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Answer: Enable Spark Streaming‘s backpressure mechanism by setting `spark.streaming.backpressure.enabled` to `true`, allowing Spark to automatically adjust ingestion rates.
Enabling Spark Streaming‘s backpressure mechanism by setting `spark.streaming.backpressure.enabled` to `true` is the most effective approach for dynamically managing backpressure in scenarios with fluctuating loads. This mechanism allows Spark to automatically adjust the ingestion rates based on the system's processing capacity, ensuring a steady processing rate and avoiding system overload. It monitors the processing times of each batch and adjusts the ingestion rate accordingly, making it ideal for handling varying workloads. Other methods, such as configuring a fixed rate limit or implementing a custom rate limiter, may not offer the same level of efficiency and adaptability. Disabling backpressure and relying solely on dynamically increasing executors can lead to resource wastage and is less effective in maintaining consistent processing rates.
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In the context of a Spark Streaming job that reads from a Kafka source and encounters varying load patterns, what is the most effective method to dynamically manage backpressure to ensure consistent processing rates and prevent system overload?
A
Implement a custom rate limiter within the streaming application to control read rates from Kafka, adjusting based on processing times.
B
Disable backpressure entirely and rely on increasing the number of executors dynamically to handle load spikes.
C
Enable Spark Streaming‘s backpressure mechanism by setting spark.streaming.backpressure.enabled to true, allowing Spark to automatically adjust ingestion rates.
D
Configure a fixed rate limit for ingestion using spark.streaming.kafka.maxRatePerPartition based on peak load observations.
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