Mapped tasks for parallel processing in Apache Airflow - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of mapped tasks in Airflow?

Mapped tasks allow you to run the same task multiple times in parallel with different inputs, making workflows more efficient and scalable.

beginner

How do you define a mapped task in Airflow using Python?

You use the .expand() method on a task to pass a list of inputs, which creates multiple task instances running in parallel.

intermediate

What happens if the input list for a mapped task is empty?

No task instances are created, so the mapped task effectively skips execution.

intermediate

Can mapped tasks in Airflow handle dynamic input sizes at runtime?

Yes, mapped tasks can dynamically adjust the number of parallel runs based on the input list size determined at runtime.

beginner

What is a key benefit of using mapped tasks over manually creating multiple similar tasks?

Mapped tasks reduce code duplication and make workflows easier to maintain by automatically generating parallel task instances.

Which Airflow method is used to create mapped tasks for parallel processing?

A .repeat()

B .map()

C .parallelize()

D .expand()

The .expand() method is used to pass a list of inputs to a task, creating multiple parallel task instances.

What happens if you pass an empty list to a mapped task's .expand() method?

A No task instances are created

B The task runs once with no input

C The task fails with an error

D The task runs infinitely

If the input list is empty, no task instances are created and the mapped task is skipped.

Mapped tasks in Airflow help to:

A Run the same task multiple times in parallel

B Reduce the number of tasks in a DAG

C Run tasks sequentially

D Automatically retry failed tasks

Mapped tasks run the same task multiple times in parallel with different inputs.

Which of the following is NOT a benefit of mapped tasks?

A Reduce code duplication

B Automatically fix task errors

C Handle dynamic input sizes

D Improve workflow scalability

Mapped tasks do not automatically fix errors; they help with parallelism and code simplicity.

In Airflow, mapped tasks are best suited for:

A Tasks that must run once

B Tasks that require manual triggering

C Tasks with varying inputs that can run in parallel

D Tasks that depend on external APIs only

Mapped tasks are ideal for running the same task multiple times with different inputs in parallel.

Explain how mapped tasks improve parallel processing in Airflow and give an example of when to use them.

Think about running the same job many times with different data.

Describe what happens internally when you use the .expand() method on a task in Airflow.

Focus on how Airflow creates multiple tasks from one.