I would implement a read-through caching strategy with a time-based expiration policy and potentially use cache invalidation mechanisms when the underlying data changes. This allows the API to quickly serve cached responses while ensuring data consistency with respect to updates.
In depth
A read-through caching strategy allows the system to check the cache first before querying the underlying data source. If the data exists in the cache, it is returned immediately, which reduces latency. If the data is not present, it is fetched from the database and stored in the cache for future requests. Implementing a time-to-live (TTL) on cached items can help balance performance with freshness, ensuring that stale data is not served for too long. Furthermore, establishing an invalidation policy that triggers cache updates when the source data is modified can help maintain consistency across the system, especially in cases where data is updated sporadically. The challenge lies in ensuring that the invalidation logic is efficient and not overly burdensome on the system's architecture.
Example scenario
In a large e-commerce platform, we had an API that served product details, which were read frequently but only updated when an inventory change occurred. We implemented a caching layer using Redis with a TTL of one hour. When the inventory was updated, we triggered an event that invalidated the cache for that product ID, ensuring that subsequent requests would fetch the fresh data from the database. This strategy significantly reduced database load and improved the response time for users accessing product information.
Common mistakes
One common mistake is not implementing proper cache expiration, leading to stale data being served for extended periods. Developers sometimes underestimate how quickly data can become outdated, which can result in user dissatisfaction. Another mistake is failing to account for concurrency issues during cache invalidation, where multiple updates can lead to inconsistent reads across different instances of the application. This can create situations where one user sees outdated data while another sees the updated version, undermining trust in the API.
Follow-up questions
- What factors would influence your choice of cache store?
- How would you handle cache warm-up strategies?
- Can you explain the difference between strong and eventual consistency in caching?
- How would you test the effectiveness of your caching strategy?
Preparing for a system design interview?
- A 7-step method for answering any system design question, plus estimation numbers
- 5 worked designs: URL shortener, rate limiter, chat, news feed and file storage
- Guides to caching, databases, queues, consistency and reliability, with a 4-week study plan
- 306 practice questions with answers on caching, databases, queues, microservices, APIs and more
5 worked designs, a 4-week plan and 306 practice questions with answers.
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