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distributed-cache

Analyze distributed cache design for high read load scenarios.

Updated Jun 5, 2026
Search Tags:#consistent hashing#Memcached#distributed caching#cache design#Redis Cluster
One-click install
npx skills add https://github.com/hung-phan/system-skills --skill distributed-cache
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: distributed-cache
Source: https://github.com/hung-phan/system-skills/tree/main/skills/system-review/references/interview-templates/distributed-cache
Command: npx skills add https://github.com/hung-phan/system-skills --skill distributed-cache

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill guides you through the complexities of designing and implementing distributed caches, helping to address high read load scenarios and mitigate performance bottlenecks in distributed systems.

Core Features & Use Cases

  • Consistent Hashing: Discusses consistent hashing algorithms for even key distribution and its impact on system scaling.
  • Eviction Policies: Analyzes different eviction policies and their trade-offs, including LRU, LFU, and allkeys-lru.
  • Cache Stampede Prevention: Introduces techniques such as singleflight, request coalescing, and TTL jitter to mitigate cache stampedes.
  • Replication & Failover: Covers strategies for replication and failover mechanisms in Memcached and Redis Cluster.
  • Use Case: Helps system designers understand how to choose between Memcached and Redis Cluster for cache solutions, considering aspects like data model, sharding, and consistency.

Quick Start

Analyze the architecture of a distributed cache using the provided interview template on "Memcached vs Redis Cluster".

Frequently Asked Questions about distributed-cache

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

FAQPage Schema
How does consistent hashing work in a distributed cache for high read load scenarios?▼

Consistent hashing in a distributed cache distributes keys evenly across nodes to minimize remapping when scaling. It ensures even key distribution and reduces cache misses during node additions or removals in high read load systems.

What is the best way to prevent cache stampedes in Redis Cluster?▼

Preventing cache stampedes in Redis Cluster involves using singleflight, request coalescing, and TTL jitter. These techniques mitigate simultaneous cache misses by controlling concurrent backend requests and varying expiration times.

Memcached vs Redis Cluster: how do I choose a distributed caching strategy?▼

Choosing between Memcached and Redis Cluster for distributed caching requires comparing data models, sharding approaches, and consistency levels. Analyze your replication and failover needs to select the appropriate cache solution.

What are the trade-offs of different eviction policies like LRU and LFU?▼

Eviction policy trade-offs between LRU and LFU depend on access patterns. LRU evicts least recently used keys, while LFU targets least frequently used ones, with allkeys-lru offering broader key selection for cache management.

Do I need to understand replication and failover mechanisms to design a distributed cache?▼

Understanding replication and failover mechanisms is required to design a distributed cache. You must analyze strategies for Memcached and Redis Cluster to ensure high availability and data consistency during node failures.

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