Dynamic DAG Generation

This document describes creation of DAGs that have a structure generated dynamically, but where the number of tasks in the DAG does not change between DAG Runs. If you want to implement a DAG where number of Tasks (or Task Groups as of Airflow 2.6) can change based on the output/result of previous tasks, see Dynamic Task Mapping.


Consistent sequence of generating tasks and task groups

In all cases where you generate DAGs dynamically, you should make sure that Tasks and Task Groups are generated with consistent sequence every time the DAG is generated, otherwise you might end up with Tasks and Task Groups changing their sequence in the Grid View every time you refresh the page. This can be achieved for example by using a stable sorting mechanism in your Database queries or by using sorted() function in Python.

Dynamic DAGs with environment variables

If you want to use variables to configure your code, you should always use environment variables in your top-level code rather than Airflow Variables. Using Airflow Variables in top-level code creates a connection to the metadata DB of Airflow to fetch the value, which can slow down parsing and place extra load on the DB. See the best practices on Airflow Variables to make the best use of Airflow Variables in your DAGs using Jinja templates.

For example you could set DEPLOYMENT variable differently for your production and development environments. The variable DEPLOYMENT could be set to PROD in your production environment and to DEV in your development environment. Then you could build your DAG differently in production and development environment, depending on the value of the environment variable.

deployment = os.environ.get("DEPLOYMENT", "PROD")
if deployment == "PROD":
    task = Operator(param="prod-param")
elif deployment == "DEV":
    task = Operator(param="dev-param")

Generating Python code with embedded meta-data

You can externally generate Python code containing the meta-data as importable constants. Such constant can then be imported directly by your DAG and used to construct the object and build the dependencies. This makes it easy to import such code from multiple DAGs without the need to find, load and parse the meta-data stored in the constant - this is done automatically by Python interpreter when it processes the “import” statement. This sounds strange at first, but it is surprisingly easy to generate such code and make sure this is a valid Python code that you can import from your DAGs.

For example assume you dynamically generate (in your DAG folder), the my_company_utils/common.py file:

# This file is generated automatically !
ALL_TASKS = ["task1", "task2", "task3"]

Then you can import and use the ALL_TASKS constant in all your DAGs like that:

from my_company_utils.common import ALL_TASKS

with DAG(
    start_date=datetime(2021, 1, 1),
    for task in ALL_TASKS:
        # create your operators and relations here

Don’t forget that in this case you need to add empty __init__.py file in the my_company_utils folder and you should add the my_company_utils/.* line to .airflowignore file (if using the regexp ignore syntax), so that the whole folder is ignored by the scheduler when it looks for DAGs.

Dynamic DAGs with external configuration from a structured data file

If you need to use a more complex meta-data to prepare your DAG structure and you would prefer to keep the data in a structured non-python format, you should export the data to the DAG folder in a file and push it to the DAG folder, rather than try to pull the data by the DAG’s top-level code - for the reasons explained in the parent Top level Python Code.

The meta-data should be exported and stored together with the DAGs in a convenient file format (JSON, YAML formats are good candidates) in DAG folder. Ideally, the meta-data should be published in the same package/folder as the module of the DAG file you load it from, because then you can find location of the meta-data file in your DAG easily. The location of the file to read can be found using the __file__ attribute of the module containing the DAG:

my_dir = os.path.dirname(os.path.abspath(__file__))
configuration_file_path = os.path.join(my_dir, "config.yaml")
with open(configuration_file_path) as yaml_file:
    configuration = yaml.safe_load(yaml_file)
# Configuration dict is available here

Registering dynamic DAGs

You can dynamically generate DAGs when using the @dag decorator or the with DAG(..) context manager and Airflow will automatically register them.

from datetime import datetime
from airflow.decorators import dag, task

configs = {
    "config1": {"message": "first DAG will receive this message"},
    "config2": {"message": "second DAG will receive this message"},

for config_name, config in configs.items():
    dag_id = f"dynamic_generated_dag_{config_name}"

    @dag(dag_id=dag_id, start_date=datetime(2022, 2, 1))
    def dynamic_generated_dag():
        def print_message(message):



The code below will generate a DAG for each config: dynamic_generated_dag_config1 and dynamic_generated_dag_config2. Each of them can run separately with related configuration.

If you do not wish to have DAGs auto-registered, you can disable the behavior by setting auto_register=False on your DAG.

Changed in version 2.4: As of version 2.4 DAGs that are created by calling a @dag decorated function (or that are used in the with DAG(...) context manager are automatically registered, and no longer need to be stored in a global variable.

Optimizing DAG parsing delays during execution

New in version 2.4.

This is an experimental feature.

Sometimes when you generate a lot of Dynamic DAGs from a single DAG file, it might cause unnecessary delays when the DAG file is parsed during task execution. The impact is a delay before a task starts.

Why is this happening? You might not be aware but just before your task is executed, Airflow parses the Python file the DAG comes from.

The Airflow Scheduler (or rather DAG File Processor) requires loading of a complete DAG file to process all metadata. However, task execution requires only a single DAG object to execute a task. Knowing this, we can skip the generation of unnecessary DAG objects when a task is executed, shortening the parsing time. This optimization is most effective when the number of generated DAGs is high.

There is an experimental approach that you can take to optimize this behaviour. Note that it is not always possible to use (for example when generation of subsequent DAGs depends on the previous DAGs) or when there are some side-effects of your DAGs generation. Also the code snippet below is pretty complex and while we tested it and it works in most circumstances, there might be cases where detection of the currently parsed DAG will fail and it will revert to creating all the DAGs or fail. Use this solution with care and test it thoroughly.

A nice example of performance improvements you can gain is shown in the Airflow’s Magic Loop blog post that describes how parsing during task execution was reduced from 120 seconds to 200 ms. (The example was written before Airflow 2.4 so it uses undocumented behaviour of Airflow.)

In Airflow 2.4 instead you can use get_parsing_context() method to retrieve the current context in documented and predictable way.

Upon iterating over the collection of things to generate DAGs for, you can use the context to determine whether you need to generate all DAG objects (when parsing in the DAG File processor), or to generate only a single DAG object (when executing the task).

The get_parsing_context() return the current parsing context. The context is of AirflowParsingContext and in case only single DAG/task is needed, it contains dag_id and task_id fields set. In case “full” parsing is needed (for example in DAG File Processor), dag_id and task_id of the context are set to None.

from airflow.models.dag import DAG
from airflow.utils.dag_parsing_context import get_parsing_context

current_dag_id = get_parsing_context().dag_id

for thing in list_of_things:
    dag_id = f"generated_dag_{thing}"
    if current_dag_id is not None and current_dag_id != dag_id:
        continue  # skip generation of non-selected DAG

    with DAG(dag_id=dag_id, ...):

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