Got a working solution down and wrote my own test cases to verify it, which felt good.
Start by clarifying requirements (e.g., window size, data volume, latency, accuracy) and then design a scalable system using a streaming architecture with a time-based data structure. Implement a solution using a sliding window approach, such as a ring buffer or a time-series database, and optimize for memory and throughput by using efficient data structures and possibly approximate algorithms.
Pro tip: Demonstrate awareness of trade-offs between exact and approximate counting (e.g., using Count-Min Sketch for high-cardinality ad IDs) and discuss how to handle late-arriving data with watermarks or allowed lateness.
Ask about window size (e.g., last 5 minutes), data volume (events per second), latency requirements, accuracy needs, and whether the system should be distributed.
Outline components: data ingestion (e.g., Kafka), stream processing (e.g., Flink, Spark Streaming), storage (e.g., Redis, time-series DB), and query API. Discuss partitioning by ad ID or time.
Propose a sliding window implementation using a circular buffer or a time-bucketed approach (e.g., per-second buckets). For distributed, use windowing with event time and watermarks.
Code a simple in-memory version (e.g., using a deque or ring buffer) and then discuss scaling: sharding, replication, and using approximate counting for memory efficiency.
Discuss optimizations: batch processing, compression, TTL for old data, and trade-offs between accuracy, memory, and latency. Mention handling out-of-order events and exactly-once semantics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came naturally out of the first part.
Start by briefly restating your solution and its current complexity, then systematically identify optimization opportunities across time, space, and scalability dimensions. For each opportunity, explicitly state the tradeoff (e.g., increased memory for faster lookups) and justify why the tradeoff is acceptable or not for Uber's scale and constraints.
Pro tip: Tie every optimization to a concrete Uber scenario (e.g., real-time matching, surge pricing) and quantify the impact—interviewers love candidates who think in terms of latency, throughput, and cost at scale.
Briefly describe your solution's approach and its current time/space complexity, and any known bottlenecks. This sets the stage for optimization.
Consider algorithmic improvements (better data structures, caching, parallelism), system-level optimizations (sharding, load balancing), and cost optimizations (memory, network).
For each optimization, explicitly state what you gain and what you sacrifice (e.g., time vs. space, consistency vs. availability, complexity vs. maintainability).
Rank optimizations by impact and feasibility given Uber's scale, latency requirements, and team resources. Explain why some tradeoffs are unacceptable.
Summarize which optimizations you would pursue first and why, showing engineering judgment and awareness of business goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.