astrocyte-ingestion-redis

v0.15.0 suspicious
4.0
Medium Risk

Redis Streams IngestSource adapter for Astrocyte

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a low risk profile with no network calls, shell executions, or credential harvesting attempts. However, its low maintenance effort and lack of author information raise concerns about potential abandonment or poor governance.

  • Low maintenance effort indicated by metadata.
  • Lack of author information.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no immediate risk of command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate threat to secrets or credentials.
  • Metadata: The package shows low maintenance effort and lacks an author's name, which raises some suspicion but does not definitively indicate malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_redis_stream_source.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1165 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

  • 4 type-annotated function signatures (partial)
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with astrocyte-ingestion-redis 
Create a real-time data streaming application using the Python package 'astrocyte-ingestion-redis'. This application will serve as a bridge between various data sources and a Redis database, utilizing the power of Redis Streams for efficient data ingestion. Your task is to develop a mini-application that allows users to ingest data from different sources into Redis Streams and monitor the data flow in real-time. Here’s a step-by-step guide on how to proceed:

1. **Setup Environment**: Begin by setting up your development environment. Install Python and necessary libraries including 'astrocyte-ingestion-redis', Redis server, and any other dependencies you might need.
2. **Data Sources Integration**: Integrate multiple data sources such as CSV files, live API endpoints, or even a simple user input form. Each source should be capable of generating data streams that can be ingested into Redis.
3. **Ingestion Logic**: Use 'astrocyte-ingestion-redis' to create ingestion pipelines for each data source. Ensure that the data is properly formatted and streamed into Redis Streams according to the specifications provided by the package documentation.
4. **Real-Time Monitoring**: Implement a feature within the application that allows users to monitor the data being ingested in real-time. This could include displaying statistics about the data flow, such as the number of messages per second, total messages ingested, etc.
5. **Visualization**: For better understanding, provide visual representations of the data flow. This could be achieved through simple charts or more advanced visualization tools like Matplotlib or Plotly.
6. **Error Handling & Logging**: Ensure robust error handling and logging mechanisms are in place to track any issues during the data ingestion process. Logs should capture important details such as timestamps, error types, and error messages.
7. **User Interface**: Develop a user-friendly interface where users can interact with the application, choose data sources, and view real-time data monitoring and visualizations.
8. **Documentation**: Finally, write comprehensive documentation for your application. Include setup instructions, usage guidelines, and explanations of how 'astrocyte-ingestion-redis' is utilized in your project.

By following these steps, you will create a powerful tool for real-time data ingestion and monitoring, leveraging the capabilities of 'astrocyte-ingestion-redis'.

💬 Discussion Feed

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