Optimizing Resource Allocation and Parallel Processing for 20GB Spark Jobs
Briefly

Optimally configuring executors with specific core and memory allocations can improve job performance and prevent resource limitations.
Understanding memory allocation for storage, execution, and off-heap usage is essential in maximizing parallelism and avoiding OOM errors.
Careful consideration of data partitioning, memory peak usage, and core utilization is key in determining the optimal number of executors and worker nodes.
Read at Medium
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