juniorApache Spark
Explain the concept of Partitioning in Spark.
Updated May 5, 2026
Short answer
Partitioning is the division of a large dataset into smaller logical chunks that can be processed in parallel.
Deep explanation
Partitions are the basic unit of parallelism in Spark. One partition is typically processed by one task on one executor core.
Real-world example
Reading a 1GB file on a 4-core cluster; Spark might create 4 partitions so each core handles 250MB simultaneously.
Common mistakes
- Having too few partitions (underutilizing the cluster) or too many (scheduler overhead).
Follow-up questions
- Difference between repartition and coalesce?