airflow-plugin-watchdog

v0.6.4 safe
3.0
Low Risk

A lightweight, zero-dependency Airflow plugin that monitors DAG/task health by querying the metadata DB — runtime anomalies, failure spikes, missed deadlines, stuck tasks, and schedule anomalies.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories with no network calls, shell executions, or obfuscations detected. The metadata risk is slightly elevated due to sparse author details, but it does not indicate any malicious intent.

  • No network calls detected
  • Sparse author details
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's details are sparse, but there are no other suspicious indicators.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 11 test file(s) detected (e.g. _seed.py)
◈ Medium Documentation 7.0

Some documentation present

  • 1 documentation file(s) (e.g. gen_preview.py)
  • Detailed PyPI description (17259 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 47 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 46 commits in Redevil10/airflow-plugin-watchdog
  • Single author but highly active (46 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Redevil10/airflow-plugin-watchdog 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 airflow-plugin-watchdog 
Develop a monitoring tool for Apache Airflow using the 'airflow-plugin-watchdog' package. This tool will help administrators and operators quickly identify issues within their DAGs and tasks, such as runtime anomalies, failure spikes, missed deadlines, stuck tasks, and schedule anomalies. The application should have a user-friendly interface, ideally a web-based dashboard, where users can view real-time status updates of their workflows.

Steps to develop the project:
1. Set up your development environment with Python and necessary libraries, including 'airflow-plugin-watchdog'.
2. Design the architecture of your application, focusing on how it will interact with the Airflow metadata database to retrieve task and DAG information.
3. Implement the core functionalities of the watchdog, which includes monitoring for the mentioned anomalies and alerting mechanisms (e.g., email, Slack).
4. Develop a web-based dashboard using Flask or Django, integrating it with the watchdog functionalities to display real-time data.
5. Test the application thoroughly under different scenarios to ensure it works as expected.
6. Document the setup process, configuration options, and usage instructions for end-users.

Suggested Features:
- Real-time visualization of DAG and task statuses.
- Historical data analysis for trend identification.
- Customizable alerts based on anomaly detection thresholds.
- Integration with popular notification services like Slack, PagerDuty, etc.
- Support for filtering and searching through DAGs and tasks.

How 'airflow-plugin-watchdog' is Utilized:
- Use 'airflow-plugin-watchdog' to periodically query the Airflow metadata database for the latest state of DAGs and tasks.
- Leverage its ability to detect runtime anomalies, failure spikes, missed deadlines, stuck tasks, and schedule anomalies to trigger alerts and update the dashboard accordingly.