Why Mapped tasks for parallel processing in Apache Airflow? - Purpose & Use Cases

The Big Idea

What if you could turn a long, boring task into a fast, automatic process with just a few lines of code?

The Scenario

Imagine you have a list of 100 files to process one by one in a data pipeline.

You run each file through the same steps manually, waiting for one to finish before starting the next.

The Problem

This manual approach takes a long time because tasks run one after another.

If one file causes an error, you must stop and fix it before continuing.

It's hard to track progress and wastes valuable time.

The Solution

Mapped tasks let you automatically create many similar tasks from a list.

Airflow runs these tasks in parallel, speeding up the whole process.

If one task fails, others keep running, and you can easily retry just the failed ones.

Before vs After

✗ Before

for file in files:
    process(file)

✓ After

process_task.expand(file=files)

What It Enables

It enables fast, scalable workflows that handle many items at once without extra manual work.

Real Life Example

Processing thousands of daily sales reports in parallel to generate quick business insights.

Key Takeaways

Manual sequential tasks are slow and fragile.

Mapped tasks automate creating many parallel tasks from a list.

This speeds up pipelines and improves reliability.

Practice

1. What is the main purpose of using mapped tasks in Airflow?

easy

A. To run many similar tasks in parallel using a list of inputs

B. To schedule tasks only once per day

C. To create a single task that runs sequentially

D. To manually trigger tasks one by one

Solution

1. Step 1: Understand mapped tasks concept

   Mapped tasks allow running multiple similar jobs at the same time using different inputs.

2. Step 2: Compare options with concept

   Only To run many similar tasks in parallel using a list of inputs describes running many similar tasks in parallel using a list of inputs, which matches mapped tasks.

3. Final Answer:

   To run many similar tasks in parallel using a list of inputs -> Option A

4. Quick Check:

   Mapped tasks = parallel jobs with input list [OK]

Hint: Mapped tasks run many jobs at once using input lists [OK]

Common Mistakes:

• Thinking mapped tasks run sequentially
• Confusing mapped tasks with scheduling frequency
• Assuming mapped tasks require manual triggers

2. Which of the following is the correct syntax to map a task with a list of inputs in Airflow?

easy

A. task.execute(inputs=[1, 2, 3])

B. task.map(input_list=[1, 2, 3])

C. task.run_parallel([1, 2, 3])

D. task.expand(input_list=[1, 2, 3])

Solution

1. Step 1: Recall the method to map tasks

   In Airflow, .expand() is used to map tasks with a list of inputs.

2. Step 2: Match syntax with options

   Only task.expand(input_list=[1, 2, 3]) uses task.expand(input_list=[1, 2, 3]), which is correct syntax.

3. Final Answer:

   task.expand(input_list=[1, 2, 3]) -> Option D

4. Quick Check:

   Use .expand() to map tasks [OK]

Hint: Use .expand() method with input list to map tasks [OK]

Common Mistakes:

• Using .map() instead of .expand()
• Calling .run_parallel() which does not exist
• Using .execute() with inputs incorrectly

3. Given the following code snippet, what will be the output of the mapped task?

def greet(name):
    return f"Hello, {name}!"

mapped_task = greet.expand(name=["Alice", "Bob", "Carol"])

medium

A. ["Hello, Alice!", "Hello, Bob!", "Hello, Carol!"]

B. "Hello, ['Alice', 'Bob', 'Carol']!"

C. ["Hello, Alice!", "Hello, Alice!", "Hello, Alice!"]

D. Error: greet function cannot be expanded

Solution

1. Step 1: Understand .expand() behavior

   The .expand() method runs the function for each item in the list separately, producing a list of results.

2. Step 2: Apply greet function to each name

   For names "Alice", "Bob", and "Carol", greet returns "Hello, Alice!", "Hello, Bob!", and "Hello, Carol!" respectively.

3. Final Answer:

   ["Hello, Alice!", "Hello, Bob!", "Hello, Carol!"] -> Option A

4. Quick Check:

   .expand() applies function to each input [OK]

Hint: Mapped tasks output list of results for each input [OK]

Common Mistakes:

• Thinking .expand() passes whole list as one input
• Expecting repeated output for first input only
• Assuming .expand() causes an error on functions

4. Identify the error in this Airflow mapped task code:

def process(item):
    return item * 2

mapped_task = process.expand(items=[1, 2, 3])

medium

A. The function cannot return a value

B. The argument name in expand() should match the function parameter

C. expand() cannot be used with lists

D. The function must be decorated with @task

Solution

1. Step 1: Check function parameter and expand argument

   The function parameter is named item, but expand uses items. They must match.

2. Step 2: Understand expand() argument matching

   expand() requires the argument name to match the function parameter exactly to map inputs correctly.

3. Final Answer:

   The argument name in expand() should match the function parameter -> Option B

4. Quick Check:

   expand() argument name = function parameter [OK]

Hint: expand() argument name must match function parameter exactly [OK]

Common Mistakes:

• Using different names for expand() argument and function parameter
• Thinking function cannot return values
• Believing expand() can't use lists
• Forgetting @task decorator (not always required)

5. You want to process a list of user IDs with a mapped task, but only for IDs greater than 10. Which code correctly applies this filter before mapping?

def process_user(user_id):
    return f"Processed {user_id}"

user_ids = [5, 12, 7, 20, 15]

# Which line correctly maps filtered user_ids?

hard

A. mapped_task = process_user.expand(user_id=user_ids if user_ids > 10 else [])

B. mapped_task = process_user.expand(user_id=user_ids.filter(lambda x: x > 10))

C. mapped_task = process_user.expand(user_id=[uid for uid in user_ids if uid > 10])

D. mapped_task = process_user.expand(user_id=user_ids.map(lambda x: x > 10))

Solution

1. Step 1: Filter user_ids list correctly

   Use a list comprehension to select IDs greater than 10: [uid for uid in user_ids if uid > 10].

2. Step 2: Pass filtered list to expand()

   Pass the filtered list as the argument to expand() matching the function parameter name.

3. Final Answer:

   mapped_task = process_user.expand(user_id=[uid for uid in user_ids if uid > 10]) -> Option C

4. Quick Check:

   Filter with list comprehension before expand() [OK]

Hint: Filter list with comprehension before .expand() [OK]

Common Mistakes:

• Using .filter() or .map() which are not list methods
• Trying to compare list directly to number
• Passing boolean list instead of filtered values