![]() Task groups effectively divide tasks into smaller groups to make the DAG structure more manageable and easier to understand.Īside from developing good DAG code, one of the most demanding parts of creating a successful DAG is making tasks reproducible. The new Airflow 2 workgroup feature helps manage these complex systems. Group tasks together using task groups: Complex DAG airflow can be challenging to understand due to the number of operations required.Fortunately, getting connection data from the Airflow connection store makes it easy to persist credentials for custom code. Centralized credential management: As the Airflow DAG interacts with various systems, many other credentials generate, such as databases, cloud storage, etc.When writing code, the easiest way to make it more transparent and understandable is to use commonly used styles. Use style conventions: Adopting a consistent and clean programming style and applying it consistently to all Apache Airflow DAGs is one of the first steps to creating clean and consistent DAGs.For example, DAG code can quickly become unnecessarily complex or difficult to understand, especially when the team members make their DAG vastly different programming styles. ![]() It depends on your use case, what is your goal, a highly or loosely coupled system? Is your data easy to listen to? Depending on your answers, you will probably choose one option or another.It’s easy to get confused when creating an Airflow DAG. However, you can also have situations where having such enforced constraint because it will require much more effort to evolve things.Īs you can see then, there is not a single YES/NO answer to the question. In other words, it simplifies - if you don't have any efficient data lineage tool - the vision of the data producers and consumers in your ETL. By using it you will enforce yourself today and yourself from the future to think like "I have to move this DAG but wait, it's used here as data provider". When to use a DAG sensor? If you want to enforce the dependencies and control that a given DAG will always execute after another, ExternalTaskSensor seems to be a better option than any data sensor. Task_1 = DummyOperator(task_id='task_1', dag=dag_target) Task_1 = DummyOperator(task_id='task_1', dag=dag_source) The idea here is to use the specific execution of a task as the trigger for our DAG's execution. The second category of sensors are DAG sensors and in Apache Airflow they can be implemented with _task_sensor.ExternalTaskSensor. If the difference goes far beyond the latency, it means that the processing can start. An idea to handle that could be a sensor that checks the time of the last written object for the hour key and compares it with some allowed latency. FYI, it's also hard, and maybe even harder, to do with DAG sensor. Let's imagine that you synchronize your Apache Kafka topic to S3 and want to start the processing once all data for a given hour is written. How can you estimate the completeness of your writing? You will probably need to either implement your sensor, or extend the generation by the logic managing the listened data part, for instance by generating an "_SUCCESS"-like file in your own.ĭata-sensors can also be hard to implement for the case of continuously arriving data. Let's imagine that you write the data into a relational database. However, data-based sensors aren't easy to implement for everything. Today you can generate the data needed by DAG A with DAG B but tomorrow you can do it with DAG C, or even export the generation logic somewhere else, without any problem. Task_1 = DummyOperator(task_id='task_1', dag=file_sensor_dag)ĭata sensor brings this nice possibility to decouple DAGs. Below you can find an example with FileSensor: S3KeySensor) and start to process as soon as this _SUCCESS is created. Now, if you want to start your other pipeline directly after the first one, you can use one of available sensors (e.g. ![]() It can be particularly useful for Apache Spark pipelines which, at the end of a successful processing, create a file called _SUCCESS. For instance, you can use 3_key_sensor.S3KeySensor to check whether an object is created on S3. ![]() The idea behind data sensors is to waiting for a specific data to be created. In this blog post I will consider these 2 options and try to see the use context of everyone.
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