c8be9d7414
- Add Prometheus metrics for marketplace API throughput and error rates with new dashboard panels - Implement confidential transaction models with encryption support and access control - Add key management system with registration, rotation, and audit logging - Create services and registry routers for service discovery and management - Integrate ZK proof generation for privacy-preserving receipts - Add metrics instru
280 lines
11 KiB
Python
Executable File
280 lines
11 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Autoscaling Validation Script
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This script generates synthetic traffic to test and validate HPA behavior.
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It monitors pod counts and metrics while generating load to ensure autoscaling works as expected.
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Usage:
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python test_autoscaling.py --service coordinator --namespace default --target-url http://localhost:8011 --duration 300
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"""
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import asyncio
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import aiohttp
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import time
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import argparse
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import logging
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import json
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from typing import List, Dict, Any
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from datetime import datetime
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import subprocess
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import sys
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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class AutoscalingTest:
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"""Test suite for validating autoscaling behavior"""
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def __init__(self, service_name: str, namespace: str, target_url: str):
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self.service_name = service_name
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self.namespace = namespace
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self.target_url = target_url
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self.session = None
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async def __aenter__(self):
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self.session = aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=30))
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return self
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async def __aexit__(self, exc_type, exc_val, exc_tb):
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if self.session:
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await self.session.close()
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async def get_pod_count(self) -> int:
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"""Get current number of pods for the service"""
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cmd = [
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"kubectl", "get", "pods",
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"-n", self.namespace,
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"-l", f"app.kubernetes.io/name={self.service_name}",
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"-o", "jsonpath='{.items[*].status.phase}'"
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]
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try:
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result = subprocess.run(cmd, capture_output=True, text=True, check=True)
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# Count Running pods
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phases = result.stdout.strip().strip("'").split()
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return len([p for p in phases if p == "Running"])
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except subprocess.CalledProcessError as e:
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logger.error(f"Failed to get pod count: {e}")
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return 0
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async def get_hpa_status(self) -> Dict[str, Any]:
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"""Get current HPA status"""
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cmd = [
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"kubectl", "get", "hpa",
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"-n", self.namespace,
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f"{self.service_name}",
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"-o", "json"
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]
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try:
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result = subprocess.run(cmd, capture_output=True, text=True, check=True)
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data = json.loads(result.stdout)
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return {
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"min_replicas": data["spec"]["minReplicas"],
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"max_replicas": data["spec"]["maxReplicas"],
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"current_replicas": data["status"]["currentReplicas"],
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"desired_replicas": data["status"]["desiredReplicas"],
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"current_cpu": data["status"].get("currentCPUUtilizationPercentage"),
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"target_cpu": None
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}
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# Extract target CPU from metrics
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for metric in data["spec"]["metrics"]:
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if metric["type"] == "Resource" and metric["resource"]["name"] == "cpu":
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self.target_cpu = metric["resource"]["target"]["averageUtilization"]
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break
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except subprocess.CalledProcessError as e:
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logger.error(f"Failed to get HPA status: {e}")
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return {}
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async def generate_load(self, duration: int, concurrent_requests: int = 50):
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"""Generate sustained load on the service"""
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logger.info(f"Generating load for {duration}s with {concurrent_requests} concurrent requests")
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async def make_request():
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try:
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if self.service_name == "coordinator":
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# Test marketplace endpoints
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endpoints = [
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"/v1/marketplace/offers",
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"/v1/marketplace/stats"
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]
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endpoint = endpoints[hash(time.time()) % len(endpoints)]
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async with self.session.get(f"{self.target_url}{endpoint}") as response:
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return response.status == 200
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elif self.service_name == "blockchain-node":
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# Test blockchain endpoints
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payload = {
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"from": "0xtest_sender",
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"to": "0xtest_receiver",
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"value": "1000",
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"nonce": int(time.time()),
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"data": "0x",
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"gas_limit": 21000,
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"gas_price": "1000000000"
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}
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async with self.session.post(f"{self.target_url}/v1/transactions", json=payload) as response:
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return response.status == 200
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else:
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# Generic health check
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async with self.session.get(f"{self.target_url}/v1/health") as response:
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return response.status == 200
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except Exception as e:
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logger.debug(f"Request failed: {e}")
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return False
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# Generate sustained load
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start_time = time.time()
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tasks = []
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while time.time() - start_time < duration:
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# Create batch of concurrent requests
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batch = [make_request() for _ in range(concurrent_requests)]
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tasks.extend(batch)
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# Wait for batch to complete
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await asyncio.gather(*batch, return_exceptions=True)
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# Brief pause between batches
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await asyncio.sleep(0.1)
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logger.info(f"Load generation completed")
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async def monitor_scaling(self, duration: int, interval: int = 10):
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"""Monitor pod scaling during load test"""
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logger.info(f"Monitoring scaling for {duration}s")
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results = []
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start_time = time.time()
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while time.time() - start_time < duration:
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timestamp = datetime.now().isoformat()
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pod_count = await self.get_pod_count()
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hpa_status = await self.get_hpa_status()
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result = {
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"timestamp": timestamp,
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"pod_count": pod_count,
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"hpa_status": hpa_status
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}
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results.append(result)
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logger.info(f"[{timestamp}] Pods: {pod_count}, HPA: {hpa_status}")
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await asyncio.sleep(interval)
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return results
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async def run_test(self, load_duration: int = 300, monitor_duration: int = 400):
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"""Run complete autoscaling test"""
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logger.info(f"Starting autoscaling test for {self.service_name}")
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# Record initial state
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initial_pods = await self.get_pod_count()
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initial_hpa = await self.get_hpa_status()
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logger.info(f"Initial state - Pods: {initial_pods}, HPA: {initial_hpa}")
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# Start monitoring in background
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monitor_task = asyncio.create_task(
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self.monitor_scaling(monitor_duration)
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)
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# Wait a bit to establish baseline
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await asyncio.sleep(30)
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# Generate load
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await self.generate_load(load_duration)
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# Wait for scaling to stabilize
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await asyncio.sleep(60)
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# Get monitoring results
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monitoring_results = await monitor_task
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# Analyze results
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max_pods = max(r["pod_count"] for r in monitoring_results)
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min_pods = min(r["pod_count"] for r in monitoring_results)
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scaled_up = max_pods > initial_pods
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logger.info("\n=== Test Results ===")
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logger.info(f"Initial pods: {initial_pods}")
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logger.info(f"Min pods during test: {min_pods}")
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logger.info(f"Max pods during test: {max_pods}")
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logger.info(f"Scaling occurred: {scaled_up}")
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if scaled_up:
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logger.info("✅ Autoscaling test PASSED - Service scaled up under load")
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else:
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logger.warning("⚠️ Autoscaling test FAILED - Service did not scale up")
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logger.warning("Check:")
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logger.warning(" - HPA configuration")
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logger.warning(" - Metrics server is running")
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logger.warning(" - Resource requests/limits are set")
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logger.warning(" - Load was sufficient to trigger scaling")
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# Save results
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results_file = f"autoscaling_test_{self.service_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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with open(results_file, "w") as f:
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json.dump({
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"service": self.service_name,
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"namespace": self.namespace,
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"initial_pods": initial_pods,
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"max_pods": max_pods,
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"min_pods": min_pods,
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"scaled_up": scaled_up,
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"monitoring_data": monitoring_results
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}, f, indent=2)
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logger.info(f"Detailed results saved to: {results_file}")
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return scaled_up
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async def main():
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parser = argparse.ArgumentParser(description="Autoscaling Validation Test")
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parser.add_argument("--service", required=True,
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choices=["coordinator", "blockchain-node", "wallet-daemon"],
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help="Service to test")
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parser.add_argument("--namespace", default="default",
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help="Kubernetes namespace")
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parser.add_argument("--target-url", required=True,
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help="Service URL to generate load against")
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parser.add_argument("--load-duration", type=int, default=300,
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help="Duration of load generation in seconds")
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parser.add_argument("--monitor-duration", type=int, default=400,
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help="Total monitoring duration in seconds")
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parser.add_argument("--local-mode", action="store_true",
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help="Run in local mode without Kubernetes (load test only)")
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args = parser.parse_args()
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if not args.local_mode:
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# Verify kubectl is available
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try:
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subprocess.run(["kubectl", "version"], capture_output=True, check=True)
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except (subprocess.CalledProcessError, FileNotFoundError):
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logger.error("kubectl is not available or not configured")
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logger.info("Use --local-mode to run load test without Kubernetes monitoring")
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sys.exit(1)
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# Run test
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async with AutoscalingTest(args.service, args.namespace, args.target_url) as test:
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if args.local_mode:
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# Local mode: just test load generation
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logger.info(f"Running load test for {args.service} in local mode")
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await test.generate_load(args.load_duration)
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logger.info("Load test completed successfully")
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success = True
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else:
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# Full autoscaling test
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success = await test.run_test(args.load_duration, args.monitor_duration)
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sys.exit(0 if success else 1)
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if __name__ == "__main__":
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asyncio.run(main())
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