EmailOperator for notifications in Apache Airflow - Time & Space Complexity

Time Complexity: EmailOperator for notifications

O(n)

Understanding Time Complexity

We want to understand how the time it takes to send emails using Airflow's EmailOperator changes as the number of emails grows.

How does the work increase when we send more notifications?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

from airflow.operators.email import EmailOperator

def send_notifications(email_list):
    for email in email_list:
        email_task = EmailOperator(
            task_id=f'send_email_{email}',
            to=email,
            subject='Notification',
            html_content='Hello!'
        )
        email_task.execute(context={})

This code sends an email notification to each address in the list one by one.

Identify Repeating Operations

• Primary operation: Sending an email using EmailOperator's execute method.
• How many times: Once for each email address in the input list.

How Execution Grows With Input

As the number of emails increases, the total time grows roughly in direct proportion.

Input Size (n)	Approx. Operations
10	10 email sends
100	100 email sends
1000	1000 email sends

Pattern observation: Doubling the number of emails roughly doubles the work.

Final Time Complexity

Time Complexity: O(n)

This means the time to send emails grows linearly with the number of emails.

Common Mistake

[X] Wrong: "Sending multiple emails at once takes the same time as sending one email."

[OK] Correct: Each email requires its own send operation, so time adds up with each email.

Interview Connect

Understanding how tasks scale with input size helps you design efficient workflows and predict performance in real projects.

Self-Check

"What if we batch multiple emails into one EmailOperator call? How would the time complexity change?"