apache-flink-libraries

v2.2.1 suspicious
4.0
Medium Risk

Apache Flink Libraries

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network, shell, obfuscation, and credential risks. However, the metadata risk score is elevated due to the author's lack of an associated GitHub repository and having only one published package, which raises suspicion.

  • Low risk scores across network, shell, obfuscation, and credential checks.
  • Elevated metadata risk due to single package from author with no associated GitHub repository.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a library focused on local processing.
  • Shell: No shell execution detected, indicating no unexpected system command executions.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has only one package and no associated GitHub repository, which could indicate a less established or potentially suspicious account.

📦 Package Quality Overall: Low (2.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (466 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ 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

Email domain looks legitimate: flink.apache.org

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Apache Software Foundation" 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-flink-libraries 
Develop a real-time data processing application using Apache Flink Libraries in Python. Your application should be capable of ingesting streaming data from multiple sources, performing complex transformations on the data in real-time, and then outputting the processed data to a database or another stream. Here’s a detailed breakdown of the project requirements:

1. **Project Overview**: Create a mini-application that monitors live financial market data streams from various sources (simulated or real). The application should process this data in real-time, identifying trends, calculating moving averages, and detecting anomalies.

2. **Data Sources**: Utilize simulated financial market data streams. You can generate these streams using tools like Kafka or RabbitMQ, simulating different types of financial data such as stock prices, cryptocurrency values, or commodity prices.

3. **Core Features**:
   - **Real-Time Data Ingestion**: Use Apache Flink to ingest data from the chosen data source(s).
   - **Data Processing**: Implement real-time calculations such as moving averages over a sliding window, trend analysis, and anomaly detection based on predefined thresholds.
   - **Output**: Store the processed data into a PostgreSQL database or output it to another stream for further analysis.

4. **Implementation Steps**:
   - Set up a local development environment with Python, Apache Flink, and the necessary libraries.
   - Configure the data sources and sinks.
   - Develop the data ingestion pipeline using Apache Flink.
   - Implement the data processing logic, including calculations and anomaly detection.
   - Integrate the output mechanism to store results in a PostgreSQL database.

5. **Additional Features**:
   - Implement a user interface (UI) that visualizes the processed data in real-time using a library like Plotly or Bokeh.
   - Add logging capabilities to monitor the application's performance and troubleshoot issues.
   - Ensure the application can handle high volumes of data without significant latency.

6. **Utilization of 'apache-flink-libraries' Package**: This package will be crucial for setting up and managing the data processing pipeline within Apache Flink. It provides the necessary functionalities to define and execute data transformations, manage stateful computations, and handle data streams efficiently.

7. **Deliverables**: The final deliverable should include a fully functional application, complete with setup instructions, sample data generation scripts, and documentation explaining the architecture and implementation details.

This project aims to showcase the power of Apache Flink for real-time data processing and analytics, providing a practical example of how businesses can leverage such technologies to gain insights from live data feeds.

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