I went straight for a circular linked list and an index pointer.
Start by clarifying requirements and constraints, then propose a data structure that supports O(1) add, remove, and next-server operations. Explain how to maintain strict round-robin order using a circular linked list or dynamic array with an index, and discuss trade-offs and edge cases.
Pro tip: Demonstrate awareness of real-world concerns like concurrency and failure handling, and mention how you would test the solution for correctness and performance.
Ask about expected number of servers, frequency of add/remove operations, concurrency requirements, and whether the server list can be empty.
Select a data structure that allows O(1) add, remove, and next-server. A circular doubly linked list with a hash map for O(1) removal is ideal.
Detail how add appends a node, remove deletes a node using the hash map, and next-server returns the current node and advances the pointer.
Address empty list, removing the current server, and concurrent modifications with locks or atomic operations.
Compare with alternatives like arrays (O(n) removal) and discuss time/space complexity, scalability, and fault tolerance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This tripped me up more than it should have.
Start by clarifying the requirements and constraints of the round-robin load balancer, then propose a solution that dynamically excludes unhealthy servers while maintaining fairness among the remaining healthy ones. Discuss trade-offs such as detection mechanisms, rebalancing strategies, and potential impacts on latency and throughput.
Pro tip: Mention that fairness should be defined in terms of the healthy set, not the original set, and that you would use a consistent hashing or weighted round-robin approach to avoid overloading any single server when health status changes.
Ask about the scale, health check mechanism, and whether the system needs to handle transient failures or permanent removals. This shows you consider the context before diving into solutions.
Explain that fairness should be relative to the current healthy server pool, not the original set. This means redistributing traffic proportionally among healthy servers.
Suggest maintaining a list of healthy servers and using an index that wraps around only that list. When a server becomes unhealthy, remove it from the list and adjust the index to avoid skipping or favoring any server.
Discuss how to handle servers recovering from unhealthy state, such as gradually reintroducing them with a warm-up period to avoid sudden load spikes.
Compare with other algorithms like least connections or consistent hashing, and explain why round-robin with dynamic exclusion might be suitable for the given scenario.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Read-write locks were the obvious answer and I said so pretty quickly.
Start by clarifying the concurrency model and the data structures involved in nextServer and server updates, then discuss synchronization primitives (locks, atomics, or immutable snapshots) and their trade-offs. Emphasize correctness, performance, and scalability, and mention how you would test for race conditions.
Pro tip: Show awareness that thread-safety is not just about locks—consider read-heavy patterns and use copy-on-write or read-write locks to avoid contention. Also, mention that you'd measure lock contention and consider lock-free alternatives if needed.
Ask about the concurrency level, read/write ratio, and consistency requirements for nextServer and server updates. Understand if these operations are on a shared data structure like a list or map.
Explain potential race conditions: e.g., two threads updating server state simultaneously, or a read of nextServer happening during an update. Highlight the need for atomicity and visibility.
Propose appropriate mechanisms: mutexes for simple mutual exclusion, read-write locks for read-heavy workloads, atomic variables for simple counters, or immutable data structures with copy-on-write for lock-free reads.
Compare options: locks are simple but can cause contention and deadlocks; lock-free approaches improve scalability but are complex. Consider performance, fairness, and ease of debugging.
Mention how to test thread-safety: stress tests, race detectors (e.g., ThreadSanitizer), and monitoring lock contention in production. Suggest using immutable snapshots for consistent reads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about passive vs active health checks, TTL-based expiry, and circuit breaker patterns.
Start by clarifying the scope: what 'selection' means (e.g., order assignment, courier selection) and the failure modes (server crash, network partition). Then walk through a concrete failure scenario, describing detection, impact, and recovery. Finally, design a health check system that proactively monitors service health and triggers failover or graceful degradation.
Pro tip: Emphasize idempotency and state reconciliation: after a crash, the system should be able to resume or roll back safely without double-processing. Also, mention that health checks should be lightweight and not cause cascading failures.
Ask clarifying questions to understand what 'selection' entails (e.g., order assignment, courier selection) and the expected scale. Define what a 'server crash' means (process crash, node failure, network partition).
Describe a specific failure scenario: a server crashes mid-selection. Explain the immediate impact (e.g., in-flight requests lost, state inconsistency) and how clients experience it.
Propose how to detect the failure (health checks, heartbeats) and recover (retry, failover, state reconciliation). Discuss trade-offs between consistency and availability.
Outline a health check system: types of checks (liveness, readiness), frequency, thresholds, and actions (e.g., remove from load balancer, trigger alerts). Consider cascading failures and avoid false positives.
Summarize the design, highlighting how it addresses the failure scenario. Mention monitoring, alerting, and continuous improvement (e.g., chaos engineering).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran out of time and only sketched two or three test cases.
Start by outlining the load balancer's core functionality and the key scenarios to test, then describe a comprehensive unit test suite covering normal operation, edge cases, and failure modes. Finally, analyze the time and space complexity of the main operations, explaining trade-offs and how they scale with the number of servers and requests.
Pro tip: Emphasize testability in your design: use dependency injection and mock external services so unit tests are fast and deterministic. Also, relate complexity analysis to real-world constraints like server count and request rate, showing you understand production implications.
Briefly restate the load balancer's responsibilities (e.g., distributing requests, health checks) and the algorithms used (e.g., round-robin, least connections). This sets the context for testing and complexity analysis.
List the key test cases: normal request distribution, server addition/removal, health check failures, and edge cases like no servers or all servers down. Mention using mocks for external dependencies.
For each core operation (e.g., picking a server, updating server list), derive the time complexity in terms of number of servers (N) and requests (R). Explain how data structures (e.g., heap, ring) affect performance.
Determine the space required for maintaining server states, health check data, and any auxiliary structures. Discuss how it scales with N and concurrent connections.
Compare different algorithms (e.g., round-robin vs. least connections) in terms of complexity, fairness, and overhead. Suggest potential optimizations and their impact on complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.