YARN vs MapReduce v1 in Hadoop - Performance Comparison

Time Complexity: YARN vs MapReduce v1

O(n)

Understanding Time Complexity

We want to understand how the time to run jobs changes when using YARN compared to MapReduce v1.

How does the system handle tasks as the data size grows?

Scenario Under Consideration

Analyze the time complexity of the resource management in these simplified code snippets.

// MapReduce v1
while (job not finished) {
  allocate slots;
  run map tasks;
  run reduce tasks;
}

// YARN
while (job not finished) {
  request containers;
  run tasks in containers;
  monitor and reallocate;
}

These snippets show how each system manages resources and tasks during job execution.

Identify Repeating Operations

Look at what repeats as the job runs.

• Primary operation: Looping over tasks to allocate resources and run them.
• How many times: Once per task batch until all tasks finish.

How Execution Grows With Input

As data size grows, more tasks need resources and time.

Input Size (n)	Approx. Operations
10	About 10 task allocations and runs
100	About 100 task allocations and runs
1000	About 1000 task allocations and runs

Pattern observation: The number of operations grows roughly in direct proportion to the number of tasks.

Final Time Complexity

Time Complexity: O(n)

This means the time grows linearly with the number of tasks or data size.

Common Mistake

[X] Wrong: "YARN always runs faster because it is newer and more complex."

[OK] Correct: Both systems handle tasks in loops; YARN improves resource use but the total work still grows linearly with data size.

Interview Connect

Understanding how resource management affects time helps you explain system design choices clearly and confidently.

Self-Check

What if YARN could run multiple tasks in parallel containers without waiting? How would the time complexity change?

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