The correct answer is: B. standard raid levels
Hadoop achieves reliability by replicating the data across multiple hosts. This means that if one host fails, the data is still available on the other hosts. This makes it unnecessary to use standard RAID levels, which are designed to protect data from disk failures.
RAID (Redundant Array of Independent Disks) is a data storage technology that combines multiple disk drives into a logical unit, providing greater performance, reliability, and capacity than a single disk drive. There are many different RAID levels, each with its own trade-offs between performance, reliability, and capacity.
Standard RAID levels are designed to protect data from disk failures. For example, RAID 1 provides data redundancy by mirroring data across two disks. If one disk fails, the data is still available on the other disk. RAID 5 provides data redundancy and performance by striping data across multiple disks and using parity bits to reconstruct data if a disk fails.
However, standard RAID levels are not necessary for Hadoop, because Hadoop already achieves data redundancy by replicating data across multiple hosts. This means that if one host fails, the data is still available on the other hosts. Therefore, there is no need to use standard RAID levels, which can add complexity and cost to a Hadoop system.
In addition, standard RAID levels can actually reduce the performance of Hadoop. This is because RAID levels typically require additional hardware and software, which can add overhead to the system. Additionally, RAID levels can sometimes cause performance bottlenecks, especially if the RAID controller is not properly configured.
Therefore, it is generally recommended to avoid using standard RAID levels with Hadoop. Instead, it is better to use a distributed file system that is designed for high performance and reliability, such as HDFS.