What is a Distributed Cache?

Key components of Distributed Caching

A distributed cache is a cache with data spread across multiple nodes in a cluster and multiple clusters across multiple data centers worldwide. A distributed cache is a system that pools together the random-access memory (RAM) of multiple networked computers into a single in-memory data store used as a data cache to provide fast access to data.

While most caches are traditionally in one physical server or hardware component, a distributed cache can grow beyond the memory limits of a single computer by linking together multiple computers–referred to as a distributed architecture or a distributed cluster–for larger capacity and increased processing power.
Distributed caches are especially useful in environments with high data volume and load.
The distributed architecture allows incremental expansion/scaling by adding more computers to the cluster, allowing the cache to grow in step with the data growth.

How Distributed Cache Works?

Below is how a Distributed Cache typically works:

Data Storage: The distributed cache system allocates a portion of memory on each node or server to store cached data. This memory is typically faster to access than disk storage, enabling faster read and write operations.
Data Replication: To ensure high availability and fault tolerance, the distributed cache system replicates cached data across multiple nodes or servers. This means that if one node fails, the data can still be accessed from other nodes in the cluster.
Cache Invalidation: Cached data needs to be invalidated or updated periodically to reflect changes in the underlying data source. Distributed cache systems implement various strategies for cache invalidation, such as time-based expiration, event-based invalidation, or manual invalidation.
Cache Coherency: Maintaining cache coherency ensures that all nodes in the distributed cache system have consistent copies of cached data. This involves synchronization mechanisms to update or invalidate cached data across all nodes when changes occur.
Cache Access: Applications interact with the distributed cache system through a cache API, which provides methods for storing, retrieving, and updating cached data. When an application requests data, the distributed cache system checks if the data is already cached. If it is, the data is retrieved from the cache memory, avoiding the need to access the underlying data source.
Cache Eviction: To prevent the cache from consuming too much memory, distributed cache systems implement eviction policies to remove least recently used (LRU) or least frequently used (LFU) data from the cache when it reaches its capacity limit.

Regarding the concurrency there are several concepts involved with it such as eventual consistency, strong consistency, distributed locks, commit logs and stuff. Also, distributed cache often works with distributed system co-ordinators such as Zookeeper. It facilitates communication and helps maintain a consistent state amongst the several running cache nodes.