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.

Practice

1. What is the main difference between YARN and MapReduce v1 in Hadoop?

easy

A. MapReduce v1 supports multiple application types, YARN supports only MapReduce.

B. YARN separates resource management and job scheduling, while MapReduce v1 combines them.

C. YARN uses JobTracker for all tasks, MapReduce v1 uses ResourceManager.

D. MapReduce v1 is more scalable than YARN.

Solution

1. Step 1: Understand MapReduce v1 architecture

   MapReduce v1 uses a single JobTracker to manage both resources and job scheduling.

2. Step 2: Understand YARN architecture

   YARN splits these roles: ResourceManager handles resources, ApplicationMaster handles job scheduling, improving scalability.

3. Final Answer:

   YARN separates resource management and job scheduling, while MapReduce v1 combines them. -> Option B

4. Quick Check:

   YARN splits roles = A [OK]

Hint: Remember: YARN splits roles, MapReduce v1 combines [OK]

Common Mistakes:

• Confusing JobTracker with ResourceManager
• Thinking MapReduce v1 supports multiple app types
• Assuming YARN uses JobTracker

2. Which of the following is the correct component responsible for resource management in YARN?

easy

A. ResourceManager

B. JobTracker

C. TaskTracker

D. ApplicationMaster

Solution

1. Step 1: Identify resource management in YARN

   YARN uses ResourceManager to manage cluster resources.

2. Step 2: Differentiate other components

   JobTracker and TaskTracker belong to MapReduce v1; ApplicationMaster manages job lifecycle, not resources.

3. Final Answer:

   ResourceManager -> Option A

4. Quick Check:

   ResourceManager manages resources = B [OK]

Hint: ResourceManager manages resources in YARN [OK]

Common Mistakes:

• Choosing JobTracker which is from MapReduce v1
• Confusing ApplicationMaster with resource manager
• Selecting TaskTracker which handles tasks, not resources

3. Consider this code snippet representing YARN components interaction:

class YARN:
    def __init__(self):
        self.rm = 'ResourceManager'
        self.am = 'ApplicationMaster'

    def get_components(self):
        return [self.rm, self.am]

yarn = YARN()
print(yarn.get_components())

What will be the output?

medium

A. ['ResourceManager', 'ApplicationMaster']

B. Error: AttributeError

C. ['ResourceManager']

D. ['JobTracker', 'TaskTracker']

Solution

1. Step 1: Analyze class initialization

   The YARN class initializes two attributes: 'ResourceManager' and 'ApplicationMaster'.

2. Step 2: Check method output

   The get_components method returns a list containing both attributes.

3. Final Answer:

   ['ResourceManager', 'ApplicationMaster'] -> Option A

4. Quick Check:

   List with RM and AM = D [OK]

Hint: Check returned list matches initialized attributes [OK]

Common Mistakes:

• Confusing with MapReduce v1 components
• Expecting only one component returned
• Assuming code throws error

4. The following code tries to simulate MapReduce v1 job submission but has an error:

class MapReduceV1:
    def __init__(self):
        self.jobtracker = 'JobTracker'

    def submit_job(self):
        print(self.tasktracker)

What is the error and how to fix it?

medium

A. TypeError because print expects a string argument.

B. SyntaxError due to missing colon after method definition.

C. AttributeError because 'tasktracker' is not defined; define self.tasktracker in __init__.

D. No error; code runs fine.

Solution

1. Step 1: Identify the error cause

   The method submit_job tries to print self.tasktracker, but this attribute is not defined in __init__.

2. Step 2: Fix the error

   Define self.tasktracker in __init__, e.g., self.tasktracker = 'TaskTracker', to avoid AttributeError.

3. Final Answer:

   AttributeError because 'tasktracker' is not defined; define self.tasktracker in __init__. -> Option C

4. Quick Check:

   Undefined attribute causes AttributeError = C [OK]

Hint: Check all used attributes are defined in __init__ [OK]

Common Mistakes:

• Thinking it's a syntax error
• Assuming print causes TypeError
• Ignoring missing attribute definition

5. You want to run a new type of application on Hadoop that is not MapReduce. Which feature of YARN makes this possible compared to MapReduce v1?

hard

A. MapReduce v1 separates resource management and scheduling.

B. MapReduce v1 supports multiple application types by default.

C. YARN uses JobTracker to schedule all applications.

D. YARN's ApplicationMaster can manage different application types beyond MapReduce.

Solution

1. Step 1: Understand application support in MapReduce v1

   MapReduce v1 is limited to MapReduce jobs only, managed by JobTracker.

2. Step 2: Understand YARN's flexibility

   YARN's ApplicationMaster can be customized to manage various application types, enabling flexibility.

3. Final Answer:

   YARN's ApplicationMaster can manage different application types beyond MapReduce. -> Option D

4. Quick Check:

   YARN supports multiple app types = A [OK]

Hint: ApplicationMaster enables multi-app support in YARN [OK]

Common Mistakes:

• Thinking MapReduce v1 supports multiple apps
• Confusing JobTracker role in YARN
• Assuming MapReduce v1 separates resource management