apache-airflow-providers-http

v6.0.2 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with low risks across various categories. There are no clear indications of malicious intent or supply-chain attacks.

  • Low network, shell, and credential risks.
  • Potential minor metadata issues but no clear signs of malice.
Per-check LLM notes
  • Network: The detected network patterns are typical for making HTTP requests and testing functionalities, which align with the package's purpose.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The observed pattern is likely a standard method for extending module search paths and does not indicate malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The package shows some potential issues with author information and an insecure link, but no clear signs of malice or typosquatting.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

  • Test runner config found: conftest.py
  • 16 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-htt
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3840 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
  • 55 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 score 9.0

Found 6 network call pattern(s)

  • uests_mock session = requests.Session() adapter = requests_mock.Adapter() session.
  • `` is passed as ``aiohttp.ClientSession().get(json=obj)``. """ from tenacity import
  • """ async with aiohttp.ClientSession() as session: request = self._get_request_func(s
  • `` is passed as ``aiohttp.ClientSession().get(json=obj)``. """ try: if s
  • async).""" async with aiohttp.ClientSession() as session: resp = await self.async_hook.run(
  • obj)`` is passed as ``aiohttp.ClientSession().get(json=obj)``. 2XX or 3XX status codes """
⚠ 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-http 
Create a mini-application using Apache Airflow that fetches weather data from an API and stores it into a database for further analysis. This application will utilize the 'apache-airflow-providers-http' package to handle HTTP requests to the weather API and demonstrate the power of Airflow for data processing pipelines.

Step 1: Set up an Apache Airflow environment on your local machine or a cloud service like AWS or GCP. Ensure you have the necessary packages installed, including 'apache-airflow-providers-http'.

Step 2: Define a DAG (Directed Acyclic Graph) in Airflow that schedules the fetching of weather data at regular intervals, such as every hour. Use the 'HttpOperator' provided by 'apache-airflow-providers-http' to make GET requests to a public weather API (e.g., OpenWeatherMap).

Step 3: Implement error handling in your DAG to manage potential issues like network errors or API rate limiting. Use Airflow's task retries mechanism and ensure you log any failures or warnings properly.

Step 4: Once the data is fetched successfully, use another operator (such as 'PythonOperator') to process the raw data. Cleanse the data if necessary and transform it into a format suitable for storage.

Step 5: Store the processed weather data into a database of your choice (e.g., PostgreSQL, MySQL). Use the 'PostgresOperator' or 'MySqlOperator' from Airflow to insert the data directly into the database.

Suggested Features:
- Allow users to specify different locations for which they want to fetch weather data.
- Provide an option to choose between various weather APIs based on user preference.
- Implement data validation before storing to ensure the integrity of the dataset.
- Create visualizations of the stored data using a tool like Grafana or Apache Superset, connected to your database.

By completing this project, you will gain hands-on experience with Apache Airflow, HTTP operations within Airflow DAGs, and data pipeline management. Additionally, you'll see how easily one can integrate external services and databases into Airflow workflows.

💬 Discussion Feed

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