pygen-spark

A code generation library that extends pygen to generate Python User-Defined Table Functions (UDTFs) for CDF Data Models, enabling you to query CDF data directly from Spark SQL.

Latest Release: Version 0.3.2 reduces memory usage in generated view UDTFs for wide views (adaptive API page size, single-pass property resolution, and page memory cleanup). Version 0.3.1 updates security-related dependencies. Version 0.3.0 adds CDF audit headers to generated UDTF HTTP clients. Earlier releases: 0.2.x improved error handling, time series UDTFs, and reserved-word property names.

Full release notes are published on GitHub Releases. Patch releases may update locked dependencies and minimum constraints without API changes.

Note: This document uses PyPI package names for references:

• PyPI: cognite-pygen (repository: pygen)
• PyPI: cognite-pygen-spark (repository: pygen-spark)
• Import paths: cognite.pygen, cognite.pygen_spark

Overview

cognite.pygen_spark (PyPI: cognite-pygen-spark) is a generic Spark UDTF code generation library that works with any Spark cluster (standalone, YARN, Kubernetes, or local development). It generates strongly-typed Python UDTF functions from CDF Data Models using Jinja2 templates, allowing you to query CDF data directly from Spark SQL.

Package Purpose:

• Generic Spark Support: Works with any Spark cluster, not limited to Databricks
• Template-Based Generation: Uses Jinja2 templates to generate UDTF code for both Data Model UDTFs and Time Series UDTFs
• Type Conversion Utilities: Provides TypeConverter class for converting between CDF types, PySpark DataTypes, and SQL DDL
• Connection Configuration: Provides CDFConnectionConfig Pydantic model for managing CDF credentials from TOML/YAML files
• Utility Functions: Helper functions for consistent UDTF naming and other generic Spark utilities

Features

• UDTF Generation: Automatically generates Python UDTF functions for each View in a CDF Data Model
• Time Series UDTFs: Template-generated UDTFs for querying CDF time series datapoints (single, multiple, latest) using the same template-based generation as Data Model UDTFs
• Type Safety: Leverages pygen's internal representation for strongly-typed code generation
• Predicate Pushdown: Generated UDTFs push WHERE filters, LIMIT, and COUNT/MIN/MAX (numeric properties) to CDF instances/list and instances/aggregate. A trailing base_url parameter is read at query time for Private Link and dedicated clusters.
• CDF audit headers: Generated UDTF HTTP clients send pygen-main-style x-cdp-app, x-cdp-sdk, and User-Agent for attributable CDF traffic
• Configuration File Support: Uses TOML/YAML configuration files for secure credential management
• Generic Spark Support: Works with any Spark cluster, not limited to Databricks
• Type Conversion Utilities: TypeConverter class for converting between CDF types, PySpark DataTypes, and SQL DDL
• Connection Configuration: CDFConnectionConfig Pydantic model for managing CDF credentials
• Utility Functions: Helper functions for consistent UDTF naming and other utilities

Using Generic Spark Utilities

pygen-spark provides generic utilities that work with any Spark cluster:

from cognite.pygen_spark import TypeConverter, CDFConnectionConfig, to_udtf_function_name

# Type conversion utilities
from cognite.client import data_modeling as dm
from pyspark.sql.types import StringType

# Convert CDF property type to PySpark DataType
spark_type = TypeConverter.cdf_to_spark(dm.Text(), is_array=False)
# Returns: StringType()

# Convert PySpark DataType to SQL DDL
sql_ddl = TypeConverter.spark_to_sql_ddl(spark_type)
# Returns: "STRING"

# Connection configuration from TOML
config = CDFConnectionConfig.from_toml("config.toml")
client = config.create_client()

# Convert view external_id to UDTF function name
udtf_name = to_udtf_function_name("MyView")
# Returns: "my_view_udtf"

These utilities are generic and work with any Spark cluster, not just Databricks.

Installation

pip install cognite-pygen-spark

Quick Start

from pathlib import Path
from cognite.client.data_classes.data_modeling.ids import DataModelId
from cognite.pygen import load_cognite_client_from_toml
from cognite.pygen_spark import SparkUDTFGenerator

# Load client from TOML file
client = load_cognite_client_from_toml("config.toml")

# Create generator
generator = SparkUDTFGenerator(
    client=client,
    output_dir=Path("./generated_udtfs"),
    data_model=DataModelId(space="sailboat", external_id="sailboat", version="1"),
    top_level_package="cognite_udtfs",
)

# Generate UDTFs for a Data Model
result = generator.generate_udtfs()

print(f"Generated {result.total_count} UDTF(s)")
for view_id, file_path in result.generated_files.items():
    print(f"  - {view_id}: {file_path}")

# Generate time series UDTFs (template-generated, same as data model UDTFs)
ts_result = generator.generate_time_series_udtfs()
print(f"Generated {ts_result.total_count} time series UDTF(s)")
for udtf_name, file_path in ts_result.generated_files.items():
    print(f"  - {udtf_name}: {file_path}")

See the User Guide for complete documentation on generating, registering, and querying UDTFs.

Architecture

cognite.pygen_spark extends cognite.pygen's architecture:

• Reuses pygen's View parsing: Leverages pygen's internal representation of CDF Data Models
• Custom template engine: Uses Jinja2 templates to generate UDTF Python code and SQL Views
• Extends MultiAPIGenerator: Builds on pygen's code generation infrastructure
• Consistent template-based generation: Both Data Model UDTFs and Time Series UDTFs use the same Jinja2 template-based generation approach for consistent behavior, error handling, and initialization patterns

See the Technical Plan for detailed architecture documentation.

Requirements

• Python 3.9+
• PySpark 3.5+ (required for UDTF support)
• cognite-pygen (PyPI package name; import: cognite.pygen)
• cognite-sdk-python (must be installed on all Spark worker nodes)
• Spark cluster (standalone, YARN, Kubernetes, or local)

Package Structure

pygen-spark/
├── cognite/
│   └── pygen_spark/
│       ├── __init__.py
│       ├── generator.py          # SparkUDTFGenerator
│       ├── udtf_generator.py    # SparkMultiAPIGenerator
│       └── templates/
│           ├── udtf_function.py.jinja
│           ├── view_sql.py.jinja
│           └── udtf_init.py.jinja
├── pyproject.toml
└── README.md

Development

Setup

git clone <repository-url>
cd pygen-spark
pip install -e ".[dev]"

Running Tests

pytest tests/

Spark Cluster Compatibility

This package generates UDTF code that works with any Spark cluster:

• Code Generation: Works on all Spark versions ✅
• UDTF Templates: Compatible with PySpark 3.5+ ✅
• Dependency Management: Requires cognite-sdk on all Spark worker nodes ⚠️

For standalone Spark clusters, ensure cognite-sdk is installed on all worker nodes. See the Installation Guide for details.

Related Packages

• pygen: Base code generation library for CDF Data Models
• cognite-databricks: Helper SDK for Databricks-specific features (Unity Catalog, Secret Manager)
• cognite-sdk-python: Python SDK for CDF APIs

Documentation

User Guide

• Getting Started: Complete user guide for pygen-spark
• Installation: Installation and setup instructions
• Generation: Generate UDTF code from CDF Data Models
• Registration: Register UDTFs in Spark sessions
• Querying: Query UDTFs using SQL
• Filtering: Filter data with WHERE clauses
• Joining: Join data from different UDTFs
• Troubleshooting: Common issues and solutions

Examples

• Basic Generation: Generate UDTFs from a Data Model
• Registration: Register and query UDTFs
• Querying Data: Various querying patterns
• Filtering Queries: Filter examples
• Joining UDTFs: Join examples

Technical Documentation

• Technical Plan - CDF Databricks Integration (UDTF-Based)
• Pygen Developer Documentation

License

[License information]

Contributing

[Contributing guidelines]