Data that has its importance for a certain duration of time, which is transient in nature, can be termed as ephemeral data. Such data needs to be flushed out of the system after the intended duration and computer resources have to be freed in order to be made available for newer datasets. In some datastores where there is no in-built capability to do this, scripts and programs have to be written to clean them, or in other words, the onus is on the user to cleanse the system. Before we get into details of the mechanisms Redis has to offer, let's look at the types of data that can be termed as ephemeral. Data types that fall in this category are the following:
Event data: Stock tickers have importance over a small period of time and then lose their value in the context form in which they are viewed. Suppose the value of the tech stock of a dummy corporation is $100 at 1300 hours, and for all the algorithms interested in calculating the whatever index of the tech...