nw-sd-patterns

Catalog distributed systems patterns for load balancing, caching, sharding, replication, and messaging.

Updated Apr 15, 2026

Search Tags:#messaging #caching #patterns #distributed #replication #sharding #load-balancing

Ready to run

npx skills add https://github.com/StudentCristian/nWave-github --skill nw-sd-patterns

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nw-sd-patterns
Source: https://github.com/StudentCristian/nWave-github/tree/main/.github/skills/nw-sd-patterns
Command: npx skills add https://github.com/StudentCristian/nWave-github --skill nw-sd-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Core knowledge base of essential distributed systems patterns to help engineers design scalable, resilient architectures across load balancing, caching, sharding, replication, and messaging.

Core Features & Use Cases

Load Balancing: strategies to distribute traffic and prevent SPOF, enabling high availability.
Caching & Data Locality: patterns to reduce latency and database load through cache-aside, write-through, and read-through approaches.
Data Partitioning: sharding, consistent hashing, and directory-based patterns to scale storage and queries.
Replication & Consistency: master-slave and multi-master setups with CAP-aware trade-offs for reliability and performance.
Messaging & Queuing: decoupling and asynchronous processing using queues, pub/sub, and dead-letter strategies.

Quick Start

Provide a recommended pattern set for a given workload to improve scalability and resilience.

Frequently Asked Questions about nw-sd-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What distributed systems patterns should I use for scalable microservices?▼

Distributed systems patterns for scalable microservices include load balancing to distribute traffic, caching to reduce database load, and messaging queues for asynchronous processing. These techniques ensure high availability and resilience across cloud-native architectures.

How do I choose between cache-aside and write-through caching patterns?▼

Choose between cache-aside and write-through caching patterns by evaluating their trade-offs. Cache-aside loads data on demand reducing latency, while write-through maintains cache consistency synchronously. The correct pattern depends on your specific read-write ratio and data freshness requirements.

When do I need consistent hashing for data partitioning?▼

You need consistent hashing for data partitioning when scaling storage and queries dynamically across distributed nodes. This sharding pattern minimizes data redistribution when adding or removing nodes, ensuring minimal disruption to large-scale data processing workloads.

What are the CAP-aware trade-offs for master-slave replication?▼

CAP-aware trade-offs for master-slave replication involve choosing between consistency and availability during network partitions. Master-slave setups prioritize strong consistency through a single write node, while multi-master configurations increase availability but introduce complex conflict resolution.

Does asynchronous messaging with dead-letter queues improve system resilience?▼

Asynchronous messaging with dead-letter queues improves system resilience by decoupling services and handling failed messages. Pub/sub and queuing strategies prevent cascading failures, allowing problematic events to be stored and reprocessed without blocking the main pipeline.

What is the best way to distribute traffic and prevent single points of failure?▼

The best way to distribute traffic and prevent single points of failure is applying load balancing patterns. These strategies route incoming requests across multiple server instances, enabling high availability and ensuring no single node becomes a bottleneck.