reflowfy 0.23

A horizontally scalable data movement and transformation framework

Reflowfy

A horizontally scalable data movement and transformation framework

Reflowfy enables you to build pipelines that fetch data from sources, apply custom transformations, and send results to destinations—all with millions+ record scalability.

🎯 Key Features

• Horizontally Scalable: Process millions of records in parallel
• Kafka-Based: Reliable message queue for job distribution
• Kubernetes-Native: KEDA autoscaling from 0 to N workers
• Order-Independent: Maximum parallelism without coordination overhead
• User-Extensible: Plugin architecture for sources, destinations, and transformations
• Two Execution Modes: Local testing and distributed production execution

🏗 Architecture

User Request
    ↓ HTTP
API (FastAPI) ────→ ReflowManager Service (port 8001)
    │                    ↓
    │                PostgreSQL (state + checkpoints)
    │                    ↑                   ↓
    │                    │               Kafka Producer (rate limited) → Kafka Topic (reflow.jobs)
    │                    │                   ↓
    └─→ Execution Tracking               Worker Pool (KEDA scaled)
                                             ↓
                                        Destinations

Components:

• ReflowManager: Orchestrates jobs, enforces rate limits, and tracks state.
• PostgreSQL: Central source of truth for execution state and checkpoints.
• Kafka: Reliable job queue for load balancing.
• Workers: Consumers that process jobs and report status directly to PostgreSQL.
• KEDA: Autoscales workers based on Kafka lag.

🚀 Quick Start

Get up and running in minutes using the CLI.

1. Install

pip install reflowfy

2. Initialize Project

Create a new project directory with a sample pipeline and Docker configuration:

reflowfy init my_project
cd my_project

3. Run Locally

Start the full stack (API, Manager, Worker, Kafka, Postgres) locally using Docker Compose:

# Verify everything builds
reflowfy run --build

# Run in background
reflowfy run -d

4. Deploy

Deploy to OpenShift/Kubernetes with a single command:

reflowfy deploy

---

🧠 Core Concepts

Reflowfy uses a class-based architecture for pipelines, allowing for dynamic configuration and modular design.

1. Create Custom Transformations

Transformations are reusable units of logic that process batches of records. To create one, subclass BaseTransformation:

from reflowfy import BaseTransformation

class XmlToJson(BaseTransformation):
    name = "xml_to_json"  # Unique identifier
    
    def apply(self, records, context):
        """
        Process a batch of records.
        Records are passed as a list of dictionaries (or source-specific format).
        Return the modified list.
        """
        return [self._parse_xml(r) for r in records]

    def _parse_xml(self, record):
        # ... logic ...
        return record

2. Build a Pipeline

Pipelines connect sources, transformations, and destinations. Subclass AbstractPipeline to define your logic:

from reflowfy import AbstractPipeline, pipeline_registry
from reflowfy import elastic_source, kafka_destination
from .transformations import XmlToJson

class ElasticXmlPipeline(AbstractPipeline):
    name = "elastic_xml_pipeline"
    rate_limit = {"jobs_per_second": 50}

    def define_source(self, params):
        """
        Define source based on runtime parameters.
        Parameters allow you to change behavior at runtime (e.g., time ranges).
        """
        return elastic_source(
            url="http://elasticsearch:9200",
            index="logs-*",
            base_query={
                "query": {
                    "range": {
                        "@timestamp": {
                            "gte": "{{ start_time }}",  # Jinja template support
                            "lte": "{{ end_time }}"
                        }
                    }
                }
            }
        )

    def define_transformations(self, params):
        """List of transformations to apply in order."""
        return [XmlToJson()]

    def define_destination(self, params):
        """Define where data should go."""
        return kafka_destination(
            bootstrap_servers="kafka:9092",
            topic="processed-logs"
        )

# Register the pipeline so the worker and API can find it
pipeline_registry.register(ElasticXmlPipeline())

3. Run Pipeline

You can run your pipeline locally or in production via the API:

# Production Execution (Async via Kafka)
curl -X POST http://localhost:8001/run \
  -H "Content-Type: application/json" \
  -d '{
    "pipeline_name": "elastic_xml_pipeline",
    "runtime_params": {
      "start_time": "2024-01-01",
      "end_time": "2024-01-02"
    }
  }'

# Dry Run (Preview without side effects)
curl -X POST http://localhost:8001/run ... -d '{..., "dry_run": true}'

📦 Installation

# Using pip
pip install -e .

# Using Docker
docker build -f Dockerfile.api -t reflowfy-api .
docker build -f Dockerfile.worker -t reflowfy-worker .

🔌 Built-in Connectors

Sources

• Elasticsearch: Scroll-based pagination with runtime parameters
• SQL: ID range and offset-based splitting (Postgres, MySQL, etc.)
• HTTP API: Offset/cursor pagination with authentication

Destinations

• Kafka: Batching, compression, health checks
• HTTP: Webhooks with retry logic

⚙️ Configuration

Environment Variables

API:

API_HOST=0.0.0.0
API_PORT=8000
KAFKA_BOOTSTRAP_SERVERS=kafka:9092
KAFKA_TOPIC=reflow.jobs

Worker:

KAFKA_BOOTSTRAP_SERVERS=kafka:9092
KAFKA_TOPIC=reflow.jobs
KAFKA_GROUP_ID=reflowfy-workers

Mode	Endpoint	Use Case	Kafka	Workers
Distributed	POST /run	Production execution	✅	✅
Dry Run	POST /run (dry_run=true)	Preview/Testing	❌	❌

🐳 Kubernetes Deployment

Reflowfy streamlines deployment to Kubernetes/OpenShift using the CLI and Helm.

Deployment Concept

The deployment process uses your local configuration to configure the cluster.

1. Configuration: The .env file in your project root is the source of truth. It defines connection strings (Kafka, Registry, DB).
2. CLI: The reflowfy deploy command reads this config and triggers a Helm upgrade.

Deployed Objects

When you run reflowfy deploy, the following objects are created in your namespace:

• ReflowAPI (Deployment + Service): The entry point for triggering pipeline runs.
• ReflowManager (Deployment + Service): Orchestrates job distribution and manages state.
• ReflowWorker (Deployment + KEDA ScaledObject): The worker pool that processes jobs. KEDA automatically scales this deployment based on Kafka lag (0 to N replicas).
• PostgreSQL (Optional): If DEPLOY_POSTGRES=True, a dedicated Postgres instance is deployed. Otherwise, the system connects to your external DB.

How to Deploy

1. Configure environment: Ensure your .env file has the correct registry and Kafka settings.

   REGISTRY=my.registry.com
   dataset=my-project
   KAFKA_BOOTSTRAP_SERVERS=my-kafka:9092
   

2. Run Deploy:

   reflowfy deploy
   

   This will build/push images (if requested), generate the Helm values from your .env, and apply the chart.

📝 License

MIT

🤝 Contributing

Contributions welcome! This is a production-grade framework designed for real-world data processing at scale.