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Answer: Create distinct resource pools for batch and streaming jobs, enabling dynamic allocation within each pool and adjusting settings based on the unique demands of each job type.
Configuring separate pools for batch and streaming jobs with dynamic allocation enabled and finely tuned to each job type's characteristics ensures efficient resource use. This method allows for dynamic adjustment of resources based on workload demands, preventing resource wastage during low activity and scaling up during peak demands. Tailoring the dynamic allocation settings to the specific needs of each job type, such as memory and CPU requirements, optimizes performance and resource utilization. This strategy leads to enhanced performance and cost-effectiveness in mixed workload environments.
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In a mixed Spark workload environment that includes both batch and streaming jobs, what is the best approach to configure dynamic allocation for optimal resource utilization?
A
Disable dynamic allocation and manually allocate resources for each job type to avoid any potential inefficiencies.
B
Utilize static partitioning exclusively for streaming jobs while applying dynamic allocation solely to batch jobs to minimize resource contention.
C
Create distinct resource pools for batch and streaming jobs, enabling dynamic allocation within each pool and adjusting settings based on the unique demands of each job type.
D
Apply dynamic allocation across the board with highly responsive scaling parameters to quickly adapt to varying workloads.
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