In PySpark, caching and persistence refer to storing an RDD (Resilient Distributed Dataset) in memory or on disk for faster access.

Caching is a way of storing an RDD in memory so it can be reused without recomputing it. This is useful for RDDs that are used frequently or are expensive to compute. You can cache an RDD by calling the cache() or persist() method.

Persistence goes a step further and allows you to specify the storage level for an RDD, which determines where the RDD is stored (memory, disk, or both) and how it is serialized. The storage level can be specified using the StorageLevel class, which has several predefined options like MEMORY_ONLY, DISK_ONLY, MEMORY_ONLY_SER, MEMORY_ONLY_DISK_SER, etc.

It’s worth noting that caching and persistence are only sometimes necessary and can lead to negative performance if used correctly. For example, if the RDD fits entirely in memory, caching it will not provide any performance gain. If the RDD is too large to fit in memory, caching it will cause the application to run out of memory.

When an RDD is no longer needed, it can be unpersisted using the unpersist() method. This releases the memory or disk space used by the RDD and allows it to be garbage collected.

How Do I Persist or Cache an RDD or DataFrame in PySpark?

In PySpark, you can persist or cache an RDD or DataFrame using the following methods:

• persist(): This method is used to persist an RDD or DataFrame in memory and/or disk. By default, it uses the MEMORY_ONLY storage level, which stores the RDD or DataFrame in memory in a serialized format. You can specify a different storage level by passing it as an argument to the persist() method. For example, to persist an RDD in memory and disk:

rdd.persist(StorageLevel.MEMORY_AND_DISK)

• cache(): This method is an alias for persist() and is used to cache an RDD or DataFrame in memory. It uses the MEMORY_ONLY storage level by default.
• unpersist(): This method removes an RDD or DataFrame from memory and/or disk. If the RDD or DataFrame is no longer needed, it’s a good practice to unpersist it to release the memory and/or disk space it uses.

rdd.unpersist()

It’s worth noting that if you are working with DataFrames, persisting and caching will be done automatically by Spark’s query optimizer, called Catalyst. You can control the storage level of DataFrames by using the spark.sql.autoBroadcastJoinThreshold configuration property, which can be set to the maximum size of the table in bytes before it will be broadcast to worker nodes.

In summary, caching and persistence can improve the performance of PySpark applications by storing RDDs in memory or on disk for faster access. However, it’s important to use them judiciously and consider the size and computation cost of the RDDs.