Explanation:
Analyzing garbage collection logs from the Spark UI in Databricks is the most efficient approach to identify and resolve memory leaks in a Databricks cluster. Garbage collection logs provide detailed insights into memory usage, allocation, and deallocation, allowing you to detect abnormal patterns or trends indicative of a memory leak. This method enables proactive monitoring and data-driven decision-making to optimize cluster performance and stability.
Ultimate access to all questions.
Your team suspects a memory leak in a Databricks cluster is leading to performance issues. What is the most effective method to identify and resolve this problem?
A
Utilizing Databricks cluster metrics to monitor memory usage over time
B
Running a memory diagnostic tool within a Databricks notebook against the cluster
C
Analyzing garbage collection logs from the Spark UI in Databricks
D
Reviewing Databricks cluster event logs in Azure Log Analytics for anomalies
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