apache-airflow-providers-apache-tinkerpop

v1.1.3 safe
3.0
Low Risk

Provider package apache-airflow-providers-apache-tinkerpop for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with it, with no network calls, shell executions, or credential harvesting activities detected. While there is some obfuscation and metadata risk, these do not indicate any malicious intent.

  • No network calls detected.
  • No shell execution detected.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external services.
  • Shell: No shell execution detected, indicating no immediate risk of executing system commands.
  • Obfuscation: The observed pattern is likely a standard method for extending package paths and not indicative of malicious obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were detected.
  • Metadata: The package shows some minor red flags but lacks clear signs of malice.

📦 Package Quality Overall: Medium (7.4/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

  • Test runner config found: conftest.py
  • 15 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-apa
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3723 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
✦ 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-apache-tinkerpop 
Create a mini-application that leverages the 'apache-airflow-providers-apache-tinkerpop' package to manage and automate workflows involving Apache TinkerPop-based graph databases. Your application will serve as a bridge between Apache Airflow and Apache TinkerPop, allowing users to define, schedule, and monitor tasks that interact with graph databases using Gremlin queries. Here’s a detailed plan on how to approach this project:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with necessary dependencies including Apache Airflow, TinkerPop, and the 'apache-airflow-providers-apache-tinkerpop' package.
2. **Define Workflow Tasks**: Design several workflow tasks that can be executed against a TinkerPop-enabled graph database. These tasks could include operations such as creating vertices, adding edges, querying data, updating properties, etc., all performed via Gremlin queries.
3. **Implement Task Operators**: Using the 'apache-airflow-providers-apache-tinkerpop' package, implement custom operators in Apache Airflow that encapsulate these tasks. Each operator should be able to connect to a specified graph database, execute the appropriate Gremlin query, and handle any responses or errors.
4. **Build DAGs**: Create Directed Acyclic Graphs (DAGs) in Apache Airflow that utilize these custom operators. These DAGs should represent different scenarios of interacting with a graph database, such as periodic data synchronization between two databases, scheduled data cleanup, or automated data enrichment processes.
5. **User Interface**: Develop a simple user interface that allows users to select and trigger specific DAGs. This UI could be a web-based frontend that interacts with Apache Airflow's API to start, stop, and monitor DAG executions.
6. **Monitoring & Logging**: Integrate comprehensive monitoring and logging capabilities into your application. Users should be able to view the status of their DAG runs, see detailed logs of executed tasks, and receive alerts for any failures or issues.
7. **Testing & Documentation**: Finally, ensure thorough testing of all components and functionalities. Provide clear documentation on how to set up, use, and extend the application, including examples of how to write custom operators and DAGs.

By following these steps, your mini-application will become a powerful tool for automating and managing workflows involving Apache TinkerPop-based graph databases.

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