Final answer:
In a Hadoop system, disk latency is commonly the primary cause of poor performance due to its impact on read/write operations which are essential for big data processing. Other components such as CPU, network, and RAM also affect performance but to a lesser extent.
Step-by-step explanation:
When it comes to Hadoop systems, disk latency is often the primary cause of poor performance. Hadoop is designed to handle large volumes of data, and most of the operations consist of reading from and writing to the disk. Therefore, the speed at which this can be done is crucial. While other factors like CPU, network, and RAM do play significant roles in the overall performance, it is the latency in accessing the disk that typically has the most significant impact. Disk latency can slow down data processing, especially when dealing with the high volume, velocity, and variety of data common to Big Data applications. Optimizing disk I/O can lead to better Hadoop performance. For example, using Solid State Drives (SSD) over traditional Hard Disk Drives (HDD) can reduce latency. Additionally, proper configuration of Hadoop's file system (HDFS) and ensuring a balanced load across the disks can mitigate the impact of disk latency.