Scaling Memcache Deep Dive for System Design
Explore challenges and solutions in scaling Memcache for large distributed systems. Understand data replication, handling hotspots, maintaining cache hit rates, and balancing latency with consistency across server, cluster, regional, and cross-regional levels.
Key-value stores and big data
When designing large-scale social media websites, we need to understand that there are huge volumes of data and that a number of requests are being made. The large scale of data on such platforms entails very intense load and spontaneous I/O requests, which might not be optimized using
This is where caching in memory can be used to allow for linear scaling. A key-value store is one way to cache data. Key-value stores can run on network-enabled systems that share information with other machines without requiring specialized hardware. This allows architects to scale their key-value stores by adding more front-end servers. One such key-value store is Memcached.
Note: We'll call the distributed key-value store as
and the source code or the running binary as Memcached. Memcache Memcache without the 'd'.
Why scale Memcache?
Memcache allows developers to add caching to their applications while having simple operations like set, get, and delete. Although it comes with a lot of features for single-server use, there ...