YARN vs MapReduce v1 in Hadoop - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the resource manager used in YARN.

Hadoop

Configuration conf = new Configuration();
conf.set("yarn.resourcemanager.address", "[1]");

Drag options to blanks, or click blank then click option'

Alocalhost:9000

Blocalhost:8032

Clocalhost:50070

Dlocalhost:8088

Attempts:

3 left

💡 Hint

Common Mistakes

Using the NameNode port instead of the resource manager port.

Confusing the web UI port with the resource manager port.

The resource manager in YARN listens on port 8032 by default. This address is used to communicate with the resource manager.

2fill in blank

medium

Complete the code to get the number of map tasks in MapReduce v1.

Hadoop

JobConf conf = new JobConf();
int numMaps = conf.[1]();

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AgetMapTaskCount

BgetNumReduceTasks

CgetNumTasks

DgetNumMapTasks

Attempts:

3 left

💡 Hint

Common Mistakes

Using getNumReduceTasks() instead of map tasks method.

Using a non-existent method like getMapTaskCount().

In MapReduce v1, getNumMapTasks() returns the number of map tasks configured for the job.

3fill in blank

hard

Fix the error in the code to submit a YARN application.

Hadoop

YarnClient yarnClient = YarnClient.createYarnClient();
yarnClient.init(conf);
yarnClient.start();
ApplicationId appId = yarnClient.[1]();

Drag options to blanks, or click blank then click option'

AgetNewApplication

BcreateApplication

CsubmitApplication

DstartApplication

Attempts:

3 left

💡 Hint

Common Mistakes

Using submitApplication() which does not exist on YarnClient.

Using startApplication() which is not a valid method.

The correct method to create a new application in YARN is getNewApplication(). It returns an ApplicationId for the new app.

4fill in blank

hard

Fill both blanks to create a MapReduce job configuration and set the number of reduce tasks.

Hadoop

Job job = Job.getInstance(conf, "wordcount");
job.setJarByClass([1].class);
job.[2](2);

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AWordCount

BsetNumReduceTasks

CsetReduceTasks

DMain

Attempts:

3 left

💡 Hint

Common Mistakes

Using setReduceTasks() which is not a valid method.

Using a wrong class name like 'Main' if the job class is 'WordCount'.

The class name is usually the main class like WordCount. The method to set reduce tasks is setNumReduceTasks().

5fill in blank

hard

Fill all three blanks to create a YARN container launch context with local resources and environment variables.

Hadoop

ContainerLaunchContext ctx = ContainerLaunchContext.newInstance(
    [1],
    [2],
    [3],
    null,
    null,
    null
);

Drag options to blanks, or click blank then click option'

AlocalResources

Benv

Ccommands

Dconf

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing the order of arguments.

Passing configuration object instead of commands.

The newInstance method takes local resources, environment variables, and commands as the first three arguments.

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