System-Design-Question

Design Distributed Cache

Category: system_design Date: 2026-03-15

Design Distributed Cache

Problem Statement: Design a distributed cache system to store frequently accessed data, reducing the load on the main database and improving the overall system performance.

Requirements (Functional + Non-functional):

Functional Requirements:

  1. Store and retrieve data from the cache efficiently.
  2. Handle cache expiration and eviction.
  3. Support multiple data types (e.g., strings, integers, objects).
  4. Provide a simple API for clients to interact with the cache.

Non-functional Requirements:

  1. High availability and fault tolerance.
  2. Scalability to handle large amounts of data and traffic.
  3. Low latency (less than 10ms) for cache operations.
  4. Data consistency across all nodes in the cluster.

High-Level Architecture: The distributed cache system will consist of the following components:

  1. Cache Nodes: Each node will store a portion of the cached data.
  2. Cache Manager: Responsible for distributing data across nodes, handling cache expiration, and eviction.
  3. Client: Interacts with the cache using a simple API.
  4. Load Balancer: Distributes incoming traffic across cache nodes.

Database Design: We will use a distributed NoSQL database (e.g., Redis Cluster) to store cached data. The database will be designed to handle high write and read throughput.

  1. Key-Value Store: Store cached data as key-value pairs.
  2. Expiration: Store expiration timestamps for each key-value pair.
  3. Eviction: Implement a least recently used (LRU) eviction policy.

Scaling Strategy: To handle increasing traffic and data, we will implement the following scaling strategies:

  1. Horizontal Scaling: Add more cache nodes to handle increased load.
  2. Load Balancing: Distribute incoming traffic across nodes using a load balancer.
  3. Data Partitioning: Split data across nodes using consistent hashing.

Bottlenecks:

  1. Cache Hit Ratio: High cache miss rates can lead to increased load on the main database.
  2. Cache Node Failures: Node failures can lead to data loss and increased latency.
  3. Network Latency: High network latency can lead to increased latency for cache operations.

Trade-offs:

  1. Data Consistency: Implementing strong consistency across nodes can lead to increased latency.
  2. Scalability: Horizontal scaling can lead to increased complexity and cost.

Design using the First Principle of System Design:

The first principle of system design is to “Keep it Simple, Stupid” (KISS). We can apply this principle by:

  1. Minimizing the number of components: Using a simple key-value store and load balancer.
  2. Avoiding over-engineering: Focusing on the core requirements and avoiding unnecessary complexity.
  3. Focusing on a single responsibility: Assigning a single responsibility to each component (e.g., cache nodes only store data).

By applying the KISS principle, we can design a simple, scalable, and efficient distributed cache system.

Learning Links:

  1. Redis Cluster
  2. Distributed NoSQL databases
  3. Load balancing
  4. Scalability

Note: This is a high-level design discussion. In a real-world scenario, you would need to consider additional factors such as security, monitoring, and backup/restore procedures.

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