Apache Spark Certification Practice Test

Spark's MLlib improves performance over traditional disk-based systems primarily through the use of in-memory computation. This feature allows Spark to store intermediate data in the memory (RAM) rather than writing it to disk after each transformation. It greatly reduces the latency associated with disk I/O, which can be a significant bottleneck in data processing.

When data is processed in-memory, the system can quickly access and manipulate it without the overhead of repeatedly reading from or writing to disk. This capability is especially beneficial for machine learning tasks, where iterative algorithms require multiple passes over the data. By keeping data in memory across these iterations, MLlib can leverage caching and optimize computation, leading to substantial performance gains compared to systems that rely on slower disk storage.

While increasing the number of nodes and parallel processing are also important aspects of Spark's distributed architecture, they do not specifically address the core performance advantage provided by in-memory computation. Similarly, data size reduction can improve efficiency, but it is not a direct feature of how MLlib enhances performance over disk-based systems.