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?

Real Exam

Implement a custom rate limiter within the streaming application to control read rates from Kafka, adjusting based on processing times.

8.5%

Disable backpressure entirely and rely on increasing the number of executors dynamically to handle load spikes.

3.4%

Enable Spark Streaming‘s backpressure mechanism by setting spark.streaming.backpressure.enabled to true, allowing Spark to automatically adjust ingestion rates.

78.0%

Configure a fixed rate limit for ingestion using spark.streaming.kafka.maxRatePerPartition based on peak load observations.

10.2%

Databricks Certified Data Engineer - Professional

Get started today

Comments

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?