sparkpl 2.0.1

A utility package for converting between PySpark and Polars DataFrames

sparkpl

A lightweight, pandas-free Python package for seamless conversion between PySpark and Polars DataFrames.

Installation

pip install sparkpl

Features

• 🚀 Direct Arrow conversion - Uses native Arrow for maximum performance (Spark 4.0+)
• ⚡ Zero pandas dependency - Pure Polars ↔ Spark conversion
• 🔄 Bidirectional conversion - Seamless data exchange between frameworks
• 🛡️ Type preservation - Maintains data types during conversion
• 📊 Batch processing - Handles large datasets efficiently
• 🔍 Smart logging - Structured logging with loguru
• 🎯 Simple API - Both functional and class-based interfaces
• 💾 Minimal footprint - Lightweight with essential dependencies only

Quick Start

import polars as pl
from pyspark.sql import SparkSession
from sparkpl.converter import spark_to_polars, polars_to_spark

# Initialize Spark
spark = SparkSession.builder.appName("example").getOrCreate()

# Create sample data
spark_df = spark.createDataFrame([(1, "Alice"), (2, "Bob")], ["id", "name"])

# Convert Spark → Polars
polars_df = spark_to_polars(spark_df)
print(polars_df)

# Convert Polars → Spark
spark_df_back = polars_to_spark(polars_df)
spark_df_back.show()

Advanced Usage

Class-based API

from sparkpl.converter import DataFrameConverter

converter = DataFrameConverter(spark)

# With Arrow optimization (default)
polars_df = converter.spark_to_polars(spark_df, use_arrow=True)

# Native fallback for compatibility
polars_df = converter.spark_to_polars(spark_df, use_arrow=False)

# Batch processing for large datasets
polars_df = converter.spark_to_polars(large_spark_df, batch_size=100000)

# Create temporary view
spark_df = converter.polars_to_spark(polars_df, table_name="my_table")

Error Handling

from sparkpl.converter import DataFrameConverterError

try:
    polars_df = spark_to_polars(spark_df)
except DataFrameConverterError as e:
    print(f"Conversion failed: {e}")

Logging Configuration

from loguru import logger

# Configure structured logging
logger.add("sparkpl.log", rotation="10 MB", level="INFO")

# Conversions automatically log progress
polars_df = spark_to_polars(spark_df)  # Logs conversion metrics

Performance

SparkPL automatically selects the optimal conversion method:

• Spark 4.0+: Direct Arrow conversion (toArrow() → createDataFrame(arrow_table))
• Older versions: Native collection methods with fallback
• Large datasets: Automatic batching to manage memory

Type Support

Polars Type	Spark Type	Notes
pl.Utf8	StringType
pl.Int32	IntegerType
pl.Int64	LongType
pl.Float32	FloatType
pl.Float64	DoubleType
pl.Boolean	BooleanType
pl.Date	DateType
pl.Datetime	TimestampType
pl.Binary	BinaryType
pl.Time	StringType	Converted to string
pl.Duration	LongType	Microseconds

Requirements

• Python >=3.8
• polars >=0.18.0
• pyspark >=3.0.0
• pyarrow >=5.0.0
• loguru >=0.6.0

API Reference

Functions

• spark_to_polars(spark_df, **kwargs) - Convert Spark DataFrame to Polars
• polars_to_spark(polars_df, **kwargs) - Convert Polars DataFrame to Spark

DataFrameConverter Class

• spark_to_polars(spark_df, use_arrow=True, batch_size=None)
• polars_to_spark(polars_df, use_arrow=True, table_name=None)
• validate_conversion(original_df, converted_df, check_data=False)

Why No Pandas?

SparkPL eliminates pandas dependency for:

• Reduced footprint - Fewer dependencies to manage
• Better performance - Direct conversion without intermediate steps
• Simplified deployment - No pandas version conflicts
• Pure workflow - Stay within Polars/Spark ecosystem

Examples

Basic Conversion

# Sample data
data = [("Alice", 25), ("Bob", 30), ("Charlie", 35)]
spark_df = spark.createDataFrame(data, ["name", "age"])

# Convert and process
polars_df = spark_to_polars(spark_df)
filtered = polars_df.filter(pl.col("age") > 28)
result_spark = polars_to_spark(filtered)

Working with Large Data

# Process large dataset in chunks
converter = DataFrameConverter(spark)
large_polars = converter.spark_to_polars(
    huge_spark_df, 
    batch_size=50000  # Process 50k rows at a time
)

Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/my-feature
3. Make changes with tests
4. Commit: git commit -am 'Add feature'
5. Push: git push origin feature/my-feature
6. Create pull request

Development Setup

git clone https://github.com/yourusername/sparkpl.git
cd sparkpl
pip install -e ".[dev]"
pytest tests/

License

MIT License - see LICENSE file.

Support

• Issues: GitHub Issues
• Documentation: Coming soon
• Community: Discussions welcome

---

Built with ❤️ for the Python data community.