How to retry failed requests without overwhelming servers. Exponential backoff, jitter, and when to give up. Java implementations and real-world patterns.

How to make operations safe to retry. Idempotency keys, database patterns, and why retrying non-idempotent operations causes data corruption.

Read-your-writes and monotonic reads aren’t just buzzwords. They’re the difference between a database that feels broken and one that makes sense to users.

Comparing vertical scaling (scale up) and horizontal scaling (scale out). When to use each, trade-offs, and the complexity that comes with horizontal scaling.

How circuit breakers prevent cascading failures in microservices. State transitions, Java implementation with Resilience4j, and real-world thresholds.

Comparing load balancing algorithms - Round Robin, Least Connections, Weighted Round Robin, and IP Hash. Java implementations and real-world trade-offs.

How do stale replicas catch up in distributed systems? Compare read repair and anti-entropy strategies with Merkle trees for healing data divergence.

Concurrent writes in distributed databases don’t merge automatically. Learn to detect conflicts with version vectors and resolve them without losing data.

Users see stale data after writes. It’s not a bug, it’s replication lag. Learn to handle read-after-write problems and causality violations in production.

Eventual consistency doesn’t mean milliseconds. Understand linearizability, causal consistency, and quorum reads to pick the right consistency model.

Querying partitioned databases by non-partition keys? Learn the tradeoffs between local and global secondary indexes in distributed systems.

Understand virtual nodes in consistent hashing through a simple three-layer model that decouples data distribution from server topology in distributed systems.