Distributed Systems & Data

Consistency, replication, partitioning, caching and messaging.

During a network partition you choose consistency or availability; when there's no partition you still trade latency against consistency.

From linearizable to eventual — what each guarantee means for users, and practical models like read-your-writes and monotonic reads.

Keep copies of data on multiple nodes for availability, durability and read scaling — single-leader, multi-leader and leaderless, sync vs async.

Split data across nodes so storage and throughput scale horizontally — choosing shard keys, range vs hash, hot spots and rebalancing.

Cache-aside, read/write-through, write-behind; TTLs, invalidation, stampede protection and multi-layer caches (browser → CDN → app → DB).

RabbitMQ/SQS-style queues distribute work; Kafka-style logs retain ordered events for many consumers and replay. Know which one your problem needs.

Data modelling by access pattern, indexing (ESR rule), transactions, change streams and the operational gotchas of large multi-tenant collections.