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Home/Questions/Spark/Big Data/Cache() vs Persist(): Explain the difference and use cases for caching and persisting data in Spark with memory levels.

Cache() vs Persist(): Explain the difference and use cases for caching and persisting data in Spark with memory levels.

Spark/Big Dataeasy0.5 min readPremium
Frequency
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Asked at 1 company
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questions in Spark/Big Data
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Asked at these companies
Capgemini
Key Concepts Tested
spark
Expert AnswerPremium
93 wordsInterview-ready
**Why this matters**: Wrong storage level = OOM or unnecessary disk I/O. **cache()** = `persist(StorageLevel.MEMORY_AND_DISK)`—default for quick reuse. **persist()** accepts levels: MEMORY_ONLY (fast, evicted if OOM), MEMORY_AND_DISK (spill to disk), MEMORY_ONLY_SER (serialized, less memory), MEMORY_AND_DISK_SER, DISK_ONLY. **Use cases**: MEMORY_ONLY for hot small DataFrames; MEMORY_AND_DISK when data may not fit; serialized for memory savings....
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