How do you make a task dynamic in Airflow?

How do you make a task dynamic in Airflow?

Basically what you want to do is have two subdags with the following:

1. Xcom push a list (or what ever you need to create the dynamic workflow later) in the subdag that gets executed first (see test1.py def return_list() )
2. Pass the main dag object as a parameter to your second subdag.

How can I create DAGs dynamically?

Single-File Methods. One method for dynamically generating DAGs is to have a single Python file which generates DAGs based on some input parameter(s) (e.g. a list of APIs or tables). A common use case for this is an ETL or ELT-type pipeline where there are many data sources or destinations.

How do I set dependencies between DAGs in Airflow?

This post has shown how to create those dependencies even if you don’t control the upstream DAGs: add a new DAG that relies on using the ExternalTaskSensor (one sensor per upstream DAG), encode the dependencies between the DAGs as dependencies between the sensor tasks, run the DAG encoding the dependencies in the same …

How do you make DAGs in Airflow?

Creating your first DAG In Airflow, a DAG is simply a Python script that contains a set of tasks and their dependencies. What each task does is determined by the task’s operator. For example, using PythonOperator to define a task means that the task will consist of running Python code.

What is dummy operator in Airflow?

Operator that does literally nothing. It can be used to group tasks in a DAG. The task is evaluated by the scheduler but never processed by the executor.

How do you create a dynamic workflow?

Creating a Dynamic Workflow

1. Go to Projects (select a project) > Gear Icon > Project Settings > Workflows.
2. Create a Workflow or scroll to the workflow you’d like to configure, then click Add Steps.
3. Click on the + icon before or after any step to create the Decision step, or router.

How do you make an Airflow operator?

Creating a custom Operator

1. Constructor – Define the parameters required for the operator. You only need to specify the arguments specific to your operator.
2. Execute – The code to execute when the runner calls the operator. The method contains the airflow context as a parameter that can be used to read config values.

How do I trigger one DAG from another?

What’s the best way to trigger a DAG with another DAG?

1. Both DAGs would have to be set on the same schedule.
2. The DAG execution date of that sensor MUST match the DAG execution date of the task it’s sensing. That’s why they must have the same schedule.
3. Consider upgrading to 1.10. 2 (details below)

Is airflow an ETL tool?

Airflow is not a data streaming platform. Tasks represent data movement, they do not move data in themselves. Thus, it is not an interactive ETL tool. Airflow is a Python script that defines an Airflow DAG object.

Does Kubeflow use airflow?

Airflow is a generic task orchestration platform, while Kubeflow focuses specifically on machine learning tasks, such as experiment tracking. Both tools allow you to define tasks using Python, but Kubeflow runs tasks on Kubernetes.

What is Airflow DAG?

In Airflow, a DAG – or a Directed Acyclic Graph – is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies. A DAG is defined in a Python script, which represents the DAGs structure (tasks and their dependencies) as code.

Which is an example of a DAG in airflow?

A DAG (Directed Acyclic Graph) is the core concept of Airflow, collecting Tasks together, organized with dependencies and relationships to say how they should run. Here’s a basic example DAG: It defines four Tasks – A, B, C, and D – and dictates the order in which they have to run, and which tasks depend on what others.

Is it good to use dynamic operators in airflow?

I would like to know if what I did to achieve to goal of dynamic operators within an Airflow DAG (Directed Acyclic Graph) is a good or a bad practice. Create a ‘x’ amount of operators within a DAG based on the result of an API call. This DAG will run for example every week.

Are there any issues with dynamically generating DAGs?

Dynamically generating DAGs can cause performance issues when used at scale. Whether or not any particular method will cause problems is dependent on your total number of DAGs, your Airflow configuration, and your infrastructure. Here are a few general things to look out for:

How to keep Dag names static in airflow scheduler?

You can keep the dag and task names static, just assign them ids dynamically in order to differentiate one dag from the other. You put this python script in the dags folder. When you start the airflow scheduler, it runs through this script on every heartbeat and writes the DAGs to the dag table in the database.