Advanced Caching and Data Pipeline Design Questions
Distributed caching, cache coherency, specialized stores (search engines, column stores, time-series databases). Data pipeline architecture: batch processing, stream processing, ETL design. Understanding Lambda and Kappa architectures.
MediumSystem Design
46 practiced
Design a streaming pipeline that uses Kafka for ingestion and Apache Flink (or equivalent) for sessionization and aggregations, then writes results to ClickHouse for analytics queries. Specify how to achieve fault tolerance, windowing/session semantics, and state management; discuss delivery semantics (at-least-once vs exactly-once) and their impact on downstream storage.
HardTechnical
35 practiced
Design a monitoring and SLO framework for caches and streaming pipelines. Specify metrics to collect (e.g., cache hit rate, eviction rate, tail latency, consumer lag, watermark lag), alert thresholds, and dashboards. Explain how you would correlate cache issues with downstream pipeline anomalies and automate remediation.
MediumTechnical
42 practiced
Compare Lambda and Kappa architectures for data processing. Describe the operational complexity, code duplication risks, reprocessing strategies, and which architecture you'd choose for a company that wants a single codebase for both batch reprocessing and real-time analytics.
MediumTechnical
41 practiced
Explain how Elasticsearch (or a search engine) interacts with caching layers. Discuss query caching, resultset caching, document TTLs, index refresh rate, and strategies to keep search results consistent with the primary data source when frequent updates occur.
HardTechnical
41 practiced
Design a time-series data pipeline for ingesting IoT sensor readings (100k sensors, sending every 10s). Include ingestion, hot-path queries, downsampling/rollups, cold storage, retention policies, and a caching layer for dashboards. Specify the time-series DB choice (e.g., TimescaleDB, InfluxDB), compression strategy, and how to handle out-of-order or late events.
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