apache-airflow-providers-cncf-kubernetes

v10.17.1 safe
3.0
Low Risk

Provider package apache-airflow-providers-cncf-kubernetes for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across all categories, with only minor issues related to metadata and obfuscation techniques that do not indicate malicious intent.

  • Low network and shell risk
  • Minor obfuscation and metadata issues
  • No evidence of credential risk or supply-chain attack
Per-check LLM notes
  • Network: No network calls detected; expected for this type of package.
  • Shell: No shell execution patterns detected; expected for this type of package.
  • Obfuscation: The observed pattern is likely a standard technique for extending module search paths and not indicative of malicious activity.
  • Credentials: No suspicious patterns indicating credential harvesting were detected.
  • Metadata: The package has some minor issues with maintainer history and a non-secure link, but no clear signs of malice.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 34 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • 34 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-cnc
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4026 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 15 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 46 unique contributor(s) across 100 commits in apache/airflow
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: airflow.apache.org>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository apache/airflow appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with apache-airflow-providers-cncf-kubernetes 
Create a mini-application using Apache Airflow and the 'apache-airflow-providers-cncf-kubernetes' package to automate the deployment and management of Kubernetes jobs. This application will allow users to define and schedule Kubernetes jobs directly from Airflow's UI, providing a seamless experience for executing tasks on Kubernetes clusters. Here’s a detailed breakdown of the project requirements and steps:

1. **Setup Environment**: Ensure you have Docker and Kubernetes installed locally or access to a remote Kubernetes cluster. Install Apache Airflow and the 'apache-airflow-providers-cncf-kubernetes' package.
2. **Define Tasks**: Develop Python operators within Airflow that leverage KubernetesPodOperator to submit jobs to your Kubernetes cluster. These jobs could include data processing scripts, machine learning model training, or any other task that benefits from Kubernetes orchestration.
3. **Job Scheduling**: Create DAGs (Directed Acyclic Graphs) in Airflow that utilize these operators to schedule the execution of Kubernetes jobs at specific intervals or based on certain conditions.
4. **Monitoring & Logging**: Implement functionality to monitor the status of submitted jobs and log their outputs back into Airflow for easy tracking and debugging.
5. **User Interface**: Utilize Airflow’s web interface to allow users to visualize the DAGs, trigger jobs manually, and view job logs and statuses.
6. **Security Considerations**: Address security aspects such as authentication, authorization, and encryption when interacting with the Kubernetes API server.
7. **Testing & Deployment**: Write unit tests for your Airflow DAGs and operators to ensure reliability. Deploy the application to a production-like environment for testing.
8. **Documentation**: Provide comprehensive documentation covering installation, configuration, usage, and best practices for maintaining the application.

Suggested Features:
- Support for dynamic job creation based on input parameters.
- Integration with cloud storage services like S3 or GCS for storing job artifacts.
- Automated cleanup of completed or failed jobs to maintain cluster efficiency.
- Enhanced logging with structured logging formats for better readability and analysis.
- Customizable retry policies for failed jobs.

The 'apache-airflow-providers-cncf-kubernetes' package plays a crucial role in this project by enabling the creation and management of Kubernetes jobs through Airflow. It abstracts away much of the complexity involved in interacting with Kubernetes APIs, allowing developers to focus on defining the logic and flow of their workflows.

💬 Discussion Feed

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