59 PySpark Interview Questions

Published 2026-08-16 in PySpark

🚀 59 PySpark Interview Questions I Compiled After Going Through Data Engineering Interviews — Here’s What Most Candidates Get Wrong Most candidates can define an RDD. But when an interviewer asks “Why is Spark faster than MapReduce?” , many struggle to explain the actual reason. Here’s the one-liner that often separates a basic answer from a strong one: 👉 MapReduce writes intermediate results to disk between stages, while Spark can keep data in memory across operations and iterations. That’s a major reason Spark can be significantly faster for many workloads. Here are a few other PySpark concepts that frequently trip candidates up: 📌 Cache() vs Persist() • cache() → Uses the default storage level. • persist() → Lets you explicitly choose the storage level, such as memory, disk, or a combination. 📌 Repartition() vs Coalesce() • repartition() → Performs a full shuffle and can increase or decrease partitions. • coalesce() → Typically avoids a full shuffle and is mainly used to reduce partitions. 💡 Interview tip: Understand why you would choose one over the other—not just the definition. 📌 groupByKey() vs reduceByKey() • groupByKey() → Shuffles data before aggregation, which can be…

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