YARN vs MapReduce v1 in Hadoop - Hands-On Comparison

Understanding YARN vs MapReduce v1

📖 Scenario: You are working with big data processing frameworks. Hadoop originally used MapReduce v1 to manage resources and run jobs. Later, YARN was introduced to improve resource management and scalability. You want to explore the differences by simulating job resource allocation data.

🎯 Goal: Create a simple data structure representing jobs and their resource usage in MapReduce v1 and YARN. Then, compare how many jobs can run simultaneously under each system based on resource limits.

📋 What You'll Learn

Create a dictionary called jobs with job names as keys and their resource needs as values (CPU cores).

Create a variable called max_cores representing the total CPU cores available.

Use a loop to calculate how many jobs can run simultaneously under MapReduce v1 (which runs jobs sequentially).

Use a loop to calculate how many jobs can run simultaneously under YARN (which can run multiple jobs in parallel until cores run out).

Print the results clearly showing the number of jobs running simultaneously in each system.

💡 Why This Matters

🌍 Real World

Big data platforms use resource managers like YARN to efficiently run many jobs on shared clusters, improving speed and utilization.

💼 Career

Understanding resource management concepts is key for data engineers and data scientists working with Hadoop and distributed computing.

1

Create the jobs dictionary

Create a dictionary called jobs with these exact entries: 'Job1': 2, 'Job2': 4, 'Job3': 1, 'Job4': 3 representing CPU cores needed for each job.

Hadoop

# Create the jobs dictionary with CPU cores needed
# Your code here

Hint

Use curly braces to create a dictionary with job names as keys and integers as values.

2

Set the total CPU cores available

Create a variable called max_cores and set it to 7 representing total CPU cores available in the cluster.

Hadoop

jobs = {'Job1': 2, 'Job2': 4, 'Job3': 1, 'Job4': 3}
# Set max_cores to 7
# Your code here

Hint

Just assign the number 7 to the variable max_cores.

3

Calculate simultaneous jobs for MapReduce v1 and YARN

Use a for loop with variables job and cores to iterate over jobs.items(). Calculate mapreduce_jobs as 1 because MapReduce v1 runs jobs one by one. Calculate yarn_jobs as the maximum number of jobs that can run simultaneously without exceeding max_cores. Use a variable used_cores to track cores used.

Hadoop

jobs = {'Job1': 2, 'Job2': 4, 'Job3': 1, 'Job4': 3}
max_cores = 7
# Calculate mapreduce_jobs and yarn_jobs
# Your code here

Hint

Remember MapReduce v1 runs jobs one at a time, so mapreduce_jobs is 1. For YARN, add jobs while total cores used is within max_cores.

4

Print the comparison results

Print the number of jobs running simultaneously in MapReduce v1 using print(f"MapReduce v1 runs {mapreduce_jobs} job at a time"). Then print the number of jobs running simultaneously in YARN using print(f"YARN runs {yarn_jobs} jobs simultaneously").

Hadoop

jobs = {'Job1': 2, 'Job2': 4, 'Job3': 1, 'Job4': 3}
max_cores = 7
mapreduce_jobs = 1
used_cores = 0
yarn_jobs = 0
for job, cores in jobs.items():
    if used_cores + cores <= max_cores:
        used_cores += cores
        yarn_jobs += 1
# Print the results
# Your code here

Hint

Use f-strings to print the variables with descriptive text.

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