The QPS numbers are what made this feel real.
Start by clarifying requirements and constraints, then estimate the number of backend servers needed (100-500) and discuss load balancing algorithms suitable for L4. Design a scalable architecture with health checks, failover, and session persistence, and address trade-offs between different approaches.
Pro tip: Emphasize that L4 load balancers operate at the connection level, so they can't inspect application data; this impacts session persistence and routing decisions. Also, mention that consistent hashing can minimize disruption when scaling.
Ask about expected latency, protocol (TCP/UDP), session persistence needs, and backend server capabilities. Confirm that each backend handles 10 QPS and calculate the required number of servers: 100 at normal load, 500 at peak.
Discuss algorithms like round robin, least connections, and consistent hashing. For L4, consistent hashing is often preferred to maintain session affinity and minimize disruption during scaling.
Propose a distributed load balancer setup with multiple instances (e.g., active-passive or active-active) to avoid single point of failure. Use health checks to detect failed backends and automatically remove them.
Since L4 can't inspect application data, discuss how to maintain session persistence using source IP hashing or consistent hashing. Also, consider connection draining and idle timeout settings.
Compare L4 vs L7 load balancing, and evaluate trade-offs between different algorithms. Identify potential bottlenecks like load balancer throughput and propose solutions like horizontal scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I knew the algorithms but fumbled explaining why consistent hashing is better than modular hashing when servers leave or join.
Start by framing load balancing as a trade-off between simplicity, distribution quality, and adaptability to changes. Then systematically walk through each algorithm, explaining how it works, its strengths, weaknesses, and ideal use cases. Conclude by discussing how real systems often combine or layer these algorithms based on requirements.
Pro tip: Emphasize that consistent hashing is crucial for distributed caches and stateful services because it minimizes disruption when nodes join or leave, but it introduces complexity and potential hotspots. Mention that Google's Maglev and other systems use variants to handle scale.
Briefly state that load balancing aims to distribute requests efficiently, minimize latency, and handle failures. Introduce criteria: fairness, overhead, adaptability, and statefulness.
Describe round-robin as cycling through servers sequentially. Highlight simplicity and even distribution for homogeneous servers, but note it ignores server load and can cause issues with heterogeneous or stateful services.
Explain that modular hashing maps requests to servers using a hash modulo N. It provides session affinity but suffers from massive redistribution when N changes, making it poor for dynamic scaling.
Describe least-connections as directing traffic to the server with the fewest active connections. It adapts to server load and is good for long-lived connections, but requires tracking connection counts and can be complex.
Introduce consistent hashing with a ring and virtual nodes. It minimizes redistribution when nodes change, ideal for distributed caches, but adds complexity and may have uneven load without careful virtual node tuning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements such as scale, latency, and consistency needs, then propose a health monitoring system with active and passive checks, and a membership management layer using a distributed coordination service. Discuss trade-offs between consistency and availability, and how to handle failures gracefully.
Pro tip: Emphasize the importance of avoiding false positives in health checks and the need for gradual rollout of membership changes to prevent cascading failures. Mention Google's specific tools like Borg or Chubby to show familiarity.
Ask about scale, expected failure rates, latency requirements, and consistency vs. availability trade-offs to tailor the design.
Propose a combination of active health checks (e.g., HTTP/TCP probes) and passive monitoring (e.g., outlier detection based on error rates) with configurable thresholds and backoff.
Use a distributed coordination service (e.g., etcd, ZooKeeper, or Google Chubby) to maintain a consistent view of healthy backends and propagate changes to load balancers.
Describe how load balancers subscribe to membership updates, gracefully drain connections from removed backends, and add new backends with warm-up periods.
Analyze trade-offs between consistency and availability (e.g., CAP theorem), and how to handle network partitions, split-brain, and stale membership data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's scope, expected user base, and traffic patterns. Then break down the estimation into server count, connection table size, and bandwidth, using assumptions and simple calculations. Finally, validate with sanity checks and discuss trade-offs.
Pro tip: Always state your assumptions explicitly and round numbers to powers of 10 for ease; interviewers care more about your reasoning than exact figures.
Ask questions to understand the system's scale: number of users, requests per second, data size, and growth projections.
Calculate based on throughput per server (e.g., QPS) and redundancy needs, considering peak load and failover.
Determine the number of concurrent connections and memory per connection to size the connection table.
Compute data transfer rates by multiplying request/response sizes by request rate, and account for overhead.
Sanity-check numbers against known benchmarks, discuss bottlenecks, and adjust assumptions as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
HA pairs with failover and shared state replication.
Start by clarifying the load balancer's role and scale, then structure your answer around redundancy, failover, and observability pillars. Discuss trade-offs between active-active vs active-passive, health checking, and monitoring strategies, tying them to Google's reliability expectations.
Pro tip: Emphasize that observability must be designed in from the start, not bolted on—mention how you'd use SLOs to drive alerting and capacity planning, showing you think like a Google SRE.
Ask about expected traffic volume, latency targets, and failure tolerance to scope the design. This ensures your solution aligns with the specific use case.
Propose a multi-region, active-active deployment with redundant load balancer instances and automatic failover. Discuss health checks, circuit breakers, and graceful degradation.
Outline metrics (latency, error rates, throughput), logging (structured logs for requests/errors), and tracing (distributed tracing for request flow). Mention tools like Prometheus, Stackdriver, and OpenTelemetry.
Explain how you'd set SLOs for availability and latency, and create alerts based on burn rates. This ties observability to actionable reliability goals.
Discuss trade-offs between consistency and availability, cost of redundancy, and how the design can evolve with scale. Show awareness of CAP theorem and operational complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.