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Performance Optimization Guide

This guide provides comprehensive information about the Firefly Framework Rule Engine's performance optimization features, configuration options, and best practices for high-load production environments.

Overview

The Firefly Framework Rule Engine includes three major performance optimization systems:

  1. AST Caching - Dual cache providers (Caffeine/Redis) for parsed AST models
  2. Connection Pool Tuning - Optimized R2DBC connection pools for high-load scenarios
  3. Batch Operations - Concurrent rule evaluation with configurable concurrency limits

🚀 AST Caching System

Cache Provider Selection

The system supports two cache providers that can be selected via configuration:

Caffeine Cache Provider (Default)

  • Ultra-fast local caching - 664x faster reads than Redis
  • Memory-efficient - Automatic eviction and size management
  • Zero network overhead - Perfect for single-instance deployments
  • Thread-safe - Concurrent access without locks

Redis Cache Provider (Optional)

  • Distributed caching - Share cache across multiple instances
  • Persistence - Survive application restarts
  • Scalability - Handle large cache datasets
  • High availability - Redis cluster support

Configuration

firefly:
  rules:
    cache:
      provider: caffeine  # or 'redis'
      
      # Caffeine Configuration (Default)
      caffeine:
        ast-cache:
          maximum-size: 1000
          expire-after-write: 2h
          expire-after-access: 30m
        constants-cache:
          maximum-size: 500
          expire-after-write: 15m
          expire-after-access: 5m
        validation-cache:
          maximum-size: 200
          expire-after-write: 1h
          expire-after-access: 15m
        
      # Redis Configuration (Optional)
      redis:
        host: ${REDIS_HOST:localhost}
        port: ${REDIS_PORT:6379}
        password: ${REDIS_PASSWORD:}
        database: ${REDIS_DATABASE:0}
        timeout: 5s
        ttl:
          ast-cache: 2h
          constants-cache: 15m
          validation-cache: 1h

Performance Comparison

Operation Caffeine Redis Performance Gain
Read Operations 0.26 ms 175.12 ms 664x faster
Write Operations 0.25 ms 44.28 ms 180x faster
Network Overhead None TCP/Redis Protocol Zero vs Network
Use Case Single instance Multi-instance Deployment specific

Cache Usage Patterns

  • AST Cache: Stores parsed AST models using SHA-256 hash of YAML content
  • Constants Cache: Caches system constants to avoid repeated database queries
  • Validation Cache: Caches validation results for frequently validated rules

Monitoring Cache Performance

# Get cache statistics
curl http://localhost:8080/api/v1/rules/cache/statistics

# Response includes hit rates, miss rates, eviction counts
{
  "astCache": {
    "provider": "caffeine",
    "hitRate": 85.5,
    "missRate": 14.5,
    "evictionCount": 12,
    "averageLoadTime": "2.3ms",
    "size": 750
  },
  "constantsCache": {
    "hitRate": 92.1,
    "missRate": 7.9,
    "size": 245
  }
}

🔗 Connection Pool Optimization

Environment-Specific Configuration

Production Settings (High Load)

spring:
  r2dbc:
    pool:
      initial-size: 10
      max-size: 20
      max-idle-time: 30m
      max-acquire-time: 60s
      max-create-connection-time: 30s
      max-life-time: 1800s
      validation-query: SELECT 1
      validation-depth: LOCAL

Development Settings (Resource Efficient)

spring:
  r2dbc:
    pool:
      initial-size: 5
      max-size: 10
      max-idle-time: 15m
      max-acquire-time: 30s
      max-create-connection-time: 15s
      max-life-time: 900s

Pool Monitoring

Monitor connection pool health through actuator endpoints:

# Connection pool metrics
curl http://localhost:8080/actuator/metrics/r2dbc.pool.acquired
curl http://localhost:8080/actuator/metrics/r2dbc.pool.allocated
curl http://localhost:8080/actuator/metrics/r2dbc.pool.pending

📦 Batch Processing Optimization

Batch Evaluation API

curl -X POST http://localhost:8080/api/v1/rules/batch/evaluate \
  -H "Content-Type: application/json" \
  -d '{
    "evaluationRequests": [
      {
        "requestId": "req-001",
        "ruleDefinitionCode": "LOAN_APPROVAL",
        "inputData": {"creditScore": 750, "income": 75000},
        "priority": 1
      }
    ],
    "batchOptions": {
      "maxConcurrency": 10,
      "timeoutSeconds": 300,
      "failFast": false,
      "returnPartialResults": true,
      "sortByPriority": true
    }
  }'

Configuration Options

Parameter Description Default Range Impact
maxConcurrency Concurrent evaluations 10 1-50 Throughput
timeoutSeconds Batch timeout 300 30-1800 Reliability
failFast Stop on first error false true/false Error Handling
returnPartialResults Return partial success true true/false Availability
sortByPriority Process by priority false true/false Ordering

Performance Characteristics

  • Throughput: Up to 2000 rules/minute with optimal configuration
  • Latency: Average 45ms per rule evaluation
  • Concurrency: Configurable from 1-50 concurrent evaluations
  • Error Resilience: Partial results with detailed error reporting

Batch Statistics

# Get real-time batch statistics
curl http://localhost:8080/api/v1/rules/batch/statistics

{
  "batchSummary": {
    "totalRequests": 1000,
    "successfulEvaluations": 985,
    "failedEvaluations": 15,
    "successRate": 98.5,
    "averageProcessingTimeMs": 45.2,
    "cacheHits": 750,
    "cacheHitRate": 75.0
  },
  "performanceMetrics": {
    "totalBatchesProcessed": 50,
    "averageConcurrency": 8.5,
    "peakThroughput": "2000 rules/minute"
  }
}

📊 Performance Monitoring

Key Performance Indicators (KPIs)

Metric Target Description
Cache Hit Rate >80% Percentage of cache hits vs total requests
Average Response Time <50ms Average time per rule evaluation
Throughput >1000 rules/min Rules processed per minute in batch mode
Error Rate <1% Percentage of failed evaluations
Resource Utilization CPU <70%, Memory <80% System resource usage

Health Checks

# Overall system health
curl http://localhost:8080/api/v1/rules/batch/health

# Cache provider health
curl http://localhost:8080/actuator/health/cache

# Database connection health  
curl http://localhost:8080/actuator/health/r2dbc

🎯 Best Practices

Cache Optimization

  1. Use Caffeine for single-instance deployments - 664x faster than Redis
  2. Use Redis for multi-instance deployments - Shared cache across instances
  3. Monitor cache hit rates - Target >80% for optimal performance
  4. Tune cache sizes - Balance memory usage vs hit rates
  5. Set appropriate TTL values - Balance freshness vs performance

Connection Pool Tuning

  1. Size pools for peak load - Monitor connection usage patterns
  2. Set appropriate timeouts - Balance responsiveness vs resource usage
  3. Enable connection validation - Ensure connection health
  4. Monitor pool metrics - Track acquired, allocated, pending connections

Batch Processing

  1. Tune concurrency for your hardware - Start with 10, adjust based on CPU cores
  2. Use priority sorting for critical rules - Process high-priority rules first
  3. Enable partial results - Improve availability in error scenarios
  4. Monitor batch statistics - Track throughput and error rates
  5. Set appropriate timeouts - Balance completeness vs responsiveness

Memory Management

  1. Monitor heap usage - Ensure adequate memory for caches
  2. Tune garbage collection - Use G1GC for low-latency applications
  3. Set cache size limits - Prevent out-of-memory errors
  4. Monitor memory leaks - Watch for growing heap usage

🚨 Troubleshooting

Common Performance Issues

Low Cache Hit Rate

  • Symptoms: High response times, frequent database queries
  • Solutions: Increase cache sizes, adjust TTL values, check cache key patterns

High Connection Pool Contention

  • Symptoms: High acquire times, pending connections
  • Solutions: Increase pool size, optimize query performance, check for connection leaks

Batch Processing Timeouts

  • Symptoms: Batch operations timing out, partial results
  • Solutions: Increase timeout values, reduce concurrency, optimize rule complexity

Memory Issues

  • Symptoms: OutOfMemoryError, high GC pressure
  • Solutions: Increase heap size, reduce cache sizes, tune GC settings

Performance Tuning Checklist

  • Cache provider selected based on deployment architecture
  • Cache hit rates >80%
  • Connection pool sized for peak load
  • Batch concurrency tuned for hardware
  • Memory usage <80% of available heap
  • Response times <50ms average
  • Error rates <1%
  • Monitoring and alerting configured