Which of the following best describes Mesos mode?

Which of the following best describes Mesos mode?

• A. A mode for executing Spark locally
• B. A resource manager for monitoring Spark jobs
• C. A cluster manager that shares resources across different applications
• D. A standalone method for executing Spark without a cluster

Answer: (C)

Mesos mode refers to a cluster manager that effectively handles resource allocation across multiple applications, allowing them to share resources dynamically. Apache Mesos facilitates this by managing the distributed environment, enabling various frameworks, such as Spark, Hadoop, and others, to run simultaneously on the same physical or virtual infrastructure. What sets Mesos apart is its capability to scale resources as needed, distributing CPU and memory across applications while ensuring that they don’t interfere with one another’s operations. This resource-sharing capability leads to improved efficiency and utilization of computational resources when managing large-scale distributed systems. In contrast, the other options do not accurately capture the essence of Mesos mode. While local execution, monitoring of jobs, and standalone execution of Spark are important concepts, they pertain to different operational contexts. Local execution refers to running Spark on a single machine, monitoring jobs typically involves tools rather than a mode, and standalone execution indicates an environment without resource sharing, differing fundamentally from what Mesos offers.

Mesos mode refers to a cluster manager that effectively handles resource allocation across multiple applications, allowing them to share resources dynamically. Apache Mesos facilitates this by managing the distributed environment, enabling various frameworks, such as Spark, Hadoop, and others, to run simultaneously on the same physical or virtual infrastructure.

What sets Mesos apart is its capability to scale resources as needed, distributing CPU and memory across applications while ensuring that they don’t interfere with one another’s operations. This resource-sharing capability leads to improved efficiency and utilization of computational resources when managing large-scale distributed systems.

In contrast, the other options do not accurately capture the essence of Mesos mode. While local execution, monitoring of jobs, and standalone execution of Spark are important concepts, they pertain to different operational contexts. Local execution refers to running Spark on a single machine, monitoring jobs typically involves tools rather than a mode, and standalone execution indicates an environment without resource sharing, differing fundamentally from what Mesos offers.