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Answer: Enable detailed query execution logs in Databricks, analyze them with the built-in Spark UI, and optimize based on the identified bottlenecks.
Enabling detailed query execution logs in Databricks allows for the capture of comprehensive information about each query's execution, including step durations, data shuffling, and resource usage. The built-in Spark UI facilitates the analysis of these logs, offering visual insights into query execution to easily identify bottlenecks like slow-running queries, inefficient data shuffling, or resource contention. This method enables the identification of stages with prolonged execution times, data skew issues, or suboptimal resource utilization, guiding targeted optimizations such as query rewriting, resource allocation adjustments, or data restructuring. This approach is superior as it harnesses Databricks and Spark's native monitoring and analysis features for detailed query performance insights, supporting real-time issue detection and resolution.
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Your analytics team is experiencing slow query performance in Azure Databricks notebooks. What steps would you take to use logging and monitoring to pinpoint and resolve these performance issues?
A
Implement custom logging within notebooks to track execution times, then manually review these logs for any patterns or outliers.
B
Utilize Azure Databricks' event logs, export them to a data lake for analysis with Azure Data Lake Analytics, focusing on query execution patterns.
C
Enable detailed query execution logs in Databricks, analyze them with the built-in Spark UI, and optimize based on the identified bottlenecks.
D
Capture performance metrics using Azure Monitor, correlate them with specific queries logged in Azure Log Analytics, and apply optimizations.