apache-airflow-providers-apache-hdfs

v4.12.0 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across various categories, with no evidence of malicious activity or supply-chain attacks.

  • Low shell and credential risks
  • Some obfuscation and metadata risks, but within normal range for package development
Per-check LLM notes
  • Network: The use of requests.Session and authentication suggests network interaction with Apache HDFS, which is expected for an Airflow provider package.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The observed pattern is likely related to package management and path extension rather than malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were 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 — 12 test file(s) found

  • Test runner config found: conftest.py
  • 12 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-apa
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4399 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
  • 15 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 1.5

Found 1 network call pattern(s)

  • {namenode}" session = requests.Session() if password is not None: session.auth
⚠ 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-apache-hdfs 
Your task is to develop a mini-application that automates the process of ingesting data from a local directory into HDFS (Hadoop Distributed File System), using Apache Airflow along with the 'apache-airflow-providers-apache-hdfs' package. This application will serve as a simple yet powerful tool for anyone who needs to move large datasets into HDFS for processing or storage purposes. Here’s a step-by-step guide on how to proceed:

1. **Set Up Your Environment**: Ensure you have Python installed, then install Apache Airflow and the 'apache-airflow-providers-apache-hdfs' package. Also, make sure your HDFS cluster is accessible.
2. **Create a DAG**: Define a Directed Acyclic Graph (DAG) in Airflow. This DAG should include tasks for reading data from a specified local directory, processing it if necessary, and then writing it to HDFS.
3. **Data Ingestion Task**: Implement a task that reads files from a local directory. This task should be flexible enough to handle different file types (e.g., CSV, JSON).
4. **Processing Data (Optional)**: Depending on your use case, add a data processing step. For example, you could transform CSV data into Parquet format before moving it to HDFS.
5. **Writing to HDFS**: Use the 'apache-airflow-providers-apache-hdfs' package to write the processed data into HDFS. Ensure that the destination path in HDFS is configurable.
6. **Error Handling and Logging**: Incorporate robust error handling and logging mechanisms to ensure that any issues during execution are captured and reported.
7. **Scheduling**: Configure your DAG to run on a schedule (e.g., daily, hourly). This allows for continuous data ingestion into HDFS.
8. **User Interface**: Optionally, create a simple user interface where users can specify the source directory, HDFS destination, and other parameters without needing to modify the code directly.

This mini-application not only demonstrates the power of Apache Airflow and the 'apache-airflow-providers-apache-hdfs' package but also provides a practical solution for managing data flow into HDFS. It showcases automation, flexibility, and integration capabilities, making it a valuable addition to any data engineer's toolkit.

💬 Discussion Feed

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