How to handle skewed data in PySpark?

𝗛𝗼𝘄 𝗗𝗼 𝗬𝗼𝘂 𝗛𝗮𝗻𝗱𝗹𝗲 𝗦𝗸𝗲𝘄𝗲𝗱 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸?

This is a critical PySpark interview question! Here’s the breakdown: 

✅ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗦𝗸𝗲𝘄𝗲𝗱 𝗗𝗮𝘁𝗮?
– Skewed data occurs when some partitions have 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁𝗹𝘆 𝗺𝗼𝗿𝗲 𝗱𝗮𝘁𝗮 than others, leading to inefficient resource usage and slower processing. 

✅ 𝗛𝗼𝘄 𝘁𝗼 𝗛𝗮𝗻𝗱𝗹𝗲 𝗦𝗸𝗲𝘄𝗲𝗱 𝗗𝗮𝘁𝗮: 
1. 𝗦𝗮𝗹𝘁𝗶𝗻𝗴: Add a random prefix to keys to distribute data more evenly. 
2. **𝗖𝘂𝘀𝘁𝗼𝗺 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴**: Use a custom partitioner to balance the load. 
3. 𝗕𝗿𝗼𝗮𝗱𝗰𝗮𝘀𝘁 𝗝𝗼𝗶𝗻𝘀: For small skewed datasets, use broadcast joins to avoid shuffling. 
4. 𝗥𝗲𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴: Use 𝚛𝚎𝚙𝚊𝚛𝚝𝚒𝚝𝚒𝚘𝚗() to increase the number of partitions and reduce skew. 

Example (Salting): 

𝚏𝚛𝚘𝚖 𝚙𝚢𝚜𝚙𝚊𝚛𝚔.𝚜𝚚𝚕.𝚏𝚞𝚗𝚌𝚝𝚒𝚘𝚗𝚜 𝚒𝚖𝚙𝚘𝚛𝚝 𝚌𝚘𝚕, 𝚌𝚘𝚗𝚌𝚊𝚝, 𝚕𝚒𝚝, 𝚛𝚊𝚗𝚍  

# 𝙰𝚍𝚍 𝚊 𝚛𝚊𝚗𝚍𝚘𝚖 𝚜𝚊𝚕𝚝 𝚝𝚘 𝚝𝚑𝚎 𝚔𝚎𝚢  
𝚍𝚏_𝚜𝚊𝚕𝚝𝚎𝚍 = 𝚍𝚏.𝚠𝚒𝚝𝚑𝙲𝚘𝚕𝚞𝚖𝚗(“𝚜𝚊𝚕𝚝𝚎𝚍_𝚔𝚎𝚢”, 𝚌𝚘𝚗𝚌𝚊𝚝(𝚌𝚘𝚕(“𝚔𝚎𝚢”), 𝚕𝚒𝚝(“_”), (𝚛𝚊𝚗𝚍() * 𝟷𝟶).𝚌𝚊𝚜𝚝(“𝚒𝚗𝚝”)))  

# 𝙿𝚎𝚛𝚏𝚘𝚛𝚖 𝚘𝚙𝚎𝚛𝚊𝚝𝚒𝚘𝚗𝚜 𝚘𝚗 𝚝𝚑𝚎 𝚜𝚊𝚕𝚝𝚎𝚍 𝚔𝚎𝚢  
𝚛𝚎𝚜𝚞𝚕𝚝 = 𝚍𝚏_𝚜𝚊𝚕𝚝𝚎𝚍.𝚐𝚛𝚘𝚞𝚙𝙱𝚢(“𝚜𝚊𝚕𝚝𝚎𝚍_𝚔𝚎𝚢”).𝚌𝚘𝚞𝚗𝚝()  


𝗣𝗿𝗼 𝗧𝗶𝗽: Monitor your Spark UI to identify skewed partitions and apply the right strategy! 

What’s your go-to method for handling skewed data? Share your tips below! 👇 

#PySpark #DataEngineering #InterviewPrep #BigData #TechTips