Technology Selection & Deep Technical Knowledge Questions

Deep understanding of specific technologies relevant to complex system design. Master databases (PostgreSQL, Cassandra, DynamoDB, Elasticsearch), message queues (Kafka, RabbitMQ), caching systems (Redis), search engines, and frameworks. Understand their strengths, weaknesses, trade-offs, operational characteristics, scaling patterns, and common pitfalls. Be able to justify technology choices based on specific system requirements.

EasyTechnical

40 practiced

You're designing storage for high-cardinality time-series metrics with high ingest rates. Compare schema choices and operational trade-offs for: PostgreSQL with partitioning, a TSDB (e.g., Prometheus, Influx), Cassandra wide-row model, and DynamoDB. Discuss retention policies, downsampling, compaction, query types, and operational implications for SREs.

HardSystem Design

40 practiced

Design a data platform that supports both low-latency OLTP transactions and near-real-time OLAP analytics on the same dataset. Explain storage separation (OLTP vs OLAP), use of CDC/streaming ETL, ensuring transactional integrity for OLTP while enabling freshness for analytics, and how SRE ensures reliability and backpressure across layers.

EasyTechnical

42 practiced

List and justify the core monitoring metrics, logs, and traces you would collect for a production PostgreSQL cluster (primary + read replicas) to maintain reliability. Include metrics for query latency distributions, lock/wait statistics, replication lag, resource usage, WAL generation, and what alert thresholds or anomaly detections you'd start with.

MediumTechnical

39 practiced

You must choose storage for a multi-tenant analytics service with heavy scans and per-tenant isolation. Compare: (A) relational DB with read replicas and materialized views, (B) distributed NoSQL/column-store. Discuss isolation guarantees, cost, per-tenant performance, scaling, and the SRE operational differences for backups, restores, and resource isolation.

MediumSystem Design

54 practiced

Design Elasticsearch index mapping and shard sizing for an application with 100M documents (avg 2 KB/doc) supporting full-text search and aggregations. Decide number of shards, shard size target, mapping choices (types, analyzers), and index lifecycle management (ILM) for retention and hot/warm/cold tiers.

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