apache-airflow-providers-influxdb

v2.11.0 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all assessed categories, with only minor metadata concerns. There are no indications of malicious behavior or supply-chain attacks.

  • Low network and shell risk
  • Minor metadata issues but no signs of malicious intent
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires online services.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: The observed pattern is likely a standard practice for extending package paths and not indicative of malicious activity.
  • Credentials: No suspicious patterns related to credential harvesting have been detected.
  • Metadata: The package has some minor issues with maintainer history and a non-secure link, but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

  • Test runner config found: conftest.py
  • 14 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-inf
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3617 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
  • 11 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-influxdb 
Create a data monitoring and alerting system using Apache Airflow and the 'apache-airflow-providers-influxdb' package. This system will collect metrics from various sources, store them in InfluxDB, and trigger alerts based on predefined thresholds. The application should include the following components:

1. **Data Collection DAGs**: Develop Directed Acyclic Graphs (DAGs) that periodically fetch data from different sources such as APIs, databases, or log files. These DAGs will use operators provided by Apache Airflow to handle the data collection process.
2. **InfluxDB Integration**: Use the 'apache-airflow-providers-influxdb' package to write collected metrics into InfluxDB. Ensure that each metric has appropriate tags and fields for easy querying and visualization.
3. **Threshold Checking Task**: Implement a task within the DAG that checks if the collected metrics exceed predefined thresholds. If any threshold is breached, this task should trigger an alert.
4. **Alerting Mechanism**: Set up an alerting mechanism that sends notifications via email or Slack when a threshold is exceeded. Use Airflow's Alerting Operator or custom logic to achieve this.
5. **Visualization Dashboard**: Although not part of Airflow itself, suggest integrating Grafana with InfluxDB to visualize the collected metrics and thresholds in real-time.

The application should demonstrate how to set up an environment with Apache Airflow, configure InfluxDB connection details, and create a sample DAG for collecting CPU usage metrics from a local machine. Additionally, include instructions on how to run the DAG, view the collected data in InfluxDB, and test the alerting functionality.

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