What should you consider when deploying Hadoop on Oracle Cloud Infrastructure?

What should you consider when deploying Hadoop on Oracle Cloud Infrastructure?

• A. Normalize either OCPU or Memory against OCI shapes used as workers
• B. Utilize only DenseIO storage regardless of workload
• C. Always use a replication factor of 1 for efficiency
• D. Employ only fiber network connections to enhance speed

Answer: (A)

When deploying Hadoop on Oracle Cloud Infrastructure, normalizing either OCPU or Memory against the OCI shapes used as workers is crucial because it directly impacts the performance and efficiency of a Hadoop cluster. Each workload may have different requirements based on the data being processed and the computational tasks involved. Ensuring that the resources allocated in terms of CPU and memory are balanced with the capabilities of the worker nodes helps in optimizing resource usage and enhancing performance. Simply using DenseIO storage regardless of workload can lead to inefficiencies, as different workloads may have varying input/output operations per second (IOPS) and throughput requirements. Therefore, understanding the specific needs of your workload is more beneficial than applying a one-size-fits-all storage solution. A replication factor of 1 does not provide data redundancy and can increase the risk of data loss in case of node failures. Hadoop is designed with fault tolerance in mind, and typically, a replication factor of 2 or 3 is recommended to ensure data durability and availability. Employing only fiber network connections, while advantageous for certain high-throughput scenarios, may not be necessary or feasible for all deployments. The choice of networking should be tailored to the specific requirements of the Hadoop cluster and the workloads it will support. Thus, normalizing O

When deploying Hadoop on Oracle Cloud Infrastructure, normalizing either OCPU or Memory against the OCI shapes used as workers is crucial because it directly impacts the performance and efficiency of a Hadoop cluster. Each workload may have different requirements based on the data being processed and the computational tasks involved. Ensuring that the resources allocated in terms of CPU and memory are balanced with the capabilities of the worker nodes helps in optimizing resource usage and enhancing performance.

Simply using DenseIO storage regardless of workload can lead to inefficiencies, as different workloads may have varying input/output operations per second (IOPS) and throughput requirements. Therefore, understanding the specific needs of your workload is more beneficial than applying a one-size-fits-all storage solution.

A replication factor of 1 does not provide data redundancy and can increase the risk of data loss in case of node failures. Hadoop is designed with fault tolerance in mind, and typically, a replication factor of 2 or 3 is recommended to ensure data durability and availability.

Employing only fiber network connections, while advantageous for certain high-throughput scenarios, may not be necessary or feasible for all deployments. The choice of networking should be tailored to the specific requirements of the Hadoop cluster and the workloads it will support.

Thus, normalizing O