pydagu 0.2.3

Python client library for Dagu - A cron alternative with a Web UI

pydagu

A Python client library for Dagu - providing type-safe DAG creation and HTTP API interaction with Pydantic validation.

Features

• 🎯 Type-safe: Built with Pydantic models for full type safety and validation
• 🏗️ Builder Pattern: Fluent API for constructing DAGs and steps
• 🔌 HTTP Client: Complete client for Dagu's HTTP API
• 🔄 Webhook Support: Built-in patterns for webhook integration
• ✅ Well-tested: Comprehensive test suite with 95%+ coverage
• 📝 Examples: Production-ready integration examples

Installation

pip install pydagu

Prerequisites

You need a running Dagu server to run the tests. Install Dagu:

# macOS
brew install dagu-org/brew/dagu

# Linux
curl -L https://github.com/dagu-org/dagu/releases/latest/download/dagu_linux_amd64.tar.gz | tar xz
sudo mv dagu /usr/local/bin/

# Start the server
dagu server

Quick Start

Create and Run a Simple DAG

from pydagu.builder import DagBuilder, StepBuilder
from pydagu.http import DaguHttpClient
from pydagu.models.request import StartDagRun

# Initialize client
client = DaguHttpClient(
    dag_name="my-first-dag",
    url_root="http://localhost:8080/api/v2/"
)

# Build a DAG
dag = (
    DagBuilder("my-first-dag")
    .description("My first DAG with pydagu")
    .add_step_models(
        StepBuilder("hello-world")
        .command("echo 'Hello from pydagu!'")
        .build()
    )
    .build()
)

# Post the DAG to Dagu
client.post_dag(dag)

# Start a run
dag_run_id = client.start_dag_run(StartDagRun(dagName=dag.name))

# Check status
status = client.get_dag_run_status(dag_run_id.dagRunId)
print(f"Status: {status.statusLabel}")

HTTP Executor with Retry

from pydagu.builder import StepBuilder

step = (
    StepBuilder("api-call")
    .command("POST https://api.example.com/webhook")
    .http_executor(
        headers={
            "Content-Type": "application/json",
            "Authorization": "Bearer ${API_TOKEN}"
        },
        body={"event": "user.created", "user_id": "123"},
        timeout=30
    )
    .retry(limit=3, interval=5)
    .build()
)

Chained Steps with Dependencies

from pydagu.builder import DagBuilder, StepBuilder

dag = (
    DagBuilder("data-pipeline")
    .add_step_models(
        StepBuilder("extract")
        .command("python extract_data.py")
        .output("EXTRACTED_FILE")
        .build(),

        StepBuilder("transform")
        .command("python transform_data.py ${EXTRACTED_FILE}")
        .depends_on("extract")
        .output("TRANSFORMED_FILE")
        .build(),

        StepBuilder("load")
        .command("python load_data.py ${TRANSFORMED_FILE}")
        .depends_on("transform")
        .build()
    )
    .build()
)

Development

# Clone the repository
git clone https://github.com/yourusername/pydagu.git
cd pydagu

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests (requires Dagu server running)
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=pydagu --cov-report=html

Documentation

• PyDagu Reference
• Dagu Documentation

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details.

Related Projects

• Dagu - The underlying DAG execution engine
• Airflow - Alternative workflow orchestration platform
• Fluvial Diligence - TPRM Platform with customisable workflows