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Dag integrity test

As explained in Data Pipelines with Apache Airflow, Second Edition

A DAG integrity test is a test that validates the structural correctness of Airflow DAG definitions, including acyclicity and unique task identifiers.

A DAG integrity test catches structural mistakes before deployment. It validates that Airflow DAGs are correct, acyclic, and free of duplicate task identifiers. Airflow performs similar checks while loading DAGs, but running them in a test suite exposes failures earlier. section 10.1.1

Catch a cycle with pytest

A workflow containing t1 >> t2 >> t3 >> t1 violates DAG acyclicity. A parameterized pytest test can discover every DAG file, instantiate it through DagBag(dag_folder=dag_file, include_examples=False), and convert logged errors into test failures. A detected cycle raises AirflowDagCycleException and identifies the faulty task.

A practical test suite can:

  • Run pytest tests locally or in CI/CD to catch defects before deployment. section 10.5

How Data Pipelines with Apache Airflow, Second Edition covers it

How deep it goes
Demonstrates recursive discovery, pytest parameterization, DagBag instantiation, log inspection, and cycle diagnostics, but not operator unit tests.
Examples
Uses a three-task cycle, duplicate task identifiers from a loop, missing DAG files, and a faulty BashOperator instantiation.
What it assumes
Assumes Airflow DAG definitions and introduces pytest installation, test-file organization, and basic pytest execution.
What you can do after
Diagnose/debug structural DAG failures with pytest, DagBag logs, and Airflow exception messages before deployment.
How it connects
Connects Airflow DAGs with pytest, DagBag, unit testing, integration testing, and CI/CD pipelines.
Cover of Data Pipelines with Apache Airflow, Second Edition
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Data Pipelines with Apache Airflow, Second Edition. The section links point to the parts of the book it draws on.