A distributed cache is a cache that spans multiple nodes in order to provide high availability, partition tolerance, and potentially increased capacity. It enables faster access to data by storing it across different nodes, thus reducing the load on the backend system.
Distributed caching systems use a form of hashing or consistent hashing to distribute data across the cache nodes. Each node can operate independently but also coordinate with other nodes to ensure that the most frequently accessed data is available for faster retrieval.
This improves performance by reducing the latency time in retrieving the data and can also reduce the load on databases by serving frequently requested data from the cache.
Most distributed caching systems offer features such as:
Examples of distributed caching systems include Memcached, Redis (when configured in distributed mode), Hazelcast, etc.
Here's an example of how you might use Redis as a distributed cache:
In this code snippet, we're using Python's
redis library to connect to a Redis server and store a key-value pair. This example assumes a single Redis instance, but Redis can be configured to work in a distributed environment using techniques like sharding or replication.
Remember, while distributed caches can significantly improve performance, they also introduce complexity into your system architecture, so they should be used judiciously based on the use case and requirements.