Design a large-scale Caching Platform

Question

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

Codemia · Accepted Answer

- **Functional Requirements**:
  - Support for GET and PUT requests with a low-latency response time.
  - Ability to handle millions of requests per second.
  - Cache eviction strategies (e.g., LRU, LFU) to manage memory efficiently.
  - User authentication and authorization for secure access.
  - API for cache management and monitoring (e.g., cache hit/miss statistics).

- **Non-Functional Requirements**:
  - High availability (99.99% uptime).
  - Scalability to handle increasing data loads and request rates.
  - Consistency model that suits Two Sigma’s trading applications, likely eventual consistency.
  - Fast recovery from failures to minimize downtime.
  - Comprehensive logging and monitoring for operational insights.

Assuming Two Sigma needs to support 10 million requests per second (RPS):
- **Request Size**: Average request size is 100 bytes, and response size is 1 KB.
- **Daily Requests**: 10 million RPS * 86,400 seconds = 864 billion requests per day.
- **Data Size**: If we cache 1% of the requests, we would need 8.64 TB of cache space per day.
- **Cache Duration**: Assuming a TTL of 1 day for cached items, we would need enough capacity to store 8.64 TB, plus additional space for eviction policies and operational overhead.

The architecture consists of the following components:
- **Load Balancer**: Distributes incoming requests among caching servers. (e.g., NGINX)
- **Caching Layer**:
  - **Redis**: An in-memory data store, chosen for its high throughput and low latency.
  - **Clustered Redis Setup**: Using Redis Sentinel for high availability and Redis Cluster for scaling horizontally.
- **Data Storage**: Persistent storage (e.g., **Amazon S3** or **Cassandra**) for cache warm-up and backup.
- **Monitoring and Logging**: Use **Prometheus** for metrics collection and **Grafana** for visualization, along with **ELK Stack** for logging.
- **Authentication Service**: A microservice that handles user authentication (e.g., using OAuth2).

The schema for the cached data could look like this:
- **CacheEntry**:
  - `key`: STRING (Unique identifier for the cache item)
  - `value`: BLOB (The actual cached data)
  - `expiry`: TIMESTAMP (Time when the cache entry expires)
  - `last_accessed`: TIMESTAMP (To support eviction policy)
  - `user_id`: STRING (To enforce access controls)

- **Access Patterns**: The schema is designed for quick lookups (GET) and updates (PUT), with the expiry timestamp facilitating automatic eviction, and last_accessed for LRU eviction strategy.

1. **Consistency vs. Availability**: Choosing eventual consistency to provide high availability and performance; this is acceptable for cache data as stale reads are tolerable in trading systems.
2. **Memory vs. Disk**: Using Redis (in-memory) for speed, but the trade-off is limited memory capacity, leading to potential cache misses.
3. **Single Point of Failure**: While Redis Sentinel provides high availability, introducing complexity; careful monitoring needed to ensure failover processes function correctly.
4. **Scaling**: Horizontal scaling of Redis adds complexity with shard management, but is necessary for handling high request loads.
5. **Operational Overhead**: The complexity of managing a distributed caching system requires robust monitoring and alerting to quickly identify and resolve issues.

Design a large-scale Caching Platform

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