midApache Spark
Explain Data Skew and how to handle it in Spark.
Updated May 5, 2026
Short answer
Data Skew is when certain partitions have significantly more data than others, causing 'straggler' tasks.
Deep explanation
Skew usually happens during joins or groupBys on keys with high frequency (e.g., 'Unknown' user ID). One task might take 1 hour while the rest take 1 minute.
Real-world example
Joining a 'UserClicks' table where 50% of clicks are from one bot ID.
Common mistakes
- Increasing the number of partitions blindly, which doesn't solve skew if all records with the same key still go to one partition.
Follow-up questions
- Can Adaptive Query Execution (AQE) help?