Start by framing the problem: moving from a single-node LRU cache to a distributed system requires partitioning data, routing requests, and handling failures. Then walk through the core components: sharding with consistent hashing and virtual nodes, rebalancing strategies for node changes, and techniques for hot keys. Emphasize trade-offs and practical considerations for a production system like DoorDash.
Pro tip: Mention that consistent hashing with virtual nodes reduces rebalancing overhead and improves load distribution, but also discuss the need for a coordination service (e.g., ZooKeeper, etcd) to manage membership and detect failures. This shows you understand real-world complexity beyond the algorithm.
Ask about scale (QPS, data size), latency SLAs, consistency needs, and failure tolerance. This sets the stage for design decisions.
Explain how to partition the key space using consistent hashing with virtual nodes. Describe how clients or a proxy layer route requests to the correct node.
Detail how consistent hashing minimizes data movement. Discuss strategies like gradual rebalancing, data handoff, and ensuring availability during transitions.
Propose solutions like key splitting, local caching, request coalescing, or dynamic replication. Discuss trade-offs of each approach.
Cover monitoring, failure detection, consistency guarantees, and how to handle cache invalidation across nodes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with async replication to a fixed number of replicas and talked through gossip-based failure detection.
Start by clarifying requirements (read/write ratio, consistency needs, latency SLOs) and then propose a replication strategy like consistent hashing with replication factor 3, explaining trade-offs between consistency and availability. Cover failure detection using gossip or heartbeats, and request routing via a smart client or proxy that maintains cluster membership.
Pro tip: Tie your choices to DoorDash's specific needs: low-latency reads for menu data, high availability during peak hours, and tolerance for eventual consistency. Mention how you'd monitor and tune the system over time.
Ask about read/write patterns, data size, consistency requirements, latency targets, and failure tolerance to tailor your design.
Propose a replication approach (e.g., primary-backup, multi-primary, or quorum-based) and justify it based on the requirements, discussing trade-offs.
Explain how nodes detect failures (heartbeats, gossip, phi accrual) and how the system reacts (e.g., marking nodes as suspect, triggering re-replication).
Describe how clients or proxies route requests to the correct replicas, including handling of failures and consistency guarantees (e.g., read-your-writes).
Summarize key trade-offs (consistency vs. availability, latency vs. durability) and suggest optimizations like hinted handoff or read repair.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard once you've done the rest of the design.
Start by clarifying the requirements and constraints of the distributed cache (e.g., consistency, latency, scale). Then walk through the read and write paths step by step, explaining how data is stored, retrieved, and kept consistent across nodes. Finally, discuss trade-offs and how you would handle failures and scaling.
Pro tip: Always tie your design to DoorDash's specific use cases, such as caching restaurant menus or delivery ETAs, and mention how you'd measure and monitor cache hit rates and latency in production.
Ask about expected read/write ratio, data size, consistency needs, and latency SLAs. State your assumptions clearly to guide the design.
Describe the cache cluster, partitioning strategy (e.g., consistent hashing), replication, and how clients interact with it (e.g., via a cache client library).
Explain how a read request is routed to the correct node, how cache hits/misses are handled, and how data is fetched from the backing store on a miss and then populated.
Detail how writes are handled: write-through vs. write-back, cache invalidation strategies, and how consistency is maintained across replicas.
Discuss trade-offs (e.g., consistency vs. availability), and how to handle node failures, hot keys, and cache stampedes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Write-back vs write-through is a classic trade-off question and I've answered it before, so this felt more comfortable.
Start by framing cache consistency as a trade-off between latency, throughput, and data freshness, then walk through TTL expiry, write-through vs write-back, and invalidation strategies with concrete examples. Tie your answer to DoorDash's use cases like menu data, restaurant availability, or order status to show practical relevance.
Pro tip: Mention that cache invalidation is not just about deletion—it's about ordering and atomicity; for example, invalidating after a DB write but before the next read can cause stale reads, so consider versioning or write-through with short TTLs. Also, highlight that DoorDash likely uses a hybrid approach: TTL for non-critical data and explicit invalidation for critical data like order state.
Clarify what level of consistency is needed for different data types (e.g., menu items vs. order status) and the acceptable staleness window. This sets the context for choosing strategies.
Describe how TTL provides eventual consistency by automatically expiring stale entries, and discuss trade-offs like stale reads within TTL and thundering herd on expiry.
Contrast write-through (synchronous, strong consistency, higher write latency) with write-back (asynchronous, lower latency, risk of data loss) and when to use each.
Cover invalidation approaches: write-invalidate (delete/update cache on write), write-update (update cache on write), and versioning. Address race conditions and ordering.
Propose a combination: e.g., write-through with short TTL for critical data, and TTL-based expiry for less critical data, with explicit invalidation on updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the failure model and the specific guarantees your system provides (e.g., availability vs. consistency). Then walk through the mechanisms that enforce those guarantees during partitions, such as quorum-based replication, idempotent operations, and graceful degradation. Finally, quantify the trade-offs and residual risks, showing awareness of CAP theorem and business impact.
Pro tip: Tie your fault tolerance guarantees to concrete SLAs and business metrics (e.g., order loss rate, p99 latency during partitions) to demonstrate that you think beyond pure technical correctness.
Clarify what a network partition means in your system (e.g., split-brain, asymmetric failures) and which components are affected. State your assumptions about node failures and message loss.
Explicitly list the guarantees your design provides during partitions, such as availability, durability, or consistency. Reference CAP theorem and explain which trade-off you chose and why.
Describe the technical mechanisms that enforce those guarantees: replication strategies (e.g., quorum, leader election), conflict resolution (e.g., CRDTs, last-write-wins), and idempotency to handle retries.
Explain how the system degrades gracefully (e.g., read-only mode, cached responses) and how it recovers after the partition heals (e.g., anti-entropy, reconciliation).
Acknowledge the costs of your choices (e.g., increased latency, potential data loss) and any residual risks. Tie these to business impact and SLAs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Saved this for the end and kind of rushed it.
Start by defining the key metrics that reflect system health and business success, then outline a layered monitoring strategy covering infrastructure, application, and business levels. For capacity planning, describe how you would use historical trends, load testing, and forecasting to ensure the system scales with demand while optimizing cost.
Pro tip: Tie monitoring metrics directly to DoorDash's core business KPIs like order throughput, delivery time, and Dasher utilization to show you understand the product context. Also, mention setting up automated alerts with clear escalation paths and runbooks to demonstrate operational maturity.
Identify the critical user journeys and system components, then define SLIs (e.g., latency, error rate, throughput) and set SLOs/SLAs for each. Ensure metrics cover infrastructure, application, and business levels.
Choose tools (e.g., Prometheus, Grafana, Datadog) to collect and visualize metrics, logs, and traces. Set up alerts with thresholds and anomaly detection, and integrate with incident management systems like PagerDuty.
Regularly review dashboards and alerts to identify trends, false positives, and gaps. Use post-mortems and feedback loops to refine metrics and thresholds continuously.
Use historical data and business growth projections to forecast future resource requirements (CPU, memory, storage, network). Perform load testing to validate assumptions and identify bottlenecks.
Develop a capacity plan with scaling strategies (horizontal/vertical, auto-scaling) and cost optimization (reserved instances, spot instances). Schedule regular reviews to adjust based on actual usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.