Rust Rule Engine v1.14.1 🦀⚡🚀

A blazing-fast production-ready rule engine for Rust supporting both Forward and Backward Chaining. Features RETE-UL algorithm with Alpha Memory Indexing and Beta Memory Indexing, parallel execution, goal-driven reasoning, and GRL (Grule Rule Language) syntax.

🔗 GitHub | Documentation | Crates.io

🎯 Reasoning Modes

🔄 Forward Chaining (Data-Driven)

"When facts change, fire matching rules"

• Native Engine - Simple pattern matching for small rule sets
• RETE-UL - Optimized network for 100-10,000 rules with O(1) indexing
• Parallel Execution - Multi-threaded rule evaluation

Use Cases: Business rules, validation, reactive systems, decision automation

🎯 Backward Chaining (Goal-Driven)

"Given a goal, find facts/rules to prove it"

• Unification - Pattern matching with variable bindings
• Search Strategies - DFS, BFS, Iterative Deepening
• Aggregation - COUNT, SUM, AVG, MIN, MAX
• Negation - NOT queries with closed-world assumption
• Explanation - Proof trees with JSON/MD/HTML export
• Disjunction - OR patterns for alternative paths
• Nested Queries - Subqueries with shared variables
• Query Optimization - Automatic goal reordering for 10-100x speedup

Use Cases: Expert systems, diagnostics, planning, decision support, AI reasoning

🌊 Stream Processing (Event-Driven) 🆕

"Process real-time event streams with time-based windows"

• GRL Stream Syntax - Declarative stream pattern definitions
• StreamAlphaNode - RETE-integrated event filtering & windowing
• Time Windows - Sliding (continuous) and tumbling (non-overlapping)
• Multi-Stream Correlation - Join events from different streams
• WorkingMemory Integration - Stream events become facts for rule evaluation

Use Cases: Real-time fraud detection, IoT monitoring, financial analytics, security alerts, CEP

Example:

rule "Fraud Alert" {
    when
        login: LoginEvent from stream("logins") over window(10 min, sliding) &&
        purchase: PurchaseEvent from stream("purchases") over window(10 min, sliding) &&
        login.user_id == purchase.user_id &&
        login.ip_address != purchase.ip_address
    then
        Alert.trigger("IP mismatch detected");
}

🚀 Quick Start

Forward Chaining Example

use rust_rule_engine::{RuleEngine, Facts, Value};

let mut engine = RuleEngine::new();

// Define rule in GRL
engine.add_rule_from_grl(r#"
    rule "VIP Discount" {
        when
            Customer.TotalSpent > 10000
        then
            Customer.Discount = 0.15;
    }
"#)?;

// Add facts and execute
let mut facts = Facts::new();
facts.set("Customer.TotalSpent", Value::Number(15000.0));
engine.execute(&mut facts)?;

// Result: Customer.Discount = 0.15 ✓

Backward Chaining Example

use rust_rule_engine::backward::BackwardEngine;

let mut engine = BackwardEngine::new(kb);

// Query: "Can this order be auto-approved?"
let result = engine.query(
    "Order.AutoApproved == true",
    &mut facts
)?;

if result.provable {
    println!("Order can be auto-approved!");
    println!("Proof: {:?}", result.proof_trace);
}

Stream Processing Example 🆕

use rust_rule_engine::parser::grl::stream_syntax::parse_stream_pattern;
use rust_rule_engine::rete::stream_alpha_node::{StreamAlphaNode, WindowSpec};
use rust_rule_engine::rete::working_memory::WorkingMemory;

// Parse GRL stream pattern
let grl = r#"login: LoginEvent from stream("logins") over window(5 min, sliding)"#;
let (_, pattern) = parse_stream_pattern(grl)?;

// Create stream processor
let mut node = StreamAlphaNode::new(
    &pattern.source.stream_name,
    pattern.event_type,
    pattern.source.window.as_ref().map(|w| WindowSpec {
        duration: w.duration,
        window_type: w.window_type.clone(),
    }),
);

// Process events in real-time
let mut wm = WorkingMemory::new();
for event in event_stream {
    if node.process_event(&event) {
        // Event passed filters and is in window
        wm.insert_from_stream("logins".to_string(), event);
        // Now available for rule evaluation!
    }
}

// Run: cargo run --example streaming_fraud_detection --features streaming

✨ What's New in v1.14.0 🎉

⚡ Alpha Memory Indexing - Up to 800x Faster Queries!

New hash-based indexing for alpha node fact filtering, complementing Beta Memory Indexing for complete RETE optimization!

🔍 Alpha Memory Indexing

Problem: Alpha nodes scan all facts linearly to find matches - O(n) complexity becomes slow with large datasets.

Solution: Hash-based indexing provides O(1) fact lookups - up to 800x speedup for filtered queries!

use rust_rule_engine::rete::{AlphaMemoryIndex, FactValue, TypedFacts};

// Create alpha memory with indexing
let mut mem = AlphaMemoryIndex::new();

// Create index on frequently-queried field
mem.create_index("status".to_string());

// Insert facts (index updated automatically)
for i in 0..10_000 {
    let mut fact = TypedFacts::new();
    fact.set("id", i as i64);
    fact.set("status", if i % 100 == 0 { "active" } else { "pending" });
    mem.insert(fact);
}

// Query using index - O(1) lookup!
let active = mem.filter("status", &FactValue::String("active".to_string()));
println!("Found {} active facts", active.len());
// Without index: 10,000 comparisons (O(n))
// With index: 1 hash lookup (O(1)) → ~800x faster!

Real Benchmark Results:

Key Features:

• ✅ Auto-tuning - Automatically creates indexes after 50+ queries on a field
• ✅ Multiple indexes - Index different fields independently
• ✅ Statistics tracking - Monitor index hit rates and effectiveness
• ✅ Low overhead - ~7-9% memory per index

When to Use:

// ✅ Use when:
// - Dataset > 10K facts
// - Read-heavy workload (query > insert)
// - High selectivity queries (<10% match rate)
// - Same queries repeated multiple times

// ❌ Skip when:
// - Dataset < 1K facts (overhead > benefit)
// - Write-heavy workload (insert > query)
// - Query each field only once

// 🤖 Auto-tuning mode (recommended):
let mut mem = AlphaMemoryIndex::new();

// Query many times...
for _ in 0..100 {
    mem.filter_tracked("status", &FactValue::String("active".to_string()));
}

// Auto-create index when query count > 50
mem.auto_tune();  // Indexes "status" automatically!

Memory Overhead:

Recommendation: Use 1-3 indexes max (~20% overhead) for best ROI.

✨ What's New in v1.13.0

⚡ Beta Memory Indexing - Up to 1,235x Faster Joins!

Comprehensive RETE optimization system with Beta Memory Indexing providing exponential speedup for multi-pattern rules!

🚀 Beta Memory Indexing

Problem: Join operations use nested loops (O(n²)) which becomes a bottleneck with large fact sets.

Solution: Hash-based indexing changes O(n²) to O(n) - providing 11x to 1,235x speedup!

use rust_rule_engine::rete::optimization::BetaMemoryIndex;
use rust_rule_engine::rete::TypedFacts;

// Create sample facts (e.g., orders with customer IDs)
let mut orders = Vec::new();
for i in 0..1000 {
    let mut order = TypedFacts::new();
    order.set("OrderId", format!("O{}", i));
    order.set("CustomerId", format!("C{}", i % 100));  // 100 unique customers
    order.set("Amount", (i * 50) as i64);
    orders.push(order);
}

// Build index on join key (CustomerId)
let mut index = BetaMemoryIndex::new("CustomerId".to_string());
for (idx, order) in orders.iter().enumerate() {
    index.add(order, idx);  // O(1) insertion
}

// Perform O(1) lookup instead of O(n) scan
// Note: Key format is the Debug representation of FactValue
let key = "String(\"C50\")";  // Looking for customer C50's orders
let matches = index.lookup(key);  // O(1) hash lookup!

println!("Found {} orders for customer C50", matches.len());
// Without indexing: 1,000 comparisons (O(n))
// With indexing: 1 hash lookup (O(1)) → 1,000x faster!

Real Benchmark Results:

Key Insight: At 5,000 facts, the difference between 2.6 SECONDS and 2ms is production-critical!

🔧 Memory Optimizations

Three additional optimizations focus on reducing memory footprint:

1. Node Sharing - Deduplicate identical alpha nodes

use rust_rule_engine::rete::optimization::NodeSharingRegistry;

let mut registry = NodeSharingRegistry::new();

// Register 10,000 nodes with 100 unique patterns
for (idx, node) in nodes.iter().enumerate() {
    registry.register(node, idx);
}

// Result: 98.1% memory reduction (689.84 KB saved)
let stats = registry.stats();
println!("Memory saved: {:.1}%", stats.memory_saved_percent);

2. Alpha Memory Compaction - Eliminate duplicate facts

use rust_rule_engine::rete::optimization::CompactAlphaMemory;

let mut memory = CompactAlphaMemory::new();

// Insert 10,000 facts with duplicates
for fact in facts {
    memory.add(&fact);
}

// Result: 98.7% memory reduction (925.00 KB saved)
println!("Unique facts: {} (saved {:.1}%)",
    memory.len(), memory.memory_savings());

3. Token Pooling - Reduce allocations

use rust_rule_engine::rete::optimization::TokenPool;

let mut pool = TokenPool::new(100);

// Process 10,000 events with token reuse
for event in events {
    let mut token = pool.acquire();
    token.set_fact(event);
    // ... process ...
    pool.release(token);
}

// Result: 99% fewer allocations
let stats = pool.stats();
println!("Reuse rate: {:.1}%", stats.reuse_rate);

📊 When to Use Each Optimization

💡 Recommended Usage

Default (Most Production Systems):

// Use Beta + Alpha Indexing for maximum performance
use rust_rule_engine::rete::{AlphaMemoryIndex, BetaMemoryIndex};

// Alpha indexing: for filtering (auto-tune recommended)
let mut alpha_mem = AlphaMemoryIndex::new();
// Will auto-create indexes for frequently-queried fields

// Beta indexing: for joins (always use)
let mut beta_index = BetaMemoryIndex::new("user_id".to_string());
// 150-1,235x faster joins - no downsides!

Memory-Constrained + Large Rule Sets:

use rust_rule_engine::rete::optimization::{
    BetaMemoryIndex,      // For speed (always)
    NodeSharingRegistry,  // For memory (if 10K+ rules)
};

High-Duplicate Workloads:

use rust_rule_engine::rete::optimization::{
    BetaMemoryIndex,      // For speed (always)
    CompactAlphaMemory,   // For deduplication (if >50% duplicates)
};

🔬 Try It Yourself

# Run interactive demos
cargo run --example alpha_indexing_demo          # Alpha Memory Indexing
cargo run --example rete_optimization_demo       # Beta Memory Indexing
cargo run --example grl_optimization_demo        # GRL rules + indexing

# Run benchmarks
cargo bench --bench engine_comparison_benchmark  # Compare all optimizations
cargo bench --bench alpha_indexing_benchmark     # Alpha indexing details
cargo run --bin memory_usage_benchmark --release # Memory analysis


# View detailed HTML reports
open target/criterion/report/index.html

📚 Complete Documentation

• RETE Optimization Guide - Comprehensive optimization guide
• Benchmark Results - Real benchmark data & analysis
• Optimization Demo - Interactive demonstration
• GRL + Optimization Demo - Real GRL rules with Beta Indexing
• Memory Analysis - Actual KB/MB measurements with RETE engine

New in v1.13.0:

• ✅ Beta Memory Indexing (11x to 1,235x speedup)
• ✅ Node Sharing (98.1% memory reduction)
• ✅ Alpha Memory Compaction (98.7% memory reduction)
• ✅ Token Pooling (99% fewer allocations)
• ✅ Comprehensive benchmarks with scaled datasets
• ✅ Real memory measurements (KB/MB)
• ✅ Production-ready optimization manager
• ✅ 30+ optimization tests

✨ Previous Update - v1.12.1

🌊 Stream Processing Foundation!

GRL Stream Syntax - Parse and process real-time event streams with time-based windows!

🆕 Stream Processing Features

GRL Stream Pattern Syntax:

// Stream with sliding window
login: LoginEvent from stream("logins") over window(10 min, sliding)

// Stream with tumbling window
metric: MetricEvent from stream("metrics") over window(5 sec, tumbling)

// Simple stream without window
event: Event from stream("events")

StreamAlphaNode - RETE Integration:

use rust_rule_engine::parser::grl::stream_syntax::parse_stream_pattern;
use rust_rule_engine::rete::stream_alpha_node::{StreamAlphaNode, WindowSpec};

// Parse GRL pattern
let grl = r#"login: LoginEvent from stream("logins") over window(5 min, sliding)"#;
let (_, pattern) = parse_stream_pattern(grl)?;

// Create stream processor
let mut node = StreamAlphaNode::new(
    &pattern.source.stream_name,
    pattern.event_type,
    pattern.source.window.as_ref().map(|w| WindowSpec {
        duration: w.duration,
        window_type: w.window_type.clone(),
    }),
);

// Process events
if node.process_event(&event) {
    let handle = working_memory.insert_from_stream("logins".to_string(), event);
    // Event now in RETE network for rule evaluation!
}

Real-World Example - Fraud Detection:

// 4 fraud detection rules implemented:
// 1. Suspicious IP changes (multiple IPs in 15 min)
// 2. High velocity purchases (>3 purchases in 15 min)
// 3. Impossible travel (location change too fast)
// 4. IP mismatch (login IP != purchase IP)

// Result: 7 alerts triggered from 16 events
cargo run --example streaming_fraud_detection --features streaming

Features Implemented:

• ✅ GRL stream syntax parser (nom-based, 15 tests)
• ✅ StreamAlphaNode for event filtering & windowing (10 tests)
• ✅ Sliding windows (continuous rolling)
• ✅ Tumbling windows (non-overlapping)
• ✅ WorkingMemory integration (stream → facts)
• ✅ Duration units: ms, sec, min, hour
• ✅ Optional event type filtering
• ✅ Multi-stream correlation

Test Coverage:

• 58 streaming tests (100% pass)
• 8 integration tests (fraud, IoT, trading, security)
• 3 end-to-end tests (GRL → RETE → WorkingMemory)
• 2 comprehensive examples

✨ Previous Update - v1.11.0

🎯 Nested Queries & Query Optimization!

Complete Phase 1.1 with nested queries (subqueries) and intelligent query optimization for 10-100x performance improvements!

🆕 Nested Queries

use rust_rule_engine::backward::*;

// Find grandparents using nested queries
let results = engine.query(
    "grandparent(?x, ?z) WHERE
        parent(?x, ?y) AND
        (parent(?y, ?z) WHERE child(?z, ?y))",
    &mut facts
)?;

// Complex eligibility with nested OR
query "CheckEligibility" {
    goal: (eligible(?x) WHERE (vip(?x) OR premium(?x))) AND active(?x)
    on-success: { LogMessage("Eligible!"); }
}

⚡ Query Optimization

// Enable optimization in GRL
query "OptimizedSearch" {
    goal: item(?x) AND expensive(?x) AND in_stock(?x)
    enable-optimization: true  // Automatically reorders goals!
}

// Manual optimization
let mut optimizer = QueryOptimizer::new();
optimizer.set_selectivity("in_stock(?x)".to_string(), 0.1);   // 10% in stock
optimizer.set_selectivity("expensive(?x)".to_string(), 0.3);  // 30% expensive
optimizer.set_selectivity("item(?x)".to_string(), 0.9);       // 90% items

let optimized = optimizer.optimize_goals(goals);
// Result: in_stock → expensive → item (10-100x faster!)

Performance Benefits:

• Before: 1000 items → 900 expensive → 270 in_stock = 2170 evaluations
• After: 10 in_stock → 8 expensive → 8 items = 26 evaluations
• Speedup: ~83x faster! 🚀

New Features:

• Nested queries with WHERE clauses
• Query optimizer with goal reordering
• Selectivity estimation (heuristic & custom)
• Join order optimization
• enable-optimization flag in GRL
• 19 new tests + 9 integration tests

Testing: 485/485 tests pass (368 unit + 117 integration) • Zero regressions

📖 Nested Query Demo • Optimizer Demo • GRL Integration

📚 Documentation

Comprehensive documentation organized by topic:

🚀 Getting Started

• Quick Start - Get up and running in 5 minutes
• Installation - Installation and setup guide
• Basic Concepts - Core concepts explained
• First Rules - Write your first rules

🎯 Core Features

• GRL Syntax - Grule Rule Language reference
• Features Overview - All engine capabilities

⚡ Advanced Features

• RETE Optimization - 1,235x join speedup & memory optimizations (v1.13.0+)
• RETE Benchmarks - Real performance data & analysis (v1.13.0+)
• Streaming & CEP - Complex Event Processing
• Streaming Architecture - Deep dive into streaming
• Plugins - Custom plugins and extensions
• Performance - Optimization techniques
• Redis State - Distributed state management

📖 API Reference

• API Reference - Complete public API
• GRL Query Syntax - Backward chaining queries (v1.11.0+)
• Parser Cheat Sheet - Quick syntax reference

📝 Guides

• Backward Chaining Quick Start - Goal-driven reasoning
• RETE Integration - Combine forward + backward
• Module Management - Organize rules into modules
• Troubleshooting - Common issues and solutions

💡 Examples

• AI Integration - Combine with ML models

📚 Full Documentation Index →

📜 Older Releases

See CHANGELOG.md for full version history (v0.1.0 - v0.19.0).