18 docs tagged with "consistency"

TL;DR

Standardize API design across organizations with governance and API-first development

Overview of latency, throughput, concurrency, reliability, time, idempotency, and CAP/PACELC trade‑offs in distributed systems.

Master cache-aside, write-through, write-behind, and read-through patterns to optimize latency, consistency, and durability trade-offs in distributed systems.

Understand the fundamental trade-offs: Consistency, Availability, Partition tolerance, and how PACELC refines these choices for modern systems.

Preserve cause-before-effect visibility across replicas without global total order.

Understand the spectrum of consistency models from strong to eventual, and how to choose the right model for your data and operations.

Core data concepts every engineer should master: modeling, transactions & isolation, indexing, consistency models, and lifecycle.

Master DRY principle: eliminate knowledge duplication for maintainable code, improved consistency, and reduced bugs.

Store the complete history of changes as immutable events rather than storing only the current state.

Replicas converge over time—high availability and low latency with stale‑read trade‑offs.

Ground yourself in core architecture, systems thinking, paradigms, and data basics to make sound design decisions.

Build your foundation in distributed systems theory. Master the fallacies, theorems, and consistency models that guide all architecture decisions.

Balance data consistency and query performance through normalization and strategic denormalization

Design systems where behavior matches user expectations, minimizing surprise and confusion.

Distribute read traffic across replicas while maintaining consistency

All reads reflect the latest successful write—simpler reasoning at higher latency/cost.

Coordinate distributed transactions across services using choreography and orchestration patterns.