Build a multi-tenant Caching Pipeline

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:**
    - Support caching of API responses for multiple tenants (e.g., financial data requests).
    - Provide a cache invalidation mechanism for tenants to ensure data freshness.
    - Allow tenants to specify cache TTL (Time to Live) settings.
    - Support read and write-through caching strategies to optimize performance.
    - Implement rate limiting per tenant to prevent abuse.

- **Non-Functional Requirements:**
    - **Scalability:** Handle millions of requests per second with latency under 100ms.
    - **Availability:** 99.99% uptime to ensure that cached data is accessible at all times.
    - **Consistency:** Support eventual consistency for cached data while allowing for configurable strong consistency options.
    - **Security:** Isolate tenant data and implement authentication mechanisms to secure cache access.

- **User Load Estimation:**
    - Assuming 10,000 tenants, with each generating approximately 100 requests per second on average.
    - Total requests per second = 10,000 tenants * 100 requests/tenant = 1,000,000 requests/second.

- **Data Size Estimation:**
    - If each cached response averages 1KB in size, the total cached data size would be approximately:
      - 1,000,000 requests/second * 1KB/request = 1,000,000 KB = ~1 TB per second of cache data.
    - Assuming a TTL of 10 minutes, we might need to handle 600 TB of data in cache at peak times.

- **Storage Requirements:**
    - Given the data retention needs and scaling, we may require a distributed cache with at least 10-20 TB of capacity per region.

The architecture consists of the following components:
- **API Gateway:** Handle incoming requests and route them to the appropriate caching layer.
- **Caching Layer:** Utilize a distributed caching solution like Redis Cluster or Memcached for in-memory caching.
- **Tenant Management Service:** Manage tenant configurations, including cache TTL settings and rate limiting.
- **Data Ingestion Service:** Ingest API responses and handle cache writes.
- **Invalidation Service:** Manage cache invalidation events from tenants.

- **Technology Choices:**
    - **API Gateway:** AWS API Gateway for managing traffic and security.
    - **Caching:** Redis Cluster for in-memory caching.
    - **Database:** Amazon DynamoDB for persistent storage of cache metadata and tenant configurations.
    - **Deployment:** Kubernetes for container orchestration across multiple regions.

The data model consists of the following key entities:
- **Tenant:**
    - `tenant_id`: STRING (Primary Key)
    - `cache_ttl`: INTEGER (Cache time-to-live in seconds)
    - `rate_limit`: INTEGER (Max requests per second)

- **Cache Entry:**
    - `cache_key`: STRING (Unique identifier for the cached data)
    - `tenant_id`: STRING (Foreign Key to Tenant)
    - `data`: BLOB (The actual cached data)
    - `timestamp`: TIMESTAMP (When the entry was last cached)

- **Invalidation Log:**
    - `log_id`: STRING (Unique identifier for the log entry)
    - `cache_key`: STRING (Key of the invalidated cache)
    - `tenant_id`: STRING (Foreign Key to Tenant)
    - `timestamp`: TIMESTAMP (When the invalidation occurred)

- **Access Patterns:**
    - Frequent reads for cache entries and occasional writes/invalidation requests.

- **Consistency vs. Availability:**
   - Opt for eventual consistency for cached data to ensure high availability, allowing for faster response times but requiring mechanisms to handle stale reads.
   - Implement a fallback mechanism to fetch fresh data from the database if cache misses occur.

- **Caching Strategy:**
   - Choosing a write-through caching strategy to ensure that writes are also cached, reducing complex cache invalidation logic.
   - Trade-offs include potential increased latency during write operations, but this ensures data consistency across reads and writes.

- **Operational Excellence:**
   - Implement chaos engineering practices to test fault tolerance in the caching pipeline.
   - Define SLOs such as 99.99% availability and <100ms latency for 95th percentile requests. Monitor SLIs to ensure operational health and make iterative improvements.

Build a multi-tenant Caching Pipeline

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