Design a large-scale Caching Platform

Question

Design a multi-tenant caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

Codemia · Accepted Answer

- **Functional Requirements:**  
  - Multi-tenant support to allow different clients to store and retrieve cached data.  
  - Support for various data types (e.g., strings, JSON objects, binary data).  
  - APIs for storing, retrieving, and invalidating cache entries.  
  - TTL (Time-to-Live) functionality for cache entries to automatically expire.  
  - Monitoring and logging features for usage analytics.

- **Non-Functional Requirements:**  
  - High availability with 99.99% uptime.  
  - Low latency response time (under 50 ms for 95% of requests).  
  - Scalability to handle millions of requests per second.  
  - Consistency level configurable per tenant (strong, eventual).  
  - Secure access control for tenant data.

- **User Load Estimation:**  
  - Assume 1 million active users with an average of 100 requests per user per day.  
  - Total requests per day = 1,000,000 users * 100 requests = 100,000,000 requests/day.  
  - Average requests per second = 100,000,000 / (24 * 60 * 60) ≈ 1,157 requests/second.  
  
- **Data Size Estimation:**  
  - Assume each cache entry averages 1 KB.  
  - If each user stores 10 cache entries, total data stored = 1,000,000 users * 10 entries * 1 KB = 10 GB.  
  - Considering TTL and churn, estimate a maximum storage requirement of 100 GB.

- **Component Diagram:**  
  - **Load Balancer:** Distributes incoming requests across multiple cache nodes.  
  - **Cache Nodes:** Use Redis Cluster for in-memory data storage with partitioning support.  
  - **Data Store:** Persistent storage (e.g., MongoDB) for cache entries that exceed TTL or need durability.  
  - **API Gateway:** Handles incoming API requests and provides authentication and rate limiting.  
  - **Monitoring Service:** Use Prometheus and Grafana for performance monitoring and alerting.  
  
- **Technology Choices:**  
  - Redis for caching layer (supports high throughput and low latency).  
  - MongoDB for persistent storage (flexible schema for different tenant data).  
  - Kubernetes for orchestration and scaling of cache nodes.

- **Schema Design:**  
  - **CacheEntry Table:**  
    - `id` (string, primary key)  
    - `tenant_id` (string)  
    - `data` (blob)  
    - `expiration_time` (timestamp)  
    - `created_at` (timestamp)  
  - **Access Patterns:**  
    - Read and write operations based on `tenant_id` for multi-tenancy.  
    - Frequent reads require efficient indexing on `tenant_id` and `expiration_time`.

- **Consistency vs. Availability:**  
  - Choosing Redis with eventual consistency for higher availability across tenants.  
  - Allows faster response times at the cost of potential stale reads.  
  
- **Latency vs. Durability:**  
  - Using in-memory (Redis) for high-speed access with a backup strategy to MongoDB for durability.  
  - Potential for data loss in case of a complete cache node failure, mitigated by backup persistence.  
  
- **Simplicity vs. Scalability:**  
  - Keeping the architecture simple with Redis and MongoDB promotes easier maintenance but may require complex sharding strategies as traffic grows.

Design a large-scale Caching Platform

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