apache-airflow-providers-mysql

v6.6.0 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package appears safe with low risk scores across all categories except metadata where it shows minor issues. There are no indications of malicious activities.

  • Low network and shell risks
  • Minor obfuscation and metadata concerns
  • No signs of credential risk or supply-chain attack
Per-check LLM notes
  • Network: No network calls detected, which is normal for a library focused on local database operations.
  • Shell: No shell execution patterns detected, which aligns with the expected behavior of a library designed to interact with MySQL.
  • Obfuscation: The observed pattern is a common technique used for extending module search paths and is not indicative of malicious activity.
  • Credentials: No patterns indicative of credential harvesting or secret theft were detected.
  • Metadata: The package has some minor issues but no clear signs of malice.

📦 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-mys
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (5271 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
  • 24 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 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-mysql 
Develop a small but comprehensive data pipeline application using Apache Airflow that integrates with MySQL databases. Your task is to create a data ingestion pipeline that extracts data from a MySQL database, transforms it into a more useful format, and loads it into another MySQL database. This application will serve as a simple ETL (Extract, Transform, Load) tool tailored for MySQL databases.

**Step-by-Step Guide:**
1. **Setup Environment**: Install Apache Airflow and the `apache-airflow-providers-mysql` package. Ensure you have a local MySQL server running and accessible.
2. **Define Databases**: Set up two MySQL databases - one for source data and another for transformed data.
3. **Create DAGs**: Use Apache Airflow to define Directed Acyclic Graphs (DAGs) that represent your workflow. Each DAG should include tasks for connecting to the MySQL database, extracting data, transforming the data, and loading it into the destination database.
4. **Implement Extraction Task**: Write a Python operator that connects to the source MySQL database and extracts specific data based on given criteria.
5. **Data Transformation**: Implement a transformation step where you manipulate the extracted data. For example, you might clean the data, aggregate it, or convert certain fields.
6. **Loading Data**: Design a task that loads the transformed data into the destination MySQL database.
7. **Testing and Validation**: Ensure that the pipeline works as expected by testing the entire process. Validate the data integrity between the source and destination databases.
8. **Documentation**: Document each step of your development process, including configuration settings, code snippets, and any challenges faced during implementation.

**Suggested Features**:
- Support for multiple MySQL databases and schemas.
- Ability to schedule the ETL process at regular intervals (e.g., daily).
- Logging and monitoring capabilities within Airflow to track the status of each task.
- Error handling mechanisms to manage issues such as database connection failures.
- Optional: Integration with other data sources or destinations besides MySQL.

**How 'apache-airflow-providers-mysql' is Utilized**:
This package provides operators and hooks necessary for interacting with MySQL databases through Apache Airflow. Specifically, you'll use the `MySqlHook` class to establish connections to your MySQL databases and perform SQL queries. Additionally, the `MySqlOperator` can be used to run SQL scripts directly within your DAGs, streamlining the process of executing complex SQL commands as part of your ETL tasks.

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