ML SQL Functions Design for Orbit-RS

Machine Learning capabilities integrated directly into the SQL engine for scalable data processing

Vision & Objectives

Transform Orbit-RS into a “Database + ML Engine” that provides:

Architecture Overview

1. SQL Function Registry

-- Linear Regression
SELECT name, ML_LINEAR_REGRESSION(features, target) OVER (PARTITION BY category) 
FROM sales_data;

-- Clustering
SELECT *, ML_KMEANS(features, 3) AS cluster_id 
FROM customer_data;

-- Neural Network Inference
SELECT text, ML_PREDICT('sentiment_model', text) AS sentiment 
FROM reviews;

-- Vector Similarity with ML
SELECT title, ML_SEMANTIC_SEARCH(embedding, 'query text', 10) AS similarity
FROM documents;

2. ML Function Categories

Statistical Functions

Machine Learning Models

Model Management

Feature Engineering

Vector & Embedding Operations

Time Series Functions

Natural Language Processing

Implementation Architecture

Core Components

// New ML module structure
orbit-protocols/src/ml/
├── mod.rs                    // ML module entry point
├── functions/                // ML function implementations
│   ├── statistical.rs        // Statistical functions
│   ├── supervised.rs         // Supervised learning
│   ├── unsupervised.rs       // Clustering, PCA, etc.
│   ├── neural.rs             // Neural networks
│   ├── nlp.rs                // NLP functions
│   ├── timeseries.rs         // Time series functions
│   └── vectors.rs            // Advanced vector operations
├── models/                   // Model management
│   ├── registry.rs           // Model storage and retrieval
│   ├── serialization.rs      // Model persistence
│   └── versioning.rs         // Model versioning
├── engines/                  // ML computation engines
│   ├── candle_engine.rs      // Candle/Torch integration
│   ├── onnx_engine.rs        // ONNX runtime
│   └── distributed.rs        // Distributed training
└── sql_integration/          // SQL engine integration
    ├── function_registry.rs  // Register ML functions
    ├── executor.rs           // ML function execution
    └── optimizer.rs          // ML-aware query optimization

SQL Engine Integration

// Extended FunctionCall enum in AST
pub enum MLFunction {
    // Statistical
    LinearRegression { features: Vec<Expression>, target: Expression },
    LogisticRegression { features: Vec<Expression>, target: Expression },
    
    // Clustering  
    KMeans { features: Vec<Expression>, k: u32 },
    DBSCAN { features: Vec<Expression>, eps: f64, min_samples: u32 },
    
    // Model Management
    TrainModel { name: String, algorithm: String, features: Vec<Expression>, target: Expression },
    Predict { model_name: String, features: Vec<Expression> },
    
    // Feature Engineering
    Normalize { values: Vec<Expression>, method: NormalizationMethod },
    PCA { features: Vec<Expression>, components: u32 },
    
    // Vector Operations
    EmbedText { text: Expression, model: String },
    SimilaritySearch { query: Expression, vectors: Expression, k: u32 },
    
    // NLP
    SentimentAnalysis { text: Expression },
    ExtractEntities { text: Expression },
    
    // Time Series
    Forecast { timeseries: Expression, periods: u32 },
    AnomalyDetection { timeseries: Expression },
}

Distributed ML Processing

// ML Actor for distributed processing

#[async_trait]
pub trait MLActor: Addressable {
    async fn train_model(&self, request: TrainModelRequest) -> OrbitResult<ModelMetadata>;
    async fn predict(&self, request: PredictRequest) -> OrbitResult<PredictionResult>;
    async fn evaluate_model(&self, request: EvaluateRequest) -> OrbitResult<EvaluationResult>;
    async fn update_model(&self, request: UpdateModelRequest) -> OrbitResult<()>;
}

// Distributed training coordination
pub struct DistributedTrainer {
    coordinator: ActorRef<MLCoordinator>,
    workers: Vec<ActorRef<MLWorker>>,
}

Scalability Features

1. Distributed Training

2. Query-Time Inference

3. Vector Database Integration

ML Libraries Integration

Primary: Candle (Rust-Native)

[dependencies]
candle-core = "0.6"
candle-nn = "0.6"  
candle-transformers = "0.6"
candle-datasets = "0.6"

Secondary: ONNX Runtime

ort = "2.0"  # ONNX Runtime for pre-trained models

Statistics: Statrs

statrs = "0.16"  # Statistical functions

Linear Algebra: Ndarray

ndarray = "0.15"
ndarray-linalg = "0.16"

Performance Optimizations

1. Vectorized Operations

2. Memory Management

3. Query Optimization

Use Cases & Examples

Real-Time Analytics

-- Real-time fraud detection
SELECT 
    transaction_id,
    amount,
    ML_PREDICT('fraud_model', 
        ARRAY[amount, merchant_category, hour_of_day, day_of_week]) AS fraud_score
FROM transactions 
WHERE timestamp > NOW() - INTERVAL '1 hour'
  AND ML_PREDICT('fraud_model', 
        ARRAY[amount, merchant_category, hour_of_day, day_of_week]) > 0.8;

Customer Analytics

-- Customer segmentation and lifetime value
WITH customer_features AS (
    SELECT 
        customer_id,
        ARRAY[total_spent, order_frequency, avg_order_value, days_since_last_order] as features
    FROM customer_metrics
)
SELECT 
    customer_id,
    ML_KMEANS(features, 5) AS segment,
    ML_PREDICT('clv_model', features) AS predicted_lifetime_value
FROM customer_features;

Content Recommendation

-- Semantic content recommendations
SELECT 
    c.title,
    c.content,
    ML_SIMILARITY_SEARCH(
        ML_EMBED_TEXT(c.content, 'sentence-transformers'), 
        ML_EMBED_TEXT('machine learning tutorials', 'sentence-transformers'),
        10
    ) AS similarity_score
FROM content c
WHERE ML_SIMILARITY_SEARCH(
    ML_EMBED_TEXT(c.content, 'sentence-transformers'), 
    ML_EMBED_TEXT('machine learning tutorials', 'sentence-transformers'),
    10
) > 0.7
ORDER BY similarity_score DESC;

Time Series Forecasting

-- Sales forecasting with seasonality
SELECT 
    date,
    actual_sales,
    ML_FORECAST(
        actual_sales OVER (ORDER BY date ROWS 365 PRECEDING),
        30
    ) AS forecasted_sales,
    ML_ANOMALY_DETECTION(
        actual_sales OVER (ORDER BY date ROWS 90 PRECEDING)
    ) AS is_anomaly
FROM daily_sales
ORDER BY date;

Security & Privacy

Model Security

Data Privacy

Implementation Roadmap

Phase 1: Foundation

Phase 2: Core ML

Phase 3: Advanced ML

Phase 4: Production Features

Success Metrics

Performance Targets

Functionality Goals

This design transforms Orbit-RS into a “Intelligent Database” that brings ML computation directly to the data, eliminating the need for complex ETL pipelines and enabling real-time intelligent applications.