📝 Session 3: Performance Optimization¶
Prerequisites¶
Complete these documents before starting:
1. 🎯 Observer Path
2. 📝 Production Implementation Guide
This document covers advanced performance optimization techniques for production vector search systems.
Part 1: Advanced Caching Strategies¶
Intelligent Cache Management¶
Beyond simple query caching, production systems benefit from sophisticated cache strategies that adapt to usage patterns:
import time
import threading
from collections import OrderedDict
from dataclasses import dataclass
from typing import Dict, Any, Optional
import hashlib
import pickle
@dataclass
class CacheEntry:
"""Enhanced cache entry with metadata."""
data: Any
created_at: float
last_accessed: float
access_count: int
size_bytes: int
def is_expired(self, ttl_seconds: int) -> bool:
"""Check if entry has expired."""
return time.time() - self.created_at > ttl_seconds
class AdaptiveQueryCache:
"""Production-grade cache with adaptive eviction and TTL."""
def __init__(self, max_size_mb: int = 100, default_ttl_seconds: int = 3600):
self.max_size_bytes = max_size_mb * 1024 * 1024
self.default_ttl = default_ttl_seconds
self.cache = OrderedDict()
self.current_size_bytes = 0
self.stats = {
'hits': 0,
'misses': 0,
'evictions': 0,
'expired_removals': 0
}
self._lock = threading.RLock()
def _calculate_size(self, obj: Any) -> int:
"""Estimate object size in bytes."""
try:
return len(pickle.dumps(obj))
except:
# Fallback estimation
return len(str(obj)) * 4
def _create_key(self, query: str, **kwargs) -> str:
"""Create consistent cache key."""
key_data = f"{query}_{sorted(kwargs.items())}"
return hashlib.sha256(key_data.encode()).hexdigest()[:16]
The adaptive cache uses size-based and time-based eviction strategies. The size calculation enables memory-aware caching, while the threading lock ensures thread safety.
def get(self, query: str, **kwargs) -> Optional[Any]:
"""Retrieve from cache with intelligent access tracking."""
with self._lock:
cache_key = self._create_key(query, **kwargs)
if cache_key not in self.cache:
self.stats['misses'] += 1
return None
entry = self.cache[cache_key]
# Check expiration
if entry.is_expired(self.default_ttl):
del self.cache[cache_key]
self.current_size_bytes -= entry.size_bytes
self.stats['expired_removals'] += 1
self.stats['misses'] += 1
return None
# Update access metadata
current_time = time.time()
entry.last_accessed = current_time
entry.access_count += 1
# Move to end (LRU)
self.cache.move_to_end(cache_key)
self.stats['hits'] += 1
return entry.data
def put(self, query: str, data: Any, **kwargs) -> bool:
"""Store in cache with intelligent eviction."""
with self._lock:
cache_key = self._create_key(query, **kwargs)
data_size = self._calculate_size(data)
# Don't cache oversized items
if data_size > self.max_size_bytes * 0.1: # Max 10% of cache size
return False
current_time = time.time()
entry = CacheEntry(
data=data,
created_at=current_time,
last_accessed=current_time,
access_count=1,
size_bytes=data_size
)
# Remove existing entry if present
if cache_key in self.cache:
old_entry = self.cache[cache_key]
self.current_size_bytes -= old_entry.size_bytes
del self.cache[cache_key]
# Evict entries if needed
while (self.current_size_bytes + data_size > self.max_size_bytes and
self.cache):
self._evict_lru()
# Add new entry
self.cache[cache_key] = entry
self.current_size_bytes += data_size
return True
def _evict_lru(self):
"""Evict least recently used item."""
if not self.cache:
return
# Find LRU item
lru_key = next(iter(self.cache))
lru_entry = self.cache[lru_key]
# Remove it
del self.cache[lru_key]
self.current_size_bytes -= lru_entry.size_bytes
self.stats['evictions'] += 1
The intelligent eviction strategy prioritizes frequently accessed items while preventing oversized objects from dominating the cache. The size tracking prevents memory exhaustion.
Cache Warming and Precomputation¶
class CacheWarmer:
"""Proactive cache warming for common queries."""
def __init__(self, search_engine, cache, query_log_path: str):
self.search_engine = search_engine
self.cache = cache
self.query_log_path = query_log_path
self.popular_queries = []
def analyze_query_patterns(self, min_frequency: int = 5) -> List[str]:
"""Analyze query logs to identify popular queries."""
query_counts = {}
try:
with open(self.query_log_path, 'r') as f:
for line in f:
# Assume log format: timestamp|query|results_count
parts = line.strip().split('|')
if len(parts) >= 2:
query = parts[1].strip()
query_counts[query] = query_counts.get(query, 0) + 1
except FileNotFoundError:
logging.warning(f"Query log not found: {self.query_log_path}")
return []
# Filter by frequency
popular = [query for query, count in query_counts.items()
if count >= min_frequency]
# Sort by frequency
popular.sort(key=lambda q: query_counts[q], reverse=True)
self.popular_queries = popular[:100] # Top 100
return self.popular_queries
def warm_cache(self, max_queries: int = 50):
"""Proactively warm cache with popular queries."""
if not self.popular_queries:
self.analyze_query_patterns()
warmed_count = 0
for query in self.popular_queries[:max_queries]:
try:
# Check if already cached
cached_result = self.cache.get(query, top_k=10)
if cached_result is None:
# Execute search and cache result
result = self.search_engine.hybrid_search(query, top_k=10)
self.cache.put(query, result, top_k=10)
warmed_count += 1
# Rate limiting to avoid overwhelming system
time.sleep(0.1)
except Exception as e:
logging.error(f"Failed to warm cache for query '{query}': {str(e)}")
logging.info(f"Cache warming completed: {warmed_count} queries precomputed")
Cache warming proactively computes results for popular queries during off-peak hours. The query pattern analysis identifies optimization opportunities based on actual usage.
Part 2: Comprehensive Performance Monitoring¶
Real-time Performance Metrics¶
import asyncio
import statistics
from collections import deque, defaultdict
from datetime import datetime, timedelta
import json
class PerformanceMonitor:
"""Real-time performance monitoring with alerting."""
def __init__(self, window_size_minutes: int = 5):
self.window_size = timedelta(minutes=window_size_minutes)
self.metrics_buffer = deque(maxlen=1000) # Keep last 1000 operations
self.alerts = []
self.thresholds = {
'p95_latency_ms': 500,
'error_rate_percent': 5.0,
'cache_hit_rate_percent': 60.0
}
def record_operation(self, operation_type: str, latency_ms: float,
success: bool, cache_hit: bool = False):
"""Record a single operation for monitoring."""
record = {
'timestamp': datetime.now(),
'operation_type': operation_type,
'latency_ms': latency_ms,
'success': success,
'cache_hit': cache_hit
}
self.metrics_buffer.append(record)
def get_current_metrics(self) -> Dict[str, float]:
"""Calculate current performance metrics."""
if not self.metrics_buffer:
return {}
# Filter to current window
cutoff_time = datetime.now() - self.window_size
recent_ops = [op for op in self.metrics_buffer
if op['timestamp'] >= cutoff_time]
if not recent_ops:
return {}
# Calculate metrics
latencies = [op['latency_ms'] for op in recent_ops if op['success']]
total_ops = len(recent_ops)
successful_ops = len([op for op in recent_ops if op['success']])
cache_hits = len([op for op in recent_ops if op['cache_hit']])
metrics = {
'total_operations': total_ops,
'success_rate_percent': (successful_ops / total_ops * 100) if total_ops > 0 else 0,
'error_rate_percent': ((total_ops - successful_ops) / total_ops * 100) if total_ops > 0 else 0,
'cache_hit_rate_percent': (cache_hits / total_ops * 100) if total_ops > 0 else 0,
'operations_per_minute': total_ops / self.window_size.total_seconds() * 60
}
if latencies:
latencies.sort()
metrics.update({
'avg_latency_ms': statistics.mean(latencies),
'p50_latency_ms': statistics.median(latencies),
'p95_latency_ms': latencies[int(len(latencies) * 0.95)] if len(latencies) > 1 else latencies[0],
'p99_latency_ms': latencies[int(len(latencies) * 0.99)] if len(latencies) > 1 else latencies[0],
'min_latency_ms': min(latencies),
'max_latency_ms': max(latencies)
})
return metrics
def check_alerts(self) -> List[Dict]:
"""Check for performance threshold violations."""
metrics = self.get_current_metrics()
new_alerts = []
# Check P95 latency
if 'p95_latency_ms' in metrics:
if metrics['p95_latency_ms'] > self.thresholds['p95_latency_ms']:
new_alerts.append({
'type': 'high_latency',
'message': f"P95 latency ({metrics['p95_latency_ms']:.1f}ms) exceeds threshold ({self.thresholds['p95_latency_ms']}ms)",
'severity': 'warning',
'timestamp': datetime.now(),
'value': metrics['p95_latency_ms']
})
# Check error rate
if 'error_rate_percent' in metrics:
if metrics['error_rate_percent'] > self.thresholds['error_rate_percent']:
new_alerts.append({
'type': 'high_error_rate',
'message': f"Error rate ({metrics['error_rate_percent']:.1f}%) exceeds threshold ({self.thresholds['error_rate_percent']}%)",
'severity': 'critical',
'timestamp': datetime.now(),
'value': metrics['error_rate_percent']
})
# Check cache hit rate
if 'cache_hit_rate_percent' in metrics:
if metrics['cache_hit_rate_percent'] < self.thresholds['cache_hit_rate_percent']:
new_alerts.append({
'type': 'low_cache_hit_rate',
'message': f"Cache hit rate ({metrics['cache_hit_rate_percent']:.1f}%) below threshold ({self.thresholds['cache_hit_rate_percent']}%)",
'severity': 'warning',
'timestamp': datetime.now(),
'value': metrics['cache_hit_rate_percent']
})
self.alerts.extend(new_alerts)
return new_alerts
The performance monitor provides real-time metrics calculation with configurable alerting thresholds. The sliding window approach keeps metrics current and relevant.
Load Testing Framework¶
import concurrent.futures
import random
from typing import Callable, List
import numpy as np
class LoadTester:
"""Comprehensive load testing framework."""
def __init__(self, search_function: Callable, monitor: PerformanceMonitor):
self.search_function = search_function
self.monitor = monitor
def generate_test_queries(self, base_queries: List[str], count: int) -> List[str]:
"""Generate varied test queries from base set."""
test_queries = []
for _ in range(count):
base_query = random.choice(base_queries)
# Add variations
variations = [
base_query, # Original
base_query + " details", # Extended
base_query.replace(" ", " and "), # Modified
f"What about {base_query}?", # Question format
]
test_queries.append(random.choice(variations))
return test_queries
def run_load_test(self, test_queries: List[str],
concurrent_users: int = 10,
duration_minutes: int = 5) -> Dict:
"""Execute comprehensive load test."""
end_time = time.time() + (duration_minutes * 60)
test_results = {
'total_queries': 0,
'successful_queries': 0,
'failed_queries': 0,
'latencies': [],
'errors': []
}
def worker_function():
"""Worker function for single user simulation."""
worker_queries = 0
worker_errors = 0
while time.time() < end_time:
query = random.choice(test_queries)
try:
start_time = time.time()
result = self.search_function(query)
latency_ms = (time.time() - start_time) * 1000
# Record in monitor
self.monitor.record_operation(
'load_test_query',
latency_ms,
success=True
)
test_results['latencies'].append(latency_ms)
worker_queries += 1
except Exception as e:
worker_errors += 1
test_results['errors'].append(str(e))
# Record failure in monitor
self.monitor.record_operation(
'load_test_query',
0,
success=False
)
# Brief pause between queries
time.sleep(random.uniform(0.1, 1.0))
return worker_queries, worker_errors
# Run concurrent workers
with concurrent.futures.ThreadPoolExecutor(max_workers=concurrent_users) as executor:
futures = [executor.submit(worker_function) for _ in range(concurrent_users)]
for future in concurrent.futures.as_completed(futures):
queries, errors = future.result()
test_results['total_queries'] += queries
test_results['failed_queries'] += errors
test_results['successful_queries'] = test_results['total_queries'] - test_results['failed_queries']
# Calculate summary statistics
if test_results['latencies']:
latencies = test_results['latencies']
test_results['latency_stats'] = {
'mean_ms': np.mean(latencies),
'median_ms': np.median(latencies),
'p95_ms': np.percentile(latencies, 95),
'p99_ms': np.percentile(latencies, 99),
'std_ms': np.std(latencies)
}
return test_results
The load testing framework simulates realistic user behavior with varied query patterns and timing. The concurrent execution provides accurate load simulation.
Part 3: Adaptive Parameter Tuning¶
Intelligent Performance Optimization¶
class AdaptiveOptimizer:
"""Automatic performance parameter optimization."""
def __init__(self, search_engine, monitor: PerformanceMonitor):
self.search_engine = search_engine
self.monitor = monitor
self.optimization_history = []
self.current_params = {
'cache_size': 1000,
'hnsw_ef_search': 100,
'batch_size': 1000,
'timeout_seconds': 30
}
def should_optimize(self) -> bool:
"""Determine if optimization is warranted."""
metrics = self.monitor.get_current_metrics()
# Optimize if performance is degraded
conditions = [
metrics.get('p95_latency_ms', 0) > 300, # High latency
metrics.get('error_rate_percent', 0) > 2, # High error rate
metrics.get('cache_hit_rate_percent', 100) < 50, # Low cache hits
len(self.optimization_history) == 0 # Never optimized
]
return any(conditions)
def optimize_cache_size(self) -> Dict:
"""Optimize cache size based on hit rates."""
metrics = self.monitor.get_current_metrics()
current_hit_rate = metrics.get('cache_hit_rate_percent', 0)
optimization = {
'parameter': 'cache_size',
'old_value': self.current_params['cache_size'],
'new_value': self.current_params['cache_size'],
'reason': 'No change needed'
}
if current_hit_rate < 50:
# Increase cache size
new_size = min(5000, int(self.current_params['cache_size'] * 1.5))
optimization.update({
'new_value': new_size,
'reason': f'Increasing cache size due to low hit rate ({current_hit_rate:.1f}%)'
})
self.current_params['cache_size'] = new_size
elif current_hit_rate > 90:
# Potentially reduce cache size to free memory
new_size = max(500, int(self.current_params['cache_size'] * 0.8))
optimization.update({
'new_value': new_size,
'reason': f'Reducing cache size - high hit rate ({current_hit_rate:.1f}%) suggests over-caching'
})
self.current_params['cache_size'] = new_size
return optimization
def optimize_search_parameters(self) -> List[Dict]:
"""Optimize search-related parameters."""
metrics = self.monitor.get_current_metrics()
optimizations = []
# Optimize HNSW ef_search based on latency
current_latency = metrics.get('p95_latency_ms', 0)
if current_latency > 200:
# Reduce ef_search for speed
new_ef = max(32, self.current_params['hnsw_ef_search'] - 32)
optimizations.append({
'parameter': 'hnsw_ef_search',
'old_value': self.current_params['hnsw_ef_search'],
'new_value': new_ef,
'reason': f'Reducing ef_search due to high latency ({current_latency:.1f}ms)'
})
self.current_params['hnsw_ef_search'] = new_ef
elif current_latency < 50:
# Increase ef_search for better accuracy
new_ef = min(256, self.current_params['hnsw_ef_search'] + 32)
optimizations.append({
'parameter': 'hnsw_ef_search',
'old_value': self.current_params['hnsw_ef_search'],
'new_value': new_ef,
'reason': f'Increasing ef_search due to low latency ({current_latency:.1f}ms) - room for better accuracy'
})
self.current_params['hnsw_ef_search'] = new_ef
return optimizations
def run_optimization_cycle(self) -> Dict:
"""Execute complete optimization cycle."""
if not self.should_optimize():
return {'optimizations_applied': 0, 'reason': 'No optimization needed'}
logging.info("Starting adaptive optimization cycle")
# Collect current performance baseline
baseline_metrics = self.monitor.get_current_metrics()
# Apply optimizations
optimizations = []
# Cache optimization
cache_opt = self.optimize_cache_size()
if cache_opt['new_value'] != cache_opt['old_value']:
optimizations.append(cache_opt)
# Search parameter optimization
search_opts = self.optimize_search_parameters()
optimizations.extend(search_opts)
# Record optimization attempt
optimization_record = {
'timestamp': datetime.now(),
'baseline_metrics': baseline_metrics,
'optimizations': optimizations,
'parameters_after': self.current_params.copy()
}
self.optimization_history.append(optimization_record)
logging.info(f"Optimization cycle completed: {len(optimizations)} parameters adjusted")
return {
'optimizations_applied': len(optimizations),
'details': optimizations,
'new_parameters': self.current_params
}
The adaptive optimizer automatically adjusts parameters based on observed performance patterns. The optimization history enables learning from past adjustments.
Part 4: Advanced Monitoring Dashboard¶
Metrics Export and Visualization¶
import json
from datetime import datetime
from typing import Dict, List
class MetricsExporter:
"""Export metrics for external monitoring systems."""
def __init__(self, monitor: PerformanceMonitor, optimizer: AdaptiveOptimizer):
self.monitor = monitor
self.optimizer = optimizer
def export_prometheus_metrics(self) -> str:
"""Export metrics in Prometheus format."""
metrics = self.monitor.get_current_metrics()
prometheus_lines = []
# Add help and type information
prometheus_lines.extend([
"# HELP vector_search_latency_ms Query latency in milliseconds",
"# TYPE vector_search_latency_ms histogram",
"# HELP vector_search_requests_total Total number of search requests",
"# TYPE vector_search_requests_total counter",
"# HELP vector_search_cache_hit_rate Cache hit rate percentage",
"# TYPE vector_search_cache_hit_rate gauge"
])
# Export current metrics
timestamp = int(time.time() * 1000)
if 'p95_latency_ms' in metrics:
prometheus_lines.append(
f'vector_search_latency_ms{{quantile="0.95"}} {metrics["p95_latency_ms"]} {timestamp}'
)
prometheus_lines.append(
f'vector_search_latency_ms{{quantile="0.99"}} {metrics["p99_latency_ms"]} {timestamp}'
)
if 'total_operations' in metrics:
prometheus_lines.append(
f'vector_search_requests_total {metrics["total_operations"]} {timestamp}'
)
if 'cache_hit_rate_percent' in metrics:
prometheus_lines.append(
f'vector_search_cache_hit_rate {metrics["cache_hit_rate_percent"]} {timestamp}'
)
return '\n'.join(prometheus_lines)
def export_json_dashboard(self) -> str:
"""Export comprehensive dashboard data as JSON."""
current_metrics = self.monitor.get_current_metrics()
recent_alerts = self.monitor.alerts[-10:] # Last 10 alerts
optimization_history = self.optimizer.optimization_history[-5:] # Last 5 optimizations
dashboard_data = {
'timestamp': datetime.now().isoformat(),
'system_status': {
'overall_health': self._determine_health_status(current_metrics),
'active_alerts': len([a for a in recent_alerts if
datetime.now() - a['timestamp'] < timedelta(hours=1)]),
'last_optimization': optimization_history[-1]['timestamp'].isoformat() if optimization_history else None
},
'performance_metrics': current_metrics,
'recent_alerts': [
{
'type': alert['type'],
'message': alert['message'],
'severity': alert['severity'],
'timestamp': alert['timestamp'].isoformat(),
'value': alert['value']
}
for alert in recent_alerts
],
'optimization_history': [
{
'timestamp': opt['timestamp'].isoformat(),
'optimizations_count': len(opt['optimizations']),
'parameters': opt['parameters_after']
}
for opt in optimization_history
],
'configuration': {
'cache_size': self.optimizer.current_params['cache_size'],
'hnsw_ef_search': self.optimizer.current_params['hnsw_ef_search'],
'monitoring_window_minutes': 5
}
}
return json.dumps(dashboard_data, indent=2)
def _determine_health_status(self, metrics: Dict) -> str:
"""Determine overall system health."""
if not metrics:
return 'unknown'
issues = []
# Check key metrics
if metrics.get('error_rate_percent', 0) > 5:
issues.append('high_error_rate')
if metrics.get('p95_latency_ms', 0) > 500:
issues.append('high_latency')
if metrics.get('cache_hit_rate_percent', 100) < 40:
issues.append('poor_cache_performance')
if not issues:
return 'healthy'
elif len(issues) == 1:
return 'degraded'
else:
return 'unhealthy'
class PerformanceDashboard:
"""Simple web dashboard for performance monitoring."""
def __init__(self, exporter: MetricsExporter, port: int = 8080):
self.exporter = exporter
self.port = port
def generate_html_dashboard(self) -> str:
"""Generate HTML dashboard."""
dashboard_data = json.loads(self.exporter.export_json_dashboard())
html = f"""
<!DOCTYPE html>
<html>
<head>
<title>Vector Search Performance Dashboard</title>
<style>
body {{ font-family: Arial, sans-serif; margin: 20px; }}
.metric-box {{ border: 1px solid #ddd; padding: 15px; margin: 10px; border-radius: 5px; }}
.healthy {{ border-color: #28a745; }}
.degraded {{ border-color: #ffc107; }}
.unhealthy {{ border-color: #dc3545; }}
.alert {{ background-color: #f8d7da; border-color: #dc3545; color: #721c24; }}
table {{ border-collapse: collapse; width: 100%; }}
th, td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
th {{ background-color: #f2f2f2; }}
</style>
<meta http-equiv="refresh" content="30">
</head>
<body>
<h1>Vector Search Performance Dashboard</h1>
<p>Last Updated: {dashboard_data['timestamp']}</p>
<div class="metric-box {dashboard_data['system_status']['overall_health']}">
<h2>System Status: {dashboard_data['system_status']['overall_health'].upper()}</h2>
<p>Active Alerts: {dashboard_data['system_status']['active_alerts']}</p>
<p>Last Optimization: {dashboard_data['system_status']['last_optimization'] or 'Never'}</p>
</div>
<div class="metric-box">
<h2>Performance Metrics</h2>
<table>
<tr><th>Metric</th><th>Value</th></tr>
"""
# Add performance metrics
metrics = dashboard_data['performance_metrics']
for key, value in metrics.items():
if isinstance(value, float):
formatted_value = f"{value:.2f}"
else:
formatted_value = str(value)
html += f"<tr><td>{key.replace('_', ' ').title()}</td><td>{formatted_value}</td></tr>"
html += """
</table>
</div>
<div class="metric-box">
<h2>Recent Alerts</h2>
"""
# Add alerts
if dashboard_data['recent_alerts']:
html += "<ul>"
for alert in dashboard_data['recent_alerts'][-5:]: # Last 5 alerts
html += f'<li class="alert"><strong>{alert["severity"].upper()}</strong>: {alert["message"]} ({alert["timestamp"]})</li>'
html += "</ul>"
else:
html += "<p>No recent alerts</p>"
html += """
</div>
</body>
</html>
"""
return html
The dashboard provides comprehensive visibility into system performance with both machine-readable (Prometheus, JSON) and human-readable (HTML) formats.
Part 5: Production Optimization Checklist¶
Performance Optimization Validation¶
class OptimizationValidator:
"""Validate optimization effectiveness."""
def __init__(self, search_engine, test_queries: List[str]):
self.search_engine = search_engine
self.test_queries = test_queries
def validate_optimizations(self, pre_optimization_metrics: Dict,
post_optimization_metrics: Dict) -> Dict:
"""Compare before and after optimization metrics."""
improvements = {}
regressions = {}
# Key metrics to compare
key_metrics = [
'p95_latency_ms',
'cache_hit_rate_percent',
'error_rate_percent',
'operations_per_minute'
]
for metric in key_metrics:
if metric in pre_optimization_metrics and metric in post_optimization_metrics:
old_value = pre_optimization_metrics[metric]
new_value = post_optimization_metrics[metric]
# Calculate percentage change
if old_value != 0:
change_percent = ((new_value - old_value) / old_value) * 100
else:
change_percent = float('inf') if new_value > 0 else 0
# Determine if improvement or regression
if metric == 'error_rate_percent':
# Lower is better for error rate
if change_percent < -5: # 5% improvement threshold
improvements[metric] = {
'old': old_value,
'new': new_value,
'improvement_percent': abs(change_percent)
}
elif change_percent > 5:
regressions[metric] = {
'old': old_value,
'new': new_value,
'regression_percent': change_percent
}
else:
# Higher is better for other metrics
if change_percent > 5: # 5% improvement threshold
improvements[metric] = {
'old': old_value,
'new': new_value,
'improvement_percent': change_percent
}
elif change_percent < -5:
regressions[metric] = {
'old': old_value,
'new': new_value,
'regression_percent': abs(change_percent)
}
return {
'improvements': improvements,
'regressions': regressions,
'net_score': len(improvements) - len(regressions),
'recommendation': 'keep' if len(improvements) >= len(regressions) else 'revert'
}
The validation framework ensures optimizations actually improve performance and don't introduce regressions.
Key Performance Optimization Principles¶
Essential Takeaways¶
Advanced Caching:
- Implement size-aware and time-based cache eviction
- Use cache warming for popular queries
- Monitor cache hit rates continuously
Performance Monitoring:
- Track P95/P99 latencies, not just averages
- Implement real-time alerting for threshold violations
- Use comprehensive load testing for capacity planning
Adaptive Optimization:
- Automatically adjust parameters based on observed performance
- Validate optimization effectiveness with A/B testing
- Maintain optimization history for learning
Production Monitoring:
- Export metrics in standard formats (Prometheus, JSON)
- Provide both human and machine-readable dashboards
- Implement comprehensive health checks
Next Steps¶
⚙️ Ready for Advanced Topics?¶
If you've mastered performance optimization, explore advanced implementer topics:
- Advanced HNSW Tuning
- Advanced Hybrid Search
🧭 Navigation¶
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