In Spark's Local Mode, the entire application—driver, master, and executor—runs within a single Java Virtual Machine (JVM) on a single machine. Parallelism is achieved through multi-threading. To ensure optimal resource utilization, specifically of the CPU, the developer should configure the number of threads Spark can use for task execution. By setting the master URL to local[k], where k is the number of threads, or more commonly local[], Spark is instructed to create as many worker threads as there are logical CPU cores on the machine. This maximizes parallelism and ensures the machine's processing capacity is fully utilized during testing.

A. Configuring the application for cluster mode is a different deployment strategy and does not optimize the existing Local Mode setup as requested.

C. Dynamic allocation is a feature for cluster managers (like YARN or Kubernetes) to add or remove executors; it is not applicable to Local Mode, which runs in a single JVM.

D. While memory is important, spark.executor.memory is less relevant in Local Mode where everything shares the driver's memory. The primary lever for CPU utilization is the thread count, not memory allocation.

1. Apache Spark 3.5.1 Documentation

Submitting Applications

Master URLs: The documentation explicitly states for Local Mode: "local[K]: Run Spark locally with K worker threads (ideally

set this to the number of cores on your machine)." and "local[] Run Spark locally with as many worker threads as logical cores on your machine." This directly supports configuring the number of threads based on CPU cores for optimal use.

2. Apache Spark 3.5.1 Documentation

Configuration

Dynamic Allocation: This section details the spark.dynamicAllocation.enabled property and specifies its use with Spark Standalone mode

YARN mode

and Kubernetes mode. It makes no mention of Local Mode

confirming the feature is for cluster environments.

3. Karau

H.

Konwinski

A.

Wendell

P.

& Zaharia

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

Inc. Chapter 4

"Deploying Spark": This foundational text explains that in Local Mode

"you can specify the number of threads to use by passing a number in brackets (for example

local[4])." It further clarifies that using local[] is a common practice to use one thread per core

which is the standard approach for maximizing local resource utilization.