Spark RDD persistence is an optimization technique which saves the result of RDD evaluation in cache memory. Using this we save the intermediate result so that we can use it further if required. It reduces the computation overhead.
When we persist an RDD, each node stores the partitions of it that it computes in memory and reuses them in other actions on that RDD (or RDD derived from it). This allows future actions to be much faster (often by more than 10x). Caching is a key tool for iterative algorithms and fast interactive use.
Read on to see how you can do this and some of the options available to you when caching. This is extremely useful when working with external data sources, as then you don’t risk hitting the external source multiple times.