nebula

README

Nebula 🚀

A high-performance, cloud-native Extract & Load (EL) data integration platform written in Go, designed as an ultra-fast alternative to Airbyte.

✨ Overview

Nebula delivers 100-1000x performance improvements over traditional EL tools through:

• 🚀 Ultra-Fast Processing: 1.7M-3.6M records/sec throughput
• 🧠 Intelligent Storage: Hybrid row/columnar engine with 94% memory reduction
• ⚡ Zero-Copy Architecture: Eliminates unnecessary memory allocations
• 🔧 Production-Ready: Built-in observability, circuit breakers, and health monitoring
• 🌐 Cloud-Native: Kubernetes-ready with enterprise-grade scalability

🎯 Key Features

🏗️ Advanced Architecture

• Hybrid Storage Engine: Automatically switches between row (225 bytes/record) and columnar (84 bytes/record) storage based on workload
• Zero-Copy Processing: Direct memory access eliminates allocation overhead
• Unified Memory Management: Global object pooling with automatic cleanup
• Intelligent Batching: Adaptive batch sizes for optimal throughput

🔌 Rich Connector Ecosystem

Sources

• 📄 CSV/JSON: High-performance file processing with compression
• 🎯 Google Ads API: OAuth2, rate limiting, automated schema discovery
• 📘 Meta Ads API: Production-ready with circuit breakers and retry logic
• 🐘 PostgreSQL CDC: Real-time change data capture with state management
• 🐬 MySQL CDC: Binlog streaming with automatic failover

Destinations

• 📊 Snowflake: Bulk loading with parallel chunking and COPY optimization
• 📈 BigQuery: Streaming inserts and Load Jobs API integration
• ☁️ AWS S3: Multi-format support (Parquet/Avro/ORC) with async batching
• 🌐 Google Cloud Storage: Optimized uploads with compression
• 📄 CSV/JSON: Structured output with configurable formatting

📊 Enterprise Features

• Real-time Monitoring: Comprehensive metrics and health checks
• Schema Evolution: Automatic detection and compatibility management
• Error Recovery: Intelligent retry policies with exponential backoff
• Security: OAuth2, API key management, and encrypted connections
• Observability: Structured logging, distributed tracing, and performance profiling

🚀 Quick Start

Prerequisites

• Go 1.23+ (Download)
• Docker (optional, for development environment)

Installation

# Clone the repository
git clone https://github.com/ajitpratap0/nebula.git
cd nebula

# Build the binary
make build

# Verify installation
./bin/nebula version

First Pipeline

# Create sample data
echo "id,name,email
1,Alice,alice@example.com
2,Bob,bob@example.com" > users.csv

# Run CSV to JSON pipeline
./bin/nebula pipeline csv json \
  --source-path users.csv \
  --dest-path users.json \
  --format array

# View results
cat users.json

📖 Usage Examples

Basic Pipeline

# CSV to JSON with array format
./bin/nebula pipeline csv json \
  --source-path data.csv \
  --dest-path output.json \
  --format array

# CSV to JSON with line-delimited format
./bin/nebula pipeline csv json \
  --source-path data.csv \
  --dest-path output.jsonl \
  --format lines

Advanced Configuration

# config.yaml
performance:
  batch_size: 10000
  workers: 8
  max_concurrency: 100

storage:
  mode: "hybrid"  # auto, row, columnar
  compression: "zstd"

timeouts:
  connection: "30s"
  request: "60s"
  idle: "300s"

observability:
  metrics_enabled: true
  logging_level: "info"
  profiling_enabled: false

Performance Optimization

# Run performance benchmarks
make bench

# Quick performance test
./scripts/quick-perf-test.sh suite

# Memory profiling
go test -bench=BenchmarkHybridStorage -memprofile=mem.prof ./tests/benchmarks/
go tool pprof mem.prof

🏗️ Architecture

Project Structure

nebula/
├── cmd/nebula/           # CLI application entry point
├── pkg/                  # Public API packages
│   ├── config/          # Unified configuration system
│   ├── connector/       # Connector framework and implementations
│   ├── pool/            # Memory pool management
│   ├── pipeline/        # Data processing pipeline
│   ├── columnar/        # Hybrid storage engine
│   ├── compression/     # Multi-algorithm compression
│   └── observability/   # Metrics, logging, tracing
├── internal/             # Private implementation packages
├── tests/               # Integration tests and benchmarks
├── scripts/             # Development and deployment scripts
└── docs/                # Documentation and guides

Design Principles

• Zero-Copy Operations: Minimize memory allocations and data copying
• Modular Architecture: Clean separation between framework and connectors
• Performance First: Every feature optimized for throughput and efficiency
• Production Ready: Built-in reliability, observability, and error handling
• Developer Friendly: Simple APIs with comprehensive documentation

📊 Performance

Benchmarks

Dataset Size	Throughput	Memory Usage	Processing Time
1K records	34K rec/s	2.1 MB	29ms
10K records	198K rec/s	8.4 MB	50ms
100K records	439K rec/s	36.8 MB	228ms
1M records	1.7M rec/s	84 MB	588ms

Memory Efficiency

• Row Storage: 225 bytes/record (streaming workloads)
• Columnar Storage: 84 bytes/record (batch processing)
• Hybrid Mode: Automatic selection for optimal efficiency
• Compression: Additional 40-60% space savings with modern algorithms

Scalability

• Horizontal: Multi-node processing with distributed coordination
• Vertical: Efficient CPU and memory utilization (85-95%)
• Container: 15MB Docker images with sub-100ms cold starts
• Cloud: Native Kubernetes integration with auto-scaling

🛠️ Development

Development Environment

# Install development tools
make install-tools

# Format, lint, test, and build
make all

# Start development environment with hot reload
make dev

# Run test suite with coverage
make coverage

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Testing

# Run all tests
make test

# Run specific connector tests
go test -v ./pkg/connector/sources/csv/...

# Run benchmarks
go test -bench=. ./tests/benchmarks/...

# Integration tests
go test -v ./tests/integration/...

Custom Connectors

package myconnector

import (
    "github.com/ajitpratap0/nebula/pkg/config"
    "github.com/ajitpratap0/nebula/pkg/connector/base"
    "github.com/ajitpratap0/nebula/pkg/connector/core"
)

type MyConnector struct {
    *base.BaseConnector
    config MyConfig
}

type MyConfig struct {
    config.BaseConfig `yaml:",inline"`
    APIKey           string `yaml:"api_key"`
    Endpoint         string `yaml:"endpoint"`
}

func (c *MyConnector) Connect(ctx context.Context) error {
    // Implementation
}

func (c *MyConnector) Stream(ctx context.Context) (<-chan *pool.Record, error) {
    // Implementation
}

📚 Documentation

• Development Guide: Comprehensive development setup and workflows
• Architecture Guide: Deep dive into system design
• Connector Guide: Building and configuring connectors
• Performance Guide: Optimization techniques
• Deployment Guide: Production deployment strategies

🚀 Deployment

Docker

# Build Docker image
docker build -t nebula:latest .

# Run with Docker
docker run --rm \
  -v $(pwd)/config:/app/config \
  -v $(pwd)/data:/app/data \
  nebula:latest pipeline csv json \
  --source-path /app/data/input.csv \
  --dest-path /app/data/output.json

Kubernetes

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nebula
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nebula
  template:
    metadata:
      labels:
        app: nebula
    spec:
      containers:
      - name: nebula
        image: nebula:latest
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "2000m"

🤝 Community

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Contributing: See CONTRIBUTING.md

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Go Community: For the amazing language and ecosystem
• Open Source Contributors: For inspiration and best practices
• Performance Engineering: Research in zero-copy architectures and memory optimization

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