Why YARN manages cluster resources in Hadoop - See It in Action

Why YARN Manages Cluster Resources

📖 Scenario: You are working with a big data system that processes large amounts of information using many computers together. This system needs a way to share and manage the computers' power fairly and efficiently.

🎯 Goal: Understand why YARN (Yet Another Resource Negotiator) is used to manage resources in a Hadoop cluster by simulating resource allocation for tasks.

📋 What You'll Learn

Create a dictionary to represent cluster nodes and their available memory

Set a memory threshold for task allocation

Write a loop to assign tasks to nodes if they have enough memory

Print the allocation results

💡 Why This Matters

🌍 Real World

Big data systems like Hadoop use YARN to manage many computers working together, making sure tasks run smoothly without crashing nodes.

💼 Career

Understanding YARN resource management is important for data engineers and system administrators who maintain big data clusters.

1

Create the cluster nodes data

Create a dictionary called cluster_nodes with these exact entries: 'Node1': 4096, 'Node2': 2048, 'Node3': 8192. These numbers represent the available memory in MB for each node.

Hadoop

# Create the cluster_nodes dictionary with node names and memory values
# Your code here

Hint

Use curly braces to create a dictionary with keys as node names and values as memory in MB.

2

Set the memory threshold for tasks

Create a variable called memory_threshold and set it to 3000. This represents the minimum memory in MB a node must have to run a task.

Hadoop

cluster_nodes = {'Node1': 4096, 'Node2': 2048, 'Node3': 8192}
# Set memory_threshold to 3000
# Your code here

Hint

Just assign the number 3000 to the variable memory_threshold.

3

Assign tasks to nodes based on memory

Create an empty dictionary called task_allocation. Then use a for loop with variables node and memory to iterate over cluster_nodes.items(). Inside the loop, if memory is greater than or equal to memory_threshold, set task_allocation[node] to 'Task assigned', else set it to 'Not enough memory'.

Hadoop

cluster_nodes = {'Node1': 4096, 'Node2': 2048, 'Node3': 8192}
memory_threshold = 3000
# Create task_allocation dictionary and assign tasks based on memory
# Your code here

Hint

Use a for loop to check each node's memory and assign tasks accordingly.

4

Print the task allocation results

Use a print statement to display the task_allocation dictionary.

Hadoop

cluster_nodes = {'Node1': 4096, 'Node2': 2048, 'Node3': 8192}
memory_threshold = 3000
task_allocation = {}
for node, memory in cluster_nodes.items():
    if memory >= memory_threshold:
        task_allocation[node] = 'Task assigned'
    else:
        task_allocation[node] = 'Not enough memory'
# Print the task_allocation dictionary
# Your code here

Hint

Just print the task_allocation dictionary to see which nodes got tasks.

Practice

1. Why does YARN manage cluster resources in a Hadoop environment?

easy

A. To replace the need for MapReduce

B. To store data permanently on the cluster

C. To share computer power fairly among all running jobs

D. To speed up network communication between nodes

Solution

1. Step 1: Understand YARN's role in resource management

   YARN is designed to allocate and manage resources like CPU and memory fairly across all jobs running on the cluster.

2. Step 2: Differentiate from other Hadoop components

   Unlike storage or network components, YARN focuses on resource sharing, not data storage or network speed.

3. Final Answer:

   To share computer power fairly among all running jobs -> Option C

4. Quick Check:

   YARN manages resources = fair sharing [OK]

Hint: YARN controls resources, not storage or network [OK]

Common Mistakes:

• Confusing YARN with HDFS storage
• Thinking YARN replaces MapReduce
• Assuming YARN speeds up network

2. Which of the following is the correct syntax to start the YARN ResourceManager service in Hadoop?

easy

A. start-mapreduce.sh

B. start-hdfs.sh

C. start-cluster.sh

D. start-yarn.sh

Solution

1. Step 1: Identify the script for YARN services

   The script start-yarn.sh is used to start YARN ResourceManager and NodeManager services.

2. Step 2: Differentiate from other Hadoop scripts

   start-hdfs.sh starts HDFS services, start-mapreduce.sh is not a standard script, and start-cluster.sh is not a valid command.

3. Final Answer:

   start-yarn.sh -> Option D

4. Quick Check:

   YARN start script = start-yarn.sh [OK]

Hint: YARN starts with start-yarn.sh script [OK]

Common Mistakes:

• Using start-hdfs.sh for YARN
• Confusing MapReduce with YARN start scripts
• Assuming start-cluster.sh exists

3. Consider this simplified YARN resource allocation code snippet in Python pseudocode:

resources = {'CPU': 16, 'Memory': 64}  # total cluster resources
jobs = [{'CPU': 4, 'Memory': 16}, {'CPU': 8, 'Memory': 32}, {'CPU': 6, 'Memory': 20}]
allocated = []
for job in jobs:
    if job['CPU'] <= resources['CPU'] and job['Memory'] <= resources['Memory']:
        allocated.append(job)
        resources['CPU'] -= job['CPU']
        resources['Memory'] -= job['Memory']
print(len(allocated))

What is the output of this code?

medium

A. 2

B. 0

C. 1

D. 3

Solution

1. Step 1: Check resource allocation for each job

   Start with CPU=16, Memory=64. Job1 needs CPU=4, Memory=16, fits, allocate and update resources to CPU=12, Memory=48. Job2 needs CPU=8, Memory=32, fits, allocate and update to CPU=4, Memory=16. Job3 needs CPU=6, Memory=20, does not fit CPU=4 < 6, so skip.

2. Step 2: Count allocated jobs

   Only Job1 and Job2 allocated, so length of allocated list is 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Allocated jobs = 2 [OK]

Hint: Subtract resources stepwise, check fit for each job [OK]

Common Mistakes:

• Assuming all jobs fit without checking resources
• Not updating resources after allocation
• Counting skipped jobs

4. The following YARN configuration snippet has an error:

yarn.resourcemanager.scheduler.class=org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler
yarn.nodemanager.resource.memory-mb=8192
yarn.scheduler.maximum-allocation-mb=4096

What is the main problem with this configuration?

medium

A. Maximum allocation memory is less than node manager memory, causing job failures

B. Scheduler class name is misspelled

C. Node manager memory is too high for YARN

D. Missing configuration for CPU resources

Solution

1. Step 1: Compare node manager memory and max allocation

   Node manager memory is 8192 MB but max allocation is only 4096 MB, which means YARN cannot allocate more than 4096 MB per container, limiting resource usage.

2. Step 2: Understand impact on job execution

   This mismatch can cause jobs requesting more memory than max allocation to fail or be rejected.

3. Final Answer:

   Maximum allocation memory is less than node manager memory, causing job failures -> Option A

4. Quick Check:

   Max allocation < node manager memory = problem [OK]

Hint: Max allocation must be ≤ node manager memory [OK]

Common Mistakes:

• Ignoring memory mismatch
• Assuming scheduler class typo
• Overlooking missing CPU config

5. You have a Hadoop cluster with 100 CPU cores and 400 GB memory. YARN is configured to allocate resources fairly. If three jobs request resources as follows:

• Job A: 40 CPU cores, 150 GB memory
• Job B: 30 CPU cores, 100 GB memory
• Job C: 50 CPU cores, 200 GB memory

Which resource allocation strategy should YARN use to ensure all jobs get some resources without starving any job?

hard

A. Allocate full requested resources to jobs in order of submission until resources run out

B. Allocate resources proportionally based on requests, limiting each to available cluster capacity

C. Allocate resources only to the largest job to maximize throughput

D. Allocate resources randomly to jobs without checking requests

Solution

1. Step 1: Understand fair resource sharing in YARN

   YARN aims to share cluster resources fairly, so no job is starved and all get a portion of requested resources.

2. Step 2: Analyze allocation strategies

   Proportional allocation limits each job to a fair share based on total cluster capacity, unlike full allocation or random assignment which can cause starvation or inefficiency.

3. Final Answer:

   Allocate resources proportionally based on requests, limiting each to available cluster capacity -> Option B

4. Quick Check:

   Fair sharing = proportional allocation [OK]

Hint: Fair sharing means proportional resource allocation [OK]

Common Mistakes:

• Allocating full resources to first jobs only
• Ignoring fairness and starving jobs
• Random allocation without resource checks