annarad.blogg.se - Airflow 2

#Airflow 2 code#

This is particularly useful when you have conflicting dependencies between Airflow itself and tasks you need to run.

#Airflow 2 code#

Airflow handles getting the code into the container and returning xcom - you just worry about your function. The decorator is one such decorator that allows you to run a function in a docker container. To support this feature, a new component has been added to Airflow, the triggerer, which is the daemon process that runs the asyncio event loop.Īirflow 2.2.0 ships with 2 deferrable sensors, DateTimeSensorAsync and TimeDeltaSensorAsync, both of which are drop-in replacements for the existing corresponding sensor.ĭeferrable Operators & Triggers Custom decorators and 2.2.0 allows providers to create custom decorators in the TaskFlow interface. This allows simple things like monitoring a job in an external system or watching for an event to be much cheaper. Most importantly, this results in the worker slot, and most notably any resources used by it, to be returned to Airflow.

More information can be found at: Customizing DAG Scheduling with Timetables Deferrable Tasks (AIP-40)ĭeferrable tasks allows operators or sensors to defer themselves until a light-weight async check passes, at which point they can resume executing. If you write your own timetables, keep in mind they should be idempotent and fast as they are used in the scheduler to create DagRuns.

data_interval_end (aka next_execution_date).

data_interval_start (same value as execution_date for cron).

Of course, backwards compatibility has been maintained - cron expressions and timedeltas are still fully supported, however, timetables are pluggable so you can add your own custom timetable to fit your needs! For example, you could write a timetable to schedule a DagRunĮxecution_date has long been confusing to new Airflowers, so as part of this change a new concept has been added to Airflow to replace it named data_interval, which is the period of data that a task should operate on. To provide more scheduling flexibility, determining when a DAG should run is now done with Timetables. For example, running daily on Monday-Friday, but not on weekends wasn’t possible. This worked for a lot of use cases, but not all. Custom Timetables (AIP-39)Īirflow has historically used cron expressions and timedeltas to represent when a DAG should run. 🐳 Docker Image: docker pull apache/airflow:2.2.0Īs the changelog is quite large, the following are some notable new features that shipped in this release. It contains over 600 commits since 2.1.4 and includes 30 new features, 84 improvements, 85 bug fixes, and many internal and doc changes. I’m proud to announce that Apache Airflow 2.2.0 has been released.