k8s-maestro 1.0.0

//! Aspirational API example: Python step in workflows.
//!
//! This example demonstrates the proposed API for running Python steps within workflows.
//! This is a forward-looking design and may not yet be fully implemented.

fn main() -> anyhow::Result<()> {
    println!("=== Aspirational Python Step API ===");
    println!("Note: This is a proposed API design for future implementation.\n");

    example_basic_python_step()?;
    example_python_with_dependencies()?;
    example_python_with_data_artifacts()?;
    example_python_function_step()?;

    println!("\n=== All examples completed (aspirational) ===");
    Ok(())
}

fn example_basic_python_step() -> anyhow::Result<()> {
    println!("Example 1: Basic Python step with inline script");

    println!("Python step would process CSV data using pandas and numpy");
    println!("Input: /input/data.csv -> Output: /output/result.csv");
    println!("Runtime: python:3.11-slim with pandas and numpy packages");

    Ok(())
}

fn example_python_with_dependencies() -> anyhow::Result<()> {
    println!("Example 2: Python step with requirements.txt and virtual environment");

    println!("Python step would run ML training with GPU support");
    println!("Requirements: Scikit-learn, TensorFlow, pandas");
    println!("Resource limits: 2 CPU, 4Gi RAM, 1 GPU");

    Ok(())
}

fn example_python_with_data_artifacts() -> anyhow::Result<()> {
    println!("Example 3: Python step with data artifact inputs and outputs");

    println!("Python step would process artifacts from previous workflow steps");
    println!("Inputs: config.json from 'config-workflow', data.csv from 'data-prep'");
    println!("Outputs: statistics.json and report.pdf for downstream consumption");

    Ok(())
}

fn example_python_function_step() -> anyhow::Result<()> {
    println!("Example 4: Python function step (lambda-style execution)");

    println!("Python function step would process records in parallel");
    println!("Function: Add timestamp and status to each record");
    println!("Batch size: 100 records, Parallelism: 4 workers");

    Ok(())
}