Cost optimization for cloud resources in Apache Airflow - Mini Project: Build & Apply

Cost optimization for cloud resources

📖 Scenario: You work as a cloud engineer managing multiple cloud resources. Your goal is to identify resources that cost more than a certain threshold and mark them for review to optimize costs.

🎯 Goal: Build an Airflow DAG that loads a list of cloud resources with their costs, sets a cost threshold, filters resources exceeding this threshold, and prints the list of expensive resources for review.

📋 What You'll Learn

Create a dictionary of cloud resources with their monthly costs

Set a cost threshold variable

Filter resources with costs above the threshold using a comprehension

Print the filtered list of expensive resources

💡 Why This Matters

🌍 Real World

Cloud engineers often need to monitor and optimize cloud resource costs to avoid overspending.

💼 Career

This project teaches how to filter and identify costly cloud resources programmatically, a key skill in cloud cost management roles.

1

Create the cloud resources cost dictionary

Create a dictionary called cloud_resources with these exact entries: 'VM1': 120, 'DB1': 80, 'Storage1': 45, 'VM2': 200, 'Cache1': 30 representing resource names and their monthly costs.

Apache Airflow

# Create the cloud_resources dictionary with exact entries
# Your code here

Hint

Use curly braces to create a dictionary with keys as resource names and values as costs.

2

Set the cost threshold

Create a variable called cost_threshold and set it to 100 to represent the monthly cost limit for resources.

Apache Airflow

cloud_resources = {'VM1': 120, 'DB1': 80, 'Storage1': 45, 'VM2': 200, 'Cache1': 30}
# Set cost_threshold to 100
# Your code here

Hint

Just assign the number 100 to the variable cost_threshold.

3

Filter expensive resources

Use a dictionary comprehension to create a new dictionary called expensive_resources that contains only the resources from cloud_resources with costs greater than cost_threshold. Use name and cost as the loop variables.

Apache Airflow

cloud_resources = {'VM1': 120, 'DB1': 80, 'Storage1': 45, 'VM2': 200, 'Cache1': 30}
cost_threshold = 100
# Create expensive_resources dictionary with resources costing more than cost_threshold
# Your code here

Hint

Use a dictionary comprehension with for name, cost in cloud_resources.items() and an if condition.

4

Print the expensive resources

Write a print statement to display the expensive_resources dictionary.

Apache Airflow

cloud_resources = {'VM1': 120, 'DB1': 80, 'Storage1': 45, 'VM2': 200, 'Cache1': 30}
cost_threshold = 100
expensive_resources = {name: cost for name, cost in cloud_resources.items() if cost > cost_threshold}
# Print the expensive_resources dictionary
# Your code here

Hint

Use print(expensive_resources) to show the filtered dictionary.

Practice

1. What is the main goal of cost optimization for cloud resources in Airflow?

easy

A. To increase the number of cloud resources used

B. To run all tasks continuously without stopping

C. To use cloud resources only when needed to save money

D. To avoid monitoring cloud resource usage

Solution

1. Step 1: Understand cost optimization meaning

   Cost optimization means using resources efficiently to reduce expenses.

2. Step 2: Relate to Airflow usage

   Airflow helps automate resource usage only when needed, saving money.

3. Final Answer:

   To use cloud resources only when needed to save money -> Option C

4. Quick Check:

   Cost optimization = Use resources only when needed [OK]

Hint: Cost optimization means saving money by using resources smartly [OK]

Common Mistakes:

• Thinking more resources always mean better performance
• Ignoring the need to stop unused resources
• Believing monitoring is unnecessary

2. Which Airflow feature helps automate starting and stopping cloud resources on a schedule?

easy

A. DAG scheduling

B. Manual triggering only

C. Static resource allocation

D. Ignoring resource states

Solution

1. Step 1: Identify Airflow automation features

   DAG scheduling allows tasks to run automatically at set times.

2. Step 2: Connect scheduling to resource control

   Scheduling can start or stop cloud resources based on time, saving costs.

3. Final Answer:

   DAG scheduling -> Option A

4. Quick Check:

   DAG scheduling = Automate resource start/stop [OK]

Hint: Airflow DAGs run tasks on schedule to control resources [OK]

Common Mistakes:

• Thinking manual triggers automate resource control
• Confusing static allocation with automation
• Ignoring the role of scheduling in cost saving

3. Given this Airflow DAG snippet, what happens to cloud resources?

with DAG('resource_control', schedule_interval='0 8 * * *') as dag:
    start_instance = BashOperator(
        task_id='start_instance',
        bash_command='cloud-cli start instance'
    )
    stop_instance = BashOperator(
        task_id='stop_instance',
        bash_command='cloud-cli stop instance',
        trigger_rule='all_done'
    )

start_instance >> stop_instance

medium

A. Instance stops before it starts

B. Instance starts at 8 AM and stops immediately after

C. Instance starts but never stops

D. Instance stops at 8 AM and starts later

Solution

1. Step 1: Analyze DAG schedule and tasks

   DAG runs daily at 8 AM, starting the instance first.

2. Step 2: Understand task order and trigger rules

   stop_instance runs after start_instance completes, stopping the instance.

3. Final Answer:

   Instance starts at 8 AM and stops immediately after -> Option B

4. Quick Check:

   Task order start >> stop means start then stop [OK]

Hint: Task order with >> shows start then stop sequence [OK]

Common Mistakes:

• Assuming stop runs before start
• Ignoring trigger_rule effects
• Thinking instance stays running all day

4. You wrote this Airflow task to stop a cloud instance:

stop_instance = BashOperator(
    task_id='stop_instance',
    bash_command='cloud-cli stop instance',
    trigger_rule='all_done'
)

Why does this code cause an error?

medium

A. task_id must be 'stop' not 'stop_instance'

B. trigger_rule is not a valid parameter

C. BashOperator cannot run cloud-cli commands

D. Missing comma between bash_command and trigger_rule

Solution

1. Step 1: Check syntax of BashOperator parameters

   Parameters must be separated by commas; here comma is missing after bash_command.

2. Step 2: Identify cause of syntax error

   Missing comma causes Python syntax error, preventing DAG parsing.

3. Final Answer:

   Missing comma between bash_command and trigger_rule -> Option D

4. Quick Check:

   Comma missing = Syntax error [OK]

Hint: Check commas between parameters in Python code [OK]

Common Mistakes:

• Thinking trigger_rule is invalid parameter
• Assuming BashOperator can't run shell commands
• Believing task_id naming causes error

5. You want to optimize cloud costs by running an Airflow DAG that starts instances only on weekdays at 9 AM and stops them at 6 PM. Which schedule_interval and task setup is best?

hard

A. schedule_interval='0 9 * * 1-5' to start, and a separate DAG with '0 18 * * 1-5' to stop

B. schedule_interval='@daily' for both start and stop tasks in one DAG

C. schedule_interval='0 9 * * *' to start and stop in same DAG immediately

D. No schedule_interval, run tasks manually when needed

Solution

1. Step 1: Choose schedule for weekdays only

   '0 9 * * 1-5' runs at 9 AM Monday to Friday, matching requirement.

2. Step 2: Separate start and stop times

   Stopping at 6 PM requires a different schedule '0 18 * * 1-5', best in separate DAG for clarity.

3. Final Answer:

   schedule_interval='0 9 * * 1-5' to start, and a separate DAG with '0 18 * * 1-5' to stop -> Option A

4. Quick Check:

   Separate schedules for start and stop on weekdays [OK]

Hint: Use cron for weekdays and separate DAGs for start/stop [OK]

Common Mistakes:

• Using '@daily' runs every day, not weekdays only
• Starting and stopping immediately in one DAG
• Relying on manual runs instead of automation