shardate 2025.9.9.4

A lightweight Python library for efficiently reading year-month-day partitioned Parquet datasets.

shardate

A lightweight Python library for efficiently reading year-month-day partitioned Parquet datasets with PySpark.

Installation

pip install shardate

Overview

shardate provides a clean, dataclass-based API for working with Parquet datasets that are partitioned in a year/month/day directory structure (e.g., y=2025/m=01/d=15/). It's built on PySpark and designed for efficient date-based filtering and data retrieval.

Features

• Date-based reading: Read data for specific dates, date ranges, or collections of dates
• End-of-month support: Dedicated functionality for reading end-of-month data
• Flexible partitioning: Customizable partition format (defaults to y=%Y/m=%m/d=%d)
• PySpark integration: Seamlessly works with existing PySpark workflows
• Type hints: Full type annotation support for better development experience
• Well-tested: Comprehensive test suite ensuring reliability

Quick Start

Basic Usage

from datetime import date
from shardate import Shardate

# Create a Shardate instance for your data path
reader = Shardate("/path/to/your/partitioned/data")

# Read data for a specific date
df = reader.read_by_date(date(2025, 1, 15))

# Read data between two dates (inclusive)
df = reader.read_between(date(2025, 1, 1), date(2025, 1, 31))

# Read data for specific dates
target_dates = [date(2025, 1, 1), date(2025, 1, 15), date(2025, 1, 31)]
df = reader.read_by_dates(target_dates)

# Read only end-of-month data within a date range
df = reader.read_eoms_between(date(2025, 1, 1), date(2025, 3, 31))

Custom Partition Format

# If your data uses a different partition format
reader = Shardate("/path/to/data", partition_format="year=%Y/month=%m/day=%d")
df = reader.read_by_date(date(2025, 1, 15))

Working with PySpark

from pyspark.sql import SparkSession
from shardate import Shardate

# Ensure you have an active SparkSession
spark = SparkSession.builder.appName("SharDate Example").getOrCreate()

# Use Shardate as normal
reader = Shardate("/path/to/data")
df = reader.read_by_date(date(2025, 1, 15))

# The returned DataFrame is a standard PySpark DataFrame
df.show()
df.filter(df.column_name == "some_value").count()

API Reference

Shardate Class

@dataclass
class Shardate:
    path: str
    partition_format: str = "y=%Y/m=%m/d=%d"

Methods

• read_by_date(target_date: date) -> DataFrame: Read data for a specific date
• read_between(start_date: date, end_date: date) -> DataFrame: Read data between two dates (inclusive)
• read_by_dates(target_dates: Iterable[date]) -> DataFrame: Read data for specific dates
• read_eoms_between(start_date: date, end_date: date) -> DataFrame: Read end-of-month data within a date range

Data Structure Requirements

Your Parquet data should be partitioned in directories following this structure:

data/
├── y=2025/
│   ├── m=01/
│   │   ├── d=01/
│   │   │   └── part-*.parquet
│   │   ├── d=02/
│   │   │   └── part-*.parquet
│   │   └── d=31/
│   │       └── part-*.parquet
│   └── m=02/
│       └── ...
└── y=2024/
    └── ...

Development

Setup

# Clone the repository
git clone https://github.com/yoichiojima-2/shardate.git
cd shardate

# Install development dependencies (using uv)
uv sync --dev

Testing

# Run all tests
make test

# Or use uv directly
uv run pytest -vvv

Code Quality

# Run linting and formatting
make lint

# Or use uv directly
uv run ruff check --fix .
uv run ruff format .

Building

# Build the package
make build

# Clean build artifacts
make clean

Requirements

• Python: 3.12+
• PySpark: 4.0+
• python-dateutil: 2.9.0.post0+

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes and add tests
4. Run the test suite (make test)
5. Run code quality checks (make lint)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Links

• Homepage: https://github.com/yoichiojima-2/shardate
• Issues: https://github.com/yoichiojima-2/shardate/issues
• PyPI: https://pypi.org/project/shardate/