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

Need a 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

Need a 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

Need a 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

Need a hint?

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