create-data-source

name	create-data-source
description	Create a new PySpark data source implementation. Use when adding a new connector, data source, or integration to the project. Use when this capability is needed.
metadata	{"author":"allisonwang-db"}

Create Data Source

Overview

This skill guides you through the process of adding a new PySpark data source to the repository. A data source typically consists of:

1. DataSource: The main entry point, defining capabilities and schema.
2. Reader: Logic for reading data (batch/stream).
3. Writer: Logic for writing data (batch/stream).
4. Tests: Unit and integration tests.
5. Documentation: Usage guide and API reference.

Workflow

1. Define Requirements: Determine if the source supports reading, writing, or both. Is it batch or streaming?
2. Implementation: Create the implementation file in pyspark_datasources/.
3. Registration: Register the new source in pyspark_datasources/__init__.py.
4. Dependencies: Add any required libraries to pyproject.toml.
5. Testing: Create a test file in tests/.
6. Documentation: Add documentation in docs/datasources/ and update mkdocs.yml and README.md.

Implementation Details

1. Create Implementation File

Create a new file pyspark_datasources/<name>.py. Use the templates in templates.md.

• Implement DataSource class.
• Implement DataSourceReader (if reading).
• Implement DataSourceWriter (if writing).
• Define the schema in the DataSource class.

2. Register Data Source

Add the new class to pyspark_datasources/__init__.py:

from .<name> import <Name>DataSource

3. Add Dependencies

If the data source requires external libraries:

1. Add them to [project.optional-dependencies] in pyproject.toml.
2. Update the all group to include the new dependencies.

4. Add Tests

Create tests/test_<name>.py.

• Use unittest.mock to mock external services/libraries.
• Test registration, reading, and writing logic.
• See templates.md for test structure.

5. Add Documentation

1. Create docs/datasources/<name>.md.
2. Add the new page to nav in mkdocs.yml.
3. Add installation and usage examples to README.md and docs/data-sources-guide.md.

Checklist

Use the checklist in checklist.md to track your progress.

Resources

• Python Data Source API Documentation
• Existing implementations in pyspark_datasources/ (e.g., github.py, salesforce.py).

---

Source: allisonwang-db/pyspark-data-sources — distributed by TomeVault.