dag-factory（达格工厂）

DAG Factory

You are helping a user build Apache Airflow DAGs declaratively with dag-factory, a library that turns YAML configuration files into Airflow DAGs. Execute steps in order and prefer the simplest configuration that meets the user's needs.

Package: dag-factory on PyPI Repo: https://github.com/astronomer/dag-factory Docs: https://astronomer.github.io/dag-factory/latest/ Targets: dag-factory v1.0+ only. For pre-1.0 projects, see reference/migration.md before applying any guidance from this skill. Requires: Python 3.10+, Airflow 2.4+ (Airflow 3 supported)

Before Starting

Confirm with the user:

1. Airflow version ≥2.4
2. Python version ≥3.10
3. dag-factory version: this skill targets v1.0+. If the project is on <1.0, follow reference/migration.md to upgrade before continuing.
4. Use case: dag-factory is for declarative, low-code DAG authoring. If the user needs reusable, validated Pythonic templates with Pydantic, suggest blueprint instead. If they need full Python flexibility, suggest the authoring-dags skill.

Determine What the User Needs

Project Setup

1. Install the Package

Add to requirements.txt:

dag-factory>=1.0.0

dag-factory does not install Airflow providers automatically. Install any provider packages your YAML references (e.g., apache-airflow-providers-slack, apache-airflow-providers-cncf-kubernetes).

2. Create the Loader

Create dags/load_dags.py so Airflow's DAG processor will pick it up:

import os
from pathlib import Path

from dagfactory import load_yaml_dags

CONFIG_ROOT_DIR = Path(os.getenv("CONFIG_ROOT_DIR", "/usr/local/airflow/dags/"))

# Option A: load every *.yml / *.yaml under a folder
load_yaml_dags(globals_dict=globals(), dags_folder=str(CONFIG_ROOT_DIR))

# Option B: load a single file
# load_yaml_dags(globals_dict=globals(), config_filepath=str(CONFIG_ROOT_DIR / "my_dag.yml"))

# Option C: load from an in-Python dict
# load_yaml_dags(globals_dict=globals(), config_dict={...})

globals_dict=globals() is required so generated DAG objects are registered into the module namespace where Airflow can discover them.

3. Verify Installation

dagfactory --version

Defining a DAG in YAML

Each top-level YAML key (other than default) defines a DAG. The key becomes the dag_id. Use the list format for tasks and task_groups — it is the recommended format since v1.0.0.

# dags/example_dag_factory.yml
default:
  default_args:
    start_date: 2024-11-11

basic_example_dag:
  default_args:
    owner: "custom_owner"
  description: "this is an example dag"
  schedule: "0 3 * * *"
  catchup: false
  task_groups:
    - group_name: "example_task_group"
      tooltip: "this is an example task group"
      dependencies: [task_1]
  tasks:
    - task_id: "task_1"
      operator: airflow.operators.bash.BashOperator
      bash_command: "echo 1"
    - task_id: "task_2"
      operator: airflow.operators.bash.BashOperator
      bash_command: "echo 2"
      dependencies: [task_1]
    - task_id: "task_3"
      operator: airflow.operators.bash.BashOperator
      bash_command: "echo 3"
      dependencies: [task_1]
      task_group_name: "example_task_group"

Key Fields

Tasks do not need to be ordered by dependency in the YAML — dag-factory resolves the DAG topology.

Dictionary Format (Legacy)

Pre-1.0 dictionary format (where tasks is a dict keyed by task_id) still works for backward compatibility, but prefer the list format for new code.

Defaults

There are four ways to set defaults, in precedence order (highest first):

1. default_args / DAG-level keys inside an individual DAG
2. The top-level default: block in the same YAML file
3. defaults_config_dict= argument to load_yaml_dags
4. A defaults.yml (or defaults.yaml) file via defaults_config_path= (or auto-detected next to the DAG YAML)

Note: loader argument names and several other field names changed in v1.0.0. See reference/migration.md if you're working on an older project.

default Block in the Same File

Powerful for templating multiple DAGs from one file:

default:
  default_args:
    owner: "data-team"
    start_date: 2025-01-01
    retries: 2
  catchup: false
  schedule: "@daily"

dag_one:
  description: "first DAG"
  tasks:
    - task_id: t1
      operator: airflow.operators.bash.BashOperator
      bash_command: "echo one"

dag_two:
  description: "second DAG"
  tasks:
    - task_id: t1
      operator: airflow.operators.bash.BashOperator
      bash_command: "echo two"

defaults.yml File

Place a defaults.yml next to the DAG YAML, or point defaults_config_path at a parent directory. dag-factory merges all defaults.yml files walking up the directory tree, with the file closest to the DAG YAML winning. DAG-level args (e.g. schedule, catchup) go at the root of defaults.yml; per-task defaults go under default_args.

# defaults.yml
schedule: 0 1 * * *
catchup: false
default_args:
  start_date: '2024-12-31'
  owner: data-team

Custom & Provider Operators

Reference any operator by its full Python import path. dag-factory passes all other task keys as kwargs to that operator.

tasks:
  - task_id: begin
    operator: airflow.providers.standard.operators.empty.EmptyOperator
  - task_id: make_bread
    operator: customized.operators.breakfast_operators.MakeBreadOperator
    bread_type: 'Sourdough'

The operator's package must be installed and importable. For Airflow 3, prefer airflow.providers.standard.operators.* over the legacy airflow.operators.* paths — the dagfactory convert CLI rewrites these automatically.

KubernetesPodOperator

Specify the operator path and pass kwargs directly. As of v1.0, dag-factory no longer does legacy type casting — use __type__ for nested k8s objects.

tasks:
  - task_id: hello-world-pod
    operator: airflow.providers.cncf.kubernetes.operators.pod.KubernetesPodOperator
    image: "python:3.12-slim"
    cmds: ["python", "-c"]
    arguments: ["print('hi')"]
    name: example-pod
    namespace: default
    container_resources:
      __type__: kubernetes.client.models.V1ResourceRequirements
      limits: {cpu: "1", memory: "1024Mi"}
      requests: {cpu: "0.5", memory: "512Mi"}

Dynamic Task Mapping

Use expand and partial keys on a task to map dynamically. dag-factory has two distinct ways to reference an upstream task's output:

• task_id.output — XCom-style reference, used inside expand op_args / op_kwargs (and the equivalent kwargs of other operators).
• +task_id — bare value reference, used when the value sits directly under expand (e.g. expand: {number: +numbers_list}) or as a TaskFlow decorator argument.

Don't mix them: +request won't resolve inside op_args, and request.output won't resolve as a bare expand value.

dynamic_task_map:
  default_args:
    start_date: 2025-01-01
  schedule: "0 3 * * *"
  tasks:
    - task_id: request
      operator: airflow.providers.standard.operators.python.PythonOperator
      python_callable_name: make_list
      python_callable_file: $CONFIG_ROOT_DIR/expand_tasks.py
    - task_id: process
      operator: airflow.providers.standard.operators.python.PythonOperator
      python_callable_name: consume_value
      python_callable_file: $CONFIG_ROOT_DIR/expand_tasks.py
      partial:
        op_kwargs:
          fixed_param: "test"
      expand:
        op_args: request.output    # XCom-style — used inside op_args / op_kwargs
      dependencies: [request]

Bare-value form (TaskFlow decorator tasks, or any non-op_args mapping):

tasks:
  - task_id: numbers_list
    decorator: airflow.sdk.definitions.decorators.task
    python_callable: sample.build_numbers_list
  - task_id: double_number
    decorator: airflow.sdk.definitions.decorators.task
    python_callable: sample.double
    expand:
      number: +numbers_list   # + resolves to upstream task `numbers_list`'s XComArg

For named map indices (Airflow 2.9+), set map_index_template: "{{task.custom_mapping_key}}" and have the callable assign context["custom_mapping_key"].

Tested patterns: simple mapping, task-generated mapping, repeated mapping, partial, multiple-parameter mapping, map_index_template. Unsupported / untested: mapping over task groups, zipping, transforming expanding data.

Datasets

Use inlets / outlets on tasks to declare dataset producers, and a list of dataset URIs as schedule to consume them.

producer_dag:
  default_args:
    start_date: '2024-01-01'
  schedule: "0 5 * * *"
  catchup: false
  tasks:
    - task_id: task_1
      operator: airflow.operators.bash.BashOperator
      bash_command: "echo 1"
      outlets: ['s3://bucket_example/raw/dataset1.json']

consumer_dag:
  default_args:
    start_date: '2024-01-01'
  schedule: ['s3://bucket_example/raw/dataset1.json']
  catchup: false
  tasks:
    - task_id: task_1
      operator: airflow.operators.bash.BashOperator
      bash_command: "echo 'consumer'"

Conditional Dataset Scheduling (Airflow 2.9+ / dag-factory 0.22+)

Nesting the logical operators __and__ / __or__ under datasets key.

schedule:
  datasets:
    __or__:
      - __and__:
          - s3://bucket-cjmm/raw/dataset_custom_1
          - s3://bucket-cjmm/raw/dataset_custom_2
      - s3://bucket-cjmm/raw/dataset_custom_3

Callbacks

Three styles, all valid at the DAG, TaskGroup, or Task level (or under default_args):

1. String pointing to a callable

- task_id: task_1
  operator: airflow.operators.bash.BashOperator
  bash_command: "echo task_1"
  on_failure_callback: include.custom_callbacks.output_standard_message

With kwargs:

- task_id: task_2
  operator: airflow.operators.bash.BashOperator
  bash_command: "echo task_2"
  on_success_callback:
    callback: include.custom_callbacks.output_custom_message
    param1: "Task status"
    param2: "Successful!"

2. File path + function name (no kwargs)

- task_id: task_3
  operator: airflow.operators.bash.BashOperator
  bash_command: "echo task_3"
  on_retry_callback_name: output_standard_message
  on_retry_callback_file: /usr/local/airflow/include/custom_callbacks.py

3. Provider callbacks

- task_id: task_4
  operator: airflow.operators.bash.BashOperator
  bash_command: "echo task_4"
  on_failure_callback:
    callback: airflow.providers.slack.notifications.slack.send_slack_notification
    slack_conn_id: slack_conn_id
    text: ":red_circle: Task Failed."
    channel: "#channel"

The provider package must be installed.

Custom Python Objects (__type__)

For anything that isn't a simple scalar — datetime, timedelta, Asset, timetables, k8s objects — use the generalized object syntax:

start_date:
  __type__: datetime.datetime
  year: 2025
  month: 1
  day: 1

execution_timeout:
  __type__: datetime.timedelta
  hours: 1

schedule:
  __type__: airflow.timetables.trigger.CronTriggerTimetable
  cron: "0 1 * * 3"
  timezone: UTC

• __type__ is the full import path to the class
• __args__ is a list of positional arguments
• Other keys become keyword arguments
• For lists of typed objects, use __type__: builtins.list with an items: key

Reserved Keys

Don't use these YAML keys for your own data — dag-factory reserves them: __type__, __args__, __join__, __and__, __or__. The key items is also reserved when used inside a __type__: builtins.list block — don't add a custom field named items to a typed list construction.

Validation Commands

After installing, the dagfactory CLI is on PATH:

Validation Workflow

# 1. Lint YAML
dagfactory lint dags/

# 2. Have Airflow parse to catch operator/import errors
#    (Astro CLI users)
astro dev parse

dagfactory lint only checks YAML syntax — operator import errors and missing kwargs surface at Airflow parse time.

Troubleshooting

"Operator not found" / ModuleNotFoundError

Cause: Provider package not installed, or wrong import path.

Fix: Install the provider (pip install apache-airflow-providers-...) and verify the path. For Airflow 3, run dagfactory convert to update legacy airflow.operators.* paths to airflow.providers.standard.operators.*.

YAML parses but the DAG doesn't appear in Airflow

Cause: Loader file missing or globals_dict=globals() not passed.

Fix: Ensure a Python file in dags/ calls load_yaml_dags(globals_dict=globals(),...). Check astro dev parse (or airflow dags list-import-errors) for parse errors.

"Argument is not JSON-serializable" / wrong kwarg type

Cause: A scalar string is being passed where a Python object is expected (e.g. start_date: "2025-01-01" for a field that needs datetime).

Fix: Use __type__: datetime.datetime (or datetime.timedelta etc.) per Custom Python Objects.

Conditional dataset schedule ignored

Cause: Airflow <2.9, dag-factory <0.22, or using legacy !and/!or keys.

Fix: Upgrade and rename to __and__ / __or__.

Multiple defaults.yml not merging as expected

Cause: defaults_config_path not pointing at a parent directory of the DAG YAML.

Fix: Set defaults_config_path to the highest ancestor folder you want included; dag-factory walks the tree from DAG file → ancestor and merges in that order, with files closer to the DAG winning.

Verification Checklist

Before finishing, verify with the user:

• dagfactory lint dags/ passes
• Loader file exists in dags/ and calls load_yaml_dags(globals_dict=globals(),...)
• Required Airflow providers are in requirements.txt
• DAG appears in Airflow UI without import errors

Related Skills

• authoring-dags — Writing Airflow DAGs in pure Python with af CLI validation. Use when YAML can't express what you need.
• testing-dags: For testing DAGs, debugging failures, and the test -> fix -> retest loop
• debugging-dags: For troubleshooting failed DAGs

Reference

• GitHub: https://github.com/astronomer/dag-factory
• Docs: https://astronomer.github.io/dag-factory/latest/
• PyPI: https://pypi.org/project/dag-factory/
• Migration Guide: https://astronomer.github.io/dag-factory/latest/migration_guide/