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This paradigm is very familiar to us from the natural world. Swarms of bees, colonies of ants, schools of fish, even planets in motion “collaborate” through the exchange of information (or forces) while remaining “autonomous” (capable of independent action) in isolation.
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Caching is one of the standard and proven technique to improve the turnaround time for a given service or application. In traditional application development, we have different patterns to follow to build, manage and serve response from cache. In the context of Microservices, some of those patterns
Why caching
It is anonymous across design community that at least few of the services in any enterprise application needs caching enabled, to ensure a better turnaround. Caching is a technique where data gets returned directly from service, rather than making a call to another application (can be a database or another service) or recomputing the value which has already computed once. We can reap very good performance benefits, if we apply right caching techniques for the right data in a given context.
Caching types
Based on the approach we follow to warm the cache, we have two types of caches:
Preloaded cache: Load the data in cache before the start of service and serve data from cache from the first request.
Lazy loaded cache: Warm the cache as data is being requested i.e. first request for a specific data will hit the server or compute, then from next request onwards, data will be served from cache.
Based on where do you maintain the cached data, we have two types of caches:
Inservice cache: Cache stays with in the memory of service instance.
Distributed cache: Cache is stored outside the service instance but accessible to all the service instances of given service.
