# Logging Performance Optimization Guide

## Overview

This guide provides comprehensive strategies for optimizing logging performance in high-frequency trading applications. Proper logging optimization can significantly improve application performance while maintaining observability.

## Performance Fundamentals

### Logging Overhead Sources

1. **Message Construction**: String formatting and concatenation
2. **I/O Operations**: File writes and console output
3. **Serialization**: JSON formatting for structured logs
4. **Handler Processing**: Multiple handlers processing the same message
5. **Lock Contention**: Thread synchronization in multi-threaded applications

### Performance Metrics

Monitor these key metrics to assess logging performance:

- **Logging Throughput**: Messages per second
- **Latency Impact**: Time added to application operations
- **Memory Usage**: Buffer sizes and memory consumption
- **CPU Overhead**: Processing time for log operations
- **I/O Wait Time**: Disk write delays

## Configuration Optimization

### Async Logging Configuration

Enable asynchronous logging for high-frequency operations:

```yaml
# config/logging_config.yaml
logging:
  enable_async_logging: true
  log_buffer_size: 1000
  async_queue_size: 10000
  async_batch_size: 100
  async_flush_interval: 1.0
  optimized_file_io: true
```

### Environment-Specific Settings

#### High-Performance Production
```yaml
logging:
  level: INFO
  format_type: structured
  console_output: false
  file_output: true
  enable_async_logging: true
  log_buffer_size: 2000
  async_queue_size: 20000
  async_batch_size: 200
  async_flush_interval: 0.5
  max_file_size: 104857600  # 100MB
  backup_count: 5
  compress_rotated_files: true
  optimized_file_io: true
```

#### Development with Performance Focus
```yaml
logging:
  level: INFO  # Avoid DEBUG in performance testing
  format_type: simple
  console_output: true
  file_output: false  # Disable file I/O for development
  enable_async_logging: false  # Simpler debugging
  enable_performance_logging: true
```

### Buffer Size Optimization

Choose buffer sizes based on your workload:

```python
# Calculate optimal buffer size
messages_per_second = 1000
flush_interval = 1.0
optimal_buffer_size = int(messages_per_second * flush_interval * 1.5)

config = LoggingConfig(
    log_buffer_size=optimal_buffer_size,
    async_queue_size=optimal_buffer_size * 10
)
```

## Code-Level Optimizations

### Lazy Evaluation

Use lazy evaluation to avoid expensive operations when logging is disabled:

```python
from genebot.logging.factory import get_logger
import logging

logger = get_logger(__name__)

# Bad: Always executes expensive_calculation()
logger.debug(f"Result: {expensive_calculation()}")

# Good: Only executes when DEBUG level is enabled
logger.debug("Result: %s", lambda: expensive_calculation())

# Better: Check level first
if logger.isEnabledFor(logging.DEBUG):
    result = expensive_calculation()
    logger.debug("Result: %s", result)

# Best: Use structured logging with conditional data
def get_debug_data():
    return expensive_calculation() if logger.isEnabledFor(logging.DEBUG) else None

logger.debug("Calculation completed", extra={
    "result": get_debug_data()
})
```

### Efficient Message Construction

Optimize how log messages are constructed:

```python
# Bad: String concatenation
logger.info("Order " + order_id + " for " + symbol + " executed")

# Good: String formatting (lazy)
logger.info("Order %s for %s executed", order_id, symbol)

# Best: Structured logging
logger.info("Order executed", extra={
    "order_id": order_id,
    "symbol": symbol,
    "timestamp": time.time()
})
```

### Conditional Logging

Implement smart conditional logging for high-frequency events:

```python
import time
from collections import defaultdict

class ThrottledLogger:
    def __init__(self, logger, interval=1.0):
        self.logger = logger
        self.interval = interval
        self.last_log_time = defaultdict(float)
    
    def info_throttled(self, key, message, **kwargs):
        """Log message only if interval has passed since last log."""
        now = time.time()
        if now - self.last_log_time[key] >= self.interval:
            self.logger.info(message, **kwargs)
            self.last_log_time[key] = now

# Usage for high-frequency market data
throttled_logger = ThrottledLogger(get_logger(__name__), interval=5.0)

for tick in market_data_stream:
    # Only log every 5 seconds per symbol
    throttled_logger.info_throttled(
        f"market_data_{tick.symbol}",
        "Market data received",
        extra={"symbol": tick.symbol, "price": tick.price}
    )
```

### Sampling for High-Volume Events

Implement sampling for very high-frequency events:

```python
import random

class SamplingLogger:
    def __init__(self, logger, sample_rate=0.01):
        self.logger = logger
        self.sample_rate = sample_rate
    
    def sample_log(self, level, message, **kwargs):
        """Log message with probability of sample_rate."""
        if random.random() < self.sample_rate:
            getattr(self.logger, level)(message, **kwargs)

# Usage for trade ticks (log 1% of events)
sampling_logger = SamplingLogger(get_logger(__name__), sample_rate=0.01)

for trade_tick in high_frequency_trades:
    sampling_logger.sample_log("debug", "Trade tick", extra={
        "symbol": trade_tick.symbol,
        "price": trade_tick.price,
        "volume": trade_tick.volume
    })
```

## Asynchronous Logging Implementation

### Custom Async Handler

```python
import asyncio
import queue
import threading
from logging import Handler, LogRecord
from typing import Optional

class AsyncLogHandler(Handler):
    """High-performance async log handler."""
    
    def __init__(self, target_handler: Handler, queue_size: int = 10000):
        super().__init__()
        self.target_handler = target_handler
        self.log_queue = queue.Queue(maxsize=queue_size)
        self.worker_thread = None
        self.shutdown_event = threading.Event()
        self.start_worker()
    
    def start_worker(self):
        """Start the background worker thread."""
        self.worker_thread = threading.Thread(
            target=self._worker_loop,
            daemon=True,
            name="AsyncLogWorker"
        )
        self.worker_thread.start()
    
    def _worker_loop(self):
        """Background worker loop for processing log records."""
        batch = []
        batch_size = 100
        timeout = 0.1
        
        while not self.shutdown_event.is_set():
            try:
                # Collect batch of records
                while len(batch) < batch_size:
                    try:
                        record = self.log_queue.get(timeout=timeout)
                        if record is None:  # Shutdown signal
                            break
                        batch.append(record)
                    except queue.Empty:
                        break
                
                # Process batch
                if batch:
                    for record in batch:
                        self.target_handler.emit(record)
                    batch.clear()
                    
            except Exception as e:
                # Handle errors in worker thread
                print(f"AsyncLogHandler error: {e}")
    
    def emit(self, record: LogRecord):
        """Emit log record asynchronously."""
        try:
            self.log_queue.put_nowait(record)
        except queue.Full:
            # Drop message if queue is full (prevents blocking)
            pass
    
    def close(self):
        """Close the handler and cleanup resources."""
        self.shutdown_event.set()
        self.log_queue.put(None)  # Signal shutdown
        if self.worker_thread:
            self.worker_thread.join(timeout=5.0)
        self.target_handler.close()
        super().close()
```

### Performance Monitoring

Monitor async logging performance:

```python
from genebot.logging.performance_logger import PerformanceLogger
import time

class MonitoredAsyncHandler(AsyncLogHandler):
    """Async handler with performance monitoring."""
    
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.perf_logger = PerformanceLogger()
        self.stats = {
            'messages_queued': 0,
            'messages_dropped': 0,
            'queue_full_events': 0,
            'processing_time': 0
        }
    
    def emit(self, record: LogRecord):
        """Emit with performance tracking."""
        start_time = time.time()
        
        try:
            self.log_queue.put_nowait(record)
            self.stats['messages_queued'] += 1
        except queue.Full:
            self.stats['messages_dropped'] += 1
            self.stats['queue_full_events'] += 1
        
        self.stats['processing_time'] += time.time() - start_time
        
        # Log performance metrics periodically
        if self.stats['messages_queued'] % 10000 == 0:
            self.perf_logger.log_metric("async_log_queue_size", self.log_queue.qsize())
            self.perf_logger.log_metric("async_log_dropped", self.stats['messages_dropped'])
```

## File I/O Optimization

### Optimized File Handlers

```python
import os
from logging.handlers import RotatingFileHandler

class OptimizedRotatingFileHandler(RotatingFileHandler):
    """Optimized rotating file handler with better I/O performance."""
    
    def __init__(self, *args, **kwargs):
        # Use larger buffer for better I/O performance
        self.buffer_size = kwargs.pop('buffer_size', 8192)
        super().__init__(*args, **kwargs)
        
        # Configure file for better performance
        if hasattr(self.stream, 'fileno'):
            try:
                # Use direct I/O hints on Linux
                if hasattr(os, 'O_DIRECT'):
                    pass  # Would need special handling for O_DIRECT
                
                # Set buffer size
                self.stream.reconfigure(buffering=self.buffer_size)
            except (AttributeError, OSError):
                pass
    
    def emit(self, record):
        """Emit with optimized I/O."""
        try:
            if self.shouldRollover(record):
                self.doRollover()
            
            # Format and write
            msg = self.format(record)
            stream = self.stream
            stream.write(msg + self.terminator)
            
            # Flush periodically instead of every write
            if hasattr(self, '_write_count'):
                self._write_count += 1
            else:
                self._write_count = 1
            
            if self._write_count % 100 == 0:  # Flush every 100 writes
                stream.flush()
                
        except Exception:
            self.handleError(record)
```

### Memory-Mapped Logging

For extremely high-performance scenarios:

```python
import mmap
import os
from logging import Handler

class MemoryMappedLogHandler(Handler):
    """Memory-mapped file handler for maximum I/O performance."""
    
    def __init__(self, filename, max_size=100*1024*1024):
        super().__init__()
        self.filename = filename
        self.max_size = max_size
        self.position = 0
        self._setup_mmap()
    
    def _setup_mmap(self):
        """Set up memory-mapped file."""
        # Create or open file
        with open(self.filename, 'a+b') as f:
            f.seek(0, 2)  # Seek to end
            current_size = f.tell()
            
            if current_size < self.max_size:
                # Extend file to max_size
                f.write(b'\x00' * (self.max_size - current_size))
        
        # Open for memory mapping
        self.file = open(self.filename, 'r+b')
        self.mmap = mmap.mmap(self.file.fileno(), self.max_size)
        
        # Find current position (end of actual data)
        self.mmap.seek(0)
        data = self.mmap.read()
        null_pos = data.find(b'\x00')
        self.position = null_pos if null_pos != -1 else len(data)
        self.mmap.seek(self.position)
    
    def emit(self, record):
        """Emit to memory-mapped file."""
        try:
            msg = self.format(record) + '\n'
            msg_bytes = msg.encode('utf-8')
            
            # Check if we have space
            if self.position + len(msg_bytes) >= self.max_size:
                self._rotate()
            
            # Write to memory-mapped file
            self.mmap.seek(self.position)
            self.mmap.write(msg_bytes)
            self.position += len(msg_bytes)
            
        except Exception:
            self.handleError(record)
    
    def _rotate(self):
        """Rotate the memory-mapped file."""
        # Flush and close current mapping
        self.mmap.flush()
        self.mmap.close()
        self.file.close()
        
        # Rotate file
        import shutil
        backup_name = f"{self.filename}.1"
        shutil.move(self.filename, backup_name)
        
        # Create new mapping
        self.position = 0
        self._setup_mmap()
    
    def close(self):
        """Close the handler."""
        if hasattr(self, 'mmap'):
            self.mmap.flush()
            self.mmap.close()
        if hasattr(self, 'file'):
            self.file.close()
        super().close()
```

## Performance Measurement and Monitoring

### Logging Performance Profiler

```python
import time
import statistics
from contextlib import contextmanager
from collections import defaultdict, deque

class LoggingProfiler:
    """Profile logging performance."""
    
    def __init__(self, max_samples=10000):
        self.max_samples = max_samples
        self.timings = defaultdict(lambda: deque(maxlen=max_samples))
        self.counters = defaultdict(int)
    
    @contextmanager
    def measure(self, operation_name):
        """Measure operation timing."""
        start_time = time.perf_counter()
        try:
            yield
        finally:
            elapsed = time.perf_counter() - start_time
            self.timings[operation_name].append(elapsed)
            self.counters[operation_name] += 1
    
    def get_stats(self, operation_name):
        """Get statistics for an operation."""
        timings = list(self.timings[operation_name])
        if not timings:
            return None
        
        return {
            'count': len(timings),
            'mean': statistics.mean(timings),
            'median': statistics.median(timings),
            'min': min(timings),
            'max': max(timings),
            'p95': statistics.quantiles(timings, n=20)[18] if len(timings) > 20 else max(timings),
            'p99': statistics.quantiles(timings, n=100)[98] if len(timings) > 100 else max(timings)
        }
    
    def report(self):
        """Generate performance report."""
        print("=== Logging Performance Report ===")
        for operation in sorted(self.timings.keys()):
            stats = self.get_stats(operation)
            if stats:
                print(f"\n{operation}:")
                print(f"  Count: {stats['count']}")
                print(f"  Mean: {stats['mean']*1000:.3f}ms")
                print(f"  Median: {stats['median']*1000:.3f}ms")
                print(f"  P95: {stats['p95']*1000:.3f}ms")
                print(f"  P99: {stats['p99']*1000:.3f}ms")
                print(f"  Min: {stats['min']*1000:.3f}ms")
                print(f"  Max: {stats['max']*1000:.3f}ms")

# Global profiler instance
profiler = LoggingProfiler()

# Usage in logging code
def optimized_log_function():
    with profiler.measure("log_message_construction"):
        message = construct_complex_message()
    
    with profiler.measure("log_emit"):
        logger.info(message)
```

### Benchmarking Tools

```python
#!/usr/bin/env python3
"""
Comprehensive logging performance benchmark.
"""

import time
import threading
from concurrent.futures import ThreadPoolExecutor
from genebot.logging.factory import get_logger, setup_global_config
from genebot.logging.config import LoggingConfig

def benchmark_logging_throughput():
    """Benchmark logging throughput under various conditions."""
    
    configurations = [
        ("Sync Simple", LoggingConfig(
            format_type="simple",
            enable_async_logging=False,
            console_output=False,
            file_output=True
        )),
        ("Sync Structured", LoggingConfig(
            format_type="structured",
            enable_async_logging=False,
            console_output=False,
            file_output=True
        )),
        ("Async Structured", LoggingConfig(
            format_type="structured",
            enable_async_logging=True,
            log_buffer_size=2000,
            console_output=False,
            file_output=True
        )),
        ("Memory Only", LoggingConfig(
            format_type="structured",
            enable_async_logging=True,
            console_output=False,
            file_output=False
        ))
    ]
    
    test_scenarios = [
        ("Single Thread", 1, 10000),
        ("Multi Thread (4)", 4, 10000),
        ("High Frequency", 1, 100000),
        ("Concurrent High", 8, 50000)
    ]
    
    results = {}
    
    for config_name, config in configurations:
        print(f"\nTesting {config_name}:")
        setup_global_config(config)
        
        for scenario_name, thread_count, message_count in test_scenarios:
            logger = get_logger(f"benchmark.{config_name.lower().replace(' ', '_')}")
            
            def log_worker(worker_id, messages_per_worker):
                for i in range(messages_per_worker):
                    logger.info("Benchmark message", extra={
                        "worker_id": worker_id,
                        "message_id": i,
                        "timestamp": time.time(),
                        "data": {"key": "value", "number": i}
                    })
            
            messages_per_worker = message_count // thread_count
            
            start_time = time.perf_counter()
            
            if thread_count == 1:
                log_worker(0, message_count)
            else:
                with ThreadPoolExecutor(max_workers=thread_count) as executor:
                    futures = [
                        executor.submit(log_worker, i, messages_per_worker)
                        for i in range(thread_count)
                    ]
                    for future in futures:
                        future.result()
            
            elapsed = time.perf_counter() - start_time
            throughput = message_count / elapsed
            
            result_key = (config_name, scenario_name)
            results[result_key] = {
                'elapsed': elapsed,
                'throughput': throughput,
                'message_count': message_count
            }
            
            print(f"  {scenario_name}: {throughput:.0f} msg/s ({elapsed:.3f}s for {message_count} messages)")
    
    return results

def benchmark_memory_usage():
    """Benchmark memory usage of different logging configurations."""
    import psutil
    import os
    
    process = psutil.Process(os.getpid())
    
    configurations = [
        ("Minimal", LoggingConfig(
            console_output=False,
            file_output=False
        )),
        ("File Only", LoggingConfig(
            console_output=False,
            file_output=True,
            enable_async_logging=False
        )),
        ("Async Large Buffer", LoggingConfig(
            console_output=False,
            file_output=True,
            enable_async_logging=True,
            log_buffer_size=10000
        ))
    ]
    
    print("\n=== Memory Usage Benchmark ===")
    
    for config_name, config in configurations:
        # Measure baseline memory
        baseline_memory = process.memory_info().rss / 1024 / 1024
        
        setup_global_config(config)
        logger = get_logger(f"memory_test.{config_name.lower()}")
        
        # Generate logs
        for i in range(10000):
            logger.info("Memory test message", extra={
                "iteration": i,
                "data": {"key": "value", "large_field": "x" * 100}
            })
        
        # Measure memory after logging
        after_memory = process.memory_info().rss / 1024 / 1024
        memory_increase = after_memory - baseline_memory
        
        print(f"{config_name}: {memory_increase:.2f}MB increase")

if __name__ == "__main__":
    print("Starting logging performance benchmark...")
    
    # Throughput benchmark
    throughput_results = benchmark_logging_throughput()
    
    # Memory benchmark
    benchmark_memory_usage()
    
    print("\n=== Benchmark Complete ===")
```

## Production Optimization Strategies

### Dynamic Log Level Adjustment

```python
import signal
import logging
from genebot.logging.factory import get_logger

class DynamicLogLevelManager:
    """Manage log levels dynamically in production."""
    
    def __init__(self):
        self.original_levels = {}
        self.setup_signal_handlers()
    
    def setup_signal_handlers(self):
        """Set up signal handlers for dynamic control."""
        signal.signal(signal.SIGUSR1, self.increase_log_level)
        signal.signal(signal.SIGUSR2, self.decrease_log_level)
    
    def increase_log_level(self, signum, frame):
        """Increase log level (reduce verbosity) for performance."""
        root_logger = logging.getLogger()
        current_level = root_logger.level
        
        if current_level == logging.DEBUG:
            new_level = logging.INFO
        elif current_level == logging.INFO:
            new_level = logging.WARNING
        elif current_level == logging.WARNING:
            new_level = logging.ERROR
        else:
            return  # Already at highest level
        
        root_logger.setLevel(new_level)
        print(f"Log level increased to {logging.getLevelName(new_level)}")
    
    def decrease_log_level(self, signum, frame):
        """Decrease log level (increase verbosity) for debugging."""
        root_logger = logging.getLogger()
        current_level = root_logger.level
        
        if current_level == logging.ERROR:
            new_level = logging.WARNING
        elif current_level == logging.WARNING:
            new_level = logging.INFO
        elif current_level == logging.INFO:
            new_level = logging.DEBUG
        else:
            return  # Already at lowest level
        
        root_logger.setLevel(new_level)
        print(f"Log level decreased to {logging.getLevelName(new_level)}")

# Usage: Send SIGUSR1 to reduce logging, SIGUSR2 to increase
# kill -USR1 <pid>  # Reduce logging for performance
# kill -USR2 <pid>  # Increase logging for debugging
```

### Circuit Breaker for Logging

```python
import time
from enum import Enum

class CircuitState(Enum):
    CLOSED = "closed"
    OPEN = "open"
    HALF_OPEN = "half_open"

class LoggingCircuitBreaker:
    """Circuit breaker to prevent logging from impacting performance."""
    
    def __init__(self, failure_threshold=10, recovery_timeout=60, performance_threshold=0.1):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.performance_threshold = performance_threshold
        
        self.failure_count = 0
        self.last_failure_time = 0
        self.state = CircuitState.CLOSED
        
    def should_log(self):
        """Determine if logging should proceed."""
        if self.state == CircuitState.OPEN:
            if time.time() - self.last_failure_time > self.recovery_timeout:
                self.state = CircuitState.HALF_OPEN
                return True
            return False
        
        return True
    
    def record_success(self):
        """Record successful logging operation."""
        if self.state == CircuitState.HALF_OPEN:
            self.state = CircuitState.CLOSED
            self.failure_count = 0
    
    def record_failure(self):
        """Record failed logging operation."""
        self.failure_count += 1
        self.last_failure_time = time.time()
        
        if self.failure_count >= self.failure_threshold:
            self.state = CircuitState.OPEN
    
    def record_performance(self, elapsed_time):
        """Record performance and trigger circuit if too slow."""
        if elapsed_time > self.performance_threshold:
            self.record_failure()
        else:
            self.record_success()

# Integration with logging
circuit_breaker = LoggingCircuitBreaker()

class CircuitBreakerHandler(logging.Handler):
    """Handler with circuit breaker protection."""
    
    def __init__(self, target_handler):
        super().__init__()
        self.target_handler = target_handler
        self.circuit_breaker = circuit_breaker
    
    def emit(self, record):
        if not self.circuit_breaker.should_log():
            return  # Skip logging when circuit is open
        
        start_time = time.perf_counter()
        try:
            self.target_handler.emit(record)
            elapsed = time.perf_counter() - start_time
            self.circuit_breaker.record_performance(elapsed)
        except Exception:
            self.circuit_breaker.record_failure()
            raise
```

### Resource-Aware Logging

```python
import psutil
import os
from genebot.logging.factory import get_logger

class ResourceAwareLogger:
    """Logger that adapts behavior based on system resources."""
    
    def __init__(self, logger_name):
        self.logger = get_logger(logger_name)
        self.process = psutil.Process(os.getpid())
        
        # Thresholds for resource usage
        self.cpu_threshold = 80.0  # Percent
        self.memory_threshold = 80.0  # Percent
        self.disk_threshold = 90.0  # Percent
        
        self.adaptive_level = logging.INFO
    
    def _check_resources(self):
        """Check current resource usage and adapt logging."""
        try:
            # Check CPU usage
            cpu_percent = self.process.cpu_percent()
            
            # Check memory usage
            memory_info = self.process.memory_info()
            memory_percent = (memory_info.rss / psutil.virtual_memory().total) * 100
            
            # Check disk usage
            disk_usage = psutil.disk_usage('/').percent
            
            # Adapt logging level based on resource pressure
            if (cpu_percent > self.cpu_threshold or 
                memory_percent > self.memory_threshold or 
                disk_usage > self.disk_threshold):
                
                # Reduce logging under resource pressure
                self.adaptive_level = logging.WARNING
            else:
                # Normal logging when resources are available
                self.adaptive_level = logging.INFO
                
        except Exception:
            # If resource checking fails, use conservative approach
            self.adaptive_level = logging.WARNING
    
    def info(self, message, **kwargs):
        """Adaptive info logging."""
        self._check_resources()
        if self.adaptive_level <= logging.INFO:
            self.logger.info(message, **kwargs)
    
    def debug(self, message, **kwargs):
        """Adaptive debug logging."""
        self._check_resources()
        if self.adaptive_level <= logging.DEBUG:
            self.logger.debug(message, **kwargs)
    
    def warning(self, message, **kwargs):
        """Always log warnings."""
        self.logger.warning(message, **kwargs)
    
    def error(self, message, **kwargs):
        """Always log errors."""
        self.logger.error(message, **kwargs)
```

## Best Practices Summary

### Configuration Best Practices

1. **Use Async Logging**: Enable for high-frequency operations
2. **Optimize Buffer Sizes**: Match your message volume
3. **Disable Console Output**: In production for better performance
4. **Use Structured Logging**: More efficient than string formatting
5. **Configure Log Rotation**: Prevent large files that impact I/O

### Code Best Practices

1. **Lazy Evaluation**: Avoid expensive operations when logging is disabled
2. **Conditional Logging**: Check log levels before expensive operations
3. **Throttling**: Limit high-frequency log messages
4. **Sampling**: Log only a subset of very high-volume events
5. **Batch Operations**: Group related log operations

### Monitoring Best Practices

1. **Track Performance Metrics**: Monitor logging overhead
2. **Resource Monitoring**: Watch CPU, memory, and disk usage
3. **Circuit Breakers**: Protect against logging failures
4. **Dynamic Adjustment**: Allow runtime log level changes
5. **Regular Profiling**: Identify performance bottlenecks

### Production Best Practices

1. **Performance Testing**: Benchmark before deployment
2. **Gradual Rollout**: Test performance impact incrementally
3. **Monitoring Alerts**: Set up alerts for performance degradation
4. **Fallback Mechanisms**: Have emergency logging configurations
5. **Regular Review**: Continuously optimize based on metrics

## Performance Checklist

### Pre-Deployment Checklist

- [ ] Async logging enabled for high-frequency operations
- [ ] Buffer sizes optimized for workload
- [ ] Console output disabled in production
- [ ] Log levels appropriate for environment
- [ ] File rotation configured properly
- [ ] Performance monitoring in place
- [ ] Circuit breakers configured
- [ ] Resource thresholds set
- [ ] Benchmark tests completed
- [ ] Fallback configurations ready

### Runtime Monitoring Checklist

- [ ] Logging throughput within acceptable range
- [ ] CPU overhead from logging < 5%
- [ ] Memory usage stable
- [ ] Disk I/O not bottlenecked by logging
- [ ] Log file sizes under control
- [ ] No dropped messages in async queues
- [ ] Error rates acceptable
- [ ] Performance metrics trending well

### Optimization Checklist

- [ ] Identified performance bottlenecks
- [ ] Optimized hot paths
- [ ] Reduced unnecessary logging
- [ ] Implemented lazy evaluation
- [ ] Added conditional logging
- [ ] Configured throttling/sampling
- [ ] Optimized message construction
- [ ] Tuned configuration parameters
- [ ] Validated improvements with benchmarks
- [ ] Documented optimizations