What is a Distributed Cache?
Caching refers to holding data – particularly data that needs to be frequently accessed – in a nearby location for ease of retrieval. Distributed caching scales this concept by spreading the cached data across multiple machines or servers.
To make cached data easier and faster to access, it is stored in RAM rather than a hard drive. The distributed cache approach is particularly useful in environments with a high data load and volume. Distributed cache architecture enables effective incremental scaling as data grows, making it a flexible, reliable solution.
How Does Distributed Caching Work?
Distributed caching can be more complex than relying on a single machine, naturally. In a distributed cache system design, data is spread across multiple machines or servers.
For consistency, administrators must decide on a strategy for partitioning data across multiple locations in a uniform way. Some data is easily partitioned, such as healthcare patients with a patient reference number. Partitions can be set by the first digit with ease.
Across more complicated caches, a hash function may be the best strategy. This hash function will transform the data in the set then assign and cache it in a location determined by modular arithmetic. This method assists in mapping disparate data or a set of arbitrary size to that of a fixed size.
With this model, there is a risk of the cache losing coherence, an element that data architects must plan for. Avoiding this issue means updating in-memory caches throughout the distributed system whenever information in the local cache is edited or deleted.
Distributed caching may also lead to timeouts or latency in the system. The more quickly and efficiently the components of the distributed cache communicate with one another (and the faster the network), the less latency in response.
Distributed Cache Use Cases
Consider a few of the more popular use cases for distributed cache:
Database Caching
Distributed cache lowers latency and the unnecessary load on the database by placing a cache layer in front of the database itself, thus eliminating the bottleneck for data retrieval.
User Sessions
By storing user session information in the cache, websites and applications can increase fault tolerance by avoiding the loss of the user state should an instance go down. Should an instance go down, a new instance spins up and user state data is retrieved from the cache, allowing the user to continue the session without interruption.
In-Memory Data Stream
By using a distributed in-memory cache, data can be processed and analyzed in streams in real-time. This is an on-demand alternative to creating and storing data then processing it in batches, and is particularly useful when fast real-time responses are needed such as in cases of fraud monitoring, fault detection, payment processing, real-time recommendations and transactions (eCommerce), or stats and leaderboards (online gaming).
