apache-airflow-providers-grpc

v3.9.4 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across all categories with no network or shell risks. While there is minor obfuscation and metadata risk, these do not indicate malicious intent.

  • Low network and shell execution risks.
  • Minor obfuscation and metadata concerns, but benign.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package that doesn't require external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: The observed pattern is likely a standard technique for extending module search paths and not indicative of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting or secret theft were detected.
  • Metadata: The package has some minor issues with maintainer history and a non-secure link, but no clear signs of malice or typosquatting.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: conftest.py
  • 7 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-grp
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3574 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
  • 6 type-annotated function signatures (partial)
✦ 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 4.0

Found 2 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # # Licensed to the Apache
✓ 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-grpc 
Your task is to develop a mini-application that integrates with a gRPC service using Apache Airflow and the 'apache-airflow-providers-grpc' package. This application will serve as a scheduler and orchestrator for tasks that involve fetching data from a gRPC service and processing it. Here’s a detailed breakdown of what your application should achieve:

1. **Setup**: Begin by setting up a virtual environment and installing necessary packages including 'apache-airflow', 'apache-airflow-providers-grpc', and any other dependencies required for gRPC communication.

2. **gRPC Service Mock-Up**: Since you won't have access to an actual gRPC service during development, create a mock gRPC server using the 'grpcio-tools' package. This server should simulate the behavior of a real gRPC service by providing endpoints that return sample data upon request.

3. **Airflow DAG Creation**: Utilize Apache Airflow to define a Directed Acyclic Graph (DAG). Within this DAG, create tasks that use the 'apache-airflow-providers-grpc' package to communicate with your mock gRPC server. Each task should perform specific actions such as:
   - Fetching data from the gRPC service.
   - Processing the fetched data (e.g., filtering, transforming).
   - Storing the processed data in a local file or database.

4. **Task Scheduling**: Configure the timing and dependencies between these tasks within your DAG. For example, ensure that the data fetching task runs before the data processing task.

5. **Monitoring & Logging**: Implement logging mechanisms within your tasks to monitor their execution status and output logs. Use Airflow's built-in monitoring capabilities to visualize the progress and results of your DAG.

6. **User Interface**: Although not mandatory, consider enhancing your application with a simple user interface where users can trigger the DAG manually or view the current state of the DAG execution.

7. **Documentation**: Provide clear documentation on how to set up and run your application, including installation steps, configuration details, and usage instructions.

This project aims to showcase the integration capabilities of 'apache-airflow-providers-grpc' while also demonstrating the power of Apache Airflow in orchestrating complex workflows involving external services.

💬 Discussion Feed

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