Top 50 Apache Spark Interview Questions

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Below are some common questions that are asked in interviews. Obviously this guide is subjective but being able to answer these questions will help you prove your core understanding of Spark.

  1. What is Apache Spark and how is it different from Hadoop MapReduce?

  2. Explain the Spark Architecture in detail (Driver, Executor, Cluster Manager)

  3. What are RDDs? Why were they replaced by DataFrames/Datasets?

  4. Explain Catalyst Optimizer phases with example

  5. What is the difference between DataFrame and Dataset?

  6. What is a "wide" vs "narrow" transformation? Impact on performance?

  7. Explain Shuffle in depth – Hash Shuffle vs Sort Shuffle vs Tungsten Sort

  8. What is the difference between repartition() and coalesce()?

  9. What is catalyst predicate pushdown? Show with Delta Lake and Parquet?

  10. Can you explain Broadcast Join in depth?

  11. What are Accumulators and Broadcast variables?

  12. What is the difference between cache() and persist()? Storage levels?

  13. What is Spark Streaming → Structured Streaming evolution?

  14. Explain Window functions with code.

  15. What is Adaptive Query Execution (AQE) in Spark 3?

  16. What is join skew? How to handle it?

  17. Explain UDF vs Spark SQL functions performance

  18. What is the role of the Spark Driver? What happens if it fails?

  19. What is the difference between groupByKey and reduceByKey?

  20. What are Spark Partitions? How to decide number?

  21. Explain Catalyst vs Tungsten – Catalyst = logical/physical optimization, Tungsten = memory/CPU (unsafe rows, codegen)

  22. What is off-heap memory? – spark.memory.offHeap.enabled=true → uses sun.misc.Unsafe, avoids GC

  23. Explain Z-ordering in Delta Lake – colocate multi-dimensional data

  24. What is Delta Cache? – Automatic caching of Delta files in SSD/NVMe

  25. Explain Spark Task, Stage, Job.

  26. What is backpressure in Structured Streaming?

  27. Explain foreachBatch, foreachPartition in streaming.

  28. What is Project Tungsten Phase 1,2,3?

  29. Explain Dynamic Partition Pruning (DPP).

  30. What is runtime code generation (whole-stage codegen)?

  31. Explain Spark shuffle spill (memory → disk).

  32. What is executor heartbeat and block manager?

  33. Explain Spark Fair Scheduler vs FIFO.

  34. What is speculation in Spark?

  35. Explain Catalyst constant folding, boolean simplification.

  36. What is the difference between mapPartitions and foreachPartition?

  37. Explain Spark SQL Thrift Server vs HiveServer2.

  38. What is Arrow memory format in PySpark?

  39. Explain Dataset Encoder serialization.

  40. What is spark.locality.wait?

  41. Explain partition pruning vs data skipping.

  42. What is the role of _SUCCESS, _STARTED files?

  43. Explain Spark on Kubernetes vs YARN.

  44. What is dynamic allocation?

  45. Explain shuffle partitions config.

  46. What is broadcast timeout?

  47. Explain Spark UI stages, tasks, SQL tabs.

  48. What is storage tab vs executors tab?

  49. Explain Event Timeline view in Spark UI.

  50. What is the future of Spark? (Spark 4.0 plans – native Kubernetes, better Python, Project Lightspeed)

Stay tuned for more questions and answers!

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