Execution date vs logical date in Apache Airflow - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the logical date of an Airflow task instance.

Apache Airflow

print(ti.[1])

Drag options to blanks, or click blank then click option'

Aexecution_date

Bstart_date

Clogical_date

Drun_date

Attempts:

3 left

💡 Hint

Common Mistakes

Using execution_date instead of logical_date

Confusing start_date with logical_date

The logical_date attribute represents the scheduled date for the task instance, which is different from the actual execution time.

2fill in blank

medium

Complete the code to get the execution date from the context dictionary in an Airflow operator.

Apache Airflow

exec_date = context.get('[1]')

Drag options to blanks, or click blank then click option'

Aexecution_date

Blogical_date

Cstart_date

Drun_date

Attempts:

3 left

💡 Hint

Common Mistakes

Using logical_date key which is not in context

Using start_date which is a DAG attribute

The execution_date key in the context dictionary holds the actual execution time of the task instance.

3fill in blank

hard

Fix the error in the code to correctly compare execution date and logical date.

Apache Airflow

if ti.execution_date == ti.[1]:
    print('Dates match')

Drag options to blanks, or click blank then click option'

Alogical_date

Brun_date

Cstart_date

Dschedule_date

Attempts:

3 left

💡 Hint

Common Mistakes

Using run_date which is not a valid attribute

Using start_date which is a DAG property, not task instance

The logical_date attribute should be used to compare with execution_date because it represents the scheduled run date.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps logical dates to execution dates for task instances.

Apache Airflow

{ti.[1]: ti.[2] for ti in task_instances}

Drag options to blanks, or click blank then click option'

Alogical_date

Bexecution_date

Cstart_date

Drun_date

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping logical_date and execution_date

Using start_date or run_date which are not task instance attributes

This comprehension creates a dictionary where keys are logical dates and values are execution dates of task instances.

5fill in blank

hard

Fill all three blanks to filter task instances where execution date is after logical date and create a dictionary with logical date keys and execution date values.

Apache Airflow

{ti.[1]: ti.[2] for ti in task_instances if ti.[3] > ti.[1]

Drag options to blanks, or click blank then click option'

Alogical_date

Bexecution_date

Cstart_date

Drun_date

Attempts:

3 left

💡 Hint

Common Mistakes

Using start_date or run_date which are not task instance attributes

Mixing up the comparison order

The dictionary comprehension filters task instances where the execution date is later than the logical date, mapping logical dates to execution dates.

Practice

1. What does the logical date in Airflow represent?

easy

A. The time period the task is processing data for

B. The exact time the task started running

C. The current system time when the task runs

D. The time when the DAG was created

Solution

1. Step 1: Understand the definition of logical date

   The logical date is the timestamp representing the data interval the task is working on, not when it runs.

2. Step 2: Differentiate from execution date

   Execution date is when the task actually runs, but logical date points to the data period it processes.

3. Final Answer:

   The time period the task is processing data for -> Option A

4. Quick Check:

   Logical date = data period [OK]

Hint: Logical date = data period, execution date = run time [OK]

Common Mistakes:

• Confusing logical date with actual run time
• Thinking logical date is current system time
• Mixing execution date with DAG creation time

2. Which of the following is the correct way to access the logical date inside an Airflow task?

easy

A. Using context['execution_date']

B. Using context['logical_date']

C. Using context['run_time']

D. Using context['start_date']

Solution

1. Step 1: Recall Airflow context variables

   Airflow provides logical_date in the context to represent the logical date of the task instance.

2. Step 2: Identify the correct key for logical date

   The key 'logical_date' is used to access the logical date, while 'execution_date' is deprecated in newer versions.

3. Final Answer:

   Using context['logical_date'] -> Option B

4. Quick Check:

   Access logical date with context['logical_date'] [OK]

Hint: Use context['logical_date'] for logical date in Airflow 2.2+ [OK]

Common Mistakes:

• Using deprecated 'execution_date' key
• Trying to access logical date with 'run_time'
• Confusing start_date with logical date

3. Given a DAG scheduled daily at midnight, if a task runs on 2024-06-10 00:00 UTC, what is its logical date?

medium

A. 2024-06-10 00:00 UTC

B. 2024-06-11 00:00 UTC

C. 2024-06-09 00:00 UTC

D. The current system time when the task runs

Solution

1. Step 1: Understand daily scheduling in Airflow

   A DAG scheduled daily at midnight runs for the data of the previous day, so the logical date is the start of that previous day.

2. Step 2: Calculate logical date for run on 2024-06-10 00:00 UTC

   The run at midnight on June 10 processes data for June 9, so logical date is 2024-06-09 00:00 UTC.

3. Final Answer:

   2024-06-09 00:00 UTC -> Option C

4. Quick Check:

   Run time = June 10, logical date = June 9 [OK]

Hint: Logical date is previous day for daily midnight runs [OK]

Common Mistakes:

• Assuming logical date equals execution date
• Using current system time as logical date
• Confusing next day as logical date

4. You see a task's logs showing execution_date as 2024-06-10 but the data processed is from 2024-06-11. What is the likely issue?

medium

A. The task is running twice for the same data

B. The DAG schedule interval is set to hourly instead of daily

C. The system clock is incorrect

D. The task is using execution_date instead of logical_date for data filtering

Solution

1. Step 1: Analyze mismatch between execution_date and data date

   If data processed is from a later date than execution_date, the task likely uses execution_date (run time) instead of logical_date (data period) for filtering.

2. Step 2: Understand correct usage of logical date

   Logical date should be used to select data for the intended period; using execution_date causes off-by-one errors.

3. Final Answer:

   The task is using execution_date instead of logical_date for data filtering -> Option D

4. Quick Check:

   Use logical_date for data period, not execution_date [OK]

Hint: Use logical_date for data filtering, not execution_date [OK]

Common Mistakes:

• Ignoring difference between execution and logical dates
• Assuming schedule interval causes date mismatch
• Blaming system clock without checking code

5. You want to process hourly data with a DAG scheduled every hour. The task should process data for the previous hour. Which date should you use inside the task to filter data correctly?

hard

A. Use logical_date which represents the data interval start

B. Use DAG start_date

C. Use current system time when the task runs

D. Use execution_date which is the task run time

Solution

1. Step 1: Understand hourly scheduling and data processing

   For hourly DAGs, the logical date marks the start of the hour for which data is processed, typically the previous hour.

2. Step 2: Choose the correct date for filtering data

   Using logical_date ensures the task filters data for the correct hour interval, not the actual run time.

3. Final Answer:

   Use logical_date which represents the data interval start -> Option A

4. Quick Check:

   Hourly data uses logical_date for correct filtering [OK]

Hint: For hourly data, always use logical_date for filtering [OK]

Common Mistakes:

• Using execution_date causes off-by-one hour errors
• Using current time ignores schedule intervals
• Confusing DAG start_date with data interval