apache-airflow-providers-apache-spark

v6.0.2 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows typical behavior for its intended functionality with no significant red flags. It's safe for use with caution.

  • No network calls detected
  • Base64 decoding present but benign
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Shell execution patterns are observed, likely for executing Spark commands, which is typical for a package dealing with Apache Spark.
  • Obfuscation: Base64 decoding is commonly used for data serialization and may not indicate malicious intent without additional context.
  • Credentials: No suspicious patterns indicative of credential harvesting were detected.
  • Metadata: The package has some minor issues with maintainer history and an insecure link, but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 23 test file(s) found

  • Test runner config found: conftest.py
  • 23 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 (4228 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
  • 21 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)

  • try: keytab = base64.b64decode(base64_keytab) except Exception as err:
  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • mand(cmd) self._sp = subprocess.Popen( spark_sql_cmd, stdout=subprocess.PIPE, stderr=s
  • try: result = subprocess.run( shlex.split(cmd), s
  • nv self._submit_sp = subprocess.Popen( spark_submit_cmd, stdout=subprocess
  • status_process: Any = subprocess.Popen( poll_drive_status_cmd, stdo
  • ll_command() with subprocess.Popen(kill_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) as
  • ccacche with subprocess.Popen( kill_cmd, env=env, stdout=subprocess.PI
✓ 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-spark 
Create a data processing pipeline using Apache Airflow and the 'apache-airflow-providers-apache-spark' package. Your task is to develop a mini-application that automates the process of ingesting raw data from a CSV file stored on an S3 bucket, performing complex transformations using Spark, and then storing the processed data back into another S3 bucket. This project will demonstrate the integration of Airflow for workflow management and Apache Spark for big data processing tasks.

Steps to follow:
1. Set up a local environment with Docker containers for Apache Airflow and Spark, ensuring that the 'apache-airflow-providers-apache-spark' package is installed within your Airflow environment.
2. Define a DAG in Airflow that schedules the execution of your data processing task at regular intervals (e.g., daily).
3. Use the 'apache-airflow-providers-apache-spark' package to create a Spark job within the DAG that reads data from a specified CSV file in an S3 bucket, applies transformations such as filtering, aggregation, and feature engineering, and writes the transformed data back to another S3 bucket.
4. Implement error handling within your DAG to ensure robustness, including retry mechanisms for transient failures and logging of all critical operations.
5. Monitor the execution of your DAG through the Airflow web interface, ensuring that all tasks complete successfully and that any issues are promptly addressed.

Suggested Features:
- Implement dynamic partitioning in the Spark job based on certain criteria (e.g., date ranges) to optimize data storage and retrieval.
- Add unit tests for your Spark job to validate its functionality and performance.
- Integrate a notification system that alerts stakeholders via email or Slack when the DAG fails or completes successfully.
- Extend the DAG to support multiple input files and output directories, allowing for batch processing of different datasets.

This project not only showcases the power of Apache Airflow for orchestrating complex workflows but also highlights the efficiency of Apache Spark for large-scale data processing tasks. By utilizing the 'apache-airflow-providers-apache-spark' package, you'll be able to streamline the development and deployment of your data processing pipeline, making it easier to manage and scale as your data volume grows.

💬 Discussion Feed

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