Start by clarifying requirements and scale, then propose a high-level architecture that decouples ingestion, processing, and serving layers. Focus on trade-offs between latency, cost, and accuracy, and explain how you would handle 500k events per second with horizontal scaling and partitioning.
Pro tip: Emphasize the importance of idempotency and exactly-once processing to avoid double-counting events, and mention how you would handle late-arriving data with watermarks or a lambda architecture.
Ask questions to understand data sources, required latency for reporting, accuracy needs, and retention. Confirm the 500k events/sec is peak and estimate data volume and growth.
Propose a scalable ingestion system using a distributed message queue (e.g., Kafka) with partitioning by ad ID or user ID. Discuss client-side batching, retries, and deduplication.
Outline stream processing (e.g., Flink, Spark Streaming) for real-time aggregation and a batch layer for historical accuracy. Choose storage: time-series DB for metrics, data lake for raw events.
Provide APIs for querying metrics with low latency. Use pre-aggregated tables and caching. Discuss trade-offs between real-time and batch views.
Discuss fault tolerance, exactly-once semantics, backpressure, and cost. Explain how to handle late data and ensure data consistency across layers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that the optimal choice depends on the specific use case and constraints, then systematically compare the two approaches across dimensions like network efficiency, latency, reliability, and battery life. Conclude with a balanced recommendation that often involves a hybrid strategy, such as adaptive batching based on network conditions and event criticality.
Pro tip: Emphasize that the decision should be data-driven: measure real-world metrics like battery consumption, network overhead, and user-perceived latency for your specific app, rather than relying on generic assumptions.
Identify the nature of the events (e.g., critical vs. non-critical), expected volume, and mobile-specific constraints like intermittent connectivity and battery limitations.
Compare one-request-per-event vs. batching in terms of network overhead, latency, reliability, battery impact, and server load.
Discuss how factors like radio state transitions, background execution limits, and data costs influence the decision.
Suggest a strategy that combines both approaches, such as batching non-critical events while sending critical events immediately, with dynamic adjustment based on network conditions.
Conclude with a clear recommendation that balances the trade-offs for the given context, and mention how you would validate it with metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the exactly-once vs at-least-once framing.
Start by clarifying the pipeline's requirements: data sources, latency tolerance, and exactly-once semantics. Then explain a layered strategy: deduplicate using idempotent keys and stateful stores, and handle late events with watermarks, allowed lateness, and reprocessing. Conclude with trade-offs and monitoring.
Pro tip: Emphasize that deduplication and late-event handling are not just technical but also business decisions—align with stakeholders on acceptable data loss or duplication. Mention that you'd instrument metrics for duplicates and late arrivals to continuously tune the system.
Ask about data sources, volume, velocity, latency requirements, and exactly-once vs at-least-once semantics. Understand business impact of duplicates and late data.
Use unique event IDs and maintain a deduplication store (e.g., Redis, RocksDB) with TTL. Consider idempotent writes and windowed deduplication for streaming.
Implement event-time processing with watermarks and allowed lateness. Use side outputs or dead-letter queues for very late events, and support reprocessing for corrections.
Choose a distributed, scalable store for dedup state and checkpointing. Use exactly-once sinks or idempotent writes to avoid duplicates on failure.
Track metrics like duplicate rate, late event count, and processing latency. Set up alerts and adjust watermarks, TTLs, and allowed lateness based on observed patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Stream-table joins were my answer for campaign/ad metadata since that data changes slowly and fits in a changelog-backed KV store.
Start by clarifying the streaming architecture and enrichment requirements, then compare the three approaches based on latency, cost, and complexity. Recommend a hybrid solution that uses stream-table joins for low-latency enrichment and batch for backfill or high-latency tolerance.
Pro tip: Emphasize the importance of handling late-arriving events and ensuring exactly-once semantics, as these are common pitfalls in streaming enrichment. Also, discuss how the choice impacts downstream analytics and real-time decision-making.
Ask about data volume, latency requirements, and consistency needs to understand the context. This will guide the choice of enrichment method.
Explain stream-stream joins (joining two streams), stream-table joins (enriching with a lookup table), and batch enrichment (processing in batches). Highlight how each works.
Compare latency, throughput, cost, complexity, and consistency for each approach. For example, stream-stream joins have low latency but high complexity, while batch enrichment is simpler but has higher latency.
Recommend a hybrid approach, such as using stream-table joins for real-time enrichment and batch for historical data, or using a lambda architecture.
Discuss handling late data, exactly-once semantics, and monitoring. Mention tools like Kafka Streams, Flink, or Spark Structured Streaming.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a lambda-ish setup: a fast OLAP store (Druid or ClickHouse) fed by the stream for real-time, and a warehouse (BigQuery or Redshift) for historical.
Start by clarifying the requirements: query patterns, latency SLAs, data volume, and freshness. Then propose a hybrid architecture that separates hot (recent) and cold (historical) data, using different storage engines optimized for each workload. Finally, discuss how to unify access via a query layer or materialized views to avoid duplicating logic.
Pro tip: Mention the trade-off between pre-aggregation and flexibility: pre-aggregated rollups speed up dashboards but limit ad-hoc analysis, so consider a lambda or kappa architecture with a serving layer that can handle both. Also, highlight the importance of partitioning and indexing strategies to keep costs down as data grows.
Ask about query patterns (e.g., dashboard filters, report granularity), latency needs (sub-second vs. minutes), data volume, retention period, and budget. This shapes the choice of storage and processing.
Propose storing recent, frequently accessed data in a fast, indexed store (e.g., columnar OLAP like ClickHouse or Druid) for real-time dashboards, and older data in a cost-effective store (e.g., S3 with Parquet, or a data warehouse like Snowflake) for batch reports.
For real-time: use a denormalized, columnar schema with pre-aggregated rollups (e.g., per-minute metrics) to enable fast slicing. For historical: use a normalized or star schema in Parquet/ORC with partitioning by date to optimize batch scans.
Implement a query federation layer (e.g., Trino/Presto) or materialized views that abstract the underlying stores, so applications query a single endpoint. Alternatively, use a lambda architecture with a batch layer for accuracy and a speed layer for freshness.
Discuss data ingestion (e.g., Kafka for streaming, batch ETL for historical), consistency guarantees, backfill strategies, and cost management (e.g., tiered storage, compression, retention policies).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.