aiotrino-patched

v0.3.5 suspicious
4.0
Medium Risk

Asyncio client for the Trino distributed SQL Engine

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate obfuscation and limited metadata, raising concerns about its origin and intent. While network and shell risks are low, the lack of maintainer details and low repository engagement warrant further investigation.

  • moderate obfuscation risk
  • minimal maintainer information
Per-check LLM notes
  • Network: Network calls to Trino indicate the package is likely designed for database interaction, which is expected behavior.
  • Shell: No shell execution patterns detected; this aligns with non-malicious intent.
  • Obfuscation: The observed patterns suggest potential obfuscation through base64 decoding, which could be used for malicious purposes but may also be part of normal functionality in some applications.
  • Credentials: No clear evidence of credential harvesting patterns detected.
  • Metadata: The package has minimal maintainer information and low engagement in the git repository, raising concerns about its legitimacy.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/gledi-ai/aiotrino
  • Detailed PyPI description (9824 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

  • 164 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 16 unique contributor(s) across 100 commits in gledi-ai/aiotrino
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • self._http_session = aiohttp.ClientSession( connector=aiotrino.client.TrinoTCPConnector
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • elf) -> bytes: return base64.b64decode(self._segment["data"]) def __repr__(self): retu
  • return None return base64.b64decode(value.encode("utf8")) class DateValueMapper(ValueMapper[da
✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 aiotrino-patched 
Create a Python-based utility application named 'TrinoQueryRunner' that leverages the 'aiotrino-patched' package to interact with a Trino cluster. This application should allow users to execute complex SQL queries asynchronously against a Trino database, retrieve query results, and optionally visualize the data using basic plots. Here’s how you can structure your application:

1. **Setup**: Begin by installing the necessary packages including 'aiotrino-patched', 'pandas', and 'matplotlib'. These will help manage asynchronous connections to Trino, handle data in a tabular format, and visualize the data respectively.

2. **Connection Management**: Implement a class named `TrinoClient` which handles establishing a connection to the Trino server. Use the `aiotrino-patched` package to create an async connection object. Ensure the class includes methods for connecting, disconnecting, and executing queries.

3. **Query Execution**: Develop a method within the `TrinoClient` class called `execute_query`. This method should accept a SQL query string as input, execute it against the Trino server asynchronously, and return the results in a pandas DataFrame for easy manipulation and analysis.

4. **Data Visualization**: Integrate basic plotting capabilities into your application. After fetching data from Trino, use matplotlib to plot simple graphs such as line charts, bar charts, or scatter plots based on user input or predefined configurations.

5. **User Interface**: Although command-line interface (CLI) is acceptable, consider adding a simple GUI using libraries like PyQt or Tkinter to make the tool more user-friendly. This UI should allow users to enter their Trino credentials, input SQL queries, select visualization types, and display the resulting graphs.

6. **Error Handling and Logging**: Implement robust error handling and logging mechanisms to ensure that any issues during the connection process or query execution are appropriately logged and displayed to the user.

7. **Documentation and Testing**: Finally, write comprehensive documentation explaining how to set up and use the application, along with examples of SQL queries and expected outputs. Additionally, include unit tests to verify the functionality of each component of your application.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!