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What is the advantage of caching in PySpark? When and why would you use it?

Spark/Big Datamedium0.5 min readPremium
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Tredence
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joinspark
Expert AnswerPremium
94 wordsInterview-ready
**Advantage**: Avoid recomputation. Same DF used multiple times = one compute, many reads. Critical for iterative algorithms (ML), multi-action pipelines. **When**: (1) DF reused 2+ times. (2) Iterative (e.g., loop with same lookup). (3) Small dimension joined repeatedly. **Storage Levels**: MEMORY_ONLY (fast, evictable), MEMORY_AND_DISK (spill), MEMORY_ONLY_SER (smaller). Cache = MEMORY_AND_DISK. **Why Care**: Recompute of 1TB = hours....
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