Answer-first summary for fast verification
Answer: Implement state timeout logic using mapGroupsWithState or flatMapGroupsWithState and specify a timeout duration to purge old state data.
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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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?
A
Configure the streaming query to restart periodically, thereby resetting the state store and preventing unbounded growth.
B
Use the state operator to explicitly define state storage level as MEMORY_ONLY_SER, forcing old state data to be serialized and stored on disk.
C
Implement state timeout logic using mapGroupsWithState or flatMapGroupsWithState and specify a timeout duration to purge old state data.
D
Regularly checkpoint the streaming state to an external durable store and manually truncate the state store at intervals.
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