Rust Rule Engine v1.16.1 🦀⚡🚀

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), tumbling (non-overlapping), and session (gap-based) 🆕
• 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 v1.16.1 🎉

🧹 Minimal Dependencies - Pure Stdlib

Removed 5 external dependencies - replaced with Rust stdlib or removed dead code:

Replaced with stdlib:

• ❌ num_cpus → ✅ std::thread::available_parallelism() (Rust 1.59+)
• ❌ once_cell → ✅ std::sync::OnceLock (Rust 1.70+)
• ❌ fastrand → ✅ std::collections::hash_map::RandomState

Removed unused:

• ❌ petgraph - Declared but never used (zero code references)
• ❌ futures - Declared but never used (tokio is sufficient)

Benefits:

• 📦 5 fewer crates - down from 12 to 7 core dependencies (41% reduction!)
• 🛡️ More reliable - 100% stdlib for threading, lazy init, randomization
• ⚡ Zero performance regression - all benchmarks unchanged
• 🔧 Modern Rust - using latest stdlib features

Final Core Dependencies: Only 7 essential crates

chrono, log, nom, regex, serde, serde_json, thiserror

Optional dependencies (by feature):

• tokio - Async runtime for streaming
• redis - State backend for streaming-redis

Code changes:

• Thread detection: num_cpus::get() → std::thread::available_parallelism()
• Lazy regex (20 patterns): once_cell::Lazy → std::sync::OnceLock
• Random generation: fastrand → RandomState::new().build_hasher()
• Fixed flaky test in session window eviction

Testing:

• ✅ All 428+ tests passing
• ✅ All 14+ examples working
• ✅ All features validated (streaming, backward-chaining, etc.)

✨ What's New in v1.16.0

🪟 Session Windows for Stream Processing

Complete implementation of session-based windowing for real-time event streams! Session windows dynamically group events based on inactivity gaps rather than fixed time boundaries.

What are Session Windows?

Unlike sliding or tumbling windows, session windows adapt to natural event patterns:

Events: A(t=0), B(t=1), C(t=2), [gap 10s], D(t=12), E(t=13)
Timeout: 5 seconds

Result:
  Session 1: [A, B, C]  - ends when gap > 5s
  Session 2: [D, E]     - starts after gap > 5s

GRL Syntax:

rule "UserSessionAnalysis" {
    when
        activity: UserAction from stream("user-activity")
            over window(5 min, session)
    then
        AnalyzeSession(activity);
}

Rust API:

use rust_rule_engine::rete::stream_alpha_node::{StreamAlphaNode, WindowSpec};
use rust_rule_engine::streaming::window::WindowType;
use std::time::Duration;

let window = WindowSpec {
    duration: Duration::from_secs(60),
    window_type: WindowType::Session {
        timeout: Duration::from_secs(5),  // Gap threshold
    },
};

let mut node = StreamAlphaNode::new("user-events", None, Some(window));

Perfect for:

• 📊 User Session Analytics - Track natural user behavior sessions
• 🛒 Cart Abandonment - Detect when users don't complete checkout
• 🔒 Fraud Detection - Identify unusual session patterns
• 📡 IoT Sensor Grouping - Group burst events from sensors

Features:

• ✅ Automatic session boundary detection based on inactivity
• ✅ Dynamic session sizes (adapts to activity patterns)
• ✅ O(1) event processing with minimal overhead
• ✅ Full integration with RETE network
• ✅ 7 comprehensive tests (all passing)
• ✅ Interactive demo: cargo run --example session_window_demo --features streaming

✨ What's New in v1.15.1

🧹 Codebase Cleanup

Major cleanup and optimization of the project structure for better maintainability and developer experience!

🔧 Dependencies Optimized (-75% dev-deps)

• Removed 9 unused dev-dependencies (axum, tower, reqwest, tracing, etc.)
• Eliminated duplicate dependencies (serde, chrono already in main deps)
• Kept only essentials: criterion, tokio, serde_yaml
• Faster build times and smaller binary size

Benefits:

• ⚡ Faster compilation and CI runs
• 📚 Easier onboarding with clear example structure
• 🧹 Less code to maintain (-76% examples)
• ✅ Production-ready with all tests passing

✨ What's New in v1.15.0

➕ Array Append Operator (+=)

Added support for the += operator to append values to arrays in GRL actions! This is particularly useful for building recommendation lists, accumulating results, and managing collections.

GRL Syntax:

rule "Product Recommendation" salience 100 no-loop {
    when
        ShoppingCart.items contains "Laptop" &&
        !(Recommendation.items contains "Mouse")
    then
        Recommendation.items += "Mouse";          // Append to array
        Recommendation.items += "USB-C Hub";      // Multiple appends
        Log("Added recommendations");
}

Rust Usage:

use rust_rule_engine::rete::{IncrementalEngine, TypedFacts, FactValue};
use rust_rule_engine::rete::grl_loader::GrlReteLoader;

let mut engine = IncrementalEngine::new();
GrlReteLoader::load_from_file("rules.grl", &mut engine)?;

let mut facts = TypedFacts::new();
facts.set("ShoppingCart.items", FactValue::Array(vec![
    FactValue::String("Laptop".to_string())
]));
facts.set("Recommendation.items", FactValue::Array(vec![]));

engine.insert_typed_facts("ShoppingCart", facts.clone());
engine.fire_all(&mut facts, 10);

// Result: Recommendation.items = ["Mouse", "USB-C Hub"] ✓

Integration with Rule Mining:

The += operator works seamlessly with rust-rule-miner for automatic rule generation:

// Mine association rules from historical data
let rules = miner.mine_association_rules()?;

// Export to GRL with += syntax
let grl = GrlExporter::to_grl(&rules);
// Generates: Recommendation.items += "Phone Case";

// Load and execute in RETE engine
GrlReteLoader::load_from_string(&grl, &mut engine)?;

Supported Everywhere:

• ✅ Forward chaining (RETE engine)
• ✅ Backward chaining (goal-driven reasoning)
• ✅ Parallel execution
• ✅ All action execution contexts

✨ 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).