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Enterprise Performance Optimization Guide

This guide provides comprehensive strategies to optimize your CyberSecFeed API v1.5 integration for maximum performance, minimal bandwidth usage, and efficient credit consumption with enterprise-grade features.

Performance Overview​

The CyberSecFeed API v1.5 delivers enterprise-grade performance with advanced optimization layers:

  • Edge Caching: 95%+ cache hit rates with Cloudflare CDN
  • Enterprise Batch Operations: 86% cost savings with POST /api/v1/cve/batch
  • Advanced Field Selection: Up to 85% payload reduction
  • Smart Sorting: 8 sort options for optimized queries
  • Exploit Filtering: Targeted vulnerability detection
  • ETag Support: Efficient client-side caching
  • Credit Optimization: Transparent, value-based pricing

Enterprise Performance Metrics (v1.5)​

OperationP95 Response TimeCache Hit RateCreditsPayload SizeOptimization
CVE Detail (basic)<150ms95%5~2KBStandard
CVE Detail + Field Selection<100ms95%5~0.3KB (85% ↓)Optimized
Enterprise Batch (50 CVEs)<500ms85%35~50KB86% savings
CVE Search + Sorting<400ms90%1~15KB (20 results)Advanced
Exploit Filtering<300ms90%1~10KBTargeted
KEV Catalog<200ms95%1~40KBStandard

1. Enterprise Feature Optimization (v1.5)​

Advanced Field Selection - 85% Payload Reduction​

The most impactful v1.5 optimization is field selection combined with intelligent data loading:

# ❌ Inefficient - full payload (5 credits)
def get_full_cve(cve_id):
    response = requests.get(
        f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}",
        headers=headers
    )
    return response.json()  # ~2KB payload

# ✅ Optimized - field selection (same 5 credits, 85% less data)
def get_essential_data(cve_id):
    response = requests.get(
        f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}",
        params={'fields': 'cve_id,severity,kev,epss'},
        headers=headers
    )
    return response.json()  # ~0.3KB payload (85% reduction!)

# ✅ Enterprise - field selection + targeted enrichment
def get_enterprise_data(cve_id, use_case='risk_assessment'):
    field_configs = {
        'risk_assessment': 'cve_id,severity,kev,epss',
        'compliance': 'cve_id,published,severity,kev',
        'threat_hunting': 'cve_id,severity,kev,epss,enrichment_status'
    }

    params = {'fields': field_configs[use_case]}
    if use_case == 'threat_hunting':
        params['include'] = 'enrichment,attack'

    response = requests.get(
        f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}",
        params=params,
        headers=headers
    )
    return response.json()

Advanced Sorting for Performance​

v1.5 introduces 8 sort options for optimized data retrieval:

# ✅ Performance-optimized CVE searches
def get_critical_vulns_optimized():
    response = requests.get(
        "https://api.cybersecfeed.com/api/v1/cves",
        params={
            'severity': 'critical',           # Named severity filtering
            'sort': 'severity_desc',          # Sort by severity (fastest)
            'fields': 'cve_id,severity,kev',  # Essential fields only
            'limit': 20
        },
        headers=headers
    )
    return response.json()

# ✅ EPSS-based prioritization
def get_high_probability_exploits():
    response = requests.get(
        "https://api.cybersecfeed.com/api/v1/cves",
        params={
            'exploit': 'true',               # Exploit filtering (v1.5)
            'sort': 'epss_desc',             # Sort by EPSS score
            'fields': 'cve_id,severity,epss,kev',
            'limit': 50
        },
        headers=headers
    )
    return response.json()

v1.5 Performance Impact Matrix​

ConfigurationResponse TimePayload SizeCreditsEfficiency Gain
Field Selection OnlyFastest85% ↓5Maximum
Default (no params)StandardBaseline5Good
Field Selection + Enrichment+20%60% ↓5Excellent
Field Selection + Full Intelligence+30%40% ↓5Premium
Enterprise Batch (50 CVEs)Single ReqBulk Opt3586% savings

Sort Performance Characteristics​

Sort OptionUse CasePerformanceIndex Optimization
severity_descCritical vulnerability monitoringFastestPrimary index
published_descRecent vulnerability trackingFastTime-based index
epss_descExploitation prioritizationFastEPSS index
modified_descChange monitoringFastModified index

2. Advanced Field Selection (v1.5)​

Field selection is the most powerful performance optimization in v1.5, reducing payload size by up to 85%:

# ❌ Inefficient - full response (2KB payload)
def get_full_cve(cve_id):
    response = requests.get(
        f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}",
        headers=headers
    )
    return response.json()

# ✅ v1.5 Optimized - field selection (0.3KB payload, 85% reduction)
def get_risk_essentials(cve_id):
    response = requests.get(
        f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}",
        params={'fields': 'cve_id,severity,kev,epss'},
        headers=headers
    )
    return response.json()

# ✅ Enterprise - field selection + comprehensive intelligence
def get_enterprise_intelligence(cve_id):
    response = requests.get(
        f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}",
        params={
            'fields': 'cve_id,published,severity,kev,epss,enrichment_status',
            'include': 'enrichment,attack'  # Full threat intelligence
        },
        headers=headers
    )
    return response.json()  # Optimized payload with complete data

v1.5 Field Selection Presets​

# Enterprise field selection patterns for maximum efficiency
V15_FIELD_PRESETS = {
    'ultra_minimal': 'cve_id,severity',                          # 90% payload reduction
    'risk_scoring': 'cve_id,severity,kev,epss',                 # Risk assessment essentials
    'dashboard': 'cve_id,published,severity,status,kev',        # Executive dashboards
    'compliance': 'cve_id,published,severity,kev,source_tag',   # Audit reporting
    'threat_hunting': 'cve_id,severity,kev,epss,enrichment_status', # SOC operations
    'enterprise_full': 'cve_id,published,modified,severity,status,kev,epss,enrichment_status' # Complete context
}

class OptimizedCVEClient:
    def __init__(self, api_key):
        self.api_key = api_key
        self.headers = {'X-API-Key': api_key}

    def get_cve_optimized(self, cve_id, preset='risk_scoring', include_intel=False):
        """Get CVE with optimized field selection"""
        params = {'fields': V15_FIELD_PRESETS[preset]}

        # Add intelligence based on use case
        if include_intel:
            if preset == 'threat_hunting':
                params['include'] = 'enrichment,attack'  # Full threat context
            elif preset == 'compliance':
                params['include'] = 'acsc'                # Regional compliance

        response = requests.get(
            f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}",
            params=params,
            headers=self.headers
        )
        return response.json()

3. Enterprise Batch Operations - 86% Cost Savings​

The enterprise batch endpoint is the most significant performance improvement in v1.5:

import requests

def enterprise_batch_optimized(cve_ids, api_key, optimization_level='standard'):
    """Enterprise batch with optimization levels"""

    url = "https://api.cybersecfeed.com/api/v1/cve/batch"
    headers = {
        'X-API-Key': api_key,
        'Content-Type': 'application/json'
    }

    # Optimization configurations
    configs = {
        'ultra_fast': {
            'fields': ['cve_id', 'severity'],  # 90% payload reduction
            'include': None
        },
        'standard': {
            'fields': ['cve_id', 'severity', 'kev', 'epss'],  # 85% reduction
            'include': 'enrichment'
        },
        'comprehensive': {
            'fields': ['cve_id', 'published', 'severity', 'kev', 'epss', 'enrichment_status'],
            'include': 'enrichment,attack'  # Full threat intelligence
        }
    }

    payload = {
        'cve_ids': cve_ids[:50],  # Maximum 50 per batch
        **configs[optimization_level]
    }

    response = requests.post(url, json=payload, headers=headers)
    return response.json()

# Cost comparison examples
def demonstrate_batch_savings():
    cve_list = [f"CVE-2024-{str(i).zfill(4)}" for i in range(1, 51)]

    print("Cost Comparison:")
    print(f"Individual calls: {len(cve_list)} × 5 = {len(cve_list) * 5} credits")
    print(f"Enterprise batch: 35 credits (flat rate)")
    print(f"Savings: {((len(cve_list) * 5 - 35) / (len(cve_list) * 5)) * 100:.1f}%")

    # Ultra-fast batch processing
    result = enterprise_batch_optimized(cve_list, api_key, 'ultra_fast')
    print(f"Processed {len(result['data']['cves'])} CVEs with maximum efficiency")

    return result

Batch Performance Optimization​

def process_large_dataset_optimized(all_cve_ids, api_key):
    """Process thousands of CVEs with enterprise efficiency"""

    batch_size = 50
    all_results = []
    total_credits = 0

    # Calculate enterprise efficiency
    num_batches = (len(all_cve_ids) + batch_size - 1) // batch_size
    enterprise_credits = num_batches * 35
    individual_credits = len(all_cve_ids) * 5
    savings_percent = ((individual_credits - enterprise_credits) / individual_credits) * 100

    print(f"Enterprise Processing: {len(all_cve_ids)} CVEs")
    print(f"Batches: {num_batches} × 35 credits = {enterprise_credits} credits")
    print(f"vs Individual: {len(all_cve_ids)} × 5 credits = {individual_credits} credits")
    print(f"Cost Savings: {savings_percent:.1f}%")

    # Process in optimized batches
    for i in range(0, len(all_cve_ids), batch_size):
        batch = all_cve_ids[i:i + batch_size]

        # Use enterprise batch with field optimization
        result = enterprise_batch_optimized(batch, api_key, 'standard')

        if result.get('data', {}).get('cves'):
            batch_cves = list(result['data']['cves'].values())
            all_results.extend(batch_cves)
            total_credits += 35

    print(f"\nCompleted: {len(all_results)} CVEs, {total_credits} credits used")
    print(f"Efficiency: {len(all_results)/total_credits:.1f} CVEs per credit")

    return all_results

# Example: Process 1000 CVEs efficiently
large_dataset = [f"CVE-2024-{str(i).zfill(4)}" for i in range(1, 1001)]
results = process_large_dataset_optimized(large_dataset, api_key)
# Result: 86% cost savings + 98% fewer HTTP requests

4. Intelligent Caching Strategies​

ETag-Based Caching​

Implement proper ETag support for maximum cache efficiency:

import hashlib

class OptimizedCyberSecFeedClient:
    def __init__(self, api_key):
        self.api_key = api_key
        self.etag_cache = {}
        self.session = requests.Session()
        self.session.headers.update({'X-API-Key': api_key})

    def get_cve_cached(self, cve_id, params=None):
        """Get CVE with ETag caching support"""

        url = f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}"
        cache_key = self._make_cache_key(url, params)

        headers = {}
        if cache_key in self.etag_cache:
            headers['If-None-Match'] = self.etag_cache[cache_key]['etag']

        response = self.session.get(url, params=params, headers=headers)

        if response.status_code == 304:
            # Cache hit - return cached data
            return self.etag_cache[cache_key]['data']
        elif response.status_code == 200:
            # Cache miss - store new data
            data = response.json()
            if 'etag' in response.headers:
                self.etag_cache[cache_key] = {
                    'etag': response.headers['etag'],
                    'data': data
                }
            return data
        else:
            response.raise_for_status()

    def _make_cache_key(self, url, params):
        """Create cache key from URL and parameters"""
        if params:
            param_str = '&'.join(f"{k}={v}" for k, v in sorted(params.items()))
            cache_input = f"{url}?{param_str}"
        else:
            cache_input = url
        return hashlib.md5(cache_input.encode()).hexdigest()

# Usage
client = OptimizedCyberSecFeedClient(api_key)

# First request - cache miss
data1 = client.get_cve_cached('CVE-2024-0001')  # ~200ms

# Second request - cache hit
data2 = client.get_cve_cached('CVE-2024-0001')  # ~20ms

Application-Level Caching​

from functools import lru_cache
import time

class CacheConfig:
    CVE_CACHE_TTL = 3600  # 1 hour
    STATS_CACHE_TTL = 300  # 5 minutes
    KEV_CACHE_TTL = 1800   # 30 minutes

class TimedCache:
    def __init__(self, ttl):
        self.cache = {}
        self.ttl = ttl

    def get(self, key):
        if key in self.cache:
            data, timestamp = self.cache[key]
            if time.time() - timestamp < self.ttl:
                return data
            else:
                del self.cache[key]
        return None

    def set(self, key, value):
        self.cache[key] = (value, time.time())

class CachedClient:
    def __init__(self, api_key):
        self.api_key = api_key
        self.cve_cache = TimedCache(CacheConfig.CVE_CACHE_TTL)
        self.stats_cache = TimedCache(CacheConfig.STATS_CACHE_TTL)

    def get_cve(self, cve_id, use_cache=True):
        if use_cache:
            cached = self.cve_cache.get(cve_id)
            if cached:
                return cached

        # Make API request
        response = requests.get(
            f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}",
            headers={'X-API-Key': self.api_key}
        )
        data = response.json()

        if use_cache:
            self.cve_cache.set(cve_id, data)

        return data

4. Batch Operation Optimization​

Efficient Batch Processing​

def optimized_batch_processing(cve_ids, api_key):
    """Process CVEs in optimized batches"""

    session = requests.Session()
    session.headers.update({'X-API-Key': api_key})

    # Use maximum batch size
    batch_size = 50
    all_results = []

    for i in range(0, len(cve_ids), batch_size):
        batch = cve_ids[i:i + batch_size]

        # Optimize batch request
        params = {
            'ids': ','.join(batch),
            'fields': 'id,cvss.baseScore,kev,epss.score',  # Only essential data
            # Include ACSC only if needed for this batch
            # 'include': 'acsc'
        }

        response = session.get(
            'https://api.cybersecfeed.com/api/v1/cves',
            params=params
        )

        if response.status_code == 200:
            batch_data = response.json()
            all_results.extend(batch_data['data']['cves'])

        # Small delay to avoid overwhelming the API
        time.sleep(0.1)

    return all_results

# Process 500 CVEs efficiently
large_cve_list = [f"CVE-2024-{str(i).zfill(4)}" for i in range(1, 501)]
results = optimized_batch_processing(large_cve_list, api_key)
print(f"Processed {len(results)} CVEs")

Parallel Processing with Rate Limiting​

import asyncio
import aiohttp
from asyncio import Semaphore

class AsyncOptimizedClient:
    def __init__(self, api_key, max_concurrent=10):
        self.api_key = api_key
        self.semaphore = Semaphore(max_concurrent)
        self.base_url = "https://api.cybersecfeed.com/api/v1"

    async def fetch_cve_batch(self, session, cve_ids, include_acsc=False):
        """Fetch a batch of CVEs asynchronously"""

        async with self.semaphore:  # Rate limiting
            params = {
                'ids': ','.join(cve_ids[:50]),
                'fields': 'id,cvss.baseScore,kev,epss.score'
            }
            if include_acsc:
                params['include'] = 'acsc'

            async with session.get(
                f"{self.base_url}/cves",
                params=params,
                headers={'X-API-Key': self.api_key}
            ) as response:
                return await response.json()

    async def process_large_list(self, all_cve_ids, include_acsc=False):
        """Process large CVE list with controlled concurrency"""

        # Split into batches of 50
        batches = [all_cve_ids[i:i+50] for i in range(0, len(all_cve_ids), 50)]

        async with aiohttp.ClientSession() as session:
            tasks = [
                self.fetch_cve_batch(session, batch, include_acsc)
                for batch in batches
            ]

            batch_results = await asyncio.gather(*tasks)

            # Combine all results
            all_cves = []
            for batch_result in batch_results:
                if batch_result.get('data', {}).get('cves'):
                    all_cves.extend(batch_result['data']['cves'])

            return all_cves

# Usage
async def main():
    client = AsyncOptimizedClient(api_key, max_concurrent=5)
    large_list = [f"CVE-2024-{str(i).zfill(4)}" for i in range(1, 1001)]

    start_time = time.time()
    results = await client.process_large_list(large_list)
    end_time = time.time()

    print(f"Processed {len(results)} CVEs in {end_time - start_time:.2f} seconds")

# Run async processing
asyncio.run(main())

5. Connection Optimization​

Connection Pooling​

from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

class OptimizedSession:
    def __init__(self, api_key):
        self.session = requests.Session()

        # Configure connection pooling
        adapter = HTTPAdapter(
            pool_connections=10,      # Number of connection pools
            pool_maxsize=20,          # Max connections per pool
            max_retries=Retry(
                total=3,
                status_forcelist=[429, 500, 502, 503, 504],
                backoff_factor=1,
                respect_retry_after_header=True
            )
        )

        self.session.mount('https://', adapter)
        self.session.headers.update({
            'X-API-Key': api_key,
            'User-Agent': 'MyApp/1.0 (Optimized)',
            'Accept-Encoding': 'gzip, deflate'
        })

    def get(self, url, **kwargs):
        return self.session.get(url, **kwargs)

# Use persistent session
session = OptimizedSession(api_key)

# Multiple requests reuse connections
for cve_id in cve_list:
    response = session.get(f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}")

Timeout Optimization​

# Configure timeouts for different operations
TIMEOUTS = {
    'ping': (2, 5),           # (connect, read) in seconds
    'cve_detail': (3, 10),
    'search': (5, 30),
    'batch': (5, 60),
    'stats': (3, 15)
}

def get_cve_with_timeout(cve_id, operation_type='cve_detail'):
    timeout = TIMEOUTS.get(operation_type, (5, 30))

    response = requests.get(
        f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}",
        headers={'X-API-Key': api_key},
        timeout=timeout
    )
    return response.json()

6. Detail Endpoint Optimization​

When to Use Detail vs Standard Endpoints​

The detail endpoint provides comprehensive data but has higher latency. Choose wisely:

class SmartCVEClient:
    """Intelligent endpoint selection based on use case"""

    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = "https://api.cybersecfeed.com/api/v1"
        self.headers = {"X-API-Key": api_key}

    def get_cve_optimized(self, cve_id, use_case="dashboard"):
        """Select optimal endpoint based on use case"""

        strategies = {
            "dashboard": self._get_minimal,
            "risk_scoring": self._get_enriched,
            "patch_management": self._get_detailed,
            "security_research": self._get_full_detail,
            "compliance": self._get_compliance_data
        }

        return strategies.get(use_case, self._get_minimal)(cve_id)

    def _get_minimal(self, cve_id):
        """Minimal data for dashboards - fastest"""
        params = {"fields": "id,cvss.baseScore,cvss.baseSeverity,kev,epss.score"}
        response = requests.get(
            f"{self.base_url}/cve/{cve_id}",
            headers=self.headers,
            params=params
        )
        return response.json()

    def _get_enriched(self, cve_id):
        """Enriched data for risk scoring"""
        params = {"include": "enrichment"}
        response = requests.get(
            f"{self.base_url}/cve/{cve_id}",
            headers=self.headers,
            params=params
        )
        return response.json()

    def _get_detailed(self, cve_id):
        """Detailed data for patch management"""
        response = requests.get(
            f"{self.base_url}/cve/{cve_id}/detail",
            headers=self.headers
        )
        return response.json()

    def _get_full_detail(self, cve_id, vendor="nvd"):
        """Full vendor-specific detail for research"""
        params = {"vendor": vendor}
        response = requests.get(
            f"{self.base_url}/cve/{cve_id}/detail",
            headers=self.headers,
            params=params
        )
        return response.json()

    def _get_compliance_data(self, cve_id):
        """Optimized for compliance reporting"""
        # First, get basic data with specific fields
        params = {
            "fields": "id,published,cvss,kev,epss",
            "include": "acsc"
        }
        response = requests.get(
            f"{self.base_url}/cve/{cve_id}",
            headers=self.headers,
            params=params
        )
        return response.json()

Detail Endpoint Performance Characteristics​

ScenarioEndpointResponse TimeCache TTLPayload Size
Quick check/cve/{id}?fields=id,cvss<150ms1 hour~500B
Risk assessment/cve/{id}?include=enrichment<250ms2 hours~2KB
Full analysis/cve/{id}/detail<500ms24 hours~10-50KB
Vendor-specific/cve/{id}/detail?vendor=cisco<600ms24 hours~10-100KB

Caching Strategy for Detail Endpoint​

import hashlib
import time
from typing import Optional, Dict, Tuple

class DetailEndpointCache:
    """Aggressive caching for detail endpoint data"""

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.cache = {}  # In production, use Redis or similar
        self.cache_ttl = {
            "detail": 86400,  # 24 hours for detailed data
            "enrichment": 7200,  # 2 hours for enrichment
            "basic": 3600  # 1 hour for basic data
        }

    def get_cve_detail(self, cve_id: str, vendor: str = "nvd",
                       force_refresh: bool = False) -> Optional[Dict]:
        """Get CVE detail with aggressive caching"""

        cache_key = self._make_cache_key(cve_id, vendor, "detail")

        # Check cache first
        if not force_refresh:
            cached_data = self._get_from_cache(cache_key, "detail")
            if cached_data:
                return cached_data

        # Fetch from API
        url = f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}/detail"
        params = {"vendor": vendor} if vendor != "nvd" else {}
        headers = {"X-API-Key": self.api_key}

        response = requests.get(url, headers=headers, params=params)

        if response.status_code == 200:
            data = response.json()

            # Check if we got full or partial data
            if data.get("data", {}).get("status") == "partial":
                # Partial data - shorter cache
                self._set_cache(cache_key, data, ttl_override=3600)
            else:
                # Full data - long cache
                self._set_cache(cache_key, data, cache_type="detail")

            return data
        elif response.status_code == 404:
            # Cache negative responses too
            self._set_cache(cache_key, None, ttl_override=3600)
            return None
        else:
            response.raise_for_status()

    def _make_cache_key(self, cve_id: str, vendor: str, cache_type: str) -> str:
        """Generate cache key"""
        key_input = f"{cve_id}:{vendor}:{cache_type}"
        return hashlib.md5(key_input.encode()).hexdigest()

    def _get_from_cache(self, key: str, cache_type: str) -> Optional[Dict]:
        """Retrieve from cache if not expired"""
        if key in self.cache:
            data, timestamp = self.cache[key]
            ttl = self.cache_ttl.get(cache_type, 3600)

            if time.time() - timestamp < ttl:
                return data
            else:
                # Expired - remove from cache
                del self.cache[key]

        return None

    def _set_cache(self, key: str, data: Optional[Dict],
                   cache_type: str = "detail", ttl_override: int = None):
        """Store in cache with TTL"""
        ttl = ttl_override or self.cache_ttl.get(cache_type, 3600)
        self.cache[key] = (data, time.time())

# Usage
cache_client = DetailEndpointCache(api_key)

# First request - API call
detail1 = cache_client.get_cve_detail("CVE-2024-0001")  # ~500ms

# Second request - cached
detail2 = cache_client.get_cve_detail("CVE-2024-0001")  # ~1ms

# Vendor-specific with caching
cisco_detail = cache_client.get_cve_detail("CVE-2024-0001", vendor="cisco")

Progressive Loading Pattern​

Optimize user experience with progressive data loading:

import asyncio
import aiohttp
from typing import Dict, Optional

class ProgressiveCVELoader:
    """Load CVE data progressively for optimal UX"""

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.headers = {"X-API-Key": api_key}
        self.base_url = "https://api.cybersecfeed.com/api/v1"

    async def load_cve_progressive(self, cve_id: str, callback):
        """Load CVE data in stages, calling callback for each stage"""

        async with aiohttp.ClientSession() as session:
            # Stage 1: Basic data (fastest)
            basic_data = await self._fetch_basic(session, cve_id)
            await callback("basic", basic_data)

            # Stage 2: Enrichment (medium speed)
            enrichment_data = await self._fetch_enrichment(session, cve_id)
            await callback("enrichment", enrichment_data)

            # Stage 3: Full detail (slowest, if needed)
            if self._needs_detail(basic_data, enrichment_data):
                detail_data = await self._fetch_detail(session, cve_id)
                await callback("detail", detail_data)

    async def _fetch_basic(self, session, cve_id: str) -> Dict:
        """Fetch minimal CVE data"""
        url = f"{self.base_url}/cve/{cve_id}"
        params = {"fields": "id,cvss,kev,epss"}

        async with session.get(url, headers=self.headers, params=params) as response:
            return await response.json()

    async def _fetch_enrichment(self, session, cve_id: str) -> Dict:
        """Fetch enrichment data"""
        url = f"{self.base_url}/cve/{cve_id}"
        params = {"include": "enrichment,attack"}

        async with session.get(url, headers=self.headers, params=params) as response:
            return await response.json()

    async def _fetch_detail(self, session, cve_id: str) -> Dict:
        """Fetch full detail data"""
        url = f"{self.base_url}/cve/{cve_id}/detail"

        async with session.get(url, headers=self.headers) as response:
            return await response.json()

    def _needs_detail(self, basic_data: Dict, enrichment_data: Dict) -> bool:
        """Determine if full detail is needed"""
        # Load detail for high-value CVEs
        cve = basic_data.get("data", {}).get("cve", {})
        cvss_score = cve.get("cvss", {}).get("baseScore", 0)
        has_kev = "kev" in cve

        return cvss_score >= 7.0 or has_kev

# Usage example
async def handle_cve_data(stage: str, data: Dict):
    """Handle data as it arrives"""
    if stage == "basic":
        print(f"Quick display: CVE {data['data']['cve']['id']}")
        # Update UI with basic info immediately
    elif stage == "enrichment":
        print("Adding enrichment data to display")
        # Enhance UI with enrichment
    elif stage == "detail":
        print("Full detail loaded")
        # Show complete information

async def main():
    loader = ProgressiveCVELoader(api_key)
    await loader.load_cve_progressive("CVE-2024-0001", handle_cve_data)

# Run progressive loading
asyncio.run(main())

7. Use Case-Specific Optimizations​

High-Frequency Monitoring​

class HighFrequencyMonitor:
    """Optimized for frequent checks with minimal data"""

    def __init__(self, api_key):
        self.client = OptimizedCyberSecFeedClient(api_key)
        self.last_check = None

    def check_new_critical_cves(self):
        """Fast check for new critical CVEs"""

        # Use minimal fields for speed
        params = {
            'severity_min': 9.0,
            'limit': 10,
            'fields': 'id,published,cvss.baseScore'
        }

        if self.last_check:
            params['published_after'] = self.last_check

        response = self.client.session.get(
            'https://api.cybersecfeed.com/api/v1/cves',
            params=params
        )

        self.last_check = datetime.utcnow().isoformat()
        return response.json()

Bulk Data Analysis​

class BulkAnalyzer:
    """Optimized for processing large datasets"""

    def __init__(self, api_key):
        self.api_key = api_key

    def analyze_cve_list(self, cve_ids):
        """Analyze large CVE list with optimal batching"""

        # Process in chunks with minimal data
        results = []
        chunk_size = 50

        for i in range(0, len(cve_ids), chunk_size):
            chunk = cve_ids[i:i + chunk_size]

            response = requests.get(
                'https://api.cybersecfeed.com/api/v1/cves',
                params={
                    'ids': ','.join(chunk),
                    'fields': 'id,cvss.baseScore,kev,epss.score'
                },
                headers={'X-API-Key': self.api_key}
            )

            if response.status_code == 200:
                chunk_data = response.json()['data']['cves']
                results.extend(chunk_data)

        return self.calculate_risk_metrics(results)

    def calculate_risk_metrics(self, cves):
        """Fast risk calculation on minimal data"""

        metrics = {
            'total_cves': len(cves),
            'critical_count': 0,
            'kev_count': 0,
            'high_epss_count': 0,
            'avg_cvss': 0
        }

        total_cvss = 0
        for cve in cves:
            cvss_score = cve.get('cvss', {}).get('baseScore', 0)
            total_cvss += cvss_score

            if cvss_score >= 9.0:
                metrics['critical_count'] += 1

            if cve.get('kev'):
                metrics['kev_count'] += 1

            if cve.get('epss', {}).get('score', 0) > 0.7:
                metrics['high_epss_count'] += 1

        metrics['avg_cvss'] = total_cvss / len(cves) if cves else 0
        return metrics

7. Performance Monitoring​

Response Time Tracking​

import time
from collections import defaultdict

class PerformanceTracker:
    def __init__(self):
        self.metrics = defaultdict(list)

    def track_request(self, operation, func, *args, **kwargs):
        """Track request performance"""

        start_time = time.time()
        try:
            result = func(*args, **kwargs)
            success = True
        except Exception as e:
            result = None
            success = False

        duration = time.time() - start_time

        self.metrics[operation].append({
            'duration': duration,
            'success': success,
            'timestamp': time.time()
        })

        return result

    def get_stats(self, operation):
        """Get performance statistics"""

        if operation not in self.metrics:
            return None

        durations = [m['duration'] for m in self.metrics[operation] if m['success']]

        if not durations:
            return None

        return {
            'count': len(durations),
            'avg': sum(durations) / len(durations),
            'min': min(durations),
            'max': max(durations),
            'p95': sorted(durations)[int(len(durations) * 0.95)]
        }

# Usage
tracker = PerformanceTracker()

# Track CVE requests
def get_cve_tracked(cve_id):
    return tracker.track_request(
        'cve_detail',
        lambda: requests.get(
            f"https://api.cybersecfeed.com/api/v1/cve/{cve_id}",
            headers={'X-API-Key': api_key}
        ).json()
    )

# Get performance stats
stats = tracker.get_stats('cve_detail')
print(f"Average response time: {stats['avg']:.3f}s")
print(f"95th percentile: {stats['p95']:.3f}s")

8. Optimization Checklist​

Pre-Optimization Audit​

  • Identify which enrichment data you actually use
  • Review field usage - are you reading all returned fields?
  • Check for duplicate or unnecessary API calls
  • Measure current response times and bandwidth usage

Implementation​

  • Implement parameter-based data loading (include parameters)
  • Add field projection for targeted data retrieval
  • Set up proper ETag-based caching
  • Use batch operations for multiple CVE lookups
  • Configure connection pooling and timeouts

Monitoring​

  • Track response times and cache hit rates
  • Monitor credit usage via /api/v1/usage endpoint (0 credits)
  • Set up alerts for performance degradation and credit efficiency
  • Regular performance testing with realistic v1.5 feature usage
  • Track enterprise batch savings and field selection effectiveness

Advanced Optimizations​

  • Implement application-level caching with TTL
  • Use async processing for bulk operations
  • Consider CDN or proxy caching for frequently accessed data
  • Optimize data processing pipelines for minimal memory usage

Enterprise Performance Gains (v1.5)​

Following this v1.5 optimization guide delivers transformational results:

OptimizationPerformance ImprovementBandwidth ReductionCredit Efficiency
Advanced Field Selection30-60% faster85% lessSame 5 credits, minimal data
Enterprise Batch Operations50x faster (bulk)Overhead eliminated86% credit savings
Smart Sorting & Filtering20-40% faster queriesTargeted resultsPrecise data retrieval
ETag Caching80-95% faster (cached)95%+ reductionNear-zero credit usage
Combined v1.5 Features10-50x improvement85-95% reductionMassive enterprise savings

Real-World Enterprise Impact​

ScenarioBefore (Individual)After (v1.5 Enterprise)Improvement
1000 CVE Analysis5,000 credits, 50+ seconds700 credits, 2-5 seconds86% cheaper, 90% faster
Daily SOC Monitoring1,000 credits/day150 credits/day85% cost reduction
Compliance ReportingHigh bandwidth, slowField-selected, instant95% bandwidth savings
Threat IntelligenceMultiple API callsSingle batch + enrichmentEnterprise efficiency

These v1.5 optimizations deliver enterprise-grade performance while dramatically reducing credit consumption and providing superior user experience through ultra-fast response times.