🚀 Terraphim Performance Analysis & Optimization Guide

📊 Performance Insights from Test Matrix

Based on our comprehensive test matrix results, we've identified significant performance patterns across different scoring function and haystack combinations.

🏆 Top Performers (7+ results/sec)

| Rank | Combination | Performance | Haystack | Notes | |------|-------------|-------------|----------|-------| | 1 | TitleScorer + QueryRs (JaroWinkler) | 7.64 results/sec | QueryRs | Fastest overall | | 2 | TitleScorer + QueryRs (BM25Plus) | 7.63 results/sec | QueryRs | Near-optimal | | 3 | TitleScorer + QueryRs (OkapiBM25) | 7.44 results/sec | QueryRs | Excellent | | 4 | TitleScorer + QueryRs (BM25) | 7.42 results/sec | QueryRs | Consistent | | 5 | TitleScorer + QueryRs (Jaro) | 7.39 results/sec | QueryRs | String similarity |

⚠️ Performance Bottlenecks Identified

Slow Combinations (>30 seconds)

• BM25 + ClickUp: 39.94 seconds (0.025 results/sec)
• TerraphimGraph + Ripgrep: 26.35 seconds (initialization overhead)

Medium Performance (1-10 seconds)

• Most Ripgrep combinations: ~0.8-1.0 seconds
• Atomic combinations: ~0.7-0.9 seconds
• MCP/Perplexity combinations: ~0.8-1.2 seconds

🔍 Performance Analysis

QueryRs Dominance

QueryRs consistently shows the best performance characteristics:

• Fastest search execution
• Best scalability
• Optimal memory usage
• Native Rust implementation benefits

Algorithm Performance Ranking

1. JaroWinkler: Best for fuzzy string matching
2. BM25Plus: Improved BM25 with better normalization
3. OkapiBM25: Classic implementation with good performance
4. Standard BM25: Reliable baseline performance
5. Jaro: Good string similarity without position weighting

🎯 Optimization Recommendations

Immediate Actions

1. Promote QueryRs as Default

{
  "recommended_haystack": "QueryRs",
  "reason": "Consistently 7-8x faster than alternatives",
  "use_cases": ["Development", "CI/CD", "Local search", "Performance-critical applications"]
}

2. Optimize ClickUp Integration

The 39-second search time for BM25 + ClickUp indicates:

• API rate limiting issues
• Network latency problems
• Inefficient pagination handling
• Missing caching layer

Recommended fixes:

• Implement intelligent request batching
• Add response caching with TTL
• Use parallel requests where possible
• Add connection pooling

3. TerraphimGraph Initialization Optimization

The long initialization time (26+ seconds) suggests:

• Heavy graph loading at startup
• Complex automata compilation
• Missing lazy loading

Recommended fixes:

• Implement lazy graph loading
• Add graph caching mechanisms
• Use incremental indexing
• Precompile automata during build

Advanced Optimizations

4. Algorithm-Specific Tuning

JaroWinkler Optimization:

// Consider pre-computing character frequency tables
// Use SIMD instructions for string comparisons
// Implement early termination for low-similarity strings

BM25Plus Enhancements:

// Optimize term frequency calculations
// Use sparse vector representations
// Implement document length normalization caching

5. Haystack-Specific Optimizations

Ripgrep Integration:

• Use parallel processing for multiple files
• Implement streaming results instead of buffering
• Add smart file filtering based on extensions

Atomic Integration:

• Implement connection pooling
• Use batch queries where possible
• Add local caching layer

📈 Performance Monitoring

Key Metrics to Track

1. Search latency (p50, p95, p99)
2. Memory usage during searches
3. CPU utilization patterns
4. Network I/O for remote haystacks
5. Cache hit rates

Benchmarking Framework

# Run performance benchmarks
./run_test_matrix.sh performance

# Generate performance reports
./scripts/performance_report.sh

# Compare against baseline
./scripts/performance_comparison.sh

🎯 Performance Targets

Target Performance Goals

• QueryRs combinations: Maintain 7+ results/sec
• Ripgrep combinations: Improve to 2+ results/sec
• ClickUp combinations: Reduce to <5 seconds
• TerraphimGraph: Reduce initialization to <5 seconds

Memory Usage Goals

• Peak memory: <500MB for typical searches
• Memory growth: <10% per 1000 searches
• Cache efficiency: >80% hit rate after warmup

🚀 Implementation Roadmap

Phase 1: Quick Wins (1-2 weeks)

• [ ] Promote QueryRs as default haystack
• [ ] Add connection pooling for ClickUp
• [ ] Implement basic caching layer
• [ ] Optimize TerraphimGraph lazy loading

Phase 2: Advanced Optimizations (3-4 weeks)

• [ ] SIMD optimizations for string algorithms
• [ ] Parallel processing for multi-haystack searches
• [ ] Advanced caching strategies
• [ ] Memory usage optimizations

Phase 3: Monitoring & Tuning (Ongoing)

• [ ] Performance monitoring dashboard
• [ ] Automated regression detection
• [ ] Continuous performance profiling
• [ ] Algorithm parameter tuning

🔧 Development Tools

Profiling Commands

# CPU profiling
cargo flamegraph --bin terraphim-tui -- --config config.json search "test query"

# Memory profiling
cargo instruments -t "Allocations" --bin terraphim-tui -- search "test query"

# Benchmark specific combinations
cargo bench --bench scoring_performance

Performance Testing

# Continuous performance testing
cargo test --test performance_regression_tests --release

# Load testing
./scripts/load_test.sh --concurrent 10 --duration 60s

📋 Conclusion

Our test matrix revealed QueryRs with TitleScorer as the clear performance winner, achieving 7+ results/sec consistently. The main optimization opportunities lie in:

1. ClickUp API optimization (biggest impact: 35+ second reduction)
2. TerraphimGraph initialization (20+ second reduction potential)
3. Algorithm-specific tuning (10-20% improvements)
4. Caching strategies (2-5x improvements for repeated queries)

By implementing these optimizations, we can achieve 2-5x performance improvements across all combinations while maintaining the exceptional performance of our top-tier QueryRs configurations.

Performance Analysis v1.0 - Generated September 17, 2025