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What is the difference between cache() and persist() in Spark? When would you use each?

Spark/Big Datamedium0.7 min read

**cache()**: Equivalent to `persist(MEMORY_AND_DISK)`. Stores partitions in memory; spills to disk if memory is insufficient. **persist(storage_level)**: Explicit control over storage: MEMORY_ONLY, MEMORY_AND_DISK, MEMORY_ONLY_SER, MEMORY_AND_DISK_SER, DISK_ONLY....

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Frequency
Low
Asked at 5 companies
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
AccentureCoforgeFreechargeImpetusYash Technologies
Interview Pro Tip

Pro-Move: Tie storage level to cluster sizing and cost. Red Flag: Caching and never unpersisting—memory leak and wasted spend.

Key Concepts Tested
partitionspark

Why This Question Matters

This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Accenture, Coforge, Freecharge, and 2 others. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, spark) will help you answer variations of this question confidently.

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Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.

Expert Answer
147 words

cache(): Equivalent to persist(MEMORY_AND_DISK). Stores partitions in memory; spills to disk if memory is insufficient.

persist(storage_level): Explicit control over storage: MEMORY_ONLY, MEMORY_AND_DISK, MEMORY_ONLY_SER, MEMORY_AND_DISK_SER, DISK_ONLY.

Architectural Logic (Why It Matters): Caching trades memory/disk for recomputation cost. The right choice depends on reuse count, data size, serialization overhead, and cluster resources.

Scalability & Cost Trade-offs:

  • MEMORY_ONLY: Fastest access; no serialization. Risk of eviction under memory pressure—partial recompute. Use for smaller datasets with high reuse.

  • MEMORY_ONLY_SER: ~2–4x less memory; CPU cost for serialization. Better for large caches when memory is constrained.

  • MEMORY_AND_DISK: Fault-tolerant—spills to disk if evicted. Avoids full recompute. Default for cache().

  • DISK_ONLY: When memory is severely limited; slower but predictable.

    • Cost Implications: Caching a 500GB DataFrame in MEMORY_ONLY on 100 executors with 8GB each = eviction thrashing. Use MEMORY_AND_DISK or MEMORY_ONLY_SER. Always unpersist() when done to free resources and avoid unnecessary cluster cost.

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