por-que 0.0.3

Pure-python parquet parser, for education

Por Qué: Pure-Python Parquet Parser

¿Por qué? ¿Por qué no?

Si, ¿pero por qué? ¡Porque, parquet, python!

[!WARNING] This is a project for education, it is NOT suitable for any production uses.

Overview

Por Qué is a pure-Python Apache Parquet parser built from scratch for educational purposes. It implements Parquet's binary format parsing without external dependencies, providing insights into how Parquet files work internally.

Features

• Pure Python implementation - No native dependencies, built from scratch
• Complete reader stack - Parse files, row groups, column chunks, and pages
• Lazy data loading - Efficient memory usage with on-demand reading
• Metadata inspection - Parse and display Parquet file metadata
• Schema analysis - View detailed schema structure with logical types
• Row group information - Inspect row group statistics and column metadata
• Compression analysis - Calculate compression ratios and storage efficiency
• HTTP support - Read Parquet files from URLs using range requests
• CLI interface - Easy-to-use command-line tools for exploration

Installation

With pip:

pip install 'por-que[cli]'

Usage

Command Line Interface

The CLI uses a unified file/URL argument that auto-detects the source:

# Show help
porque --help

# View file summary (local file or URL)
porque metadata /path/to/file.parquet summary
porque metadata https://example.com/file.parquet summary

# View detailed schema
porque metadata file.parquet schema

# View file statistics and compression info
porque metadata file.parquet stats

# View row group information
porque metadata file.parquet rowgroups
porque metadata file.parquet rowgroups --group 0

# View column metadata
porque metadata file.parquet columns

# View key-value metadata
porque metadata file.parquet keyvalue
porque metadata file.parquet keyvalue "spark.version"

Python API

from por_que import ParquetFile
from por_que.util.http_file import HttpFile

# Read from local file
with open("data.parquet", "rb") as f:
    parquet_file = ParquetFile(f)
    print(f"Total rows: {parquet_file.num_rows()}")
    print(f"Columns: {parquet_file.columns()}")

    # Access metadata
    metadata = parquet_file.metadata
    print(f"Parquet version: {metadata.version}")
    print(f"Row groups: {len(metadata.row_groups)}")

# Read from URL
with HttpFile("https://example.com/data.parquet") as f:
    parquet_file = ParquetFile(f)

    # Lazy iteration over a column
    for value in parquet_file.column("user_id"):
        print(value)  # Values are yielded one at a time

    # Access specific row group
    row_group = parquet_file.row_group(0)
    column_reader = row_group.column("email")
    for page in column_reader.read():
        # Pages contain raw data (decoding not yet implemented)
        page_header, raw_data = page
        print(f"Page type: {page_header.type}")

What You'll Learn

By exploring this codebase, you can learn about:

• Parquet file format - Binary structure, magic bytes, footer layout
• Thrift protocol - Binary serialization format used by Parquet
• Schema representation - How nested and complex data types are encoded
• Compression techniques - Various compression algorithms and their efficiency
• Column storage - Columnar storage benefits and trade-offs
• Metadata organization - How Parquet organizes file and column statistics
• Lazy loading patterns - Efficient data access without loading entire files
• Binary parsing - Low-level byte manipulation and struct unpacking

Educational Focus

This implementation prioritizes readability and understanding over performance:

• Explicit parsing logic instead of generated Thrift code
• Comprehensive comments explaining binary format details
• Step-by-step Thrift deserialization
• Clear separation of concerns between parsing and data structures
• Educational debug logging (enable with logging.basicConfig(level=logging.DEBUG))
• Structured architecture mirroring Parquet's physical layout

Requirements

• Python 3.13+
• No runtime dependencies for core parsing
• Click for CLI interface (optional)

Architecture

src/por_que/
├── cli/                     # Command-line interface
│   ├── _cli.py             # Click CLI definitions
│   ├── formatters.py       # Output formatting functions
│   └── exceptions.py       # CLI-specific exceptions
├── parsers/                 # Low-level binary parsers
│   ├── parquet/            # Parquet format parsers
│   │   ├── metadata.py     # Metadata parser
│   │   ├── page.py         # Page header parser
│   │   ├── schema.py       # Schema parser
│   │   └── ...             # Other format parsers
│   └── thrift/             # Thrift protocol implementation
│       ├── parser.py       # Core Thrift parser
│       └── enums.py        # Thrift type definitions
├── readers/                 # High-level reader classes
│   ├── row_group.py        # Row group reader
│   ├── column_chunk.py     # Column chunk reader
│   └── page.py             # Page reader
├── util/                    # Utilities
│   └── http_file.py        # HTTP range request support
├── parquet_file.py         # Main entry point
├── protocols.py            # Type protocols
├── types.py                # Data structures and types
├── enums.py                # Parquet format enums
├── stats.py                # Statistics calculation
└── exceptions.py           # Core exceptions

Current Capabilities

Implemented Features

• Complete metadata parsing - All Parquet metadata structures
• Schema parsing - Full schema tree with logical types
• Page header parsing - All page types (DATA_PAGE, DATA_PAGE_V2, DICTIONARY_PAGE)
• Row group access - Lazy readers for row groups and columns
• Statistics parsing - Min/max values and null counts
• HTTP support - Range requests for remote file reading
• Memory efficiency - Lazy loading throughout the stack

Work in Progress

• Data decoding - Converting raw page data to Python values
• Compression support - Snappy, GZIP, LZ4, Zstd decompression
• Encoding support - PLAIN, DICTIONARY, RLE, BIT_PACKED decoding
• Nested data - Handling definition and repetition levels

Future Development

• Complete value extraction with all encodings
• Schema inference and validation
• Performance optimizations

Not Planned

• Write support (creating Parquet files)

Contributing

This is primarily an educational project. Feel free to:

• Report bugs or parsing issues
• Suggest improvements for educational value
• Add more comprehensive test cases
• Improve documentation and comments

License

Apache License 2.0

Why "Por Qué"?

Because asking "why" leads to understanding! This project exists to answer "why does Parquet work the way it does?" by implementing it from first principles.