pyspark-udtf 0.1.2

A collection of PySpark User-Defined Table Functions (UDTFs)

PySpark UDTF Examples

A collection of Python User-Defined Table Functions (UDTFs) for PySpark, demonstrating how to leverage UDTFs for complex data processing tasks.

Installation

You can quickly install the package using pip:

pip install pyspark-udtf

Usage

Fuzzy Matching (Quick Start)

This UDTF demonstrates how to use Python's standard library difflib to perform fuzzy string matching in PySpark. It takes a target string and a list of candidates, returning the best match and a similarity score.

from pyspark.sql import SparkSession
from pyspark_udtf.udtfs import FuzzyMatch

spark = SparkSession.builder.getOrCreate()

# Register the UDTF
spark.udtf.register("fuzzy_match", FuzzyMatch)

# Create a sample dataframe with typos
data = [
    ("aple", ["apple", "banana", "orange"]),
    ("bananna", ["apple", "banana", "orange"]),
    ("orange", ["apple", "banana", "orange"]),
    ("grape", ["apple", "banana", "orange"]) 
]
df = spark.createDataFrame(data, ["typo", "candidates"])

# Use the UDTF in SQL
df.createOrReplaceTempView("typos")

spark.sql("""
    SELECT * 
    FROM fuzzy_match(TABLE(SELECT typo, candidates FROM typos))
""").show()

Batch Inference Image Captioning

This UDTF demonstrates how to perform efficient batch inference against a model serving endpoint. It buffers rows and sends them in batches to reduce network overhead.

from pyspark.sql import SparkSession
from pyspark_udtf.udtfs import BatchInferenceImageCaption

spark = SparkSession.builder.getOrCreate()

# Register the UDTF
spark.udtf.register("batch_image_caption", BatchInferenceImageCaption)

# View UDTF definition and parameters
help(BatchInferenceImageCaption.func)

# Usage in SQL
# Assuming you have a table 'images' with a column 'url'
spark.sql("""
    SELECT * 
    FROM batch_image_caption(
        TABLE(SELECT url FROM images), 
        10,  -- batch_size
        'your-api-token', 
        'https://your-endpoint.com/score'
    )
""").show()

Requirements

• Python >= 3.10
• PySpark >= 4.0.0
• requests
• pandas
• pyarrow

Documentation

For more detailed documentation, including design docs and guides for Unity Catalog integration, see the docs/ directory.

• Unity Catalog Guide

Development

We recommend using uv for extremely fast package management.

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install the package
uv add pyspark-udtf

Running Tests

To run the test suite:

# Run all tests
uv run pytest

# Run specific test file
uv run pytest tests/test_image_caption.py

Linting

This project uses Ruff for linting and formatting. Install dev dependencies, then run:

uv sync --extra dev   # install ruff
uv run ruff check .   # lint
uv run ruff format .  # format

Adding Dependencies

To add a new runtime dependency:

uv add package_name

To add a development dependency:

uv add --dev package_name

Bumping Version

You can bump the version automatically using uv (requires uv >= 0.7.0):

# Bump patch version (0.1.0 -> 0.1.1)
uv version --bump patch

# Bump minor version (0.1.0 -> 0.2.0)
uv version --bump minor

Alternatively, you can manually update pyproject.toml:

1. Open pyproject.toml.
2. Update the version field under [project]:

   [project]
   version = "0.1.1"  # Update this value
   

Publishing to PyPI

To build and publish the package to PyPI:

1. Build the package:

   uv build
   

   This will create distributions in the dist/ directory.

2. Publish to PyPI:

   uv publish
   

   Note: You will need to configure your PyPI credentials (API token) either via environment variables (UV_PUBLISH_TOKEN) or following uv's authentication documentation.