apache-airflow-providers-celery

v3.20.0 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators across all categories, with no evidence of network or shell risks. The obfuscation and metadata risks are minimal and do not suggest any malicious intent.

  • No network or shell execution detected
  • Low obfuscation and metadata risks
Per-check LLM notes
  • Network: No network calls detected, which is normal as the package likely does not require direct internet access.
  • Shell: No shell execution patterns detected, indicating no unexpected system command executions.
  • Obfuscation: The detected pattern is likely used for path manipulation and extension within the package's directory structure, rather than malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting or secret stealing were found.
  • Metadata: The package has a non-secure link and lacks detailed author information, but no other red flags are present.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

  • Test runner config found: conftest.py
  • 15 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-cel
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4093 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
  • 18 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-celery 
Create a fully-functional mini-app that leverages Apache Airflow along with the 'apache-airflow-providers-celery' package to manage a distributed task queue system for processing image files. Your app will be designed to handle a large number of image files, which could be resized, converted into different formats, or analyzed using machine learning models. Here’s a detailed breakdown of what your application should achieve:

1. **Task Submission Interface**: Develop a simple web interface where users can upload multiple image files at once. These images should be stored temporarily on a server.
2. **Task Scheduling & Execution**: Use Apache Airflow to schedule and execute tasks that process these images. Each task should be handled independently and concurrently using Celery as the backend for task execution.
3. **Processing Tasks**:
   - Resize images to a predefined resolution.
   - Convert images from one format to another (e.g., JPG to PNG).
   - Apply basic machine learning models to detect objects within the images (using pre-trained models).
4. **Status Tracking**: Implement a feature that allows users to track the status of their submitted images. This includes knowing whether the image is being processed, has been processed successfully, or if there was an error during processing.
5. **Result Delivery**: Once all tasks related to an image have completed, provide a download link for the processed image(s). Additionally, include any analysis results if applicable.
6. **Error Handling & Notifications**: Ensure robust error handling for cases where processing fails due to issues like unsupported file types, model errors, etc. Users should receive notifications via email or SMS about the status of their requests.
7. **Scalability Considerations**: Design your application to scale horizontally by adding more worker nodes as needed without downtime.

The 'apache-airflow-providers-celery' package is crucial here as it enables seamless integration between Apache Airflow and Celery, allowing you to define workflows where tasks are executed asynchronously across multiple workers. This setup is ideal for handling the potentially high volume of image processing tasks efficiently and reliably.

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