A data engineer is running a batch processing job on a Spark cluster with the following configuration: 10 worker nodes 16 CPU cores per worker node…

The total number of CPU cores utilized by the Spark application is determined by multiplying the number of worker nodes by the number of executors allocated per node, and then by the number of CPU cores assigned to each executor.

The calculation is as follows:

Total Executors = (Number of worker nodes) × (Executors per node) = 10 × 4 = 40 executors.

Total CPU Cores = (Total Executors) × (Cores per executor) = 40 × 4 = 160 cores.

This configuration fully utilizes the 16 available cores on each of the 10 worker nodes.

B. 64: This value might be derived from an incorrect calculation, such as multiplying the cores per node (16) by the executors per node (4), which is not the correct formula.

C. 80: This result cannot be logically derived from the given parameters. It may represent a miscalculation, such as using only half the number of nodes (5 nodes 4 executors/node 4 cores/executor).

D. 40: This represents the total number of executors (10 nodes × 4 executors per node), not the total number of CPU cores used by the application.

1. Official Apache Spark Documentation

"Application Properties": The documentation specifies the configuration properties for a Spark application. The total core count is the product of the number of executors and the cores per executor (spark.executor.cores). The number of executors is determined by the cluster setup. In this scenario

it's 10 nodes 4 executors/node.

Reference: Apache Spark 3.x Documentation

Configuration > Application Properties

see descriptions for spark.executor.cores.

2. Databricks Documentation

"Cluster configuration": The Databricks documentation explains that a cluster consists of a driver and worker nodes. Executors are processes that run on worker nodes

and each executor is allocated a specific number of cores. The total core usage for the application is the sum of cores allocated to all executors.

Reference: Databricks Documentation

Compute > Clusters > Cluster configuration.

3. Karau

H.

Konwinski

A.

Wendell

P.

& Zaharia

M. (2015). Learning Spark: Lightning-Fast Big Data Analysis. O'Reilly Media

Inc.

Chapter 4

"Spark's Execution Model

" explains that the number of cores available to an application is the number of executors multiplied by the cores per executor. This fundamental principle directly supports the calculation. (Note: While a commercial book

its authors are the creators of Spark

making it an authoritative source on its architecture).