Interview questions · medium
Explain how Adaptive Query Execution changes the economics of Spark tuning. What problems does it solve at runtime, and when might you still need manual intervention (e.g., salting, broadcast hints)?
What are the best practices for logging and monitoring bad data?
What role does the executor heap size play in preventing OOM errors?
How does improper partitioning affect Spark job performance?
What metrics would you analyze to determine if your partitioning strategy is effective?
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 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 is the usage of Optimize and REORG commands in Databricks?
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