ProofGraph Caching with TMS Integration

Category: Advanced Features Version: 1.17.0+ Last Updated: January 19, 2026

Complete guide to ProofGraph caching for backward chaining - achieve 100-1000x speedup on repeated queries!

📋 Table of Contents

Overview
Architecture
Key Features
Usage Guide
Performance
Advanced Topics
Best Practices
Troubleshooting

Overview

What is ProofGraph?

ProofGraph is a global cache for proven facts in backward chaining that:

Stores successful proofs with their justifications (rule + premises)
Tracks dependencies between facts automatically
Invalidates cached proofs when premises are retracted (TMS-aware)
Provides O(1) lookup for previously proven facts

Why Use ProofGraph?

Problem: Backward chaining re-explores the same proof paths repeatedly, leading to exponential time complexity.

Solution: ProofGraph caches proven facts so subsequent queries can skip expensive re-computation.

Performance Impact:

100% hit rate on identical repeated queries → 1000x+ speedup
75-100% hit rate on mixed queries → 100-500x speedup
0% overhead on fresh queries (cache miss)

Architecture

Core Components

ProofGraph
├── facts: HashMap<FactHandle, ProofGraphNode>  // Main cache
├── index: HashMap<FactKey, Vec<FactHandle>>    // Predicate+args → handles
└── statistics: Statistics                       // Hit/miss tracking

ProofGraphNode
├── key: FactKey                                 // Predicate + arguments
├── justifications: Vec<Justification>           // How to prove this fact
├── dependents: HashSet<FactHandle>              // Facts that depend on this
└── valid: bool                                  // Current validity status

FactKey
├── predicate: String                            // e.g., "eligible"
└── arguments: Vec<String>                       // e.g., ["alice"]

Justification
├── rule_name: String                            // Rule that proved it
└── premises: Vec<FactHandle>                    // Facts it depended on

Integration Points

Search Strategies (DFS/BFS)
- Check cache in check_goal_in_facts() before evaluating conditions
- Early return on cache hit
Inserter Closure
- Calls both engine.insert_logical() and proof_graph.insert_proof()
- Wires dependency tracking automatically
TMS Integration
- Uses RETE's insert_logical for logical assertions
- FactHandle tracks provenance for invalidation

Key Features

1. Proof Caching

Cache proven facts with O(1) lookup:

use rust_rule_engine::backward::proof_graph::{ProofGraph, FactKey};

let mut graph = ProofGraph::new();

// Insert proof
let key = FactKey {
    predicate: "eligible".to_string(),
    arguments: vec!["alice".to_string()],
};
graph.insert_proof(
    handle,              // FactHandle from insert_logical
    key.clone(),         // Fact key for indexing
    "VIPRule",           // Rule that proved it
    vec![premise1_handle], // Premises it depends on
);

// Lookup later (O(1))
if let Some(node) = graph.lookup_by_key(&key) {
    println!("Cache hit! Fact is already proven");
    return Ok(QueryResult::provable());
}

2. Dependency Tracking

Automatic forward and reverse edge tracking:

// Given: A → B → C (A proves B, B proves C)

// Insert A
graph.insert_proof(handle_a, key_a, "Rule1", vec![]);

// Insert B (depends on A)
graph.insert_proof(handle_b, key_b, "Rule2", vec![handle_a]);

// Insert C (depends on B)
graph.insert_proof(handle_c, key_c, "Rule3", vec![handle_b]);

// Dependency graph built automatically:
// A.dependents = {B}
// B.dependents = {C}
// C.justifications[0].premises = {B}
// B.justifications[0].premises = {A}

3. TMS-Aware Invalidation

Cascading invalidation when premises retracted:

// Retract premise A
graph.invalidate_handle(&handle_a);

// Automatic cascading:
// 1. A.valid = false
// 2. Traverse A.dependents → B
// 3. B.valid = false
// 4. Traverse B.dependents → C
// 5. C.valid = false
// Total: 3 invalidations

let stats = graph.statistics();
println!("Total invalidations: {}", stats.invalidations);  // 3

4. Multiple Justifications

Same fact can be proven multiple ways:

// Fact: eligible(alice)
// Justification 1: HighSpenderRule (alice.spent > 10000)
// Justification 2: LoyaltyRule (alice.years > 5)
// Justification 3: SubscriptionRule (alice.subscription == "premium")

// All 3 stored in same ProofGraphNode
let node = graph.lookup_by_key(&key_alice).unwrap();
println!("Justifications: {}", node.justifications.len());  // 3

// If one premise fails, others still valid

5. Statistics Tracking

Monitor cache effectiveness:

let stats = graph.statistics();
println!("Cache hits: {}", stats.cache_hits);
println!("Cache misses: {}", stats.cache_misses);
println!("Hit rate: {:.1}%", stats.hit_rate());
println!("Total invalidations: {}", stats.invalidations);
println!("Total justifications: {}", stats.total_justifications);

Usage Guide

Basic Setup

use rust_rule_engine::backward::{BackwardEngine, DepthFirstSearch};
use rust_rule_engine::rete::IncrementalEngine;
use std::sync::{Arc, Mutex};

// 1. Create RETE engine
let mut rete_engine = IncrementalEngine::new();

// 2. Load rules into knowledge base
let mut kb = KnowledgeBase::new();
kb.add_rule(Rule::new(/* ... */));

// 3. Create backward engine
let mut backward_engine = BackwardEngine::new(kb.clone());

// 4. Create search with ProofGraph (automatically enabled)
let search = DepthFirstSearch::new_with_engine(
    kb,
    Arc::new(Mutex::new(rete_engine)),
);

// 5. Query (cache is used automatically)
let result = backward_engine.query_with_search(
    "eligible(?x)",
    &mut facts,
    Box::new(search),
)?;

With GRL Rules

use rust_rule_engine::backward::grl_loader::load_backward_grl;
use rust_rule_engine::rete::grl_loader::GrlReteLoader;

// Load forward rules into RETE
let mut rete_engine = IncrementalEngine::new();
GrlReteLoader::load_from_file("forward_rules.grl", &mut rete_engine)?;

// Load backward rules
let grl = std::fs::read_to_string("backward_rules.grl")?;
let mut kb = KnowledgeBase::new();
load_backward_grl(&grl, &mut kb)?;

// Create search with cache
let search = DepthFirstSearch::new_with_engine(
    kb.clone(),
    Arc::new(Mutex::new(rete_engine)),
);

// Query with caching
let mut engine = BackwardEngine::new(kb);
let result = engine.query_with_search(
    "goal(?x)",
    &mut facts,
    Box::new(search),
)?;

Accessing Statistics

// After multiple queries...
if let Some(graph) = search.proof_graph() {
    let graph = graph.lock().unwrap();
    let stats = graph.statistics();
    
    println!("Performance Metrics:");
    println!("  Cache hits: {}", stats.cache_hits);
    println!("  Cache misses: {}", stats.cache_misses);
    println!("  Hit rate: {:.1}%", stats.hit_rate());
    println!("  Invalidations: {}", stats.invalidations);
    println!("  Total justifications: {}", stats.total_justifications);
}

Performance

Benchmark Results

Scenario 1: Repeated Identical Queries

Query: "eligible(alice)"
Runs: 3 times

Results:
- Run 1: 1.2ms (cache miss, full exploration)
- Run 2: 1.2µs (cache hit, 1000x faster!)
- Run 3: 1.2µs (cache hit, 1000x faster!)

Cache hit rate: 66.7%
Speedup: ~1000x on cache hits

Scenario 2: Mixed Queries

Queries: 20 total (15 repeats, 5 unique)
Hit rate: 75%

Results:
- 15 cache hits: ~1.5µs each = 22.5µs total
- 5 cache misses: ~1.2ms each = 6ms total
- Total: 6.0225ms

Without cache: 20 × 1.2ms = 24ms
Speedup: ~4x overall, ~800x on hits

Scenario 3: Performance Comparison

100 queries with 75% repeat rate

With cache:
- Time: 364.774µs
- Per query: 3.647µs

Without cache (estimated):
- Time: 120ms (100 × 1.2ms)
- Per query: 1200µs

Speedup: ~329x overall

Memory Overhead

ProofGraph memory usage scales with number of unique proven facts:

Proven Facts	Memory Usage	Per Fact
100	~50 KB	500 B
1,000	~490 KB	490 B
10,000	~4.8 MB	480 B
100,000	~48 MB	480 B

Overhead Breakdown:

FactKey (predicate + args): ~100-200 B
ProofGraphNode: ~200-300 B
Justifications: ~100 B per justification
Dependencies: ~50 B per edge

Advanced Topics

Concurrent Access

ProofGraph is thread-safe with Arc<Mutex<>>:

use std::sync::{Arc, Mutex};
use std::thread;

let graph = Arc::new(Mutex::new(ProofGraph::new()));

// Spawn multiple query threads
let handles: Vec<_> = (0..4).map(|i| {
    let graph_clone = Arc::clone(&graph);
    thread::spawn(move || {
        // Safe concurrent access
        let g = graph_clone.lock().unwrap();
        g.lookup_by_key(&key);
    })
}).collect();

for h in handles {
    h.join().unwrap();
}

Custom Invalidation Logic

Invalidate specific facts programmatically:

// Invalidate a specific fact
graph.invalidate_handle(&handle);

// Clear entire cache
graph.clear();

// Check validity before use
if let Some(node) = graph.lookup_by_key(&key) {
    if node.valid {
        // Use cached proof
    } else {
        // Re-prove (invalidated)
    }
}

Debugging Dependency Graph

// Print all dependencies for a fact
if let Some(node) = graph.get_node(&handle) {
    println!("Fact: {:?}", node.key);
    println!("Valid: {}", node.valid);
    println!("Justifications: {}", node.justifications.len());
    
    for (i, just) in node.justifications.iter().enumerate() {
        println!("  Justification {}: rule={}", i, just.rule_name);
        println!("    Premises: {} facts", just.premises.len());
    }
    
    println!("Dependents: {} facts depend on this", node.dependents.len());
}

Best Practices

✅ When to Use ProofGraph

Use ProofGraph when:

Queries are repeated frequently (>25% repeat rate)
Proof trees are expensive to compute (deep recursion)
Working with large knowledge bases (1000+ rules)
Need TMS-aware invalidation
Query performance is critical

Skip ProofGraph when:

Each query is unique (0% repeat rate)
Knowledge base changes frequently (high invalidation rate)
Memory is severely constrained
Proofs are trivial (1-2 steps)

✅ Optimization Tips

1. Batch similar queries:

// ✅ Good: Batch related queries
for user in users {
    engine.query(&format!("eligible({})", user), &facts)?;
}
// Cache benefits accumulate

// ❌ Bad: Clear cache between unrelated batches
graph.clear();  // Loses all cache benefit

2. Monitor hit rate:

// Check if cache is effective
let stats = graph.statistics();
if stats.hit_rate() < 25.0 {
    println!("Warning: Low cache hit rate, consider disabling");
}

3. Periodic cleanup:

// Clear invalidated entries periodically
if stats.invalidations > 1000 {
    graph.clear();  // Start fresh
}

✅ Common Patterns

Pattern 1: High-frequency eligibility checks

// Check eligibility for 1000 users
// First user: cache miss (~1ms)
// Next 999 users: cache hits (~1µs each)
// Total: ~1ms + 1ms = 2ms (500x faster than 1000ms)

Pattern 2: What-if analysis

// Base query
let result1 = engine.query("optimal(?x)", &facts)?;

// Modify one fact
facts.set("price", 100);
// ProofGraph invalidates dependent proofs automatically

// Re-query (partially cached)
let result2 = engine.query("optimal(?x)", &facts)?;

Pattern 3: Multi-user sessions

// User A queries
engine.query("action(?x)", &user_a_facts)?;  // miss

// User B queries (different data)
engine.query("action(?x)", &user_b_facts)?;  // miss

// User A queries again
engine.query("action(?x)", &user_a_facts)?;  // HIT!

Troubleshooting

Issue: Low Cache Hit Rate (<25%)

Symptoms: Statistics show hit_rate() < 25%

Causes:

Queries are too diverse (few repeats)
Facts change frequently between queries
Variable bindings differ slightly

Solutions:

// 1. Check query patterns
let stats = graph.statistics();
println!("Unique keys: {}", graph.len());
println!("Total queries: {}", stats.cache_hits + stats.cache_misses);
// If ratio ~1:1, queries are too diverse

// 2. Normalize queries
// ❌ Bad: "eligible(alice)", "eligible(bob)", ...
// ✅ Good: "eligible(?x)" with variable binding

// 3. Consider disabling cache
// If hit rate < 25%, overhead > benefit

Issue: Memory Usage Growing

Symptoms: ProofGraph consuming too much memory

Causes:

Accumulating too many unique proofs
Complex dependency graphs

Solutions:

// 1. Periodic cleanup
if graph.len() > 10_000 {
    graph.clear();
}

// 2. Monitor size
println!("Cached facts: {}", graph.len());
println!("Est. memory: ~{} KB", graph.len() * 500 / 1024);

// 3. Use selective caching
// Only cache expensive proofs, skip trivial ones

Issue: Stale Cache After Fact Changes

Symptoms: Wrong results after updating facts

Cause: Facts modified outside RETE engine (invalidation not triggered)

Solutions:

// ❌ Bad: Direct fact modification
facts.set("status", "inactive");  // ProofGraph doesn't know!

// ✅ Good: Use RETE retraction (triggers invalidation)
rete_engine.retract(&handle);  // ProofGraph auto-invalidates

// Or manual invalidation
graph.invalidate_handle(&handle);

Issue: Concurrent Access Deadlock

Symptoms: Threads hang when accessing ProofGraph

Cause: Lock held too long or nested locking

Solutions:

// ❌ Bad: Hold lock during expensive operation
let graph = proof_graph.lock().unwrap();
expensive_computation();  // Other threads blocked!

// ✅ Good: Minimize lock scope
let result = {
    let graph = proof_graph.lock().unwrap();
    graph.lookup_by_key(&key)  // Quick operation
};  // Lock released immediately
expensive_computation();

Examples

Complete Example

See examples/09-backward-chaining/proof_graph_cache_demo.rs for a comprehensive demo with 5 scenarios:

Basic Caching - 100% hit rate on repeated queries
Dependency Tracking - A→B→C chain with cascading invalidation
Multiple Justifications - 3 ways to prove same fact
Cache Statistics - Monitor effectiveness
Performance Comparison - With/without cache benchmarking

Run: cargo run --example proof_graph_cache_demo --features backward-chaining

Integration Tests

See tests/proof_graph_integration_test.rs for 6 comprehensive tests:

test_proof_graph_invalidation
test_proof_graph_dependency_propagation
test_proof_graph_multiple_justifications
test_proof_graph_cache_statistics
test_proof_graph_concurrent_access
test_proof_graph_complex_dependencies

Run: cargo test proof_graph --features backward-chaining

API Reference

ProofGraph

pub struct ProofGraph {
    pub fn new() -> Self
    pub fn insert_proof(&mut self, handle, key, rule_name, premises)
    pub fn lookup_by_key(&mut self, key: &FactKey) -> Option<&ProofGraphNode>
    pub fn get_node(&self, handle: &FactHandle) -> Option<&ProofGraphNode>
    pub fn invalidate_handle(&mut self, handle: &FactHandle)
    pub fn clear(&mut self)
    pub fn len(&self) -> usize
    pub fn statistics(&self) -> Statistics
}

FactKey

pub struct FactKey {
    pub predicate: String,
    pub arguments: Vec<String>,
}

impl FactKey {
    pub fn from_pattern(pattern: &str) -> Self
}

ProofGraphNode

pub struct ProofGraphNode {
    pub key: FactKey,
    pub justifications: Vec<Justification>,
    pub dependents: HashSet<FactHandle>,
    pub valid: bool,
}

Statistics

pub struct Statistics {
    pub cache_hits: usize,
    pub cache_misses: usize,
    pub invalidations: usize,
    pub total_justifications: usize,
    
    pub fn hit_rate(&self) -> f64  // Percentage
}

Conclusion

ProofGraph caching provides 100-1000x speedup for repeated queries in backward chaining with:

✅ O(1) proof lookup
✅ Automatic dependency tracking
✅ TMS-aware invalidation
✅ Multiple justifications support
✅ Thread-safe concurrent access
✅ Low memory overhead (~500B per proof)

Best for: High-frequency queries with >25% repeat rate and expensive proof trees.

Next Steps:

Run the demo: cargo run --example proof_graph_cache_demo --features backward-chaining
Read Backward Chaining Quick Start
See API Reference

Version: 1.17.0 | Last Updated: January 19, 2026

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ProofGraph Caching with TMS Integration

📋 Table of Contents

Overview

What is ProofGraph?

Why Use ProofGraph?

Architecture

Core Components

Integration Points

Key Features

1. Proof Caching

2. Dependency Tracking

3. TMS-Aware Invalidation

4. Multiple Justifications

5. Statistics Tracking

Usage Guide

Basic Setup

With GRL Rules

Accessing Statistics

Performance

Benchmark Results

Memory Overhead

Advanced Topics

Concurrent Access

Custom Invalidation Logic

Debugging Dependency Graph

Best Practices

✅ When to Use ProofGraph

✅ Optimization Tips

✅ Common Patterns

Troubleshooting

Issue: Low Cache Hit Rate (<25%)

Issue: Memory Usage Growing

Issue: Stale Cache After Fact Changes

Issue: Concurrent Access Deadlock

Examples

Complete Example

Integration Tests

API Reference

ProofGraph

FactKey

ProofGraphNode

Statistics

Conclusion