YARN vs MapReduce v1 in Hadoop - When to Use Which

The Big Idea

What if your big data jobs could run faster and never get stuck waiting for each other?

The Scenario

Imagine you have a huge pile of documents to analyze, and you try to do it all on one old computer. You wait hours, and if something breaks, you start all over. This is like using MapReduce v1, where one system tries to handle everything.

The Problem

Using MapReduce v1 means the system manages both running tasks and resources together. This causes delays, poor use of computers, and if one job crashes, others wait. It's slow and frustrating when you want quick results.

The Solution

YARN separates the job of managing resources from running tasks. It acts like a smart manager that assigns work to many computers efficiently. This way, many jobs run smoothly at the same time without waiting or crashing each other.

Before vs After

✗ Before

mapred.job.tracker=old_tracker
mapred.task.tracker=old_task_tracker

✓ After

yarn.resourcemanager.address=new_manager
yarn.nodemanager.address=new_node_manager

What It Enables

YARN lets many big data jobs run faster and smarter by sharing resources well and recovering quickly from problems.

Real Life Example

A company analyzing millions of customer reviews can run multiple analysis jobs at once using YARN, getting insights faster than with MapReduce v1.

Key Takeaways

MapReduce v1 mixes resource and job management, causing delays.

YARN separates these roles for better speed and reliability.

This leads to faster, more efficient big data processing.

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