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Jobs: Run Functions Once 🎯

Jobs are Runnable's solution for one-time function execution. Perfect for standalone tasks, testing, analysis, and automation scripts.

What Are Jobs?

Jobs wrap your Python functions to provide:

  • Execution tracking - Know what ran, when, and what happened
  • Parameter management - Pass configuration from files or environment
  • Output capture - Automatically store results and logs
  • Error handling - Graceful failure management
  • Reproducibility - Consistent execution across environments

Quick Start

from runnable import PythonJob

def analyze_data():
    """A simple analysis function."""
    data = [1, 2, 3, 4, 5]
    result = sum(data) / len(data)
    print(f"Average: {result}")
    return result

def main():
    job = PythonJob(function=analyze_data)
    job.execute()
    return job

if __name__ == "__main__":
    main()
See complete runnable code
examples/11-jobs/python_tasks.py
"""
You can execute this pipeline by:

    python examples/01-tasks/python_tasks.py

The stdout of "Hello World!" would be captured as execution
log and stored in the catalog.

An example of the catalog structure:

.catalog
└── baked-heyrovsky-0602
    └── hello.execution.log

2 directories, 1 file


The hello.execution.log has the captured stdout of "Hello World!".
"""

from examples.common.functions import hello
from runnable import PythonJob


def main():
    job = PythonJob(function=hello)

    job.execute()

    return job


if __name__ == "__main__":
    main()

Try it now: 

uv run examples/11-jobs/python_tasks.py

Common Use Cases

📊 Data Analysis

Perfect for one-off analysis tasks:

def monthly_sales_analysis():
    # run a notebook

🧪 Function Testing

Test your functions in isolation:

def test_my_algorithm():
    # Test edge cases, validate outputs
    assert my_algorithm([1, 2, 3]) == expected_result

📝 Report Generation

Generate standalone reports:

def generate_weekly_report():
    # Collect data, create visualizations, export PDF
    return "report_2024_week_47.pdf"

Job Types

Runnable supports multiple job types for different scenarios:

Job Type Purpose Example
PythonJob Execute Python functions Data analysis, calculations
NotebookJob Run Jupyter notebooks Interactive analysis, reports
ShellJob Execute shell commands System operations, deployments

Key Features

✅ Simple Execution

job = PythonJob(function=my_function)
job.execute()

⚙️ Parameter Support

job.execute(parameters_file="config.yaml")

📁 Automatic Output Storage

  • Execution logs captured automatically
  • Results stored in catalog
  • Reproducible execution history

When to Use Jobs vs Pipelines

Scenario Use Jobs Use Pipelines
Single function to run ✅ ❌
Testing a function ✅ ❌
One-off analysis ✅ ❌
Multi-step workflow ❌ ✅
Data dependencies between steps ❌ ✅
Reproducible processes ❌ ✅

Start Simple, Grow Complex

Start with a Job to test your function, then evolve it into a Pipeline when you need multiple steps or complex workflows.

What's Next?