Memcached<\/a> is a high-performance, distributed memory object caching system, generic in nature, but intended for use in speeding up dynamic web applications by alleviating Database<\/a> load. Originally developed by Brad Fitzpatrick<\/a> for LiveJournal<\/a> in 2003, it has since become a staple in the infrastructure of some of the world's largest websites, including Facebook and Twitter.<\/p> As an In-Memory Database<\/a>, Memcached<\/a> operates as a Key-Value Store<\/a> for small chunks of arbitrary data such as strings or objects. It employs a Client-Server Model<\/a> where the server maintains a reactive Hash Table<\/a>. When the table fills up, Memcached<\/a> uses a Least Recently Used<\/a> (LRU) algorithm to discard the oldest data to make room for new entries. Because it stores data purely in RAM<\/a>, it offers extremely low Latency<\/a> compared to disk-based storage solutions.<\/p> The system is designed to be simple yet powerful. It does not support complex data types or internal programming logic, which distinguishes it from Redis<\/a>. Its Distributed Computing<\/a> nature allows it to scale horizontally by adding more nodes to a Cluster<\/a>. Developers can find the source code and documentation on the Official Memcached Website<\/a> or review community-driven insights on Wikipedia<\/a>.<\/p> Typical use cases for Memcached<\/a> include Session Management<\/a>, results of API<\/a> calls, and Page Caching<\/a>. By reducing the number of times an application must read from a slow Relational Database<\/a>, it significantly improves Throughput<\/a> and user experience. It is often paired with languages like PHP<\/a>, Python<\/a>, and Node.js<\/a> using standardized client libraries.<\/p>Technical Architecture<\/h2>
Implementation and Performance<\/h2>