Comprehensive Guide to Apache Airflow Scheduling

Apache Airflow's robust scheduling capabilities allow users to orchestrate complex workflows. Historically, users scheduled Directed Acyclic Graphs (DAGs) using cron expressions, timedelta objects, or preset Airflow schedules. However, recent Airflow versions have introduced advanced scheduling features, including data-aware scheduling with datasets and the ability to define custom schedules using timetables. This guide will cover fundamental scheduling concepts and the various methods for scheduling DAGs in Apache Airflow.

Key Scheduling Concepts

Understanding DAG scheduling requires familiarity with several essential terms and parameters:

Data Interval

The data interval is a property of each DAG run representing the period of data that each task should process. For instance, an hourly scheduled DAG has a data interval starting at the top of the hour (minute 0) and ending at the close of the hour (minute 59). The DAG run typically executes at the end of this data interval.

Logical Date

The logical date marks the start of the data interval and does not indicate when the DAG will actually run. In Airflow versions prior to 2.2, this was known as the execution date.

Timetable

A timetable is a DAG property dictating the data interval and logical date for each DAG run, determining when a DAG is scheduled.

Run After

This is the earliest time a DAG can be scheduled. Displayed in the Airflow UI, this date may coincide with the end of the data interval, depending on the DAG's timetable.

Backfilling and Catchup

These terms are related to the scheduling behavior of DAGs. Backfilling refers to running historical DAG runs, and catchup involves running missed DAG runs. For more details, see the section on DAG Runs.

Essential Parameters for Scheduling

Several parameters ensure that your DAGs run at the correct time:

data_interval_start

Defines the start date and time of the data interval. The DAG's timetable automatically generates this parameter for each DAG run, or it can be specified by the user when implementing a custom timetable.

data_interval_end

Defines the end date and time of the data interval. Similar to data_interval_start, this parameter is either automatically generated by the DAG's timetable or specified by the user.

schedule

Specifies when a DAG will run. This parameter is set at the DAG configuration level and accepts cron expressions, timedelta objects, timetables, and lists of datasets. The default schedule is timedelta(days=1), running the DAG once per day if no schedule is defined. To trigger the DAG externally, set the schedule to None.

start_date

The first date your DAG will be executed. This parameter is mandatory for scheduling DAGs in Airflow.

end_date

The last date your DAG will be executed. This parameter is optional.

Scheduling in Different Airflow Versions

Airflow 2.3 and Earlier

In these versions, the schedule_interval parameter is used instead of the schedule parameter. It only accepts cron expressions or timedelta objects. Custom timetables were passed using the timetable parameter, which was deprecated in Airflow 2.4.

Airflow 2.4 and Later

The schedule parameter replaces schedule_interval and supports a broader range of scheduling options, including cron expressions, timedelta objects, timetables, and datasets. This change allows for more flexible and powerful scheduling configurations.

Advanced Scheduling Features

Data-Aware Scheduling with Datasets

Recent Airflow versions introduce data-aware scheduling, allowing DAGs to be triggered based on the availability of datasets. This method provides a more dynamic approach to scheduling by ensuring DAGs run only when necessary data is available.

Custom Schedules with Timetables

Timetables enable the definition of complex custom schedules. By implementing a custom timetable, users can specify intricate scheduling requirements that go beyond the capabilities of cron expressions and timedelta objects.

Summary

Apache Airflow's scheduling features have evolved to offer a wide range of options, from traditional cron expressions to advanced data-aware scheduling and custom timetables. Understanding the fundamental concepts and parameters involved in DAG scheduling is crucial for effectively orchestrating workflows in Airflow. Whether you are using an older version or the latest release, this guide provides the knowledge needed to leverage Airflow's powerful scheduling capabilities.