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Databricks Certified Data Engineer - Professional

Databricks Certified Data Engineer - Professional

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How can you manage the state size of a Spark Structured Streaming application with stateful processing that grows indefinitely over time to prevent resource exhaustion?

Real Exam



Explanation:

Implementing state timeout logic using mapGroupsWithState or flatMapGroupsWithState allows for the automatic purging of old state data by specifying a timeout duration. This approach prevents the state size from growing indefinitely, thereby avoiding resource exhaustion. It is more efficient and scalable compared to manual checkpointing or restarting the query periodically, as it automates state management without requiring manual intervention. This method ensures the application can handle large volumes of stateful data efficiently, maintaining optimal performance and stability.

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