I started with the ingestion pipeline since that felt most concrete: ad servers pushing into Kafka, a stream processor doing windowed aggregation by ad and hour, then writing into something like Cassandra keyed on that pair.
Start by clarifying requirements: event volume, latency, accuracy, and query patterns. Then propose a scalable architecture with a high-throughput ingestion pipeline, a windowed aggregation layer, and a low-latency serving store. Discuss trade-offs between batch and stream processing, and how to handle late data and exactly-once semantics.
Pro tip: Emphasize the importance of idempotency and deduplication in the ingestion pipeline to avoid overcounting, and suggest using a lambda or kappa architecture to balance real-time and batch processing needs.
Ask about event volume, required latency for API responses, accuracy guarantees, and query patterns (e.g., per-ad hourly counts).
Propose a pipeline: ingestion (e.g., Kafka), stream processing (e.g., Flink/Spark Streaming), storage (e.g., Cassandra/Bigtable), and serving layer (e.g., Redis).
Design keys for per-ad hourly counts, handle windowing, and decide on pre-aggregation vs. on-the-fly aggregation.
Discuss partitioning, replication, backpressure, and exactly-once processing to handle high volume and failures.
Compare batch vs. stream, discuss late data handling, and propose caching or materialized views for low-latency reads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about watermarks and a look-back window for late arrivals, which landed okay.
Start by clarifying the requirements and constraints of the ingestion pipeline, such as data volume, latency tolerance, and exactly-once semantics. Then, propose a robust strategy that combines deduplication using unique event IDs with windowing and watermarking to handle out-of-order events. Finally, discuss trade-offs between different approaches and how to monitor and handle late data.
Pro tip: Mention that you would use a combination of event-time processing and idempotent writes to downstream systems, and highlight the importance of defining a clear late-data policy based on business impact.
Ask about data volume, latency requirements, and the cost of duplicates or missing data to understand the problem scope.
Use a unique event ID (e.g., UUID) and maintain a deduplication store (e.g., Redis, Bloom filter) to filter duplicates within a time window.
Process events based on event time rather than ingestion time, using watermarks and allowed lateness to handle out-of-order events.
Design downstream systems to be idempotent, so that duplicate processing doesn't cause incorrect results.
Monitor metrics like duplicate rate and late event rate, and adjust window sizes or deduplication TTL based on observed patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and requirements (e.g., writes per second, latency, durability). Then propose a layered architecture: buffer writes using a message queue, batch them, and use a write-optimized database (e.g., Cassandra, ScyllaDB) with partitioning and replication. Discuss trade-offs between consistency, latency, and throughput, and mention monitoring and backpressure mechanisms.
Pro tip: Emphasize that you would first measure and understand the current bottleneck before optimizing, and that you'd design for failure—e.g., using idempotent writes and dead-letter queues to handle retries without data loss.
Ask about expected write volume (e.g., millions per second), latency SLAs, data durability, and consistency needs. This shapes the entire design.
Introduce a durable message queue (e.g., Kafka) between the stream processor and the database to absorb bursts and allow the database to consume at its own pace.
Use batching, asynchronous writes, and a write-optimized datastore (e.g., LSM-tree based like Cassandra). Partition data to distribute load and consider denormalization for write efficiency.
Add backpressure to slow down producers if the database is overwhelmed. Monitor key metrics (write latency, queue depth) and set up alerts.
Compare consistency vs. availability, SQL vs. NoSQL, and cost implications. Mention potential fallbacks like writing to a log first or using a write-behind cache.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.