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.
HardSystem Design
0 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.
MediumTechnical
0 practiced
Explain how secondary indexes work in Cassandra and DynamoDB (local vs global secondary indexes). Discuss performance implications, write amplification, consistency concerns, and alternatives such as maintaining manual index tables or inverted indexes. Include operational pitfalls and monitoring signals to detect index-related issues.
MediumSystem Design
0 practiced
Design a Redis-based caching strategy for a read-heavy user profile service. Cover cache key design, TTL strategies, invalidation patterns (cache-aside, write-through, write-behind), how to mitigate cache stampede, and how to warm and scale caches (local in-process cache + Redis cluster + CDN layer).
MediumTechnical
0 practiced
Design a safe, minimal-downtime migration plan to add a new non-null column with a default value to a very large PostgreSQL table used in production with many replicas. Include steps for schema change, backfill approach, application compatibility, write path handling, and rollback procedures.
EasyTechnical
0 practiced
Given this schema: transactions(transaction_id serial primary key, user_id int, amount numeric, occurred_at timestamp, status text) and this query: SELECT * FROM transactions WHERE user_id = 123 AND amount > 100 ORDER BY occurred_at DESC LIMIT 20; Explain why this query may be slow on large tables and propose indexing and schema changes to speed it up. Discuss composite indexes, covering indexes, and write cost trade-offs.
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