System-Design-Question

Design Distributed Cache

Category: system_design Date: 2026-02-14

Design Distributed Cache

Problem Statement: Design a distributed cache system that can store and retrieve data efficiently, handling high traffic and scalability requirements.

Requirements (Functional + Non-functional)

1. Functional Requirements:
   • Store and retrieve data from cache
   • Support data expiration (time-to-live, TTL)
   • Support cache eviction policies (e.g., LRU, LFU)
   • Support multi-datacenter replication for high availability
2. Non-functional Requirements:
   • High throughput (thousands of requests per second)
   • Low latency (sub-10ms response time)
   • Scalability to handle increasing traffic
   • Fault tolerance and high availability
   • Data consistency across datacenters

High-Level Architecture

1. Client: Applications that interact with the cache
2. Cache Proxy: Load balancer and gateway for client requests
3. Cache Store: Distributed cache storage (e.g., Redis, Memcached)
4. Cache Manager: Responsible for cache operations (e.g., eviction, replication)
5. Datacenter: Multiple datacenters for high availability

Database Design:

1. Cache Store: Redis or Memcached for fast data access
2. Metadata Store: MySQL or PostgreSQL for storing cache metadata (e.g., TTL, eviction policy)
3. Replication Store: Consistent hashing-based store for data replication

Scaling Strategy:

1. Horizontal Scaling: Add more cache nodes and datacenters as traffic increases
2. Sharding: Divide cache data into smaller chunks and distribute across nodes
3. Load Balancing: Use techniques like HAProxy or NGINX for efficient load distribution

Bottlenecks:

1. Cache miss rate: High cache miss rates can lead to increased load on the cache store
2. Network latency: High network latency between datacenters can impact data replication
3. Cache eviction: Frequent cache eviction can lead to cache thrashing

Trade-offs:

1. Cache size vs. data freshness: Larger caches can lead to increased latency, while smaller caches may lead to data staleness
2. Replication frequency vs. data consistency: More frequent replication can lead to increased latency, while less frequent replication may lead to data inconsistencies

Design using the First Principle of System Design:

The First Principle: “The system should be designed around the constraints, not the requirements.”

In this case, the constraint is the high throughput and low latency requirement. To design a distributed cache system that meets these constraints, we focus on:

1. Using an in-memory cache store (e.g., Redis, Memcached) to reduce latency
2. Implementing a scalable cache manager to handle high throughput
3. Employing a replication strategy to ensure data consistency across datacenters

By designing the system around the constraints, we can build a highly performant and scalable distributed cache system.

Relevant Learning Links:

• Redis: https://redis.io/
• Memcached: https://memcached.org/
• Consistent Hashing: https://en.wikipedia.org/wiki/Consistent_hashing
• HAProxy: https://www.haproxy.org/
• NGINX: https://www.nginx.com/

Note: This is a high-level design discussion and may require additional details and trade-offs depending on the specific requirements and constraints of the project.

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