When running in Local mode, what is a potential limitation of Apache Spark?

In Local mode, Apache Spark is typically executed on a single machine, which means that it can be constrained by the resources available on that machine. This setup is primarily suitable for development, testing, or scenarios where data volume is not significant. As a result, processing large datasets can lead to performance issues or memory limitations, as all operations are limited to the capabilities of that single machine.

When running locally, the size of the dataset that can be effectively processed is often restricted by the amount of RAM, CPU, and disk I/O available on that single machine. For instance, if the data size exceeds the available memory, Spark may struggle to efficiently manage the computations, causing it to fail or result in slow processing times. Thus, for heavy data workloads and substantial data processing tasks, working in Local mode is impractical, highlighting this potential limitation.

Options discussing dedicated clusters, multi-threaded environments, or node counts are not relevant in this context, as they pertain to different operational setups or capabilities not constrained by Local mode itself.