apache-airflow-providers-common-compat

v1.15.0 suspicious
4.0
Medium Risk

Provider package apache-airflow-providers-common-compat for Apache Airflow

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is from a reputable source, Apache, but it lacks detailed metadata such as author details and contains a non-secure external link, which could be a potential vector for issues.

  • Lack of author details
  • Presence of non-secure external link
Per-check LLM notes
  • Metadata: The package has a non-secure external link and lacks author details, but no clear signs of typosquatting or malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 20 test file(s) found

  • Test runner config found: conftest.py
  • 20 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-com
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3861 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
  • 6 type-annotated function signatures (partial)
✦ 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 8.0

Found 4 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
  • try: module = __import__(new_path, fromlist=[name]) return getattr(module, name) ex
  • try: module = __import__(old_path, fromlist=[old_name]) return getattr(module, old_name)
  • module = __import__(module_path, fromlist=[name]) return getattr(module, name)
✓ 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-common-compat 
Your task is to develop a mini-scheduler application using Python that integrates with Apache Airflow through the 'apache-airflow-providers-common-compat' package. This scheduler will allow users to define simple tasks that need to run periodically, and it will manage these tasks using the capabilities provided by the Apache Airflow ecosystem. Here’s a detailed breakdown of what your application should achieve:

1. **Task Definition**: Users should be able to define tasks within the application. Each task should have a name, a description, a schedule (e.g., every hour, daily at midnight), and a command or function to execute.
2. **Scheduling Engine**: Utilize the 'apache-airflow-providers-common-compat' package to interact with Apache Airflow's scheduling engine. This will ensure that tasks are scheduled and executed according to their defined schedules.
3. **Task Status Tracking**: Implement a feature to track the status of each task (e.g., pending, running, completed, failed). Users should be able to view the status of all tasks in real-time.
4. **Notifications**: When a task fails or completes, send a notification to the user via email or another preferred method. Notifications should include details about the task execution such as start time, end time, and any errors encountered.
5. **User Interface**: Develop a basic web interface using Flask or a similar framework to allow users to define tasks, monitor task statuses, and receive notifications.
6. **Security**: Ensure that user data and task configurations are securely stored and transmitted. Implement basic security measures like encryption for sensitive data.
7. **Documentation**: Provide clear documentation on how to set up and use the application, including how to integrate with Apache Airflow and configure the 'apache-airflow-providers-common-compat' package.

The 'apache-airflow-providers-common-compat' package is crucial for enabling seamless integration with Apache Airflow. Use its functionalities to handle task definitions, scheduling, and execution. Additionally, leverage other Apache Airflow components as necessary to enhance the robustness and reliability of your scheduler application.

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