (e.g., on role updates) and ensure cache entries don't outlive authorization decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.