Spark Persistence: Boost Performance with Caching

In Spark, persistence refers to caching the calculated results of RDDs or DataFrames into memory, allowing these results to be reused in subsequent operations to avoid redundant computation. Persistence can improve the performance of Spark programs, especially when the same dataset needs to be used multiple times. Persistence can be achieved by specifying the persistence levels of RDDs or DataFrames (such as MEMORY_ONLY, MEMORY_AND_DISK, DISK_ONLY, etc.). Persistence can be explicitly implemented in Spark applications by calling the persist() method or implicitly by using the cache() method when performing operations on RDDs.