Manual triggers and parameters in Apache Airflow - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a manual trigger in Airflow?

A manual trigger is when you start a workflow (DAG) by clicking a button in the Airflow UI instead of waiting for a scheduled time.

beginner

How can you pass parameters when manually triggering a DAG in Airflow?

You can pass parameters by entering a JSON object in the 'Trigger DAG' dialog box in the Airflow UI before starting the DAG.

beginner

What is the purpose of parameters in a manually triggered DAG?

Parameters let you customize the behavior of the DAG run, like choosing which data to process or setting options for tasks.

intermediate

How do you access parameters inside a DAG's Python code?

You access parameters using the 'dag_run.conf' dictionary inside your task's Python function.

beginner

Why is it useful to use manual triggers with parameters in Airflow?

It allows you to run workflows on demand with specific settings, making your workflows flexible and interactive.

How do you start a DAG manually in Airflow?

A Restart the Airflow server

B Wait for the scheduled time

C Click the 'Trigger DAG' button in the Airflow UI

D Run a shell script outside Airflow

Manual triggering is done by clicking the 'Trigger DAG' button in the Airflow UI.

What format should parameters be in when manually triggering a DAG?

A YAML

B XML

C Plain text

D JSON

Parameters must be entered as a JSON object in the trigger dialog.

Where do you find the parameters inside your DAG code?

A task_instance.xcom_pull()

B dag_run.conf

C os.environ

D airflow.cfg

Parameters passed during manual trigger are accessed via dag_run.conf.

What happens if you trigger a DAG manually without parameters?

A The DAG runs with default settings

B The DAG runs twice

C Airflow throws an error

D The DAG will not run

If no parameters are given, the DAG runs with its default behavior.

Why use manual triggers with parameters?

A To customize runs on demand

B To automate workflows only

C To schedule DAGs in the future

D To stop running DAGs

Manual triggers with parameters let you customize workflow runs when you want.

Explain how to manually trigger a DAG with parameters in Airflow.

Think about the steps you take in the Airflow web interface.

Describe how parameters passed during manual trigger affect the DAG run.

Consider how parameters change what the DAG does.

Practice

1. What is the main purpose of manually triggering a DAG in Airflow?

easy

A. To stop a running DAG

B. To schedule a DAG to run automatically at a set time

C. To delete a DAG from Airflow

D. To start a workflow immediately with optional custom parameters

Solution

1. Step 1: Understand manual trigger concept

   Manual triggers allow users to start a workflow whenever they want, not waiting for a schedule.

2. Step 2: Recognize parameter use

   Manual triggers can include parameters to customize the run, making the workflow flexible.

3. Final Answer:

   To start a workflow immediately with optional custom parameters -> Option D

4. Quick Check:

   Manual trigger = immediate start + parameters [OK]

Hint: Manual trigger means start now, not later [OK]

Common Mistakes:

• Confusing manual trigger with scheduled runs
• Thinking manual trigger stops a DAG
• Assuming manual trigger deletes DAG

2. Which of the following is the correct way to access a parameter named 'user_id' passed during a manual trigger inside a DAG task?

easy

A. user_id = dag_run.conf.get('user_id')

B. user_id = dag_run.conf['user_id']

C. user_id = dag_run.params['user_id']

D. user_id = dag_run.get('user_id')

Solution

1. Step 1: Recall parameter access method

   Parameters passed during manual trigger are accessed via dag_run.conf dictionary.

2. Step 2: Use safe access method

   Using dag_run.conf.get('user_id') safely returns the value or None if missing, avoiding errors.

3. Final Answer:

   user_id = dag_run.conf.get('user_id') -> Option A

4. Quick Check:

   Use dag_run.conf.get() to read parameters [OK]

Hint: Use .conf.get() to safely get parameters [OK]

Common Mistakes:

• Using square brackets without .get() causing KeyError
• Using non-existent dag_run.params
• Calling dag_run.get() which is invalid

3. Given this DAG task code snippet inside a PythonOperator:

def greet(**context):
    name = context['dag_run'].conf.get('name', 'Guest')
    print(f"Hello, {name}!")

What will be printed if the DAG is manually triggered with parameters {'name': 'Alice'}?

medium

A. Hello, Guest!

B. Hello, Alice!

C. Hello, None!

D. Error: 'name' not found

Solution

1. Step 1: Understand parameter retrieval

   The code uses dag_run.conf.get('name', 'Guest') which returns 'Alice' if passed, else 'Guest'.

2. Step 2: Apply given parameters

   Since 'name' is provided as 'Alice', the print statement outputs "Hello, Alice!".

3. Final Answer:

   Hello, Alice! -> Option B

4. Quick Check:

   Parameter 'name' passed = 'Alice' printed [OK]

Hint: Default used only if parameter missing [OK]

Common Mistakes:

• Ignoring default value and expecting error
• Assuming parameter is missing
• Confusing print output with variable name

4. You wrote this code inside a DAG task:

def process(**kwargs):
    param = kwargs['dag_run'].conf['param']
    print(param)

When manually triggering the DAG without passing 'param', what happens?

medium

A. Raises KeyError exception

B. Prints empty string

C. Prints None

D. Prints 'param'

Solution

1. Step 1: Analyze parameter access method

   Accessing dag_run.conf['param'] without .get() causes KeyError if 'param' is missing.

2. Step 2: Understand missing parameter effect

   Since 'param' is not passed, the code raises KeyError and does not print anything.

3. Final Answer:

   Raises KeyError exception -> Option A

4. Quick Check:

   Missing key with [] causes KeyError [OK]

Hint: Use .get() to avoid KeyError on missing params [OK]

Common Mistakes:

• Expecting None or empty string instead of error
• Assuming print outputs 'param' string
• Not realizing [] access throws error

5. You want to manually trigger a DAG that processes orders. The DAG expects a parameter 'order_id'. You want to ensure the DAG uses 'unknown' if 'order_id' is missing. Which code snippet inside the DAG task correctly implements this?

hard

A. order_id = dag_run.conf['order_id'] or 'unknown'

B. order_id = dag_run.conf['order_id'] if dag_run.conf else 'unknown'

C. order_id = dag_run.conf.get('order_id', 'unknown')

D. order_id = dag_run.conf.get('order_id') or 'unknown'

Solution

1. Step 1: Understand default parameter handling

   Using dag_run.conf.get('order_id', 'unknown') returns 'order_id' if present, else 'unknown'.

2. Step 2: Compare options for correctness

   A fails with KeyError if missing. B checks if conf exists (empty {} is falsy), but if other parameters present conf is truthy causing KeyError. C works for missing but overrides falsy values like empty string. D is clean, safe, and handles all cases correctly.

3. Final Answer:

   order_id = dag_run.conf.get('order_id', 'unknown') -> Option C

4. Quick Check:

   Use .get(key, default) for safe defaults [OK]

Hint: Use .get(key, default) to set fallback values [OK]

Common Mistakes:

• Using [] without checking key existence
• Using or without default in .get()
• Overcomplicating with if-else instead of .get()