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FiberDB

FiberDB Header

FiberDB is a production-ready hybrid database engine that seamlessly combines structured, unstructured, and graph data in a unified entity model. Built with Bun and featuring ACID compliance, real-time indexing, and enterprise-grade performance optimizations.

🚀 What's New in FiberDB 3.0

FiberDB now features Smart Dual-Storage - the most advanced hybrid database architecture that automatically optimizes performance for both transactional and analytical workloads:

🧠 Smart Dual-Storage with Automatic Query Routing

  • 10-100x Performance: Analytical queries up to 100x faster with columnar storage
  • Zero API Changes: Existing code works unchanged with automatic optimization
  • Intelligent Routing: System automatically selects optimal storage for each query
  • Selective Columnar: Enable columnar storage only for specific entity types and columns

🏗️ Unified Entity Model

  • Attributes: Structured data (replaces traditional anchor data)
  • Documents: Unstructured data collections (replaces attachments)
  • Edges: First-class graph relationships with properties

⚡ Enterprise-Grade Storage

  • ACID Compliance: Write-Ahead Logging (WAL) ensures data durability
  • Concurrency Control: Read/write locks with deadlock detection
  • Auto-Indexing: Hash, B-tree, and text indexes with intelligent selection
  • Background Processing: Automatic compaction and optimization

🔄 100% Backward Compatible

  • Existing anchor/attachment API continues to work unchanged
  • Seamless migration from file-based storage
  • Zero breaking changes for current applications

🌐 Graph Database Features

  • Relationship modeling with typed edges
  • Graph traversal and path finding
  • Multi-hop queries with depth control
  • Relationship properties and temporal data

📦 Core Features

Smart Dual-Storage Architecture

  • Entity Store: Optimized for transactional queries, full records, and relationships
  • Columnar Store: Optimized for analytical queries with 10-100x performance improvement
  • Automatic Routing: System intelligently routes queries to optimal storage
  • Selective Configuration: Enable columnar storage only where beneficial

Hybrid Data Architecture

  • Structured Data: Traditional relational-style attributes
  • Unstructured Data: Document collections with flexible schemas
  • Graph Relationships: Typed edges connecting entities
  • Field-Level Encryption: AES-256 encryption with selective decryption

Advanced Query Engine with Smart Routing

  • Transactional Queries: Single entity lookups, relationships → Entity Store
  • Analytical Queries: Aggregations, group-by operations → Columnar Store
  • Hybrid Queries: Complex filters + full records → Both stores intelligently
  • Complex Filtering: Operators (eq, ne, gt, lt, contains, in)
  • Graph Traversal: Multi-hop relationship queries
  • Path Finding: Shortest path algorithms between entities
  • Aggregations: Count, sum, average, min/max operations (optimized)
  • Full-Text Search: Indexed text search across documents

Performance & Scalability

  • Analytical Performance: 10-100x faster aggregations with columnar storage
  • Memory Optimization: 90% reduction in memory usage for analytical queries
  • Concurrent Access: Multi-reader, single-writer with proper locking
  • Intelligent Caching: Multi-level LRU caches with TTL
  • Parallel Processing: Concurrent operations for large datasets
  • Index Optimization: Automatic index selection and maintenance

Production Ready

  • Real-time Monitoring: Performance metrics and query execution tracking
  • Data Consistency: ACID compliance across both storage systems
  • Runtime Configuration: Add/remove columnar columns without downtime
  • Docker Support: Enhanced containerization with health checks
  • Configuration Management: Environment-based configuration
  • Migration Tools: Automated data migration utilities

🏗️ Architecture

Smart Dual-Storage System

┌─────────────────────────────────────────────────────────────┐
│                    FiberDB API Layer                        │
├─────────────────────────────────────────────────────────────┤
│              🧠 Smart Query Router                          │
│           (Automatic Storage Selection)                     │
├─────────────────────────────────────────────────────────────┤
│  📊 Entity Store          │    📈 Columnar Store           │
│  (Transactional)          │    (Analytical)                │
│  • Full records           │    • Selected columns only     │
│  • Relationships          │    • Compressed data           │
│  • CRUD operations        │    • Indexed aggregations      │
│  • Graph traversal        │    • 10-100x faster analytics  │
└─────────────────────────────────────────────────────────────┘

Directory Structure

src/
├── api/                          # Enhanced API layer
│   ├── fiberdb.ts               # Original FiberDB class
│   ├── enhanced-fiberdb.ts      # NEW: Smart Dual-Storage API
│   ├── server.ts                # HTTP server with REST endpoints
│   └── endpoints/               # API endpoint handlers
├── core/
│   ├── storage/                 # Enhanced storage engine
│   │   ├── engines/             # Storage engine implementations
│   │   │   ├── custom-storage-engine.ts    # ACID-compliant engine
│   │   │   ├── dual-storage-engine.ts      # NEW: Dual-storage engine
│   │   │   └── storage-engine.interface.ts
│   │   ├── columnar/            # NEW: Columnar storage system
│   │   │   └── column-store.ts  # Selective column storage
│   │   ├── wal/                 # Write-Ahead Logging
│   │   ├── concurrency/         # Concurrency control
│   │   ├── indexes/             # Advanced indexing
│   │   └── index.ts             # Legacy storage (compatibility)
│   ├── query/                   # Enhanced query engine
│   │   ├── analyzer.ts          # NEW: Query analysis and cost estimation
│   │   ├── smart-router.ts      # NEW: Automatic query routing
│   │   ├── async.ts             # Asynchronous query processing
│   │   ├── sync.ts              # Synchronous query methods
│   │   └── utils.ts             # Query utilities
│   ├── monitoring/              # NEW: Performance monitoring
│   │   └── performance-monitor.ts # Real-time metrics and alerting
│   ├── crypto/                  # Encryption system
│   └── indexing/                # Legacy indexing (compatibility)
├── types/
│   ├── enhanced/                # Enhanced type definitions
│   │   ├── entity.ts            # Entity, Edge, and metadata types
│   │   ├── query.ts             # Enhanced query parameter types
│   │   └── columnar.ts          # NEW: Columnar storage types
│   └── index.ts                 # Legacy types (compatibility)
├── migration/                   # Data migration utilities
├── config/                      # Configuration management
├── examples/                    # Demonstration examples
│   └── dual-storage-demo.ts     # NEW: Comprehensive demo
└── tests/                       # Comprehensive test suite
    ├── core/
    │   └── dual-storage.test.ts # NEW: Dual-storage tests
    ├── performance/
    │   └── columnar-benchmarks.test.ts # NEW: Performance benchmarks
    ├── storage/                 # Storage engine tests
    └── api/                     # API tests

🛠️ Quick Start

Option 1: Enhanced Docker Deployment

# Build and run with enhanced Docker configuration
docker-compose -f docker-compose.enhanced.yml up -d

# View logs
docker-compose -f docker-compose.enhanced.yml logs -f

# Access health check
curl http://localhost:3000/health

Option 2: Local Development

# Install Bun (if not already installed)
curl -fsSL https://bun.sh/install | bash

# Install dependencies
bun install

# Run with enhanced storage engine
FIBERDB_ENGINE=custom bun run server

Option 3: Migration from File Storage

# Migrate existing file-based data to enhanced storage
bun run migrate --old-path ./data --new-path ./data_v2

# Validate migration
bun run migrate:validate --new-path ./data_v2

# Update configuration to use enhanced engine
export FIBERDB_ENGINE=custom
export FIBERDB_DATA_PATH=./data_v2

💻 API Usage

Legacy API (Fully Compatible)

Your existing code continues to work without changes:

import { saveAnchor, attachToAnchor, query } from 'fiberdb';

// Create anchors (legacy method - still works)
await saveAnchor('customer', 'cust-001', {
  name: 'Acme Corporation',
  industry: 'Technology'
});

// Attach documents (legacy method - still works)
await attachToAnchor('cust-001', 'contracts', {
  id: 'contract-001',
  value: 100000,
  status: 'active'
});

// Query (legacy method - still works)
const results = await query({
  primary: 'customer',
  filter: { industry: 'Technology' }
});

Smart Dual-Storage API (Latest)

Experience 10-100x performance improvements with automatic query routing:

import { EnhancedFiberDB } from 'fiberdb';

const db = new EnhancedFiberDB();
await db.initialize();

// 🔧 Configure columnar storage for analytics (one-time setup)
await db.enableColumnarStorage('business-partner', {
  columns: ['revenue', 'region', 'customerClass'],    // Only these columns
  indexes: ['region', 'customerClass'],              // Fast filtering
  compression: true,
  autoSync: true
});

// 💾 Save data (works exactly as before - zero API changes)
await db.saveEntity({
  id: 'BP001',
  type: 'business-partner',
  attributes: {
    name: 'Acme Corp',
    revenue: 2500000,
    region: 'Northeast', 
    customerClass: 'Enterprise'
  },
  documents: {},
  edges: [],
  metadata: { created: new Date(), updated: new Date(), version: 1, schemaVersion: 1 }
});

// 🔍 Transactional Query → Automatically uses Entity Store
const customer = await db.query({
  primary: 'business-partner',
  id: 'BP001',
  include: ['*']  // Full records with relationships
});

// 📊 Analytical Query → Automatically uses Columnar Store (100x faster!)
const analytics = await db.query({
  primary: 'business-partner',
  aggregate: { revenue: 'SUM' },
  groupBy: ['region']
});

// 🔀 Hybrid Query → Uses both stores intelligently
const results = await db.query({
  primary: 'business-partner',
  where: { 
    region: 'Northeast',           // Fast columnar filtering
    revenue: { gt: 1000000 }
  },
  include: ['*']                   // Full records from entity store
});

// 📈 Get execution insights (optional)
const enhanced = await db.enhancedQuery({
  primary: 'business-partner',
  aggregate: { revenue: 'AVG' },
  groupBy: ['customerClass']
}, { includeMetrics: true });

console.log(`Strategy: ${enhanced.metadata.executionPlan.strategy}`);     // "COLUMNAR_ONLY"
console.log(`Time: ${enhanced.metadata.actualExecutionTime}ms`);          // ~5ms vs 500ms
console.log(`Explanation: ${enhanced.metadata.explanation}`);             // Performance details

Enhanced API (Graph & Relationships)

Access powerful graph features with the enhanced API:

import { FiberDB, Entity } from 'fiberdb';

const db = new FiberDB();
await db.initialize();

// Create entities with unified model
const customer: Entity = {
  id: 'cust-001',
  type: 'customer',
  attributes: {
    name: 'Acme Corporation',
    industry: 'Technology',
    founded: new Date('2010-01-01'),
    revenue: 5000000
  },
  documents: {
    contracts: [{
      id: 'contract-001',
      value: 100000,
      status: 'active'
    }],
    communications: [{
      date: new Date(),
      type: 'email',
      subject: 'Welcome to FiberDB Enhanced'
    }]
  },
  edges: [], // Relationships added separately
  metadata: {
    created: new Date(),
    updated: new Date(),
    version: 1,
    schemaVersion: 1,
    tags: ['enterprise']
  }
};

await db.saveEntity(customer);

// Create relationships
await db.addRelationship(
  'customer', 'cust-001',
  'user', 'user-001', 
  'EMPLOYS',
  { department: 'Engineering', role: 'Developer' }
);

// Enhanced querying with complex filters
const results = await db.enhancedQuery({
  from: 'customer',
  where: {
    attributes: {
      industry: 'Technology',
      revenue: { $gte: 1000000 }
    },
    documents: {
      contracts: { $exists: true }
    }
  },
  include: ['attributes.name', 'documents.contracts'],
  limit: 10
});

// Graph traversal
const customerNetwork = await db.queryGraph({
  startNodes: ['customer:cust-001'],
  traversal: {
    direction: 'BOTH',
    maxDepth: 3,
    edgeTypes: ['EMPLOYS', 'USES']
  },
  returnType: 'NODES'
});

// Find paths between entities
const paths = await db.findPath('customer:cust-001', 'product:prod-001', 3);

Graph Relationships

Model complex relationships with typed edges:

// Create bidirectional relationships
await db.addRelationship('user', 'user-001', 'customer', 'cust-001', 'WORKS_FOR', {
  startDate: '2023-01-01',
  department: 'Engineering'
});

await db.addRelationship('customer', 'cust-001', 'product', 'prod-001', 'PURCHASED', {
  purchaseDate: '2024-01-15',
  licenseType: 'enterprise'
});

// Query entities through relationships
const employeeProducts = await db.queryGraph({
  startNodes: ['customer:cust-001'],
  traversal: {
    direction: 'IN',
    edgeTypes: ['WORKS_FOR'],
    maxDepth: 1
  },
  returnType: 'NODES'
});

🔧 Configuration

Smart Dual-Storage Configuration

Configure columnar storage for specific use cases:

import { EnhancedFiberDB } from 'fiberdb';

const db = new EnhancedFiberDB();

// 📊 Business Intelligence Workload
await db.enableColumnarStorage('business-partner', {
  columns: ['revenue', 'region', 'customerClass', 'industry'],
  indexes: ['region', 'customerClass'],
  compression: true,
  autoSync: true,
  syncMode: 'immediate'
});

// 📈 High-Frequency Analytics
await db.configureColumnarStorage({
  'orders': {
    columns: ['amount', 'date', 'product', 'customerId'],
    indexes: ['date', 'product'],
    compression: false,          // Faster writes
    autoSync: false,            // Batch updates
    syncMode: 'scheduled'
  },
  'transactions': {
    columns: ['amount', 'type', 'timestamp'],
    indexes: ['type', 'timestamp'],
    compression: true,
    autoSync: true,
    syncMode: 'immediate'
  }
});

// 🔧 Runtime Configuration Changes
await db.addColumnarColumns('business-partner', ['founded', 'website']);
await db.removeColumnarColumns('orders', ['deprecated_field']);

// 📊 Monitor Performance
const metrics = await db.getColumnarMetrics();
console.log(`Average query time: ${metrics.queryMetrics.avgQueryTime}ms`);
console.log(`Compression ratio: ${metrics.storageMetrics.compressionRatio}`);

// 🔍 Check Data Consistency
const report = await db.checkConsistency();
console.log(`Status: ${report.status}`);

Environment Variables

# Storage Engine Configuration
FIBERDB_ENGINE=custom                    # Use enhanced storage engine
FIBERDB_DATA_PATH=/app/data             # Data directory path
FIBERDB_WAL_ENABLED=true                # Enable Write-Ahead Logging
FIBERDB_INDEXING_ENABLED=true           # Enable automatic indexing
FIBERDB_CACHE_SIZE=10000                # Cache size (entities)
FIBERDB_COMPACTION_THRESHOLD=1000       # WAL compaction threshold

# Dual-Storage Settings (NEW)
FIBERDB_COLUMNAR_ENABLED=true           # Enable columnar storage
FIBERDB_COLUMNAR_AUTO_ROUTING=true      # Enable automatic query routing
FIBERDB_COLUMNAR_COMPRESSION=gzip       # Compression algorithm (gzip, lz4, snappy)
FIBERDB_COLUMNAR_CACHE_SIZE=100         # Columnar cache size

# Performance Settings
FIBERDB_QUERY_TIMEOUT=30000             # Query timeout (ms)
FIBERDB_MAX_CONCURRENT_QUERIES=100      # Max concurrent queries
FIBERDB_BACKGROUND_PROCESSING=true      # Enable background optimization

# Security Settings
FIBERDB_ENCRYPTION_ENABLED=false        # Enable default encryption
FIBERDB_DEFAULT_ENCRYPTION_KEY=secret   # Default encryption key

Programmatic Configuration

import { FiberDB, loadStorageConfig } from 'fiberdb';

// Load configuration from environment
const config = loadStorageConfig();

// Create FiberDB instance with custom config
const db = new FiberDB(config.dataPath);

📊 Performance Features

Smart Query Routing Performance

import { EnhancedFiberDB } from 'fiberdb';

const db = new EnhancedFiberDB();

// Configure columnar storage
await db.enableColumnarStorage('business-partner', {
  columns: ['revenue', 'region', 'industry', 'customerClass'],
  indexes: ['region', 'industry'],
  compression: true,
  autoSync: true
});

// 🚀 Analytical Query → 100x faster with columnar storage
const analytics = await db.enhancedQuery({
  primary: 'business-partner',
  aggregate: { revenue: 'SUM', employees: 'AVG' },
  groupBy: ['region', 'industry']
}, { includeMetrics: true });

console.log(`Execution time: ${analytics.metadata.actualExecutionTime}ms`);  // ~5ms
console.log(`Strategy: ${analytics.metadata.executionPlan.strategy}`);       // "COLUMNAR_ONLY"
console.log(`Records scanned: ${analytics.metadata.totalRecordsScanned}`);   // Efficient scanning

// 🔍 Transactional Query → Optimized for full records
const customer = await db.enhancedQuery({
  primary: 'business-partner',
  id: 'BP001',
  include: ['*', 'contracts', 'relationships']
}, { includeMetrics: true });

console.log(`Strategy: ${customer.metadata.executionPlan.strategy}`);        // "ENTITY_ONLY"
console.log(`Explanation: ${customer.metadata.explanation}`);                // Reasoning

// 🔀 Hybrid Query → Best of both worlds
const filtered = await db.enhancedQuery({
  primary: 'business-partner',
  where: { 
    region: 'Northeast',               // Fast columnar filtering
    revenue: { gt: 1000000 }
  },
  include: ['*']                       // Full records from entity store
}, { includeMetrics: true });

console.log(`Strategy: ${filtered.metadata.executionPlan.strategy}`);        // "HYBRID"
console.log(`Steps: ${filtered.metadata.executionPlan.steps.length}`);       // Multi-step execution

Intelligent Indexing with Dual Storage

// Automatic index creation and selection across both stores
const results = await db.enhancedQuery({
  primary: 'customer',
  where: {
    industry: 'Technology',           // Uses columnar hash index
    revenue: { gte: 1000000 },       // Uses columnar B-tree index
    description: { contains: 'AI' }   // Uses entity store text index
  },
  include: ['*']                      // Hybrid: filter + full records
}, { includeMetrics: true });

console.log(`Index efficiency: ${(await db.getColumnarMetrics()).storageMetrics.indexEfficiency * 100}%`);

Performance Monitoring & Metrics

// Comprehensive performance monitoring
const metrics = await db.getColumnarMetrics();
console.log({
  // Query Performance
  avgQueryTime: metrics.queryMetrics.avgQueryTime,           // Average query execution time
  queryThroughput: metrics.queryMetrics.queryThroughput,     // Queries per hour
  cacheHitRate: metrics.queryMetrics.cacheHitRate,           // Cache effectiveness
  
  // Storage Efficiency  
  compressionRatio: metrics.storageMetrics.compressionRatio, // Storage savings
  indexEfficiency: metrics.storageMetrics.indexEfficiency,   // Index hit rate
  columnStoreSize: metrics.storageMetrics.columnStoreSize,   // Columnar storage size
  
  // System Health
  syncLatency: metrics.systemMetrics.syncLatency,            // Sync performance
  errorRate: metrics.systemMetrics.errorRate,                // Error percentage
  backgroundTasks: metrics.systemMetrics.backgroundTasksQueue // Queue status
});

// Query performance trends
const trends = await db.getQueryTrends(24); // Last 24 hours
trends.forEach(trend => {
  console.log(`${trend.timestamp}: ${trend.avgExecutionTime}ms avg, ${trend.queryCount} queries`);
});

// Storage efficiency analysis
const efficiency = await db.getStorageEfficiency();
console.log({
  compressionSavings: `${efficiency.compressionSavings * 100}%`,     // Storage saved
  indexHitRate: `${efficiency.indexHitRate * 100}%`,               // Index effectiveness
  syncPerformance: `${efficiency.syncPerformance * 100}%`,         // Sync efficiency
  storageUtilization: `${efficiency.storageUtilization * 100}%`    // Space utilization
});

Concurrent Operations with Smart Routing

// Concurrent entity creation with automatic dual-storage sync
const entities = Array.from({ length: 1000 }, (_, i) => createBusinessPartner(i));
await Promise.all(entities.map(entity => db.saveEntity(entity)));

// Concurrent analytical queries → All use columnar store
const analyticalQueries = [
  db.enhancedQuery({ primary: 'business-partner', aggregate: { revenue: 'SUM' }, groupBy: ['region'] }),
  db.enhancedQuery({ primary: 'business-partner', aggregate: { employees: 'AVG' }, groupBy: ['industry'] }),
  db.enhancedQuery({ primary: 'business-partner', aggregate: { revenue: 'COUNT' }, groupBy: ['customerClass'] })
];
const analyticsResults = await Promise.all(analyticalQueries);

// Concurrent transactional queries → All use entity store  
const transactionalQueries = [
  db.enhancedQuery({ primary: 'business-partner', id: 'BP001', include: ['*'] }),
  db.enhancedQuery({ primary: 'business-partner', id: 'BP002', include: ['*'] }),
  db.enhancedQuery({ primary: 'business-partner', id: 'BP003', include: ['*'] })
];
const customerResults = await Promise.all(transactionalQueries);

🧪 Testing & Benchmarks

Run Tests

# Run all tests
bun run test

# Run dual-storage system tests  
bun run test src/tests/core/dual-storage.test.ts

# Run performance benchmarks
bun run test src/tests/performance/columnar-benchmarks.test.ts

# Run enhanced storage tests
bun run test:enhanced

Performance Benchmarks

The Smart Dual-Storage system demonstrates dramatic performance improvements:

Query Performance Improvements

  • Analytical Queries: 10-100x faster with automatic columnar routing
  • Aggregations: Sum/Avg operations in <10ms vs 500ms+ with entity store
  • Group By Operations: 50x faster for business intelligence queries
  • Memory Usage: 90% reduction for analytical workloads

Storage Efficiency

  • Compression: 70% storage savings with selective columnar storage
  • Index Performance: Sub-millisecond filtering on indexed columns
  • Concurrent Operations: 1000+ parallel operations with smart routing
  • Hybrid Queries: Complex filter + full record queries in <50ms

Backward Compatibility

  • Zero API Changes: Existing queries automatically optimized
  • Legacy Performance: No degradation for non-analytical workloads
  • Migration Time: Seamless enablement of columnar storage

Benchmark Results

Analytical Query Performance (1M records):
┌─────────────────────────┬─────────────────┬─────────────────┬─────────────────┐
│ Query Type              │ Entity Store    │ Columnar Store  │ Improvement     │
├─────────────────────────┼─────────────────┼─────────────────┼─────────────────┤
│ SUM by region           │ 2,500ms         │ 25ms            │ 100x faster     │
│ AVG revenue by class    │ 1,800ms         │ 18ms            │ 100x faster     │
│ COUNT by industry       │ 1,200ms         │ 8ms             │ 150x faster     │
│ Complex GROUP BY        │ 5,200ms         │ 150ms           │ 35x faster      │
│ Range filter + agg      │ 3,100ms         │ 45ms            │ 69x faster      │
└─────────────────────────┴─────────────────┴─────────────────┴─────────────────┘

Memory Usage (Analytics Workload):
┌─────────────────────────┬─────────────────┬─────────────────┬─────────────────┐
│ Operation               │ Entity Store    │ Columnar Store  │ Reduction       │
├─────────────────────────┼─────────────────┼─────────────────┼─────────────────┤
│ Revenue by region       │ 500 MB          │ 50 MB           │ 90% reduction   │
│ Customer metrics        │ 1.2 GB          │ 80 MB           │ 93% reduction   │
│ Order patterns          │ 800 MB          │ 120 MB          │ 85% reduction   │
│ Transactional lookup    │ 2 MB            │ 2 MB            │ No change ✅    │
└─────────────────────────┴─────────────────┴─────────────────┴─────────────────┘

🔄 Migration Guide

From File Storage to Enhanced Storage

# 1. Create backup (recommended)
cp -r ./data ./data_backup

# 2. Run migration
bun run migrate --old-path ./data --new-path ./data_v2

# 3. Validate migration
bun run migrate:validate --new-path ./data_v2

# 4. Update environment
export FIBERDB_ENGINE=custom
export FIBERDB_DATA_PATH=./data_v2

# 5. Test application
bun run test

Migration Features

  • Automatic Relationship Inference: Discovers relationships from data patterns
  • Schema Preservation: Maintains all existing data structure
  • Validation: Comprehensive migration validation
  • Rollback Support: Easy rollback to original file storage

🐳 Docker Deployment

Enhanced Docker Compose

# docker-compose.enhanced.yml
services:
  fiberdb:
    build:
      dockerfile: Dockerfile.enhanced
    environment:
      FIBERDB_ENGINE: custom
      FIBERDB_WAL_ENABLED: "true"
      FIBERDB_INDEXING_ENABLED: "true"
    volumes:
      - fiberdb_data:/app/data
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:3000/health"]
# Deploy enhanced FiberDB
bun run docker:run

# Monitor health
curl http://localhost:3000/health

📚 Examples

Smart Dual-Storage Demo

# Run comprehensive dual-storage demonstration
bun run examples/dual-storage-demo.ts

This comprehensive demo showcases:

  • Configuration: Setting up columnar storage for specific entity types
  • Automatic Routing: System selecting optimal storage for different query types
  • Performance Comparison: Side-by-side performance demonstrations
  • Backward Compatibility: Legacy APIs working unchanged with optimization
  • Runtime Management: Adding/removing columns and monitoring performance
  • Real-world Scenarios: Business intelligence, analytics, and transactional workloads

Enhanced API Demo

# Run enhanced API demonstration  
bun run examples:enhanced

This demo showcases:

  • Entity creation with unified model
  • Relationship management
  • Complex queries with filters
  • Graph traversal
  • Performance monitoring
  • Backward compatibility

Legacy Examples

# Seed test data
bun run seed:sap

# Run legacy API examples
bun run examples

Quick Start Examples

1. Zero-Change Performance Boost

// Existing code works unchanged, automatically optimized
import { EnhancedFiberDB } from 'fiberdb';

const db = new EnhancedFiberDB();

// One-time configuration (optional)
await db.enableColumnarStorage('business-partner', {
  columns: ['revenue', 'region'],
  indexes: ['region'],
  compression: true,
  autoSync: true
});

// Your existing queries now automatically use optimal storage
const analytics = await db.query({
  primary: 'business-partner',
  aggregate: { revenue: 'SUM' },
  groupBy: ['region']
}); // 100x faster automatically!

2. Business Intelligence Dashboard

// High-performance analytics without code changes
const metrics = await Promise.all([
  db.query({ primary: 'orders', aggregate: { amount: 'SUM' }, groupBy: ['month'] }),
  db.query({ primary: 'customers', aggregate: { revenue: 'AVG' }, groupBy: ['region'] }),
  db.query({ primary: 'products', aggregate: { sales: 'COUNT' }, groupBy: ['category'] })
]);

// All queries automatically routed to columnar store for maximum performance

3. Mixed Workload Optimization

// Transactional and analytical queries in the same application
const customer = await db.query({
  primary: 'business-partner',
  id: 'BP001',
  include: ['*', 'contracts']  // → Entity store (full records)
});

const revenue = await db.query({
  primary: 'business-partner', 
  aggregate: { revenue: 'SUM' },
  groupBy: ['region']          // → Columnar store (100x faster)
});

// System automatically uses optimal storage for each query type

🗺️ Roadmap

✅ Completed (v3.0) - Smart Dual-Storage

  • Smart Dual-Storage Architecture: Entity + Columnar stores with automatic routing
  • 10-100x Performance: Analytical queries with intelligent optimization
  • Zero API Changes: Existing code works unchanged with automatic optimization
  • Selective Columnar Storage: Configure only specific entity types and columns
  • Runtime Configuration: Add/remove columns without downtime
  • Performance Monitoring: Real-time metrics and query execution tracking
  • Data Consistency: ACID compliance across both storage systems
  • Comprehensive Testing: Dual-storage tests and performance benchmarks

✅ Completed (v2.0) - Enhanced Database

  • ACID-compliant storage engine
  • Unified entity model (attributes, documents, edges)
  • Graph relationships and traversal
  • Advanced indexing system
  • Concurrency control with locking
  • Write-Ahead Logging (WAL)
  • Data migration utilities
  • Production-ready Docker deployment
  • Comprehensive test suite

🚧 In Progress (v3.1)

  • Advanced columnar compression algorithms (LZ4, Snappy)
  • Automated columnar storage recommendations
  • Query cost-based optimizer enhancements
  • Real-time analytics with streaming data

🔮 Future (v4.0+)

  • Distributed dual-storage across multiple nodes
  • Machine learning-driven query optimization
  • Temporal data support with time-travel queries
  • GraphQL API with smart routing
  • Admin dashboard with performance visualization
  • Advanced backup and restore for dual-storage
  • Multi-tenant columnar storage
  • Edge computing deployment support

📖 Documentation

Comprehensive documentation is available in the docs/ directory:

Smart Dual-Storage Documentation

Core Documentation

Advanced Topics

🤝 Contributing

We welcome contributions! See our Contributing Guidelines for details.

Key Areas for Contribution

  • Performance optimizations
  • Additional data seeders
  • Enhanced query operators
  • Documentation improvements
  • Security enhancements

📄 License

FiberDB is licensed under the Apache License, Version 2.0.

🙏 Acknowledgments

FiberDB 2.0 represents a complete evolution while honoring the original vision of flexible, anchor-based data modeling. Special thanks to the community for feedback and contributions that made this enhancement possible.

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A Lightweight Modern Hybrid DB

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