Download Asc Timetables Full Version [2021]

As such, Airflow allows for custom timetables to be written in plugins and used byDAGs. An example demonstrating a custom timetable can be found in theCustomizing DAG Scheduling with Timetables how-to guide.

There are two timetables CronTriggerTimetable and CronDataIntervalTimetable that accepts a cron expression.There are some differences between the two:- CronTriggerTimetable does not take care of Data Interval, while CronDataIntervalTimetable does.- The time when a DAG run is triggered by CronTriggerTimetable is more intuitive and more similar to what peopleexpect cron to behave than that of CronDataIntervalTimetable (when catchup is False).

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Here is an example showing how the first DAG run is triggered. Supposes there is a cron expression @daily or0 0 * * *, which is aimed to run at 12AM every day. If you enable DAGs using the two timetables at 3PM on January31st, CronTriggerTimetable will trigger a new DAG run at 12AM on February 1st. CronDataIntervalTimetable, on the otherhand, will immediately trigger a new DAG run which is supposed to trigger at 12AM on January 31st if the DAG had beenenabled beforehand.

This is another example showing the difference in the case of skipping DAG runs. Suppose there are two running DAGsusing the two timetables with a cron expression @daily or 0 0 * * *. If you pause the DAGs at 3PM on January31st and re-enable them at 3PM on February 2nd, CronTriggerTimetable skips the DAG runs which are supposed totrigger on February 1st and 2nd. The next DAG run will be triggered at 12AM on February 3rd. CronDataIntervalTimetable,on the other hand, skips the DAG runs which are supposed to trigger on February 1st only. A DAG run for February 2ndis immediately triggered after you re-enable the DAG.

UPDATED: 12.19.2023

Latest: Updated Cascades, Downeaster, Hartford Line, Pacific Surfliner (Full & Temporary), & San Joaquins schedules to the State Supported/Regional Services section

[All schedules have been updated as noted by "effective" date listed on the timetables]

**NOTE: For those asking about mileage on the timetables, that data comes from a completely different source that we do not have access to. Remember, this is a completely volunteer-led effort, and we must remain respectful of their time and limits to what information is publically available.**

If you feel that something is missing or needs to be updated, please reach out to our National Field Coordinator Joe Aiello ([email protected])

One of the fundamental features of Apache Airflow is the ability to schedule jobs. Historically, Airflow users scheduled their DAGs by specifying a schedule with a cron expression, a timedelta object, or a preset Airflow schedule. Timetables, released in Airflow 2.2, allow users to create their own custom schedules using Python, effectively eliminating the limitations of cron. With timetables, you can now schedule DAGs to run at any time. Datasets, introduced in Airflow 2.4, let you schedule your DAGs on updates to a dataset rather than a time-based schedule. For more information about datasets, see Datasets and Data-Aware Scheduling in Airflow.

In this guide, you'll learn Airflow scheduling concepts and the different ways you can schedule a DAG with a focus on timetables. All code used in this guide is available in the airflow-scheduling-tutorial repository.

In Airflow 2.3 and earlier, the schedule_interval is used instead of the schedule parameter and it only accepts cron expressions or timedelta objects. Additionally, timetables have to be passed using the timetable parameter, which was deprecated in Airflow 2.4 and later. In versions of Airflow 2.2 and earlier, specifying schedule_interval is the only way to define a DAG schedule.

Airflow was originally developed for extract, transform, and load (ETL) with the expectation that data is constantly flowing in from some source and then will be summarized at a regular interval. However, if you want to summarize data from Monday, you need to wait until Tuesday at 12:01 AM. This shortcoming led to the introduction of timetables in Airflow 2.2.

Custom timetables can be registered as part of an Airflow plugin. They must be a subclass of Timetable, and they should contain the following methods, both of which return a DataInterval with a start and an end:

Three sets of logic are required to account for time periods in the same timeframe (6:00 to 16:30) on different days than the day that the DAG is triggered. When you define custom timetables, keep in mind what the last complete data interval should be based on when the DAG should run. ff782bc1db