Data Model Design and Access Patterns Questions
Discuss how you'd design data models based on access patterns. Understand relational vs. NoSQL trade-offs. Know when to denormalize, how to handle distributed transactions, and strategies for scaling databases (sharding, partitioning). Discuss read vs. write optimization.
HardTechnical
0 practiced
On a 10TB OLTP database with high write throughput, outline a robust non-blocking schema change strategy for operations such as adding/removing columns and changing column types. Include the step-by-step process for online backfill, dual reads/writes (versioned reads), feature flags, verification, and safe rollback procedures.
MediumTechnical
0 practiced
Customers run ad-hoc analytical queries on your OLTP database, causing production slowdowns. Recommend strategies to support ad-hoc analytics without impacting OLTP: read-only replicas, periodic ETL to a data warehouse, query quotas/sandboxing, and schema/design changes. Evaluate pros/cons and how each strategy affects access patterns and data freshness.
HardSystem Design
0 practiced
Design a social feed system that supports personalized timelines for 200M users. Some celebrities have 50M followers. Compare fan-out-on-write vs fan-out-on-read strategies. For each approach, detail the data models, storage implications, update patterns, and how you would handle very high-fanout users without overwhelming storage or write capacity.
EasyTechnical
0 practiced
Describe the differences between B-tree and hash indexes. For each, state typical use-cases, query types they accelerate (range vs equality), and limitations (range scans, partial matches). How do these index choices affect physical data layout, disk I/O, and update costs?
MediumTechnical
0 practiced
Given a normalized blog schema: posts(id, author_id, body, created_at), comments(id, post_id, author_id, body, created_at, likes), tags(tag_id, name), post_tags(post_id, tag_id) — propose denormalized models optimized for (A) retrieving a post with the top 10 comments and tags, and (B) listing posts by tag with pagination. Discuss update anomalies and mitigation strategies (CDC, background reconciliation, partial denormalization).
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