Distributed Data Caching Using Big Data
The advent of the emerging sector of Big Data has brought with it the promise of highly scalable databases to handle terabytes of data at a time. Dealing with colossal datasets will attest to have difficulty for manipulating, storing and retrieving information in bulk quantities to determine if the cache is required or not.
In the past years, traditional RDBMS and NewSQL/NoSQL databases have mastered in-memory caching to provide caching and general in-memory capabilities. For instance, MongoDB and CouchDB can get configured to run in-memory but when Oracle 12 and SAP HANA are on the table, we already know it’s a mainstream.
If we want to deliver solutions of the digital age, we are needed to explore new approaches that will surpass the existing limitations of database technologies in present times that happens at both the network and hardware levels serve as major impediments to the management and manipulation of big data sets.
The evident importance of cache is it helps to reduce the stress on a database by setting itself as an intermediary layer between the database and the end users. It allows transferring data from a low-performance location by considering the difference in accessing data that is stored on a disk.
When we make a request, the returned data is stored inside the cache in such a way that it can be more easily accessed in the future. Any future query will initially try the cache but if it misses down, it will fell down on the database. It does make sense for applications to reuse the same information over and over for any game or message data, software rendering or scientific modeling.
For example – Consider a three-tier application which is made up of a presentation layer as a user interface, a layer for handling the logic and a data layer for the backend. These layers can be separated from each other but still, latency can be a limiting factor. Assume that each user of the app has a static data set which needs to get relayed to them every time they navigate a new page – beginning at the data layer and ending at the presentation layer.
Now, if the data layer constantly gets queried then it leads to high strain and poor user experience by latency. To resolve this, the data is accessed frequently which can be kept in temporary memory to allow get it collected rapidly to the presentation layer. Because we consider cost and speed, a cache is somewhat limited in the size it can grow up to. It is always mandatory to add any high-performance database service where efficiency is concerned.
We are aware of how many of the applications use the cache locally on a single instance running alongside an application. There can be a number of downsides to this approach but the most notable one is that it scale very well for bigger applications. In the case of failure, stages like this are likely to be irretrievable.
To this, the distributed caching offers some improvements as the name indicates.
