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"""
monitors/performance_monitor.py - LLM performance monitoring
"""
import time
import threading
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from datetime import datetime, timedelta
from statistics import mean, median, stdev
from collections import deque
from ..core.logger import SecurityLogger
from ..core.exceptions import MonitoringError
@dataclass
class PerformanceMetric:
name: str
value: float
timestamp: datetime
context: Optional[Dict[str, Any]] = None
@dataclass
class MetricThreshold:
warning: float
critical: float
window_size: int # number of samples
calculation: str # "average", "median", "percentile"
class PerformanceMonitor:
def __init__(self, security_logger: Optional[SecurityLogger] = None,
max_history: int = 1000):
self.security_logger = security_logger
self.metrics: Dict[str, deque] = defaultdict(
lambda: deque(maxlen=max_history)
)
self.thresholds = self._initialize_thresholds()
self._lock = threading.Lock()
def _initialize_thresholds(self) -> Dict[str, MetricThreshold]:
return {
"response_time": MetricThreshold(
warning=1.0, # seconds
critical=5.0,
window_size=100,
calculation="average"
),
"token_usage": MetricThreshold(
warning=1000,
critical=2000,
window_size=50,
calculation="median"
),
"error_rate": MetricThreshold(
warning=0.05, # 5%
critical=0.10,
window_size=200,
calculation="average"
),
"memory_usage": MetricThreshold(
warning=80.0, # percentage
critical=90.0,
window_size=20,
calculation="average"
)
}
def record_metric(self, name: str, value: float,
context: Optional[Dict[str, Any]] = None):
try:
metric = PerformanceMetric(
name=name,
value=value,
timestamp=datetime.utcnow(),
context=context
)
with self._lock:
self.metrics[name].append(metric)
self._check_threshold(name)
except Exception as e:
if self.security_logger:
self.security_logger.log_security_event(
"performance_monitoring_error",
error=str(e),
metric_name=name,
metric_value=value
)
raise MonitoringError(f"Failed to record metric: {str(e)}")
def _check_threshold(self, metric_name: str):
if metric_name not in self.thresholds:
return
threshold = self.thresholds[metric_name]
recent_metrics = list(self.metrics[metric_name])[-threshold.window_size:]
if not recent_metrics:
return
values = [m.value for m in recent_metrics]
if threshold.calculation == "average":
current_value = mean(values)
elif threshold.calculation == "median":
current_value = median(values)
else:
current_value = mean(values) # default to average
if current_value >= threshold.critical:
level = "critical"
elif current_value >= threshold.warning:
level = "warning"
else:
return
if self.security_logger:
self.security_logger.log_security_event(
"performance_threshold_exceeded",
metric_name=metric_name,
current_value=current_value,
threshold_level=level,
threshold_value=(
threshold.critical if level == "critical"
else threshold.warning
)
)
def get_metrics(self, metric_name: str,
window: Optional[timedelta] = None) -> List[Dict[str, Any]]:
with self._lock:
metrics = list(self.metrics[metric_name])
if window:
cutoff = datetime.utcnow() - window
metrics = [m for m in metrics if m.timestamp >= cutoff]
return [
{
"value": m.value,
"timestamp": m.timestamp.isoformat(),
"context": m.context
}
for m in metrics
]
def get_statistics(self, metric_name: str,
window: Optional[timedelta] = None) -> Dict[str, float]:
with self._lock:
metrics = self.get_metrics(metric_name, window)
if not metrics:
return {}
values = [m["value"] for m in metrics]
stats = {
"min": min(values),
"max": max(values),
"average": mean(values),
"median": median(values)
}
if len(values) > 1:
stats["std_dev"] = stdev(values)
return stats
def update_threshold(self, metric_name: str, threshold: MetricThreshold):
with self._lock:
self.thresholds[metric_name] = threshold
def clear_metrics(self, metric_name: Optional[str] = None):
with self._lock:
if metric_name:
self.metrics[metric_name].clear()
else:
self.metrics.clear()
def get_alerts(self, window: Optional[timedelta] = None) -> List[Dict[str, Any]]:
alerts = []
for name, threshold in self.thresholds.items():
stats = self.get_statistics(name, window)
if not stats:
continue
if stats["average"] >= threshold.critical:
alerts.append({
"metric_name": name,
"level": "critical",
"value": stats["average"],
"threshold": threshold.critical,
"timestamp": datetime.utcnow().isoformat()
})
elif stats["average"] >= threshold.warning:
alerts.append({
"metric_name": name,
"level": "warning",
"value": stats["average"],
"threshold": threshold.warning,
"timestamp": datetime.utcnow().isoformat()
})
return alerts |