sparkdantic 0.2.2

A pydantic -> spark schema library

SparkDantic

1️⃣ version: 0.2.2

✍️ author: Mitchell Lisle

PySpark Model Conversion Tool

This Python module provides a utility for converting Pydantic models to PySpark schemas. It's implemented as a class named SparkModel that extends the Pydantic's BaseModel.

Features

• Conversion from Pydantic model to PySpark schema.
• Determination of nullable types.
• Customizable type mapping between Python and PySpark data types.

Dependencies

This module aims to have a small dependency footprint:

• pydantic
• pyspark
• Python's built-in datetime, decimal, types, and typing modules

Usage

Creating a new SparkModel

A SparkModel is a Pydantic model, and you can define one by simply inheriting from SparkModel and defining some fields:

from sparkdantic import SparkModel
from typing import List

class MyModel(SparkModel):
    name: str
    age: int
    hobbies: List[str]

Generating a PySpark Schema

Pydantic has existing models for generating json schemas (with model_json_schema). With a SparkModel you can generate a PySpark schema from the model fields using the model_spark_schema() method:

my_model = MyModel()
spark_schema = my_model.model_spark_schema()

Provides this schema:

StructType([
    StructField('name', StringType(), True),
    StructField('age', IntegerType(), True),
    StructField('hobbies', ArrayType(StringType(), False), True)
])