What is the difference between cache and persist

Difference Between cache() and persist() in Apache Spark

Feature	cache()	persist()
Definition	Shortcut to persist() with default storage level	Stores data with customizable storage levels
Default Storage Level	DataFrame: MEMORY_AND_DISK (Deserialized) RDD: MEMORY_ONLY (Serialized)	Same as cache() by default unless specified
Custom Storage Levels	❌ Not supported	✅ Fully supported via StorageLevel
Serialization Format	DataFrame: Deserialized in memory RDD: Serialized in memory	Depends on the StorageLevel used
Control Over Storage	❌ No customization	✅ Full control – memory, disk, off-heap, etc.
Syntax	df.cache()	df.persist(StorageLevel(useDisk: bool, useMemory: bool, useOffHeap: bool, deserialized: bool, replication: int = 1))
Use Case Suitability	Simple reuse with default storage behavior	Advanced scenarios requiring tuned performance
Memory Management	DataFrame: Uses memory with disk fallback RDD: Uses memory only	Fully customizable based on need
Performance Flexibility	Limited to default behavior	High flexibility and control
Requires Action to Trigger	✅ Yes, e.g. .count()	✅ Yes, e.g. .count()
How to Remove	.unpersist()	.unpersist()
Applies To	RDDs, DataFrames, Datasets	RDDs, DataFrames, Datasets