Database Scalability and High Availability Questions

Architectural approaches and operational practices for scaling and maintaining database availability. Topics include vertical versus horizontal scaling trade offs; replication topologies, leader and follower roles, read replicas and replica lag; read write splitting and connection pooling; sharding and partitioning strategies including range based, hash based, and consistent hashing approaches; handling hot partitions and data skew; federation and multi database federation patterns; cache layers and cache invalidation; rebalancing and resharding strategies; distributed concurrency control and transactional guarantees across shards; multi region deployment strategies, cross region failover and disaster recovery; monitoring, capacity planning, automation for failover and backups, and cost optimization at scale. Candidates should be able to pick scaling approaches based on read and write patterns and explain operational complexity and trade offs introduced by distributed data.

HardSystem Design

47 practiced

Create a runbook and architecture for automated database failover tests (chaos-testing) in production for a clustered DB. Include automation steps, pre-conditions and safety checks, rollback procedures, observability metrics to watch, and how to avoid cascading failures affecting dependent services.

MediumSystem Design

47 practiced

Design a read-write splitting strategy for a relational database used by multiple microservices. Explain how routing is performed, how to handle transactions that must read their own writes, connection pooling implications, and failure modes when replicas lag or the primary becomes unavailable.

EasyTechnical

37 practiced

Describe leader-follower (primary-secondary) replication topology. What are the responsibilities of the leader versus followers, how is write and read traffic typically handled, and what considerations should be made for leader failover, promotion, and split-brain prevention?

MediumTechnical

41 practiced

Explain quorum reads and writes in distributed stores (e.g., Cassandra). How do read and write quorums affect consistency, latency, and availability? Provide example configurations (e.g., RF=3, W=2, R=2) and describe trade-offs for read-heavy and write-heavy workloads.

EasyTechnical

35 practiced

List and explain common cache invalidation strategies (write-through, write-back, write-around, TTL, explicit invalidation). For each strategy, describe operational implications, the impact on consistency, and scenarios where you would recommend it for database-backed applications.

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