YARN scheduling policies in Hadoop - Mini Project: Build & Apply

Understanding YARN Scheduling Policies

📖 Scenario: You are working as a data engineer managing a Hadoop cluster. You want to understand how YARN schedules resources for different applications to optimize cluster usage.

🎯 Goal: Learn how to represent and analyze YARN scheduling policies using Python dictionaries and loops to simulate resource allocation.

📋 What You'll Learn

Create a dictionary representing YARN scheduling policies with exact keys and values

Add a configuration variable to select a scheduling policy

Use a loop to simulate resource allocation based on the selected policy

Print the final allocation result

💡 Why This Matters

🌍 Real World

YARN scheduling policies help manage resources in big data clusters efficiently, ensuring fair and optimized use of computing power.

💼 Career

Understanding YARN scheduling is important for data engineers and Hadoop administrators to tune cluster performance and resource management.

1

Create YARN scheduling policies dictionary

Create a dictionary called yarn_policies with these exact entries: 'FIFO': 'First In First Out', 'Capacity': 'Capacity Scheduler', 'Fair': 'Fair Scheduler'.

Hadoop

# Your code here

Hint

Use curly braces to create a dictionary with keys and values as strings.

2

Add selected scheduling policy variable

Create a variable called selected_policy and set it to the string 'Fair'.

Hadoop

yarn_policies = {
    'FIFO': 'First In First Out',
    'Capacity': 'Capacity Scheduler',
    'Fair': 'Fair Scheduler'
}
# Your code here

Hint

Assign the string 'Fair' to the variable selected_policy.

3

Simulate resource allocation based on selected policy

Create a variable called allocation and set it to an empty dictionary. Then use a for loop with variables policy and description to iterate over yarn_policies.items(). Inside the loop, if policy equals selected_policy, set allocation[policy] to 'Resources allocated using ' + description.

Hadoop

yarn_policies = {
    'FIFO': 'First In First Out',
    'Capacity': 'Capacity Scheduler',
    'Fair': 'Fair Scheduler'
}
selected_policy = 'Fair'

# Your code here

Hint

Use a for loop to check each policy and assign allocation only for the selected one.

4

Print the allocation result

Write a print statement to display the allocation dictionary.

Hadoop

yarn_policies = {
    'FIFO': 'First In First Out',
    'Capacity': 'Capacity Scheduler',
    'Fair': 'Fair Scheduler'
}
selected_policy = 'Fair'
allocation = {}
for policy, description in yarn_policies.items():
    if policy == selected_policy:
        allocation[policy] = 'Resources allocated using ' + description

# Your code here

Hint

Use print(allocation) to show the final allocation dictionary.

Practice

1. Which YARN scheduling policy assigns resources to jobs in the order they arrive without considering job size or priority?

easy

A. Capacity Scheduler

B. Fair Scheduler

C. FIFO Scheduler

D. Priority Scheduler

Solution

1. Step 1: Understand FIFO Scheduler behavior

   FIFO (First In First Out) scheduler assigns resources to jobs strictly by arrival time, ignoring job size or priority.

2. Step 2: Compare with other schedulers

   Capacity Scheduler and Fair Scheduler consider resource sharing and fairness, while Priority Scheduler uses job priority.

3. Final Answer:

   FIFO Scheduler -> Option C

4. Quick Check:

   FIFO = order by arrival [OK]

Hint: FIFO means first come, first served order [OK]

Common Mistakes:

• Confusing FIFO with Fair Scheduler
• Thinking Capacity Scheduler ignores job order
• Assuming Priority Scheduler is default

2. Which of the following is the correct syntax to configure the Capacity Scheduler in YARN's capacity-scheduler.xml file?

easy

A. yarn.scheduler.capacity.root.queuesdefault,production

B. yarn.scheduler.fifo.root.queuesdefault,production

C. yarn.scheduler.fair.root.queuesdefault,production

D. yarn.scheduler.priority.root.queuesdefault,production

Solution

1. Step 1: Identify correct property prefix for Capacity Scheduler

   The Capacity Scheduler uses properties starting with yarn.scheduler.capacity.

2. Step 2: Verify queue configuration syntax

   The property yarn.scheduler.capacity.root.queues defines queues like default and production.

3. Final Answer:

   <property><name>yarn.scheduler.capacity.root.queues</name><value>default,production</value></property> -> Option A

4. Quick Check:

   Capacity Scheduler uses yarn.scheduler.capacity prefix [OK]

Hint: Capacity Scheduler uses 'yarn.scheduler.capacity' prefix [OK]

Common Mistakes:

• Using fifo or fair prefix for Capacity Scheduler
• Misspelling property names
• Confusing queue names with scheduler types

3. Given the following YARN Fair Scheduler configuration snippet:
<pre>queueA: minResources=2GB, maxResources=10GB queueB: minResources=4GB, maxResources=8GB Currently, queueA is using 3GB and queueB is using 5GB.
Which queue can get more resources if a new job requests 3GB?

medium

A. queueA only

B. Both queueA and queueB

C. queueB only

D. Neither queueA nor queueB

Solution

1. Step 1: Check current usage vs maxResources

   queueA uses 3GB, max is 10GB, so it can get up to 7GB more. queueB uses 5GB, max is 8GB, so it can get 3GB more.

2. Step 2: Assess if requested 3GB fits

   queueA can allocate 3GB (3+3=6GB <= 10GB). queueB can allocate 3GB (5+3=8GB = max), so it can also get 3GB.

3. Step 3: Consider Fair Scheduler policy

   Fair Scheduler tries to balance usage. Since queueA is below queueB usage, it will prioritize queueA for extra resources, but queueB can also allocate the requested resources up to its max.

4. Final Answer:

   Both queueA and queueB -> Option B

5. Quick Check:

   Fair Scheduler favors less used queue but both can allocate requested resources [OK]

Hint: Fair Scheduler favors queues with less current usage [OK]

Common Mistakes:

• Ignoring maxResources limits
• Assuming both queues get resources equally
• Not considering current usage in decision

4. You have configured the Capacity Scheduler but notice that some queues are starved and not getting resources. What is the most likely cause?

medium

A. Queue capacities are not properly set, causing imbalance

B. FIFO Scheduler is enabled instead of Capacity Scheduler

C. The yarn.scheduler.capacity.root.queues property is missing

D. Fair Scheduler is overriding Capacity Scheduler settings

Solution

1. Step 1: Understand Capacity Scheduler behavior

   Capacity Scheduler allocates resources based on configured queue capacities.

2. Step 2: Identify cause of starvation

   If queue capacities are not set correctly, some queues may get more resources, starving others.

3. Step 3: Rule out other options

   Missing property would cause scheduler failure, not starvation. FIFO and Fair Scheduler are different policies, not overriding Capacity Scheduler if configured correctly.

4. Final Answer:

   Queue capacities are not properly set, causing imbalance -> Option A

5. Quick Check:

   Capacity settings control resource distribution [OK]

Hint: Check queue capacity percentages to avoid starvation [OK]

Common Mistakes:

• Assuming missing property causes starvation
• Confusing scheduler types
• Ignoring capacity configuration importance

5. You want to run a multi-tenant Hadoop cluster where each department gets guaranteed minimum resources but can use more if available. Which YARN scheduling policy and configuration best fits this need?

hard

A. Priority Scheduler with fixed job priorities

B. FIFO Scheduler with strict job ordering

C. Fair Scheduler with no minimum resource guarantees

D. Capacity Scheduler with minimum and maximum queue capacities set

Solution

1. Step 1: Understand multi-tenant resource needs

   Departments need guaranteed minimum resources but flexible usage if available.

2. Step 2: Match policy features

   Capacity Scheduler supports minimum and maximum queue capacities, ensuring guarantees and flexible sharing.

3. Step 3: Rule out other policies

   FIFO does not guarantee resources per tenant. Fair Scheduler can share fairly but may lack strict minimum guarantees. Priority Scheduler focuses on job priority, not resource guarantees per tenant.

4. Final Answer:

   Capacity Scheduler with minimum and maximum queue capacities set -> Option D

5. Quick Check:

   Capacity Scheduler = guaranteed min + flexible max [OK]

Hint: Capacity Scheduler supports min and max resource guarantees [OK]

Common Mistakes:

• Choosing FIFO for guaranteed resources
• Ignoring min capacity in Fair Scheduler
• Confusing priority with resource guarantees