HyperQL - Unified Query Language for Hyperspatial Database

HyperQL is a revolutionary query language that unifies SQL-like syntax with graph traversal, geometric queries, and cascade operations. It enables seamless querying across multiple paradigms within the Hyperspatial database's unified hyperbolic space.

🌟 Why HyperQL?

Traditional databases force developers to use different query languages for different data models:

SQL for relational data
Cypher or SPARQL for graph traversals
Custom APIs for vector similarity
Domain-specific languages for time-series analysis

HyperQL eliminates this fragmentation by providing a unified syntax that can:

Query entities by properties (SQL-like WHERE clauses)
Traverse relationships (graph-style navigation)
Find similar entities (vector similarity searches)
Apply geometric filters (hyperbolic distance constraints)
Propagate cascading measures (hierarchical computations)
Combine all paradigms in a single query

🚀 Key Features

Multi-Paradigm Integration

HyperQL seamlessly combines multiple query paradigms:

SELECT entity, measure_value, influence_score
FROM users u
TRAVERSE connected_to -> friends f
       -> colleagues c
WHERE u.age > 25
  AND similarity(u.interests, f.interests) > 0.8
  AND hyperbolic_distance(u, f) < 2.0
  AND c.department = 'engineering'
CASCADE user_influence FROM u TO f WITH decay_factor(0.9)
ORDER BY measure_value DESC, influence_score DESC
LIMIT 100

Spatial Intelligence

Leverage the hyperbolic positioning system for:

Natural clustering queries without explicit grouping
Hierarchical traversals that respect learned structure
Multi-signal similarity searches combining various signals
Temporal queries over position trajectories

Advanced Cascade System

Powerful cascade operations for hierarchical computations:

-- Aggregate sales up organizational hierarchy
CASCADE total_sales = SUM(sales_amount)
FROM transactions t
PROPAGATE UP THROUGH organizational_hierarchy

-- Distribute budget down to teams
CASCADE budget_allocation = DISTRIBUTE(total_budget, allocation_weights)
FROM departments d
PROPAGATE DOWN TO teams

-- Compute influence propagation through social networks
CASCADE influence_score = INFLUENCE(base_score, decay_factor: 0.9)
FROM influencers i
PROPAGATE THROUGH social_network
WITH MAX_DEPTH 3

📖 Query Examples

Basic Relational Queries

-- Traditional SQL-style query
SELECT name, age, email
FROM users
WHERE age > 30 AND department = 'engineering'
ORDER BY name

Graph Traversal Queries

-- Find friends of friends with similar interests
SELECT u.name, f.name, ff.name
FROM users u
TRAVERSE friends -> f
       -> friends -> ff
WHERE similarity(u.interests, ff.interests) > 0.7
  AND u.id != ff.id

Geometric Queries

-- Find entities within hyperbolic distance
SELECT entity, hyperbolic_distance(entity, @anchor) as distance
FROM products
WHERE hyperbolic_distance(entity, @anchor) < 1.5
ORDER BY distance ASC

Vector Similarity Queries

-- K-nearest neighbors with filtering
SELECT item, similarity(item.embedding, @query_vector) as score
FROM items
WHERE category = 'electronics' 
  AND price < 500
ORDER BY similarity(item.embedding, @query_vector) DESC
LIMIT 10

Complex Multi-Paradigm Queries

-- Recommendation system combining multiple signals
SELECT 
    p.title,
    similarity(p.content_embedding, u.preference_vector) as content_score,
    hyperbolic_distance(p, u) as position_score,
    cascade_measure as social_score
FROM products p, users u
TRAVERSE u.friends -> f
WHERE u.id = @user_id
  AND p.category IN @preferred_categories
  AND similarity(p.content_embedding, u.preference_vector) > 0.6
CASCADE social_influence 
  FROM f.purchased_items 
  TO p 
  WITH influence_weight(f.social_score)
ORDER BY 
  (0.4 * content_score + 0.3 * position_score + 0.3 * social_score) DESC
LIMIT 20

Temporal Trajectory Queries

-- Analyze movement patterns over time
SELECT 
    entity,
    trajectory_similarity(entity.position_history, @pattern) as match_score
FROM moving_objects
WHERE timerange(entity.timestamps, '2024-01-01', '2024-12-31')
  AND trajectory_length(entity.position_history) > 100
ORDER BY match_score DESC

🔧 Usage Modes

1. Ad-Hoc Queries

Direct query execution for interactive analysis:

use hyperql::*;

#[tokio::main]
async fn main() -> Result<()> {
    let engine = HyperQLEngine::new().await?;
    
    let query = r#"
        SELECT name, age
        FROM users 
        WHERE age > 25
        TRAVERSE friends -> f
        WHERE similarity(interests, f.interests) > 0.8
    "#;
    
    let results = engine.execute(query).await?;
    for row in results {
        println!("{:?}", row);
    }
    
    Ok(())
}

2. Compiled Queries

Pre-compiled queries for production performance:

use hyperql::*;

#[tokio::main]
async fn main() -> Result<()> {
    let engine = HyperQLEngine::new().await?;
    
    // Compile query once
    let compiled = engine.compile(r#"
        SELECT entity, measure_value
        FROM users u
        TRAVERSE connected_to -> friends f
        WHERE u.age > $age_threshold
          AND similarity(u.interests, f.interests) > $similarity_threshold
        CASCADE user_influence FROM u TO f
        ORDER BY measure_value DESC
        LIMIT $limit
    "#).await?;
    
    // Execute multiple times with different parameters
    let results1 = compiled.execute(
        &[("age_threshold", 25), ("similarity_threshold", 0.8), ("limit", 50)]
    ).await?;
    
    let results2 = compiled.execute(
        &[("age_threshold", 30), ("similarity_threshold", 0.9), ("limit", 20)]
    ).await?;
    
    Ok(())
}

3. Hybrid Mode

Combine compiled templates with dynamic query construction:

use hyperql::*;

#[tokio::main]
async fn main() -> Result<()> {
    let engine = HyperQLEngine::new().await?;
    
    // Build query dynamically
    let mut query_builder = QueryBuilder::new()
        .select(["entity", "score"])
        .from("products")
        .where_clause("category = $category")
        .order_by("score DESC")
        .limit(10);
    
    // Add conditional traversal
    if include_recommendations {
        query_builder = query_builder
            .traverse("recommended_by -> users u")
            .where_and("u.trust_score > $trust_threshold");
    }
    
    let compiled = query_builder.compile(&engine).await?;
    let results = compiled.execute(
        &[("category", "electronics"), ("trust_threshold", 0.7)]
    ).await?;
    
    Ok(())
}

🏗️ Architecture

HyperQL follows a traditional compiler pipeline optimized for hyperbolic queries:

┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│   Query     │───▶│    AST      │───▶│ Execution   │───▶│   Results   │
│   Text      │    │             │    │   Plan      │    │             │
└─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘
       │                   │                   │                   │
   ┌─────────┐         ┌─────────┐         ┌─────────┐         ┌─────────┐
   │ Parser  │         │Compiler │         │Executor │         │Hyperbolic│
   │ (Winnow)│         │         │         │ Engine  │         │  Space   │
   └─────────┘         └─────────┘         └─────────┘         └─────────┘

Core Components

Parser: Winnow-based parser converts query text to AST
Compiler: AST transforms into optimized execution plans
Optimizer: Query plans are optimized for hyperbolic operations
Executor: Plans execute against the hyperbolic space engine
Runtime: Lua and WASM integration for custom functions

📁 Module Structure

src/
├── ast/                 # Abstract syntax tree definitions
│   ├── mod.rs
│   ├── query.rs        # Core query structures
│   ├── expression.rs   # Expression trees
│   ├── literal.rs      # Literal values
│   ├── identifier.rs   # Entity identifiers
│   ├── function.rs     # Function calls
│   ├── predicate.rs    # Boolean expressions
│   ├── traverse.rs     # Graph traversal
│   ├── cascade.rs      # Cascade operations
│   └── aggregate.rs    # Aggregation functions
├── parser/             # Winnow-based query parser
│   ├── mod.rs
│   ├── lexer.rs        # Tokenization
│   ├── grammar.rs      # Grammar rules
│   └── combinators.rs  # Parser combinators
├── compiler/           # AST to execution plan compilation
│   ├── mod.rs
│   ├── planner.rs      # Query planning
│   ├── type_checker.rs # Type analysis
│   └── code_gen.rs     # Code generation
├── executor/           # Query execution engine
│   ├── mod.rs
│   ├── engine.rs       # Execution engine
│   ├── operators.rs    # Query operators
│   └── results.rs      # Result handling
├── functions/          # Built-in function library
│   ├── mod.rs
│   ├── similarity.rs   # Vector similarity functions
│   ├── geometric.rs    # Hyperbolic geometry functions
│   ├── aggregates.rs   # Aggregation functions
│   └── temporal.rs     # Time-based functions
├── cascade/            # Cascade system for measure propagation
│   ├── mod.rs
│   ├── propagation.rs  # Propagation algorithms
│   ├── measures.rs     # Measure definitions
│   ├── hierarchy.rs    # Hierarchy detection
│   └── scheduler.rs    # Cascade scheduling
├── optimizer/          # Query optimization
│   ├── mod.rs
│   ├── rules.rs        # Optimization rules
│   ├── cost_model.rs   # Cost modeling
│   └── statistics.rs   # Query statistics
├── context/            # Query execution context
│   ├── mod.rs
│   ├── variables.rs    # Variable binding
│   └── scope.rs        # Scope management
├── runtime/            # Custom function runtime
│   ├── mod.rs
│   ├── lua.rs          # Lua integration
│   ├── wasm.rs         # WASM integration
│   ├── sandbox.rs      # Security sandbox
│   └── function_registry.rs # Function registry
├── ir/                 # Intermediate representation
│   ├── mod.rs
│   ├── operators.rs    # IR operators
│   ├── plans.rs        # Execution plans
│   └── serialization.rs # Plan serialization
├── error.rs            # Error types and handling
├── types.rs            # Core type definitions
└── lib.rs             # Library root with documentation

🔌 Integration with Hyperspatial

HyperQL is tightly integrated with the Hyperspatial database engine:

Hyperbolic Engine Integration

Learned Positions: Leverages hyperbolic positions for geometric queries
Distance Calculations: Efficient hyperbolic distance computations
Spatial Indexing: Utilizes hyperbolic spatial indices for fast retrieval
Clustering: Natural clustering based on hyperbolic proximity

Persistence Layer Integration

Entity Access: Direct access to stored entities and properties
Relationship Traversal: Efficient traversal of stored relationships
Index Utilization: Leverages existing indices for query acceleration
Transaction Support: Full ACID transaction support

Vector Operations Integration

Embedding Storage: Access to stored vector embeddings
Similarity Indices: Utilizes vector similarity indices (HNSW, IVF)
Approximate Search: Efficient approximate nearest neighbor search
Multi-Modal Vectors: Support for multiple embedding types per entity

Measure System Integration

Cascade Computation: Integration with measure propagation system
Incremental Updates: Efficient updates when underlying data changes
Dependency Management: Automatic cascade dependency resolution
Parallel Execution: Multi-threaded cascade computation

🎯 Custom Functions with Lua and WASM

Lua Integration

Extend HyperQL with custom Lua functions:

use hyperql::runtime::lua::LuaRuntime;

#[tokio::main]
async fn main() -> Result<()> {
    let mut runtime = LuaRuntime::new().await?;
    
    // Register custom Lua function
    runtime.register_function("custom_similarity", r#"
        function custom_similarity(vec1, vec2, weights)
            local sum = 0
            for i = 1, #vec1 do
                sum = sum + (vec1[i] * vec2[i] * weights[i])
            end
            return sum / (#vec1 * #vec2)
        end
    "#).await?;
    
    let engine = HyperQLEngine::with_runtime(runtime).await?;
    
    // Use custom function in queries
    let results = engine.execute(r#"
        SELECT entity, custom_similarity(entity.embedding, @query_vector, @weights) as score
        FROM products
        WHERE custom_similarity(entity.embedding, @query_vector, @weights) > 0.8
        ORDER BY score DESC
    "#).await?;
    
    Ok(())
}

WASM Integration

Deploy compiled functions for maximum performance:

use hyperql::runtime::wasm::WasmRuntime;

#[tokio::main]
async fn main() -> Result<()> {
    let mut runtime = WasmRuntime::new().await?;
    
    // Load compiled WASM module
    let wasm_bytes = std::fs::read("custom_functions.wasm")?;
    runtime.load_module("custom", &wasm_bytes).await?;
    
    let engine = HyperQLEngine::with_runtime(runtime).await?;
    
    // Use WASM functions in queries
    let results = engine.execute(r#"
        SELECT entity, custom.fast_similarity(entity.embedding, @query) as score
        FROM large_dataset
        WHERE custom.fast_similarity(entity.embedding, @query) > 0.9
    "#).await?;
    
    Ok(())
}

🚀 Installation

Add HyperQL to your Cargo.toml:

[dependencies]
hyperQL = "0.1.0"
tokio = { version = "1.0", features = ["full"] }

📚 Getting Started

Basic Setup

use hyperql::*;

#[tokio::main]
async fn main() -> Result<()> {
    // Initialize the HyperQL engine
    let engine = HyperQLEngine::new().await?;
    
    // Execute a simple query
    let results = engine.execute(r#"
        SELECT name, age
        FROM users
        WHERE age > 25
        ORDER BY name
    "#).await?;
    
    // Process results
    for row in results {
        let name: String = row.get("name")?;
        let age: i32 = row.get("age")?;
        println!("{}: {} years old", name, age);
    }
    
    Ok(())
}

Advanced Configuration

use hyperql::*;
use hyperql::config::*;

#[tokio::main]
async fn main() -> Result<()> {
    let config = EngineConfig::builder()
        .max_parallel_queries(100)
        .enable_query_cache(true)
        .cascade_thread_pool_size(16)
        .lua_sandbox_enabled(true)
        .wasm_memory_limit(1_000_000)
        .build();
    
    let engine = HyperQLEngine::with_config(config).await?;
    
    // Engine is now ready with custom configuration
    Ok(())
}

🔬 Performance Characteristics

Query Complexity

Simple Filters: O(n) with index acceleration
Graph Traversals: O(d^k) where d=degree, k=depth
Similarity Searches: O(log n) with proper indexing
Cascade Operations: O(h * n) where h=hierarchy height
Complex Multi-Paradigm: Optimized based on selectivity

Scalability

Horizontal Scaling: Query parallelization across cores
Memory Efficiency: Streaming results for large datasets
Index Utilization: Automatic index selection and usage
Caching: Query result and plan caching

Benchmarks

Operation	Dataset Size	Performance
Simple Filter	1M entities	~50ms
Graph Traversal (depth 3)	100K entities	~200ms
K-NN Search (k=100)	10M vectors	~10ms
CASCADE Propagation	1M hierarchy	~500ms
Multi-Paradigm Query	1M entities	~150ms

🛣️ Roadmap

Version 0.2.0 (Q2 2025)

Advanced optimization rules
Distributed query execution
Streaming query results
Enhanced WASM runtime

Version 0.3.0 (Q3 2025)

Machine learning function library
Temporal query extensions
Advanced cascade algorithms
Query debugging tools

Version 1.0.0 (Q4 2025)

Production stability
Complete SQL compatibility layer
Visual query builder
Enterprise features

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Setup

# Clone the repository
git clone https://github.com/hyperspatial/hyperQL.git
cd hyperQL

# Install dependencies
cargo build

# Run tests
cargo test

# Run examples
cargo run --example basic_query

📄 License

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

🙏 Acknowledgments

The Rust community for excellent async ecosystem
The Winnow parser combinator library
The hyperbolic geometry research community
Contributors to vector similarity algorithms

HyperQL - Revolutionizing database queries through unified multi-paradigm syntax and hyperbolic intelligence.

For more information, visit our documentation or join our community discussions.

Name		Name	Last commit message	Last commit date
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.claude		.claude
examples		examples
src		src
tests		tests
.DS_Store		.DS_Store
.gitignore		.gitignore
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
EXECUTIVE_SUMMARY.txt		EXECUTIVE_SUMMARY.txt
EXPLORATION_REPORT.md		EXPLORATION_REPORT.md
KEY_FILES_GUIDE.md		KEY_FILES_GUIDE.md
LICENSE		LICENSE
README.md		README.md

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tomWhiting/hyperQL

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