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Answer: Implement stateful transformations using `mapGroupsWithState` with a low trigger interval.
1. **Stateful Accuracy**: The `mapGroupsWithState` function ensures complete stateful accuracy by maintaining and updating state information across micro-batches, which is crucial for financial transactions. 2. **Minimize Processing Time**: Using `mapGroupsWithState` with a low trigger interval reduces processing time per micro-batch by processing data in a stateful manner within each micro-batch. 3. **Low-Latency Streaming**: For applications requiring low-latency, `mapGroupsWithState` with a low trigger interval processes data quickly and efficiently, reducing latency. 4. **Spark Structured Streaming**: Designed for stateful processing, `mapGroupsWithState` leverages Spark's optimized processing engine for streaming data, making it ideal for financial transaction processing.
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How can you optimize a Spark Structured Streaming application for low-latency processing of financial transactions to ensure minimal processing time per micro-batch without compromising stateful accuracy?
A
Utilize trigger(Trigger.ProcessingTime("1 second")) and fine-tune spark.streaming.blockInterval.
B
Enable spark.streaming.receiver.maxRate and set a high spark.sql.streaming.metricsEnabled value.
C
Implement stateful transformations using mapGroupsWithState with a low trigger interval.
D
Opt for flatMapGroupsWithState with explicit state timeout settings and adjust spark.sql.streaming.schemaInference.
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