Fundamentals of Distributed Systems: Concepts, Replication, Consistency, and Protocols

This article gives a thorough introduction to the principles and mechanisms that underpin modern distributed systems.

1. Concepts

1.1 Model

In engineering projects a node is typically a process on an operating system; the model treats a node as an indivisible whole.

1.2 Replica

Replica (copy) provides redundancy for data or services; data replicas persist the same data on different nodes to tolerate loss, while service replicas provide identical functionality without relying on local storage.

1.3 Metrics for Distributed Systems

Key metrics include performance (throughput, latency, concurrency), availability, scalability, and consistency.

2. Distributed System Principles

2.1 Data Distribution Methods

Discusses hash‑based, range‑based, and chunk‑based data placement, their trade‑offs, and the use of consistent hashing with virtual nodes to improve scalability and balance.

2.2 Basic Replica Protocols

Describes centralized (primary‑secondary) and decentralized replica control protocols, their workflows, and how they handle failures, primary election, and data synchronization.

2.3 Lease Mechanism

Explains lease‑based caching, lease issuance, expiration, and how it improves fault tolerance by guaranteeing that a server will not modify data while a lease is valid.

2.4 Quorum Mechanism

Details write‑all‑read‑one, quorum parameters (W, R, N), and how quorum ensures that reads intersect with a majority of successful writes to provide consistency.

2.5 Log Techniques

Introduces redo logs, checkpointing, and 0/1 directory structures for efficient crash recovery and atomic updates.

2.6 Two‑Phase Commit

Outlines coordinator and participant roles, the prepare and commit phases, message flow, and the protocol’s limitations in fault tolerance and performance.

2.7 MVCC

Multi‑version concurrency control creates separate versions for each transaction, allowing concurrent reads while preserving isolation.

2.8 Paxos Protocol

Describes proposer, acceptor, and learner roles, the prepare and accept phases, and how Paxos achieves strong consistency with a majority quorum.

2.9 CAP Theorem

States that a system cannot simultaneously provide strong consistency, high availability, and partition tolerance, and positions the discussed mechanisms within this trade‑off.