apache-airflow-providers-opensearch

v1.9.2 safe
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across multiple categories with only metadata indicating some concern due to a non-secure external link and sparse maintainer information.

  • Low network and shell risk
  • No evidence of obfuscation or credential theft
  • Metadata risk due to non-secure link and sparse maintainer info
Per-check LLM notes
  • Network: No network calls detected, which is normal for packages without external API integrations.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or local system compromise.
  • Obfuscation: The observed pattern is likely used for extending package paths and is not indicative of malicious activity.
  • Credentials: No patterns indicating credential harvesting were detected.
  • Metadata: The package has a non-secure external link and the maintainer information is sparse, suggesting potential lack of accountability.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 19 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • 19 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-ope
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3509 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
  • 44 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-opensearch 
Create a small, fully-functional data pipeline application using Apache Airflow and the 'apache-airflow-providers-opensearch' package. This application will automate the process of ingesting log data from various sources into an OpenSearch cluster for real-time analysis and visualization. Your task includes the following steps:

1. **Setup Environment**: Begin by setting up your development environment with Python, Apache Airflow, and the 'apache-airflow-providers-opensearch' package. Ensure you have an OpenSearch cluster ready to receive data.
2. **Define DAG Structure**: Design a Directed Acyclic Graph (DAG) within Airflow that outlines the workflow for data ingestion, transformation, and loading into OpenSearch. Each task in the DAG should represent a specific operation in the ETL (Extract, Transform, Load) process.
3. **Data Ingestion**: Implement tasks to simulate data ingestion from different sources such as local files, HTTP APIs, or other databases. These tasks should be designed to handle various formats and volumes of data efficiently.
4. **Data Transformation**: Develop transformations to clean and prepare the ingested data for OpenSearch. This might include parsing logs, normalizing data fields, and handling missing values.
5. **Loading Data into OpenSearch**: Use the 'apache-airflow-providers-opensearch' package to define operators that will load the transformed data into an OpenSearch index. Ensure that indexing operations are optimized for performance and scalability.
6. **Monitoring and Alerts**: Incorporate monitoring tasks to track the health and status of the pipeline. Additionally, set up alerting mechanisms for any failures or anomalies detected during the execution of the DAG.
7. **Visualization**: Integrate a simple visualization component that queries OpenSearch and displays key metrics or insights derived from the ingested data on a dashboard.

Suggested Features:
- Implement a dynamic scheduling mechanism based on the availability of new data sources.
- Add support for multiple OpenSearch indexes depending on the type of data being ingested.
- Include error handling and retries for failed data ingestion or indexing attempts.
- Provide a user-friendly interface for managing and monitoring the data pipeline.

How 'apache-airflow-providers-opensearch' is Utilized:
- The package provides custom operators and hooks to interact seamlessly with OpenSearch services from within Apache Airflow DAGs. It simplifies the process of connecting to OpenSearch, executing search queries, and performing bulk indexing operations, thereby streamlining the entire data pipeline workflow.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!