Distributed Caching: Memcached and Redis

Learning Objectives Explain the role of a distributed cache in improving application performance and scalability. Differentiate between the architectures and features of Memcached and Redis. Implement the Cache-Aside (lazy loading) caching pattern in pseudocode. Analyze the trade-offs of different cache eviction policies, such as LRU (Least Recently Used). Identify appropriate use cases for in-memory data stores like session management, leaderboards, and API rate limiting. Describe how consistent hashing helps distribute data evenly across a cluster of cache servers. Explain the problem of cache invalidation and strategies to maintain data consistency. Ever wonder how your favorite social media feed loads instantly, even with millions of users online? ⚡ The secret often l...

TermDefinitionExample Distributed CacheA system that pools the RAM of multiple networked computers to store data as a single, in-memory key-value store. It is used to provide fast access to frequently used data, reducing the need to access slower backend data stores like a database.A popular news website caches its top 10 articles in a distributed cache spread across three servers. When a user visits the homepage, the application fetches the articles from the fast in-memory cache instead of querying the main database, resulting in a sub-millisecond response time. Key-Value StoreA simple data storage paradigm where data is stored and retrieved using a unique identifier called a 'key'. The data itself is referred to as the 'value'. This is the fundamental model used by b...

Cache-Aside Pattern (Lazy Loading) 1. Application requests data from the cache. 2. IF data exists (Cache Hit) -> Return data to application. 3. ELSE (Cache Miss) -> a. Application requests data from the database. b. Database returns data to the application. c. Application stores the data in the cache. d. Return data to application. This is the most common caching strategy. It's called 'lazy' because data is only loaded into the cache when it's first requested and missed. Use this pattern to reduce database read load for frequently accessed data. Consistent Hashing A hashing technique that minimizes the number of keys that need to be remapped when a cache server is added or removed from the cluster. It maps both servers and keys to points o...