apache-airflow-providers-elasticsearch

v6.5.4 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across all categories, with only minor concerns about obfuscation and metadata, which do not indicate malicious intent.

  • No network or shell risks detected
  • Low obfuscation and metadata risks
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: The observed pattern is likely a standard method for extending package paths and not malicious obfuscation.
  • Credentials: No credential harvesting patterns detected.
  • Metadata: The package has some minor issues but no clear signs of malicious intent.

📦 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-ela
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3738 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
  • 7 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-elasticsearch 
Create a mini-application that integrates Apache Airflow with Elasticsearch to monitor and analyze log data in real-time. Your task is to design a pipeline that ingests log data from a simulated server into Elasticsearch using Apache Airflow. Here are the steps and features your application should include:

1. **Setup**: Install and configure Apache Airflow along with the `apache-airflow-providers-elasticsearch` package.
2. **Data Simulation**: Simulate log data generation from a mock server, which could mimic typical server logs including timestamps, request methods, URLs, response codes, etc.
3. **Pipeline Design**: Use Apache Airflow DAGs to schedule and manage the process of ingesting this log data into Elasticsearch. This includes defining operators for data extraction, transformation, and loading (ETL).
4. **Real-Time Analysis**: Implement a feature where the application periodically queries Elasticsearch to perform real-time analysis on the log data. This could include identifying common error patterns, high traffic times, or unusual spikes in activity.
5. **Visualization**: Integrate a simple dashboard or visualization tool (such as Grafana) that connects to Elasticsearch to display the analyzed data in a user-friendly format.
6. **Scalability Considerations**: Discuss how your solution could be scaled to handle larger volumes of log data.
7. **Documentation**: Provide clear documentation on how to set up and run the application, including installation instructions, configuration settings, and example usage scenarios.

In this project, the `apache-airflow-providers-elasticsearch` package plays a crucial role in facilitating the connection between Apache Airflow and Elasticsearch. It provides operators and hooks that simplify the process of interacting with Elasticsearch within Airflow workflows. Utilize these tools to streamline the ETL process and ensure efficient data ingestion and querying.

💬 Discussion Feed

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