YARN vs MapReduce v1 in Hadoop - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Difference in Resource Management between YARN and MapReduce v1

Which statement correctly describes how resource management differs between YARN and MapReduce v1?

A Both YARN and MapReduce v1 use the same JobTracker for resource management and scheduling.

B MapReduce v1 uses a ResourceManager to allocate resources, but YARN does not have a resource manager.

C YARN separates resource management and job scheduling, while MapReduce v1 combines them in a single JobTracker.

D YARN manages resources only for MapReduce jobs, while MapReduce v1 manages resources for all types of applications.

💡 Hint

Think about how YARN changed the architecture by separating concerns.

YARN introduced a ResourceManager that handles resource allocation separately from job scheduling, unlike MapReduce v1 where the JobTracker handled both.

🧠 Conceptual

intermediate

Role of NodeManager in YARN vs MapReduce v1

What is the role of the NodeManager in YARN compared to MapReduce v1?

A NodeManager in YARN manages resources and monitors containers on each node, while MapReduce v1 has no equivalent component.

B NodeManager in YARN schedules jobs, while MapReduce v1 uses TaskTracker for resource management.

C NodeManager in YARN is responsible for job history, which MapReduce v1 handles with JobTracker.

D NodeManager in YARN only manages MapReduce tasks, while MapReduce v1 manages all tasks on the node.

💡 Hint

Consider how YARN manages resources on individual nodes.

YARN's NodeManager manages resources and monitors containers on each node. MapReduce v1 uses TaskTracker, which combines resource management and task execution.

❓ data_output

advanced

YARN Application Master vs MapReduce v1 JobTracker Output

Given a YARN cluster running a MapReduce job, what output would you expect from the Application Master compared to the JobTracker in MapReduce v1?

Hadoop

print('YARN Application Master status: RUNNING')
print('JobTracker status: RUNNING')

A {'status': 'RUNNING', 'tasks_managed': 'only for its application'}

B {'status': 'RUNNING', 'tasks_managed': 'all cluster tasks'}

C {'status': 'FAILED', 'tasks_managed': 'all cluster tasks'}

D {'status': 'IDLE', 'tasks_managed': 'only for its application'}

💡 Hint

Think about the scope of responsibility for Application Master vs JobTracker.

The Application Master manages tasks only for its own application in YARN, while JobTracker manages all tasks cluster-wide in MapReduce v1.

🔧 Debug

advanced

Identify the Cause of Job Failure in YARN vs MapReduce v1

A MapReduce job fails in a YARN cluster but runs successfully in MapReduce v1. Which cause is most likely related to YARN's architecture?

A The TaskTracker failed to launch tasks in YARN but not in MapReduce v1.

B The Application Master crashed, causing the job to fail in YARN but not in MapReduce v1.

C YARN does not support MapReduce jobs, so the failure is expected.

D The JobTracker was overloaded, causing failure in YARN but not in MapReduce v1.

💡 Hint

Consider the role of Application Master in YARN and its impact on job execution.

In YARN, the Application Master manages the job lifecycle. If it crashes, the job fails. MapReduce v1 does not have an Application Master, so this failure mode is unique to YARN.

🚀 Application

expert

Analyzing Cluster Utilization Differences Between YARN and MapReduce v1

You have cluster utilization data from both YARN and MapReduce v1 running similar workloads. Which analysis best explains why YARN achieves higher cluster utilization?

A MapReduce v1 uses containers for resource allocation, which limits utilization compared to YARN's JobTracker.

B MapReduce v1 supports dynamic resource allocation, but YARN does not, causing lower utilization in YARN.

C YARN restricts resource sharing to MapReduce jobs only, reducing utilization compared to MapReduce v1.

D YARN allows multiple applications to share resources dynamically, improving utilization compared to MapReduce v1's static allocation.

💡 Hint

Think about how YARN manages resources for different applications.

YARN's architecture supports multiple types of applications sharing cluster resources dynamically, unlike MapReduce v1 which statically allocates resources to MapReduce jobs only, leading to better utilization in YARN.