How to partition database across multiple servers. Hash-based vs range-based sharding, rebalancing strategies, and the complexity that comes with it.

Protecting services from overload with rate limiting. Token bucket and leaky bucket algorithms explained with Java implementations and real-world trade-offs.

Handling slow consumers in distributed systems. Queue growth, memory exhaustion, and strategies for applying backpressure - rejection, rate limiting, and flow control.

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.

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

Bloom filters skip unnecessary disk reads in LSM trees by saying ‘definitely not here’ with zero false negatives. Learn how Cassandra and RocksDB use them.

LSM trees create SSTables fast but need compaction. Learn size-tiered vs leveled compaction strategies and the write vs read amplification tradeoff.

LSM Trees vs B-Trees: the write-fast or read-fast tradeoff. Learn when to use B-trees (MySQL) vs LSM trees (Cassandra) based on your database workload.

How do databases survive crashes and ensure durability? Learn how Write-Ahead Logging (WAL) uses sequential writes to guarantee data persistence without killing performance.

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.

Every JOIN multiplies query complexity. Learn the three JOIN strategies databases use and when denormalization beats JOIN performance by 30x.

More indexes don’t mean faster queries. Learn when to add, remove, and optimize database indexes. Real examples of 7x performance gains through strategic indexing.

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

Stop guessing at performance problems. Learn the 5-step systematic framework for debugging slow queries that helped reduce query times from 2+ seconds to 30ms.