FlowerPower 0.31.3

A simple workflow framework for building and managing data processing pipelines

FlowerPower 🌸 - Build & Orchestrate Data Pipelines

Simple Workflow Framework - Hamilton = FlowerPower

FlowerPower is a Python framework designed for building, configuring, and executing data processing pipelines with ease and flexibility. It promotes a modular, configuration-driven approach, allowing you to focus on your pipeline logic while FlowerPower handles the orchestration.

It leverages the Hamilton library for defining dataflows in a clean, functional way within your Python pipeline scripts. Pipelines are defined in Python modules and configured using YAML files, making it easy to manage and understand your data workflows. FlowerPower provides a unified project interface that makes it easy to work with pipeline execution. It also provides a web UI (Hamilton UI) for monitoring and managing your pipelines. FlowerPower is designed to be extensible, allowing you to easily add custom I/O plugins and adapt to different deployment scenarios. This flexibility makes it suitable for a wide range of data processing tasks, from simple ETL jobs to complex data workflows.

✨ Key Features

• Modular Pipeline Design: Thanks to Hamilton, you can define your data processing logic in Python modules, using functions as nodes in a directed acyclic graph (DAG).
• Configuration-Driven: Define pipeline parameters, execution logic, and scheduling declaratively using simple YAML files.
• Extensible I/O Plugins: Connect to various data sources and destinations (CSV, JSON, Parquet, DeltaTable, DuckDB, PostgreSQL, MySQL, MSSQL, Oracle, MQTT, SQLite, and more).
• Unified Project Interface: Interact with your pipelines via:
  • FlowerPowerProject API: A unified interface for pipeline execution, supporting both RunConfig objects and flexible **kwargs overrides.
  • Command Line Interface (CLI): For running, managing, and inspecting pipelines, with enhanced run command capabilities.
  • Web UI: A graphical interface for monitoring and managing pipelines and schedules. (Hamilton UI)
• Filesystem Abstraction: Simplified file handling with support for local and remote filesystems (e.g., S3, GCS).

📦 Installation

We recommend using uv for installing FlowerPower and managing your project environments. uv is an extremely fast Python package installer and resolver.

# Create and activate a virtual environment (recommended)
uv venv
source .venv/bin/activate # Or .\.venv\Scripts\activate on Windows

# Install FlowerPower
uv pip install flowerpower

# Optional: Install additional dependencies for specific features
uv pip install flowerpower[io] # For I/O plugins (CSV, JSON, Parquet, DeltaTable, DuckDB, PostgreSQL, MySQL, MSSQL, Oracle, SQLite)
uv pip install flowerpower[ui] # For Hamilton UI
uv pip install flowerpower[all] # Install all optional dependencies

(Note: Specify required Python versions if known, e.g., Python 3.8+)

🚀 Getting Started

Let's build a simple "Hello World" pipeline.

1. Initialize Your Project:

You can quickly set up the standard FlowerPower project structure using the CLI or Python.

Using the CLI:

Navigate to your desired parent directory and run:

flowerpower init --name hello-flowerpower-project

Using Python:

Alternatively, you can initialize programmatically:

from flowerpower import FlowerPowerProject

# Initialize a new project
project = FlowerPowerProject.init(
    name='hello-flowerpower-project',
)

This will create a hello-flowerpower-project directory with the necessary conf/ and pipelines/ subdirectories and default configuration files.

hello-flowerpower-project/
├── conf/
│   ├── project.yml
│   └── pipelines/
└── pipelines/

Now, navigate into your new project directory:

cd hello-flowerpower-project

Configure Project (conf/project.yml):

Open conf/project.yml and define your project name:

name: hello-flowerpower
# adapter: ... # Optional adapter configurations (e.g., Hamilton Tracker, MLflow), see `conf/project.yml` for details

2. Create Your Pipeline

You can create a new pipeline using the CLI or programmatically.

Using the CLI:

flowerpower pipeline new hello_world

Using Python:

You can create pipelines programmatically using the FlowerPowerProject interface:

from flowerpower import FlowerPowerProject

# Load the project
project = FlowerPowerProject.load('.')

# Create a new pipeline
project.pipeline_manager.new(name='hello_world')

This will create a new file hello_world.py in the pipelines/ directory and a corresponding configuration file hello_world.yml in conf/pipelines/.

Implement Pipeline (pipelines/hello_world.py):

Open pipelines/hello_world.py and write your pipeline logic using Python and Hamilton. FlowerPower makes configuration easily accessible.

# FlowerPower pipeline hello_world.py
# Created on 2025-05-03 22:34:09

####################################################################################################
# Import necessary libraries
# NOTE: Remove or comment out imports that are not used in the pipeline

from hamilton.function_modifiers import parameterize

from pathlib import Path

from flowerpower.cfg import Config

####################################################################################################
# Load pipeline parameters. Do not modify this section.

PARAMS = Config.load(
    Path(__file__).parents[1], pipeline_name="hello_world"
).pipeline.h_params


####################################################################################################
# Helper functions.
# This functions have to start with an underscore (_).


####################################################################################################
# Pipeline functions

@parameterize(**PARAMS.greeting_message) # Inject 'message' from params
def greeting_message(message: str) -> str:
  """Provides the greeting part."""
  return f"{message},"

@parameterize(**PARAMS.target_name) # Inject 'name' from params
def target_name(name: str) -> str:
  """Provides the target name."""
  return f"{name}!"

def full_greeting(greeting_message: str, target_name: str) -> str:
  """Combines the greeting and target."""
  print(f"Generating greeting: {greeting_message} {target_name}")
  return f"{greeting_message} {target_name}"

# You can add more complex Hamilton functions here...

Configure Pipeline (conf/pipelines/hello_world.yml):

Open conf/pipelines/hello_world.yml and specify parameters, run configurations, and scheduling for your pipeline.

# adapter: ... # Pipeline-specific adapter overrides

params: # Parameters accessible in your Python code
  greeting_message:
    message: "Hello"
  target_name:
    name: "World"

run: # How to execute the pipeline
  final_vars: # Specify the desired output(s) from your Hamilton DAG
    - full_greeting
  # inputs: # Optional: Specify input variables to the pipeline
    # message: "Hello"
  # config: ... # Runtime configuration overrides for Hamilton
  # executor: ... # Execution backend (e.g., threadpool, multiprocessing)

3. Run Your Pipeline 🏃‍♀️

FlowerPower allows you to execute your pipelines synchronously, with flexible configuration options.

Synchronous Execution:

For quick testing or local runs, you can execute your pipeline synchronously. This is useful for debugging or running pipelines in a local environment.

• Via CLI:

  The flowerpower pipeline run command now supports RunConfig objects (via file path or JSON string) and direct **kwargs for overriding.

  # Basic pipeline execution
  flowerpower pipeline run hello_world
  
  # Run with individual parameters (kwargs)
  flowerpower pipeline run hello_world --inputs '{"greeting_message": "Hi", "target_name": "FlowerPower"}' --final-vars '["full_greeting"]' --log-level DEBUG
  
  # Run using a RunConfig from a YAML file
  # Assuming you have a run_config.yaml like:
  # inputs:
  #   greeting_message: "Hola"
  #   target_name: "Amigo"
  # log_level: "INFO"
  flowerpower pipeline run hello_world --run-config ./run_config.yaml
  
  # Run using a RunConfig provided as a JSON string
  flowerpower pipeline run hello_world --run-config '{"inputs": {"greeting_message": "Bonjour", "target_name": "Monde"}, "log_level": "INFO"}'
  
  # Mixing RunConfig with individual parameters (kwargs overrides RunConfig)
  # This will run with log_level="DEBUG" and inputs={"greeting_message": "Howdy", "target_name": "Partner"}
  flowerpower pipeline run hello_world --run-config '{"inputs": {"greeting_message": "Original", "target_name": "Value"}, "log_level": "INFO"}' --inputs '{"greeting_message": "Howdy", "target_name": "Partner"}' --log-level DEBUG
  

• Via Python:

  The run methods (FlowerPowerProject.run, PipelineManager.run) now primarily accept a RunConfig object, but also allow individual parameters to be passed via **kwargs which override RunConfig attributes.

  from flowerpower import FlowerPowerProject
  from flowerpower.cfg.pipeline.run import RunConfig
  from flowerpower.cfg.pipeline.builder import RunConfigBuilder
  
  # Load the project
  project = FlowerPowerProject.load('.')
  
  # Basic execution
  result = project.run('hello_world')
  print(result)
  
  # Using individual parameters (kwargs)
  result = project.run(
      'hello_world',
      inputs={"greeting_message": "Hi", "target_name": "FlowerPower"},
      final_vars=["full_greeting"],
      log_level="DEBUG"
  )
  print(result)
  
  # Using RunConfig directly
  config = RunConfig(
      inputs={"greeting_message": "Aloha", "target_name": "World"},
      final_vars=["full_greeting"],
      log_level="INFO"
  )
  result = project.run('hello_world', run_config=config)
  print(result)
  
  # Using RunConfigBuilder (recommended)
  config = (
      RunConfigBuilder(pipeline_name='hello_world')
      .with_inputs({"greeting_message": "Greetings", "target_name": "Earth"})
      .with_final_vars(["full_greeting"])
      .with_log_level("DEBUG")
      .with_retries(max_attempts=3, delay=1.0)
      .build()
  )
  result = project.run('hello_world', run_config=config)
  print(result)
  
  # Mixing RunConfig with individual parameters (kwargs overrides RunConfig)
  base_config = RunConfigBuilder().with_log_level("INFO").build()
  result = project.run(
      'hello_world',
      run_config=base_config,
      inputs={"greeting_message": "Howdy", "target_name": "Partner"}, # Overrides inputs in base_config
      log_level="DEBUG" # Overrides log_level in base_config
  )
  print(result)
  

⚙️ Configuration Overview

FlowerPower uses a layered configuration system:

• conf/project.yml: Defines global settings for your project, including integrated adapters (like Hamilton Tracker, MLflow, etc.).
• conf/pipelines/*.yml: Each file defines a specific pipeline. It contains:
  • params: Input parameters for your Hamilton functions.
  • run: Execution details like target outputs (final_vars), Hamilton runtime config, and executor settings.
  • adapter: Pipeline-specific overrides for adapter settings.

🛠️ Basic Usage

You can interact with FlowerPower pipelines through multiple interfaces:

Python API (Recommended):

from flowerpower import FlowerPowerProject
from flowerpower.cfg.pipeline.run import RunConfig
from flowerpower.cfg.pipeline.builder import RunConfigBuilder

# Load the project
project = FlowerPowerProject.load('.')

# Run a pipeline using RunConfig
config = RunConfig(inputs={"greeting_message": "Hello", "target_name": "API"})
result = project.run('hello_world', run_config=config)
print(result)

# Run a pipeline using kwargs
result = project.run('hello_world', inputs={"greeting_message": "Hi", "target_name": "Kwargs"})
print(result)

CLI:

# Run a pipeline using RunConfig from a file
# flowerpower pipeline run hello_world --run-config ./path/to/run_config.yaml

# Run a pipeline using kwargs
flowerpower pipeline run hello_world --inputs '{"greeting_message": "CLI", "target_name": "Kwargs"}'

# List all available commands
flowerpower --help

🔧 Direct Module Usage

While the unified FlowerPowerProject interface is recommended for most use cases, you can also use the pipeline module directly for more granular control or when you only need specific functionality.

Pipeline-Only Usage

If you only need pipeline execution, you can use the PipelineManager directly:

from flowerpower.pipeline import PipelineManager
from flowerpower.cfg.pipeline.run import RunConfig
from flowerpower.cfg.pipeline.builder import RunConfigBuilder

# Initialize pipeline manager
pm = PipelineManager(base_dir='.')

# Create a new pipeline
pm.new(name='my_pipeline')

# Run a pipeline synchronously using RunConfig
config = RunConfig(inputs={'param': 'value'}, final_vars=['output_var'])
result = pm.run(name='my_pipeline', run_config=config)
print(result)

# Run a pipeline synchronously using kwargs
result = pm.run(name='my_pipeline', inputs={'param': 'new_value'}, final_vars=['output_var'])
print(result)

# List available pipelines
pipelines = pm.list()
print(f"Available pipelines: {pipelines}")

# Get pipeline information
info = pm.get('my_pipeline')
print(f"Pipeline config: {info}")

# Delete a pipeline
pm.delete('old_pipeline')

When to use Pipeline-only approach:

• Simple synchronous workflows
• Testing and development
• Lightweight applications with minimal dependencies

Benefits of FlowerPowerProject vs Direct Usage:

Approach	Benefits	Use Cases
FlowerPowerProject	- Unified interface - Automatic dependency injection - Simplified configuration - Best practices built-in	- Most applications - Rapid development - Full feature integration
Pipeline-only	- Lightweight - Simple synchronous execution	- Testing - Simple workflows

🖥️ UI

The FlowerPower web UI (Hamilton UI) provides a graphical interface for monitoring and managing your pipelines. It allows you to visualize pipeline runs, schedules, and potentially manage configurations.

# Start the web UI
flowerpower ui

📖 Documentation

You can find the full documentation for FlowerPower, including installation instructions, usage examples, and API references, at https://legout.github.io/flowerpower/.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details. (Placeholder - update with actual license)