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Home/Questions/Spark/Big Data/What is the advantage of caching in PySpark? When and why would you use it?

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

Spark/Big Datamedium0.5 min read

**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....

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Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Tredence
Key Concepts Tested
joinspark

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Expert Answer
94 words

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. Cache = seconds for second read.

Scalability Trade-offs: Cache consumes memory; evicts other data. Unpersist when done. Over-caching = thrashing.

Cost Implications: Cache when beneficial = 50–90% cost reduction for multi-use. Cache everything = OOM and waste.

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