Performance Optimization Strategies
Performance Optimization Strategies
Optimize scanning performance for large-scale deployments:
#!/usr/bin/env python3
# performance-optimizer.py
import os
import multiprocessing
import asyncio
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import redis
import hashlib
import json
from typing import Dict, List, Optional
class ScanningOptimizer:
def __init__(self):
self.redis_client = redis.Redis(
host='localhost',
port=6379,
decode_responses=True
)
self.cache_ttl = 3600 # 1 hour
def optimize_scanning_strategy(self, images: List[str]) -> Dict:
"""Determine optimal scanning strategy based on workload"""
strategy = {
'parallelism': self.calculate_optimal_parallelism(len(images)),
'caching_strategy': self.determine_caching_strategy(images),
'scan_order': self.prioritize_scan_order(images),
'deduplication': self.identify_duplicate_layers(images)
}
return strategy
def calculate_optimal_parallelism(self, image_count: int) -> int:
"""Calculate optimal parallel scanning threads"""
cpu_count = multiprocessing.cpu_count()
memory_gb = os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES') / (1024**3)
# Each scan typically uses ~500MB memory
memory_constrained_parallelism = int(memory_gb / 0.5)
# CPU-constrained parallelism
cpu_constrained_parallelism = cpu_count * 2
# Image count constraint
image_constrained_parallelism = min(image_count, 20)
return min(
memory_constrained_parallelism,
cpu_constrained_parallelism,
image_constrained_parallelism
)
def determine_caching_strategy(self, images: List[str]) -> Dict:
"""Determine optimal caching strategy"""
# Analyze image patterns
base_images = {}
for image in images:
base = image.split(':')[0]
base_images[base] = base_images.get(base, 0) + 1
# High reuse = aggressive caching
reuse_factor = sum(count > 1 for count in base_images.values()) / len(base_images)
return {
'cache_scan_results': reuse_factor > 0.3,
'cache_vulnerability_db': True,
'cache_layer_analysis': reuse_factor > 0.5,
'distributed_cache': len(images) > 100
}
def prioritize_scan_order(self, images: List[str]) -> List[str]:
"""Prioritize scanning order for optimal performance"""
priorities = []
for image in images:
priority_score = 0
# Production images first
if 'prod' in image or 'production' in image:
priority_score += 100
# Frequently updated images
update_frequency = self.get_update_frequency(image)
priority_score += update_frequency * 10
# Large images last (they take longer)
size = self.estimate_image_size(image)
priority_score -= size / 1000000 # MB
# Previously vulnerable images
if self.has_previous_vulnerabilities(image):
priority_score += 50
priorities.append((image, priority_score))
# Sort by priority descending
priorities.sort(key=lambda x: x[1], reverse=True)
return [image for image, _ in priorities]
async def parallel_scan_with_caching(self, images: List[str]) -> List[Dict]:
"""Perform parallel scanning with intelligent caching"""
strategy = self.optimize_scanning_strategy(images)
# Create thread pool for I/O bound operations
with ThreadPoolExecutor(max_workers=strategy['parallelism']) as executor:
# Check cache first
tasks = []
for image in strategy['scan_order']:
task = asyncio.create_task(self.scan_with_cache(image, executor))
tasks.append(task)
results = await asyncio.gather(*tasks)
return results
async def scan_with_cache(self, image: str, executor) -> Dict:
"""Scan image with caching layer"""
# Generate cache key
cache_key = f"scan:{hashlib.sha256(image.encode()).hexdigest()}"
# Check cache
cached_result = self.redis_client.get(cache_key)
if cached_result:
return json.loads(cached_result)
# Perform scan
loop = asyncio.get_event_loop()
result = await loop.run_in_executor(executor, self.scan_image, image)
# Cache result
self.redis_client.setex(
cache_key,
self.cache_ttl,
json.dumps(result)
)
return result
def implement_layer_deduplication(self, images: List[str]) -> Dict:
"""Identify and deduplicate common layers across images"""
layer_map = {}
dedup_opportunities = []
for image in images:
layers = self.get_image_layers(image)
for layer in layers:
if layer in layer_map:
layer_map[layer].append(image)
else:
layer_map[layer] = [image]
# Identify layers that appear in multiple images
for layer, images_with_layer in layer_map.items():
if len(images_with_layer) > 1:
dedup_opportunities.append({
'layer': layer,
'images': images_with_layer,
'scan_once': True
})
return {
'total_layers': len(layer_map),
'unique_layers': len([l for l, imgs in layer_map.items() if len(imgs) == 1]),
'shared_layers': len(dedup_opportunities),
'dedup_plan': dedup_opportunities
}
# Distributed scanning orchestrator
class DistributedScanner:
def __init__(self, worker_nodes: List[str]):
self.worker_nodes = worker_nodes
self.job_queue = redis.Redis(host='redis.queue.local')
async def distribute_scanning_workload(self, images: List[str]) -> Dict:
"""Distribute scanning across multiple nodes"""
# Partition images across workers
partitions = self.partition_workload(images)
# Submit jobs to workers
job_ids = []
for worker, image_batch in zip(self.worker_nodes, partitions):
job_id = await self.submit_scan_job(worker, image_batch)
job_ids.append(job_id)
# Wait for completion and aggregate results
results = await self.wait_and_aggregate_results(job_ids)
return results
def partition_workload(self, images: List[str]) -> List[List[str]]:
"""Intelligently partition workload across workers"""
# Estimate scan time for each image
image_weights = []
for image in images:
weight = self.estimate_scan_duration(image)
image_weights.append((image, weight))
# Sort by weight descending
image_weights.sort(key=lambda x: x[1], reverse=True)
# Distribute using bin packing algorithm
partitions = [[] for _ in self.worker_nodes]
partition_weights = [0] * len(self.worker_nodes)
for image, weight in image_weights:
# Add to partition with lowest current weight
min_idx = partition_weights.index(min(partition_weights))
partitions[min_idx].append(image)
partition_weights[min_idx] += weight
return partitions