I started with the ingestion layer, Kafka felt obvious, and I talked through windowing logic for the hourly buckets.
Start by clarifying requirements (scale, latency, accuracy) and then propose a streaming architecture using a distributed log (e.g., Kafka) for ingestion, a stream processor (e.g., Flink) for windowed aggregation, and a fast serving layer (e.g., Redis or Cassandra) for low-latency queries. Address late/out-of-order events by using event-time processing with watermarks and allowed lateness, and discuss trade-offs between accuracy and latency.
Pro tip: Emphasize that you would use event-time processing with watermarks and allowed lateness, and explain how you'd handle updates to already emitted results (e.g., via retractions or upserts) to maintain correctness while meeting the 30-second freshness SLA.
Ask about expected event volume, acceptable latency, accuracy requirements, and query patterns to scope the design appropriately.
Propose a scalable ingestion layer (e.g., Kafka) and a stream processing engine (e.g., Flink) that performs windowed aggregations by ad ID and hour.
Use event-time processing with watermarks to track progress, and allow a configurable lateness window. For events arriving after the window closes, either update results via retractions or store them in a side output for later reconciliation.
Use a low-latency store (e.g., Redis, Cassandra) to serve aggregated counts, ensuring it supports upserts for late updates. Consider a lambda architecture with a batch layer for eventual consistency if needed.
Explain trade-offs between latency, accuracy, and cost. Mention techniques like incremental aggregation, state management, and backpressure handling to ensure scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about write buffering, batching inserts, and using something like a columnar store or pre-aggregated counters to avoid row-level contention.
Start by clarifying the workload characteristics (write volume, read/write ratio, latency requirements, consistency needs) and then propose a layered architecture that scales writes horizontally. Discuss specific techniques like sharding, LSM-tree-based storage engines, and write-optimized data structures, while addressing trade-offs such as read amplification and consistency.
Pro tip: Emphasize that high write throughput often requires sacrificing read performance or strong consistency; explicitly state which trade-off you'd choose and why, showing you understand the business context. Also, mention monitoring and backpressure mechanisms to handle bursts gracefully.
Ask about write volume (e.g., writes per second), data size, read/write ratio, latency SLAs, consistency requirements, and durability guarantees. This ensures your solution is tailored to the actual problem.
Propose using LSM-tree based stores (e.g., RocksDB, Cassandra) that batch writes in memory and flush sequentially to disk, avoiding random I/O. Alternatively, consider append-only logs or write-ahead logging for durability.
Partition data across multiple nodes based on a shard key to distribute write load. Discuss strategies like consistent hashing, range partitioning, and handling hotspots.
Use techniques like write batching, asynchronous replication, and in-memory buffers to reduce per-write overhead. Consider compression and columnar formats for efficiency.
Discuss trade-offs: higher write throughput may increase read latency, reduce consistency, or complicate compaction. Explain how to handle failures with replication, quorum writes, and backpressure.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.