What is PySpark persist?

PySpark Persist is an optimization technique that is used in the PySpark data model for data modeling and optimizing the data frame model in PySpark. It helps in storing the partial results in memory that can be used further for transformation in the PySpark session.

What is cache persist?

Caching or persistence are optimization techniques for (iterative and interactive) Spark computations. They help saving interim partial results so they can be reused in subsequent stages. These interim results as RDD s are thus kept in memory (default) or more solid storage like disk and/or replicated.

What is persist Spark?

Both persist() and cache() are the Spark optimization technique, used to store the data, but only difference is cache() method by default stores the data in-memory (MEMORY_ONLY) whereas in persist() method developer can define the storage level to in-memory or in-disk.

What is persist and Unpersist in Spark?

When we persist or cache an RDD in Spark it holds some memory(RAM) on the machine or the cluster. Once we are sure we no longer need the object in Spark’s memory for any iterative process optimizations we can call the method unpersist(). Once this is done we can again check the Storage tab in Spark’s UI.

What is the difference between Spark checkpoint and persist to a disk?

Checkpointing stores the RDD in HDFS. It deletes the lineage which created it. When we persist RDD with DISK_ONLY storage level the RDD gets stored in a location where the subsequent use of that RDD will not reach that point in recomputing the lineage.

When should I persist spark RDD?

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.

What is the difference between broadcast and cache in spark?

Caching is a key tool for iterative algorithms and fast interactive use. Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a large input dataset in an efficient manner.

What is the maximum size of broadcast variable in spark?

8GB
The maximum size for the broadcast table is 8GB. Spark also internally maintains a threshold of the table size to automatically apply broadcast joins.