23 hashtag#Trending Pyspark hashtag#Interview Questions (Difficulty level – Medium to Hard)
1. How can you optimize PySpark jobs for better performance? Discuss
techniques like partitioning, caching, and broadcasting.
2. What are accumulators and broadcast variables in PySpark? How are
they used?
3. Describe how PySpark handles data serialization and the impact on
performance.
4. How does PySpark manage memory, and what are some common issues
related to memory management?
5. Explain the concept of checkpointing in PySpark and its importance in
iterative algorithms.
6. How can you handle skewed data in PySpark to optimize performance?
7. Discuss the role of the DAG (Directed Acyclic Graph) in PySpark’s
execution model.
8. What are some common pitfalls when joining large datasets in PySpark,
and how can they be mitigated?
9. Describe the process of writing and running unit tests for PySpark
applications.
10. How does PySpark handle real-time data processing, and what are the
key components involved?
11. Discuss the importance of schema enforcement in PySpark and how it
can be implemented.
12. What is the Tungsten execution engine in PySpark, and how does it
improve performance?
13. Explain the concept of window functions in PySpark and provide use
cases where they are beneficial.
14. How can you implement custom partitioning in PySpark, and when
would it be necessary?
15. Discuss the methods available in PySpark for handling missing or null
values in datasets.
16. What are some strategies for debugging and troubleshooting PySpark
applications?
17. What are some best practices for writing efficient PySpark code?
18. How can you monitor and tune the performance of PySpark applications
in a production environment?
19. How can you implement custom UDFs (User-Defined Functions) in
PySpark, and what are the performance considerations?
20. What are the key strategies for optimizing memory usage in PySpark
applications, and how do you implement them?
21. How does PySpark’s Tungsten execution engine improve memory and
CPU efficiency?
22. What are the different persistence storage levels in PySpark, and how
do they impact memory management?
23. How can you identify and resolve memory bottlenecks in a PySpark
application?
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