Got the structure right pretty fast, per-key list with binary search on get.
Clarify the requirements and constraints first, then propose a design using a hash map from keys to sorted lists of (timestamp, value) pairs, with binary search for efficient retrieval. Discuss time and space complexity, and consider edge cases and potential optimizations.
Pro tip: Mention that timestamps are monotonically increasing per key, so you can append to a list and use binary search to find the rightmost timestamp ≤ query timestamp. This shows you understand the data patterns and can optimize accordingly.
Ask about expected operations, constraints (e.g., timestamp range, number of keys, update frequency), and whether timestamps are unique per key. Confirm that retrieval should return the value at the largest timestamp ≤ query timestamp.
Propose a hash map where each key maps to a list of (timestamp, value) pairs, kept sorted by timestamp. Explain that since timestamps are increasing, appending maintains order.
For set: append (timestamp, value) to the list for the key. For get: binary search the list for the largest timestamp ≤ query timestamp, and return the corresponding value (or empty string if none).
State that set is O(1) amortized (append), and get is O(log n) where n is the number of timestamps for that key. Space is O(total number of set operations).
Discuss cases like query timestamp before all timestamps, after all timestamps, or non-existent key. Mention potential optimizations like using a balanced BST or skip list if timestamps are not monotonic, but note that monotonicity is typical.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview got interesting.
Start by clarifying the system's current architecture, constraints, and what 'scale' means in this context (e.g., read vs. write heavy, latency requirements). Then propose a layered scaling strategy: horizontal scaling at the service layer, caching, sharding, and asynchronous processing, while discussing trade-offs like consistency vs. availability. Finally, emphasize monitoring, load testing, and iterative improvements to validate the design.
Pro tip: At Uber's scale, no single solution works; focus on identifying bottlenecks and applying the right tool for each layer (e.g., CDN for static content, Kafka for streaming, sharded databases for writes). Show you understand that scaling is an ongoing process, not a one-time fix.
Ask about the system's current design, traffic patterns (read/write ratio, peak vs. average), latency SLAs, and data consistency needs. This ensures your answer is tailored and not generic.
Break down the system into components (e.g., load balancers, app servers, databases, caches) and discuss which are likely to become bottlenecks. Consider scaling dimensions: horizontal vs. vertical, stateless vs. stateful.
Outline specific techniques for each layer: load balancing, auto-scaling, caching (CDN, Redis), database sharding/replication, message queues for async processing, and microservices decomposition if needed.
Discuss trade-offs such as consistency vs. availability (CAP theorem), cost, complexity, and operational overhead. Mention how you'd handle data consistency, hot partitions, and failure recovery.
Explain how you'd test the scaled system (load testing, chaos engineering) and monitor it (metrics, tracing). Emphasize that scaling is iterative and requires continuous optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Per-key locks vs a global lock, I covered that.
Start by clarifying the data structure's operations and concurrency requirements, then discuss specific thread-safety mechanisms like locks, atomics, and lock-free techniques. Compare locking strategies (coarse-grained, fine-grained, optimistic) with trade-offs in performance, scalability, and complexity, and relate them to Uber's high-throughput, low-latency systems.
Pro tip: Emphasize that the best strategy depends on the read/write ratio and contention level; mention that you'd measure and profile before choosing, showing a data-driven approach.
Ask about the specific data structure, its operations, expected read/write ratio, and contention level to tailor your answer.
Explain how to ensure thread safety using locks (mutex, read-write lock), atomic operations, or lock-free techniques, and mention memory barriers and synchronization primitives.
Contrast coarse-grained locking, fine-grained locking, and optimistic concurrency control, highlighting their trade-offs in performance, scalability, and complexity.
Connect the trade-offs to Uber's scale and latency requirements, and discuss how you would choose and validate a strategy through benchmarking and profiling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.