apache-airflow-providers-apache-livy

v4.5.6 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators with no detected network or shell risks and minimal obfuscation and metadata issues. These factors do not suggest a supply-chain attack.

  • Low network and shell risk
  • No evidence of credential harvesting
  • Metadata and obfuscation issues are minor and likely benign
Per-check LLM notes
  • Network: None detected. This is expected for the package as it does not inherently require network calls.
  • Shell: None detected.
  • Obfuscation: The observed pattern is likely a standard practice for extending module search paths and not indicative of malicious activity.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The package contains a non-secure external link and has an author with incomplete information, which raises some concerns but does not strongly indicate malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

  • Test runner config found: conftest.py
  • 16 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 (3700 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
  • 36 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-apache-livy 
Create a fully functional mini-application that integrates Apache Airflow with Apache Livy for managing Spark jobs on a cluster. This application will serve as a bridge between Airflow tasks and Livy endpoints, allowing users to submit, monitor, and manage Spark jobs directly from Airflow DAGs.

### Application Requirements:
1. **User Interface**: Develop a simple web-based UI where users can input Spark job configurations (such as Spark version, file paths, and job parameters).
2. **Job Submission**: Implement functionality within Airflow to submit these configurations to a Livy endpoint, initiating the execution of Spark jobs.
3. **Job Monitoring**: Integrate real-time monitoring capabilities to track the status of submitted jobs, including job start time, end time, and any errors encountered during execution.
4. **Error Handling**: Ensure robust error handling mechanisms are in place to gracefully manage failed jobs and provide informative feedback to the user.
5. **Results Retrieval**: Once a job completes successfully, implement a feature to retrieve and display the results back to the user via the UI.

### Utilizing 'apache-airflow-providers-apache-livy':
- **Task Definitions**: Use the `apache-airflow-providers-apache-livy` package to define Airflow operators that interact with Livy endpoints. These operators should encapsulate the logic for submitting Spark jobs, monitoring their statuses, and retrieving their outputs.
- **Configuration Management**: Leverage Airflow's configuration capabilities to manage Livy endpoint URLs, authentication details, and other necessary settings.
- **Custom Operators**: Consider extending existing operators provided by the package to better fit specific use cases, such as adding custom logging or result processing steps.

### Additional Features (Optional):
- **Batch Processing Support**: Allow users to schedule multiple jobs at once, ensuring they run sequentially or in parallel based on user preferences.
- **Resource Management**: Provide tools for managing Spark resources, such as setting memory and CPU allocations for jobs.
- **Security Enhancements**: Incorporate advanced security features like role-based access control for different users or groups.

This project aims to streamline the process of running Spark jobs through Apache Airflow, making it more accessible and efficient for data engineers and analysts.

💬 Discussion Feed

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