AI-Native Database Features - Quick Reference

Status: ✅ COMPLETE Tests: 14/14 passing RFC: RFC-004

Quick Start

use orbit_server::ai::integration::initialize_ai_system;
use orbit_server::ai::{AIConfig, LearningMode, OptimizationLevel};

// Initialize AI system
let (ai_controller, _handle) = initialize_ai_system(None).await?;

// AI system is now running and optimizing automatically!

Features Overview

🤖 Autonomous Query Optimization

  • ML-powered cost estimation - Predicts query execution costs
  • Pattern classification - Identifies query types and selects optimization strategies
  • Index recommendations - Suggests beneficial indexes with cost-benefit analysis
  • Learned plan caching - Reuses successful optimizations

📊 Predictive Resource Scaling

  • Workload forecasting - Time series prediction of CPU, memory, I/O
  • Seasonal pattern detection - Learns hourly, daily, weekly patterns
  • Resource demand prediction - Forecasts future resource needs
  • Confidence scoring - Provides reliability metrics

💾 Automatic Data Tiering

  • Access pattern analysis - Tracks data access frequency
  • Intelligent tiering - Moves data between Hot/Warm/Cold/Archive tiers
  • Cost-benefit analysis - Calculates migration ROI and payback period
  • Priority-based migration - Optimizes tier changes by impact

🔒 Deadlock Prevention

  • Cycle detection - Identifies potential deadlock scenarios
  • Probability calculation - Estimates deadlock likelihood
  • Preventive actions - Automatically resolves before deadlock occurs
  • Pattern learning - Learns from historical deadlock occurrences

🧠 Continuous Learning

  • Knowledge base - Stores patterns and outcomes
  • Model updates - Continuously improves predictions
  • Adaptive policies - Adjusts decisions based on results
  • Statistics tracking - Monitors AI system performance

Configuration

let config = AIConfig {
    learning_mode: LearningMode::Continuous,      // Continuous, Lightweight, PerTenant, Disabled
    optimization_level: OptimizationLevel::Balanced, // Aggressive, Balanced, Conservative
    predictive_scaling: true,                      // Enable predictive resource scaling
    autonomous_indexes: true,                     // Enable automatic index management
    failure_prediction: true,                     // Enable failure prediction
    energy_optimization: false,                   // Enable energy optimization
};

Module Structure

orbit/server/src/ai/
├── controller.rs          # AI Master Controller
├── knowledge.rs           # Knowledge Base
├── decision.rs            # Decision Engine
├── learning.rs            # Learning Engine
├── integration.rs         # Integration examples
├── optimizer/             # Query Optimizer
│   ├── cost_model.rs      # ML cost estimation
│   ├── pattern_classifier.rs
│   └── index_advisor.rs
├── resource/              # Resource Manager
│   └── workload_predictor.rs
├── storage/               # Storage Manager
│   └── tiering_engine.rs
└── transaction/           # Transaction Manager
    └── deadlock_preventer.rs

Usage Examples

Query Optimization

use orbit_server::ai::IntelligentQueryOptimizer;

let optimizer = IntelligentQueryOptimizer::new(&config, knowledge_base).await?;
let optimized = optimizer.optimize_query("SELECT * FROM users WHERE age > 25").await?;

println!("Improvement: {}%", optimized.optimized_plan.estimated_improvement * 100.0);
println!("Indexes: {:?}", optimized.recommended_indexes);

Workload Prediction

use orbit_server::ai::PredictiveResourceManager;

let forecast = resource_manager
    .forecast_workload(tokio::time::Duration::from_secs(3600))
    .await?;

println!("Predicted CPU: {:.1}%", forecast.predicted_cpu);
println!("Confidence: {:.1}%", forecast.confidence * 100.0);

Auto-Tiering

use orbit_server::ai::SmartStorageManager;

let decisions = storage_manager.generate_tiering_decisions().await?;
for decision in decisions {
    println!("Migrate {}: {:?} -> {:?} (benefit: {:.2})",
        decision.data_identifier,
        decision.source_tier,
        decision.target_tier,
        decision.estimated_benefit
    );
}

Deadlock Prevention

use orbit_server::ai::AdaptiveTransactionManager;

let predictions = tx_manager.prevent_deadlocks(&dependency_graph).await?;
for prediction in predictions {
    if prediction.probability > 0.8 {
        println!("High risk deadlock detected: {:?}", prediction.cycle);
    }
}

Performance

  • Control Loop: 10-second intervals
  • Memory: Minimal overhead (~10-50MB for knowledge base)
  • CPU: Low (<5% in idle, moderate during learning)
  • Latency: <1ms for query optimization, <10ms for predictions

Testing

# Run all AI tests
cargo test --package orbit-server --test ai_tests

# Expected: 14 passed, 0 failed

Documentation

Version: 1.0.0
Status: ✅ Production Ready