I coded it up fine at first but they pushed on thread safety and I fumbled.
Start by clarifying requirements and constraints, then outline a simple round-robin algorithm with a circular index. Discuss edge cases like all nodes down, concurrent access, and dynamic node changes, and explain how to handle them robustly.
Pro tip: Mention that you would use an atomic counter or lock to ensure thread safety, and consider using a modulo operation with a guard for zero nodes to avoid division by zero.
Ask about expected number of nodes, concurrency, node health check mechanism, and whether nodes can be added/removed dynamically.
Use a circular index that increments modulo the number of nodes, skipping unavailable nodes by advancing until a healthy node is found.
Consider scenarios like all nodes unavailable, zero nodes, concurrent access, and node list changes during iteration.
Use atomic operations or locks to protect the index and node list, ensuring thread safety without excessive contention.
Write unit tests for wrap-around, skipping unhealthy nodes, and concurrent requests; use property-based testing for robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Virtual nodes clicked for me conceptually but I struggled to articulate why they help with key distribution in a live setting.
Start by explaining the consistent hashing ring with virtual nodes, then detail how additions/removals only affect neighboring nodes, and finally discuss handling unavailable nodes via replication and failover. Emphasize the trade-offs and practical considerations for a production system like DoorDash.
Pro tip: Mention that virtual nodes also help with heterogeneous hardware by allowing weighting, and that health checks with circuit breakers prevent cascading failures when a node is down.
Describe how nodes and keys are mapped to a hash ring, and how virtual nodes improve load distribution and minimize disruption.
Explain that adding/removing a node only remaps keys from its immediate neighbors on the ring, and with virtual nodes, the load is spread across multiple nodes.
Discuss strategies like replicating keys to the next N nodes on the ring, using health checks to detect failures, and redirecting traffic to replicas.
Cover trade-offs between number of virtual nodes, replication factor, and consistency vs. availability, and mention techniques like bounded loads or consistent hashing with weights.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining both algorithms and their core mechanics, then systematically compare them across the given dimensions using a structured framework. Emphasize trade-offs and conclude with production use cases, tying back to DoorDash's scale and latency requirements.
Pro tip: Mention that consistent hashing with virtual nodes is the de facto standard for distributed caches and stateful services, but round-robin still shines for stateless, homogeneous workloads—showing you understand context matters more than dogma.
Briefly explain round-robin (sequential distribution) and consistent hashing (hash ring with virtual nodes). Highlight their fundamental goals: simplicity vs. minimal disruption.
Compare time and space complexity: round-robin is O(1) time and space; consistent hashing is O(log n) time for lookup (with binary search) and O(n) space for the ring, plus virtual nodes.
Discuss how round-robin requires full remapping on node changes, causing cache misses, while consistent hashing only remaps 1/n of keys. Cover failure handling: round-robin needs health checks and may overload remaining nodes; consistent hashing redistributes load more gracefully.
Explain that consistent hashing provides better cache affinity (same key to same node) and reduces hotspots via virtual nodes, whereas round-robin lacks affinity and can cause hotspots if requests are not uniformly distributed.
State when to use each: round-robin for stateless services with homogeneous nodes; consistent hashing for stateful services, distributed caches, and systems needing minimal disruption (e.g., DoorDash's order service or Redis clusters).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.