cognite-pygen-spark 0.2.2
Code generation library for creating Python UDTFs from CDF Data Models
Requires Python: <4.0,>=3.10
Keywords: cognite, cdf, databricks, udtf, code-generation, pyspark

# pygen-spark

A code generation library that extends [pygen](https://github.com/cognitedata/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 0.2.0 includes improved error handling, direct REST API calls, and enhanced time series UDTF support. Version 0.2.1 fixes protobuf parsing for detailed time series UDTFs. Version 0.2.2 adds SQL-native time series UDTF template with predicate pushdown support.

**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 support filter translation from Spark SQL to CDF API filters
- **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:

```python
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

```bash
pip install cognite-pygen-spark
```

## Quick Start

```python
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](docs/guide/index.md) 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](../Technical%20Plan%20-%20CDF%20Databricks%20Integration%20(UDTF-Based).md) 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

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

### Running Tests

```bash
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](docs/guide/installation.md) for details.

## Related Packages

- **[pygen](https://github.com/cognitedata/pygen)**: Base code generation library for CDF Data Models
- **[cognite-databricks](https://github.com/cognitedata/cognite-databricks)**: Helper SDK for Databricks-specific features (Unity Catalog, Secret Manager)
- **[cognite-sdk-python](https://github.com/cognitedata/cognite-sdk-python)**: Python SDK for CDF APIs

## Documentation

### User Guide

- **[Getting Started](docs/guide/index.md)**: Complete user guide for pygen-spark
- **[Installation](docs/guide/installation.md)**: Installation and setup instructions
- **[Generation](docs/guide/generation.md)**: Generate UDTF code from CDF Data Models
- **[Registration](docs/guide/registration.md)**: Register UDTFs in Spark sessions
- **[Querying](docs/guide/querying.md)**: Query UDTFs using SQL
- **[Filtering](docs/guide/filtering.md)**: Filter data with WHERE clauses
- **[Joining](docs/guide/joining.md)**: Join data from different UDTFs
- **[Troubleshooting](docs/guide/troubleshooting.md)**: Common issues and solutions

### Examples

- **[Basic Generation](examples/basic_generation.ipynb)**: Generate UDTFs from a Data Model
- **[Registration](examples/registration.ipynb)**: Register and query UDTFs
- **[Querying Data](examples/querying_data.ipynb)**: Various querying patterns
- **[Filtering Queries](examples/filtering_queries.ipynb)**: Filter examples
- **[Joining UDTFs](examples/joining_udtfs.ipynb)**: Join examples

### Technical Documentation

- [Technical Plan - CDF Databricks Integration (UDTF-Based)](../Technical%20Plan%20-%20CDF%20Databricks%20Integration%20(UDTF-Based).md)
- [Pygen Developer Documentation](https://cognite-pygen.readthedocs-hosted.com/en/latest/developer_docs/index.html)

## License

[License information]

## Contributing

[Contributing guidelines]