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Performance Optimization

This guide covers strategies and techniques for optimizing PMDaemon performance and the applications it manages.

PMDaemon Performance Tuning​

Resource Allocation​

CPU Optimization​

[daemon]
# Adjust worker threads for concurrent operations
worker_threads = 4  # Recommended: CPU cores

# Optimize event loop performance
max_events_per_tick = 1000
event_loop_delay_threshold = 50  # milliseconds

Memory Management​

[daemon]
# Set memory limits for the daemon itself
max_memory = "512MB"
gc_interval = 30  # seconds

# Enable memory monitoring
memory_monitoring = true
heap_dump_on_oom = true

I/O Optimization​

File System Operations​

[daemon.io]
# Optimize log file operations
log_buffer_size = "64KB"
log_sync_interval = 5  # seconds

# Process file monitoring
file_watch_debounce = 100  # milliseconds

Network Performance​

[daemon.network]
# HTTP server tuning
keep_alive_timeout = 65
max_connections = 1000
request_timeout = 30

Application Performance Optimization​

Process Configuration​

Instance Scaling​

[app.myapp]
# Optimize instance count
instances = 4  # Match CPU cores for CPU-bound apps
instances = "max"  # Use all available cores

# Load balancing for better distribution
exec_mode = "cluster"

Resource Limits​

[app.myapp]
# Set appropriate limits to prevent resource exhaustion
max_memory_restart = "1GB"
max_restarts = 10
min_uptime = "10s"

# CPU limits (Linux only)
cpu_limit = 80  # percentage

Memory Optimization​

Memory Management Strategies​

[app.myapp]
# Enable memory monitoring
memory_monitoring = true
kill_timeout = 1600  # Give app time to cleanup

# Automatic restart on memory issues
max_memory_restart = "1GB"
memory_threshold = 90  # percentage

Garbage Collection Tuning (Node.js)​

[app.myapp]
# Node.js specific optimizations
node_args = [
  "--max-old-space-size=1024",
  "--optimize-for-size",
  "--gc-interval=100"
]

CPU Optimization​

CPU Affinity (Linux)​

[app.myapp]
# Bind processes to specific CPU cores
cpu_affinity = [0, 1]  # Use cores 0 and 1

Process Priority​

[app.myapp]
# Adjust process priority (-20 to 20)
nice = -5  # Higher priority for critical apps

Performance Monitoring​

Key Metrics to Monitor​

Application Metrics​

  • Response time percentiles (p50, p95, p99)
  • Throughput (requests per second)
  • Error rates
  • Memory usage patterns
  • CPU utilization

System Metrics​

  • Load average
  • Memory pressure
  • Disk I/O
  • Network latency

Performance Profiling​

Built-in Profiling​

# Enable performance profiling
pmdaemon profile start myapp

# View performance report
pmdaemon profile report myapp

# Stop profiling
pmdaemon profile stop myapp

Custom Performance Hooks​

[app.myapp.performance]
# Enable custom performance tracking
enable_hooks = true
track_memory = true
track_cpu = true
sample_interval = 1000  # milliseconds

Optimization Strategies​

Application-Level Optimizations​

Database Optimization​

  • Use connection pooling
  • Implement query caching
  • Optimize database indexes
  • Use read replicas for scaling

Caching Strategies​

[app.myapp.cache]
# Application-level caching
redis_host = "localhost"
redis_port = 6379
cache_ttl = 3600  # seconds

Asynchronous Processing​

  • Use message queues for heavy operations
  • Implement background job processing
  • Utilize worker processes for CPU-intensive tasks

Infrastructure Optimization​

Load Balancing​

[load_balancer]
# Optimize load balancing algorithm
algorithm = "least_connections"  # or "round_robin", "ip_hash"
health_check_interval = 10
session_affinity = false

Process Distribution​

[app.myapp]
# Distribute processes across available resources
instances = "max"
exec_mode = "cluster"
instance_var = "INSTANCE_ID"

Performance Benchmarking​

Load Testing Integration​

# Run performance tests with different configurations
pmdaemon benchmark --app myapp --duration 60s --concurrent 100

Performance Regression Testing​

[performance.testing]
# Automated performance testing
baseline_file = "performance.baseline.json"
threshold_cpu = 80
threshold_memory = 1024
threshold_response_time = 200  # milliseconds

Common Performance Issues​

Memory Leaks​

Symptoms: Gradually increasing memory usage Solutions:

  • Enable automatic restart on memory threshold
  • Implement proper cleanup in application code
  • Use memory profiling tools

CPU Bottlenecks​

Symptoms: High CPU usage, slow response times Solutions:

  • Scale horizontally with more instances
  • Optimize application algorithms
  • Use caching to reduce computation

I/O Bottlenecks​

Symptoms: High wait times, slow file operations Solutions:

  • Use asynchronous I/O operations
  • Implement connection pooling
  • Optimize database queries

Event Loop Blocking (Node.js)​

Symptoms: Unresponsive application, high event loop delay Solutions:

  • Move CPU-intensive operations to worker threads
  • Use streaming for large data processing
  • Implement proper error handling

Performance Best Practices​

  1. Monitor Continuously: Set up comprehensive monitoring from day one
  2. Establish Baselines: Record performance metrics during normal operation
  3. Test Under Load: Regularly perform load testing to identify bottlenecks
  4. Optimize Gradually: Make incremental improvements and measure impact
  5. Plan for Growth: Design for scalability from the beginning
  6. Use Profiling Tools: Leverage built-in and third-party profiling tools
  7. Keep Dependencies Updated: Regular updates often include performance improvements
  8. Implement Caching: Cache frequently accessed data and computations

Performance Tuning Checklist​

  • Set appropriate resource limits
  • Configure optimal instance count
  • Enable performance monitoring
  • Implement health checks
  • Set up alerting for performance issues
  • Configure log rotation
  • Optimize database connections
  • Implement caching strategies
  • Set up load balancing
  • Plan for horizontal scaling

For more specific optimization techniques, see our Monitoring Guide and Configuration Best Practices.