polyspark 0.5.0

Generate PySpark DataFrames using polyfactory for testing and development

Polyspark

Generate type-safe PySpark DataFrames effortlessly using polyfactory

---

🎯 Why Polyspark?

Creating test data for PySpark applications is tedious. Polyspark makes it effortless by generating realistic test DataFrames from your Python data models - with automatic schema inference that prevents common pitfalls.

from dataclasses import dataclass
from polyspark import spark_factory

@spark_factory
@dataclass
class User:
    id: int
    name: str
    email: str

# That's it! Generate 1000 rows instantly:
df = User.build_dataframe(spark, size=1000)

✨ Key Features

• 🏭 Factory Pattern: Leverage polyfactory's powerful data generation
• 🎯 Type-Safe Schema Inference: Your Python types become PySpark schemas automatically
• 🛡️ Robust Null Handling: Schemas inferred from types prevent DataFrame failures with null columns
• 🔌 Zero Hard Dependencies: PySpark is optional - generate data without it
• 🌳 Complex Types: Full support for nested structs, arrays, maps, and unions
• 🎨 Flexible Models: Works with dataclasses, Pydantic models, and TypedDicts
• 🚀 Simple API: One decorator and you're done
• 🧪 Testing Utilities: DataFrame comparison, schema validation, and assertion helpers
• 💾 Data I/O: Save and load DataFrames in Parquet, JSON, and CSV formats
• 🖥️ CLI Tool: Command-line interface for schema operations and data generation
• 📦 Production Ready: 258 tests, 100% handler coverage, comprehensive CI/CD

📦 Installation

pip install polyspark

Polyspark keeps PySpark optional - install it separately when needed:

pip install pyspark

For development with all optional dependencies:

pip install "polyspark[dev]"

🚀 Quick Start

The Modern Way (Recommended)

Use the @spark_factory decorator to add DataFrame generation methods directly to your model:

from dataclasses import dataclass
from typing import Optional
from polyspark import spark_factory
from pyspark.sql import SparkSession

@spark_factory
@dataclass
class Product:
    product_id: int
    name: str
    price: float
    description: Optional[str] = None  # Automatically nullable in schema
    in_stock: bool = True

# Create your SparkSession
spark = SparkSession.builder.appName("example").getOrCreate()

# Generate a DataFrame with 100 rows
df = Product.build_dataframe(spark, size=100)
df.show(5)

Output:

+----------+------------------+-------+--------------------+--------+
|product_id|              name|  price|         description|in_stock|
+----------+------------------+-------+--------------------+--------+
|    724891|Central Public ...|1842.32|       Patient sc...|    true|
|    193847|Message Total F...|7249.17|                null|    true|
|    847291|Current Certain...|3891.04|       Tonight op...|   false|
+----------+------------------+-------+--------------------+--------+

Classic Factory Pattern

For advanced use cases, create a dedicated factory class:

from polyspark import SparkFactory

class ProductFactory(SparkFactory[Product]):
    __model__ = Product

df = ProductFactory.build_dataframe(spark, size=100)

📚 Usage Guide

Schema Inference Magic

The Problem: When creating DataFrames manually, if all values in a column are None, Spark can't infer the type and fails:

# ❌ This can break if all emails are None
data = [{"id": 1, "email": None}, {"id": 2, "email": None}]
df = spark.createDataFrame(data)  # Error: Can't infer schema!

The Solution: Polyspark infers schemas from your Python types before generating data:

# ✅ This always works - schema comes from type hints
@dataclass
class User:
    id: int
    email: Optional[str]  # Spark knows this is a nullable string

df = User.build_dataframe(spark, size=100)  # Schema: id (long), email (string, nullable)

Even if all generated emails happen to be None, the DataFrame creation succeeds because the schema is defined first.

Working Without PySpark

Generate data as dictionaries without installing PySpark:

# No PySpark installation required!
dicts = Product.build_dicts(size=1000)

# Use the data however you want
import pandas as pd
pandas_df = pd.DataFrame(dicts)

# Later, convert to Spark DataFrame when needed
spark_df = Product.create_dataframe_from_dicts(spark, dicts)

Pydantic Models

Full support for Pydantic v2 with validation:

from pydantic import BaseModel, EmailStr, Field

@spark_factory
class User(BaseModel):
    id: int = Field(gt=0, description="User ID")
    username: str = Field(min_length=3, max_length=20)
    email: EmailStr
    age: int = Field(ge=18, le=120)
    is_active: bool = True

# Generate valid data according to your constraints
df = User.build_dataframe(spark, size=500)

Complex Nested Structures

Nested Structs

@spark_factory
@dataclass
class Address:
    street: str
    city: str
    state: str
    zipcode: str
    country: str = "USA"

@spark_factory
@dataclass
class Employee:
    employee_id: int
    name: str
    email: str
    address: Address  # Nested struct
    department: str

# Generates nested StructType automatically
df = Employee.build_dataframe(spark, size=100)

# Query nested fields
df.select("name", "address.city", "address.state").show()

Arrays and Maps

from typing import Dict, List

@spark_factory
@dataclass
class Product:
    product_id: int
    name: str
    tags: List[str]  # ArrayType(StringType())
    attributes: Dict[str, str]  # MapType(StringType(), StringType())
    prices_by_region: Dict[str, float]  # MapType(StringType(), DoubleType())
    related_products: List[int]  # ArrayType(LongType())

df = Product.build_dataframe(spark, size=50)

# Work with arrays
df.select("name", explode("tags").alias("tag")).show()

Array of Structs

@dataclass
class Project:
    project_id: int
    name: str
    budget: float
    start_date: datetime

@spark_factory
@dataclass
class Department:
    dept_id: int
    dept_name: str
    manager: str
    projects: List[Project]  # ArrayType(StructType(...))

df = Department.build_dataframe(spark, size=20)

# Explode nested array of structs
df.select("dept_name", explode("projects").alias("project")).show()

Explicit Schema Override

Override inferred schema when needed:

from pyspark.sql.types import StructType, StructField, IntegerType, StringType

# Define custom schema
custom_schema = StructType([
    StructField("id", IntegerType(), nullable=False),
    StructField("name", StringType(), nullable=False),
    StructField("email", StringType(), nullable=True),
])

# Use custom schema instead of inferred one
df = User.build_dataframe(spark, size=100, schema=custom_schema)

Optional and Union Types

Automatic nullable field handling:

from typing import Optional, Union

@spark_factory
@dataclass
class UserProfile:
    user_id: int
    username: str
    nickname: Optional[str]  # nullable=True in schema
    bio: Optional[str] = None
    age: Optional[int] = None
    # Union types work too (uses first type)
    contact: Union[str, int] = ""

df = UserProfile.build_dataframe(spark, size=200)

🔧 Advanced Features

Custom Polyfactory Configuration

Pass any polyfactory arguments:

from datetime import datetime

@spark_factory
@dataclass
class Event:
    event_id: int
    name: str
    timestamp: datetime
    status: str

# Customize data generation
df = Event.build_dataframe(
    spark,
    size=1000,
    __set_as_default_factory_key__=True,
    # Pass any polyfactory kwargs
)

Decorator with Pydantic

from pydantic import BaseModel

@spark_factory
class Order(BaseModel):
    order_id: int
    customer_id: int
    total: float
    items: List[str]

# Works exactly the same!
df = Order.build_dataframe(spark, size=500)

Convenience Function

Skip the decorator for quick one-offs:

from polyspark import build_spark_dataframe

@dataclass
class SimpleModel:
    id: int
    value: str

# Generate directly without decorator or factory class
df = build_spark_dataframe(SimpleModel, spark, size=100)

📊 Type Mapping Reference

Basic Types

Python Type	PySpark Type	Nullable by Default
str	StringType	❌
int	LongType	❌
float	DoubleType	❌
bool	BooleanType	❌
bytes / bytearray	BinaryType	❌
datetime.date	DateType	❌
datetime.datetime	TimestampType	❌
decimal.Decimal	DecimalType	❌

Complex Types

Python Type	PySpark Type
List[T]	ArrayType(T)
Dict[K, V]	MapType(K, V)
Optional[T]	T (nullable=True)
Dataclass / Pydantic Model	StructType(...)
Union[T, None]	T (nullable=True)

Nested Combinations

Any combination of types is supported:

• List[List[str]] → ArrayType(ArrayType(StringType()))
• Dict[str, List[int]] → MapType(StringType(), ArrayType(LongType()))
• List[MyDataclass] → ArrayType(StructType(...))
• Optional[Dict[str, float]] → Nullable MapType(StringType(), DoubleType())

🧪 Testing Utilities

Polyspark includes powerful utilities for testing Spark transformations:

DataFrame Assertions

from polyspark import (
    assert_dataframe_equal,
    assert_schema_equal,
    assert_approx_count,
    assert_column_exists,
    assert_no_duplicates,
    get_column_stats
)

# Compare DataFrames (with tolerance for floats)
assert_dataframe_equal(df1, df2, check_order=False, rtol=1e-5)

# Compare schemas
assert_schema_equal(schema1, schema2, check_nullable=True)

# Validate row count (with tolerance)
assert_approx_count(df, expected_count=1000, tolerance=0.1)

# Check columns exist
assert_column_exists(df, "user_id", "name", "email")

# Check for duplicates
assert_no_duplicates(df, columns=["user_id"])

# Get column statistics
stats = get_column_stats(df, "amount")
print(f"Mean: {stats['mean']}, Distinct: {stats['distinct_count']}")

See examples/testing_patterns.py for comprehensive testing patterns.

💾 Data I/O Utilities

Save and load DataFrames with ease:

from polyspark import (
    save_as_parquet,
    save_as_json,
    save_as_csv,
    load_parquet,
    load_and_validate
)

# Generate data
df = User.build_dataframe(spark, size=1000)

# Save with partitioning
save_as_parquet(df, "users.parquet", partition_by="date")

# Save to JSON or CSV
save_as_json(df, "users.json")
save_as_csv(df, "users.csv", header=True)

# Load and validate against schema
from polyspark import infer_schema

expected_schema = infer_schema(User)
df = load_and_validate(spark, "users.parquet", expected_schema=expected_schema)

Without PySpark:

from polyspark import save_dicts_as_json, load_dicts_from_json

# Generate and save without PySpark
dicts = User.build_dicts(size=100)
save_dicts_as_json(dicts, "users.jsonl")

# Load later
loaded = load_dicts_from_json("users.jsonl")

🖥️ CLI Tool

Polyspark includes a command-line interface for common operations:

Export Schema

# Export schema as DDL string
polyspark schema export myapp.models:User

# Save to file
polyspark schema export myapp.models:User --output user_schema.ddl

Validate Data

# Validate data file against model schema
polyspark schema validate myapp.models:User data.parquet
polyspark schema validate myapp.models:Product data.json

Generate Test Data

# Generate and save test data
polyspark generate myapp.models:User --size 1000 --output users.parquet
polyspark generate myapp.models:Product --size 500 --format json --output products.json
polyspark generate myapp.models:Order --size 10000 --format csv --output orders.csv

Example CLI Usage:

# 1. Export schema for documentation
polyspark schema export myapp.models:Transaction --output transaction_schema.ddl

# 2. Generate test data for local development
polyspark generate myapp.models:Transaction --size 10000 --output test_data.parquet

# 3. Validate data before deployment
polyspark schema validate myapp.models:Transaction production_data.parquet

📖 API Reference

Decorator: @spark_factory

Adds DataFrame generation methods to your model class.

@spark_factory
@dataclass
class MyModel:
    field: str

# Adds these methods:
MyModel.build_dataframe(spark, size=10, schema=None, **kwargs)
MyModel.build_dicts(size=10, **kwargs)
MyModel.create_dataframe_from_dicts(spark, data, schema=None)

Class: SparkFactory[T]

Base factory class for advanced use cases.

Methods

build_dataframe(spark, size=10, schema=None, **kwargs) -> DataFrame

Generate a PySpark DataFrame with typed data.

Parameters:

• spark (SparkSession): Active Spark session
• size (int): Number of rows to generate
• schema (Optional[StructType | List[str]]): Custom schema or column names
• **kwargs: Additional polyfactory arguments

Returns: PySpark DataFrame

build_dicts(size=10, **kwargs) -> List[Dict[str, Any]]

Generate data as dictionaries (no PySpark required).

Parameters:

• size (int): Number of records
• **kwargs: Additional polyfactory arguments

Returns: List of dictionaries

create_dataframe_from_dicts(spark, data, schema=None) -> DataFrame

Convert dictionaries to DataFrame with inferred schema.

Parameters:

• spark (SparkSession): Active Spark session
• data (List[Dict]): Data to convert
• schema (Optional[StructType]): Optional custom schema

Returns: PySpark DataFrame

Function: build_spark_dataframe

build_spark_dataframe(model, spark, size=10, schema=None, **kwargs) -> DataFrame

Convenience function to generate DataFrame without decorator or factory class.

Schema Utilities

infer_schema(model, schema=None) -> StructType

Infer PySpark schema from model type.

python_type_to_spark_type(python_type, nullable=True) -> DataType

Convert Python type to PySpark DataType.

dataclass_to_struct_type(dataclass_type) -> StructType

Convert dataclass to StructType.

pydantic_to_struct_type(model_type) -> StructType

Convert Pydantic model to StructType.

Runtime Utilities

is_pyspark_available() -> bool

Check if PySpark is installed and available.

🧪 Testing

Run the test suite:

# Install dev dependencies
pip install "polyspark[dev]"

# Run all tests
pytest

# Run with coverage
pytest --cov=polyspark --cov-report=html

# Run specific test file
pytest tests/test_factory.py -v

💡 Examples

Explore complete examples in the examples/ directory:

Core Examples

• basic_usage.py - Getting started with dataclasses
• decorator_usage.py - Using the @spark_factory decorator
• pydantic_models.py - Pydantic model integration
• complex_types.py - Arrays, maps, and nested structures
• direct_schema.py - Explicit PySpark schema usage

Advanced Examples

• testing_patterns.py ⭐ NEW - Unit testing, integration testing, and test fixtures
• custom_providers.py ⭐ NEW - Custom data generation for realistic test data
• production_usage.py ⭐ NEW - Large-scale generation, partitioning, and performance optimization

🐛 Troubleshooting

"PySpark not available" Error

# Make sure PySpark is installed
pip install pyspark

# Or use build_dicts() which doesn't need PySpark
dicts = MyModel.build_dicts(size=100)

Schema Inference Issues

If schema inference fails, provide an explicit schema:

from pyspark.sql.types import StructType, StructField, StringType

schema = StructType([StructField("field", StringType(), True)])
df = MyModel.build_dataframe(spark, size=100, schema=schema)

Type Not Supported

If you encounter UnsupportedTypeError, the type may not have a direct PySpark equivalent. Use a supported type or provide an explicit schema.

🤝 Contributing

Contributions are welcome! Here's how:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests (pytest)
5. Run linting (ruff check . && black --check .)
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

See CONTRIBUTING.md for detailed guidelines.

📄 License

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

🙏 Acknowledgments

• polyfactory - The powerful factory library that powers Polyspark's data generation
• PySpark - The Python API for Apache Spark
• Faker - Realistic fake data generation (used by polyfactory)

🔗 Related Projects

• polyfactory - Factory library for mock data
• PySpark - Python API for Apache Spark
• Pydantic - Data validation using Python type annotations
• pytest - Testing framework

📞 Support

• 🐛 Bug Reports: Open an issue
• 💡 Feature Requests: Start a discussion
• 📖 Documentation: Read the guide
• ⭐ Star us on GitHub if you find Polyspark helpful!

---

Built with ❤️ for the PySpark community