BhuvanChand Maddi

Understanding Start Date, Schedule Interval, and Execution Date in Apache Airflow

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bhuvanchand maddi
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Apache Airflow is a powerful platform used for orchestrating complex computational workflows and data processing pipelines. At the heart of Airflow's scheduling system are three critical concepts: the start date (start_date), schedule interval (schedule_interval), and execution date (execution_date). Understanding these terms is essential for effectively scheduling and managing workflows in Airflow.

Start Date (start_date)

The start_date is a parameter in the DAG configuration that specifies when the DAG should start being considered for scheduling. It is important to note that it is not the exact time when the DAG or tasks within it will start executing. Instead, it is the logical start time for scheduling purposes.

Example

If you set start_date to 2024-01-01 and have a daily schedule, Airflow will not execute any tasks on January 1, 2024. Instead, the scheduler will wait until the next interval, meaning the first actual run would be on January 2, 2024, for the data of January 1, 2024.

Schedule Interval (schedule_interval)

This parameter defines the frequency at which the DAG runs. It can be a precise time interval (like every 5 minutes), a cron expression (like 0 0 * * * for daily at midnight), or a preset (like @daily or @hourly).

Example

If the schedule_interval is set to @daily, the DAG will trigger once a day. Suppose the start_date is 2024-01-01. The first run will happen on 2024-01-02 (considering the data of 2024-01-01), and subsequent runs will follow daily.

Execution Date (execution_date)

The execution_date is the logical date and time for which a particular DAG run is executed. This is critical in data processing as it often determines the slice of data on which tasks will operate. It's a representation of the time period the DAG is processing data for, not the actual start time of the DAG's run.

Example

For a DAG with a start_date of 2024-01-01 and a schedule_interval of @daily, the execution_date for the first run will be 2024-01-01, even though this run physically happens on 2024-01-02. This run is meant to process the data of January 1, 2024.

Practical Examples

  1. Hourly DAG:

    • start_date: 2024-01-10 08:00:00

    • schedule_interval: @hourly

    • The first run will be physically executed at 2024-01-10 09:00:00, with an execution_date of 2024-01-10 08:00:00.

  2. Weekly DAG:

    • start_date: 2024-01-01 (a Monday)

    • schedule_interval: 0 0 * * 0 (every Sunday at midnight)

    • The first run will occur on 2024-01-08 (the following Sunday), with an execution_date of 2024-01-01.

Understanding these three concepts and their interplay is crucial for correctly timing and managing workflows in Airflow. It ensures that data is processed for the correct time period and that workflows are triggered as expected.

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