Distributed Systems

An overview of why distributed systems are needed and how replication, partitioning, consistency, transactions, communication, and common distributed components fit together.

Requirements and failure cases for locks across processes or machines, with Redis- and ZooKeeper-style approaches and the importance of ownership and fencing.

Design goals and trade-offs for globally unique IDs using database sequences, UUIDs, Redis counters, and Snowflake-style timestamp/worker/sequence layouts.

Why process-local sessions break under horizontal scaling and how replication, shared session stores, and centralized authentication address the problem.

The core building blocks of distributed storage: partitioning, replication, consistency, metadata, routing, and failure recovery.

BASE as an availability-oriented distributed-systems design idea: basic availability, soft state, eventual consistency, and its relationship to flexible transactions.

The CAP theorem: consistency, availability, partition tolerance, and the C/A trade-off a distributed system faces when a network partition occurs.

What cluster metadata represents, why routing and ownership depend on it, and centralized versus peer-to-peer metadata management.

Why replicated distributed systems face consistency problems, how consistency models define observable guarantees, and how consensus algorithms help nodes agree on state.

A compact introduction to distributing computation across machines, with batch and stream processing as two common execution models.