Real questions from top companies · medium
Discuss performance tuning concepts such as shuffle, skew, and caching.
Discuss techniques such as partitioning, broadcast joins, and caching to enhance Spark job performance.
How do you handle out-of-memory errors in Spark jobs?
How do you handle very large datasets in Spark to ensure scalability and efficiency?
Provide specific examples of challenges faced with PySpark and SQL and solutions implemented.
Split a DataFrame such that even numbers appear in one column and odd numbers in another
Steps to mount storage in Databricks.
Transformation vs. Action in PySpark?
What Hadoop command would you use to merge multiple files into one?
What are Spark Submit properties?
What are the key differences between Map and Reduce in Spark?
What are the key performance tuning techniques you apply in Spark jobs to improve performance?
What are the limitations of the REORG command with respect to large datasets?
What are the performance trade-offs of using salting to mitigate data skewness?
What are the steps to efficiently process 1 TB of data in Spark?
What causes Out of Memory (OOM) issues in Databricks, and how do you resolve them?
What causes data skewness in Spark, and how can it be resolved?
What configuration parameters are critical for enabling AQE effectively?
What determines the maximum parallelism achievable in Databricks?
What do you understand by data shuffling in Spark? Why is it important?
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