PySpark Interview Questions Series (Part 4): Window Functions, UDFs, Spark SQL & Data Skew

2026-07-09 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

Part 4 of the PySpark Interview Questions Series details advanced PySpark topics crucial for Data Engineering interviews and real-world projects. It explains Window Functions, which perform calculations across row groups while retaining all original data, contrasting them with groupBy() and illustrating row_number(), rank(), and dense_rank() with examples. The article defines User Defined Functions (UDFs) and emphasizes preferring built-in Spark functions for better performance due to Spark's optimization capabilities. It also introduces Spark SQL for querying DataFrames using SQL syntax via temporary views. A significant section addresses data skew, explaining its impact on job performance and outlining handling strategies like Broadcast Join, repartitioning (e.g., df.repartition(20)), salting, and early filtering. Finally, it presents common interview scenarios, including troubleshooting slow Spark jobs, appropriate cache() usage, risks of collect(), and when to choose Window Functions over groupBy().

Key takeaway

For Data Engineers preparing for PySpark interviews or optimizing existing Spark jobs, mastering advanced concepts like window functions, UDF performance implications, and data skew mitigation is essential. You should prioritize built-in functions over UDFs for efficiency and strategically apply window functions for row-level calculations. Actively identify and address data skew using techniques like broadcast joins or repartitioning to prevent job slowdowns and resource bottlenecks. Regularly review your Spark job's execution plan to preempt performance issues.

Key insights

Optimizing PySpark performance and understanding advanced features like window functions and data skew mitigation are critical for data engineers.

Principles

• Prefer built-in Spark functions over UDFs for performance.
• Window functions retain rows; groupBy() aggregates them.
• Data skew causes slow jobs and poor resource utilization.

Method

Handle data skew by using Broadcast Join, repartitioning, salting, or filtering data early.

In practice

• Use row_number() for unique row assignment.
• Apply cache() when a DataFrame is reused multiple times.
• Avoid collect() on large datasets to prevent memory issues.

Topics

• PySpark
• Window Functions
• User Defined Functions
• Spark SQL
• Data Skew
• Performance Optimization
• Data Engineering Interviews

Best for: Data Engineer, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.