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Cache vs. Persistent storage in Spark?

Spark/Big Datahard0.5 min readPremium

**Why the distinction matters**: Cache = ephemeral compute-layer optimization; persistent storage = durable output. **cache()/persist()**: Stores RDD/DataFrame in memory (or disk) for reuse *within* job/DAG. Levels: MEMORY_ONLY, MEMORY_AND_DISK, etc. Evicted on LRU when full....

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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
Snowflake
Key Concepts Tested
optimizationspark

Why This Question Matters

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

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
99 words

Why the distinction matters: Cache = ephemeral compute-layer optimization; persistent storage = durable output. cache()/persist(): Stores RDD/DataFrame in memory (or disk) for reuse within job/DAG. Levels: MEMORY_ONLY, MEMORY_AND_DISK, etc. Evicted on LRU when full. Persistent storage (Parquet, Delta): Durable, optimized format on disk/S3; survives cluster tear-down. Scalability trade-offs: Cache competes with execution memory; over-caching causes spill. Persistent = source of truth; cache = performance optimization. Cost implications: Cache = no extra storage cost but uses executor RAM. Persistent = storage cost; choose tier (hot/cold). Best practice: Persist intermediates reused 2+ times; store final outputs in Delta/Parquet; unpersist when done.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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