json2.5 KB

Airflow-Shaped Serialized DAG (JSON)

The serialized-DAG shape Airflow stores in its metadata database, as standalone JSON: per-task metadata with explicit downstream_task_ids. Carries the same six-task topology as the Python, Argo, Graphviz and Mermaid fixtures.

Graph data Data engineering Conversion testing

Preview, first 50 linesjson

{
  "__version": 1,
  "dag": {
    "_dag_id": "orders_etl",
    "description": "Nightly orders extract, validate, transform and publish.",
    "schedule_interval": "17 2 * * *",
    "timezone": "UTC",
    "start_date": "2026-01-01T00:00:00+00:00",
    "catchup": false,
    "max_active_runs": 1,
    "tags": [
      "orders",
      "etl",
      "example"
    ],
    "default_args": {
      "owner": "example-data-team",
      "retries": 2,
      "depends_on_past": false
    },
    "fileloc": "dags/orders_etl.py",
    "tasks": [
      {
        "task_id": "ingest_orders",
        "_task_type": "EmptyOperator",
        "_task_module": "airflow.operators.empty",
        "ui_color": "#e8f0fe",
        "pool": "default_pool",
        "retries": 2,
        "trigger_rule": "all_success",
        "downstream_task_ids": [
          "validate_orders"
        ]
      },
      {
        "task_id": "validate_orders",
        "_task_type": "EmptyOperator",
        "_task_module": "airflow.operators.empty",
        "ui_color": "#e8f0fe",
        "pool": "default_pool",
        "retries": 2,
        "trigger_rule": "all_success",
        "downstream_task_ids": [
          "transform_orders"
        ]
      },
      {
        "task_id": "transform_orders",
        "_task_type": "EmptyOperator",
        "_task_module": "airflow.operators.empty",

… 98 lines total: download for the full file.

Specifications

System: Airflow (shape only)
Tasks: 6
Edges: 6
Downstream Lists Per Task: true
Schedule Interval: 17 2 * * *
Topological Order: ingest_orders, validate_orders, transform_orders, load_warehouse, refresh_dashboard, notify_owner

Testing contract

Expected to pass

Scenario: Rebuild a DAG from serialized JSON in which edges live on each task as downstream ids
Expected result: Six tasks yield six edges, and only notify_owner carries trigger_rule all_done

What is a .json file?

JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.

How to use this file

Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.

How to use this file for testing

“Airflow-Shaped Serialized DAG (JSON)” is a deterministic Testaroo fixture for Graph data, Data engineering, Conversion testing. Node/edge datasets in GraphML and GEXF (directed and undirected, with attributes and weights), for testing network importers, layout tools, and graph converters.

Documented properties for this file: 6 edges. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

Pipeline and infrastructure fixtures are inert configuration: steps reference fictional images and scripts, and nothing here executes. Run your linter, schema validator, migrator, or policy engine against them, and expect the deprecated-syntax and intentionally invalid variants to be rejected.

Code examples

import json

with open("airflow-serialized-dag.json") as f:
    data = json.load(f)
print(type(data), len(data))

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