Files
crawl4ai/deploy/docker/tests/test_4_concurrent.py
unclecode b97eaeea4c feat(docker): implement smart browser pool with 10x memory efficiency
Major refactoring to eliminate memory leaks and enable high-scale crawling:

- **Smart 3-Tier Browser Pool**:
  - Permanent browser (always-ready default config)
  - Hot pool (configs used 3+ times, longer TTL)
  - Cold pool (new/rare configs, short TTL)
  - Auto-promotion: cold → hot after 3 uses
  - 100% pool reuse achieved in tests

- **Container-Aware Memory Detection**:
  - Read cgroup v1/v2 memory limits (not host metrics)
  - Accurate memory pressure detection in Docker
  - Memory-based browser creation blocking

- **Adaptive Janitor**:
  - Dynamic cleanup intervals (10s/30s/60s based on memory)
  - Tiered TTLs: cold 30-300s, hot 120-600s
  - Aggressive cleanup at high memory pressure

- **Unified Pool Usage**:
  - All endpoints now use pool (/html, /screenshot, /pdf, /execute_js, /md, /llm)
  - Fixed config signature mismatch (permanent browser matches endpoints)
  - get_default_browser_config() helper for consistency

- **Configuration**:
  - Reduced idle_ttl: 1800s → 300s (30min → 5min)
  - Fixed port: 11234 → 11235 (match Gunicorn)

**Performance Results** (from stress tests):
- Memory: 10x reduction (500-700MB × N → 270MB permanent)
- Latency: 30-50x faster (<100ms pool hits vs 3-5s startup)
- Reuse: 100% for default config, 60%+ for variants
- Capacity: 100+ concurrent requests (vs ~20 before)
- Leak: 0 MB/cycle (stable across tests)

**Test Infrastructure**:
- 7-phase sequential test suite (tests/)
- Docker stats integration + log analysis
- Pool promotion verification
- Memory leak detection
- Full endpoint coverage

Fixes memory issues reported in production deployments.
2025-10-17 20:38:39 +08:00

237 lines
7.8 KiB
Python
Executable File

#!/usr/bin/env python3
"""
Test 4: Concurrent Load Testing
- Tests pool under concurrent load
- Escalates: 10 → 50 → 100 concurrent requests
- Validates latency distribution (P50, P95, P99)
- Monitors memory stability
"""
import asyncio
import time
import docker
import httpx
from threading import Thread, Event
from collections import defaultdict
# Config
IMAGE = "crawl4ai-local:latest"
CONTAINER_NAME = "crawl4ai-test"
PORT = 11235
LOAD_LEVELS = [
{"name": "Light", "concurrent": 10, "requests": 20},
{"name": "Medium", "concurrent": 50, "requests": 100},
{"name": "Heavy", "concurrent": 100, "requests": 200},
]
# Stats
stats_history = []
stop_monitoring = Event()
def monitor_stats(container):
"""Background stats collector."""
for stat in container.stats(decode=True, stream=True):
if stop_monitoring.is_set():
break
try:
mem_usage = stat['memory_stats'].get('usage', 0) / (1024 * 1024)
stats_history.append({'timestamp': time.time(), 'memory_mb': mem_usage})
except:
pass
time.sleep(0.5)
def count_log_markers(container):
"""Extract pool markers."""
logs = container.logs().decode('utf-8')
return {
'permanent': logs.count("🔥 Using permanent browser"),
'hot': logs.count("♨️ Using hot pool browser"),
'cold': logs.count("❄️ Using cold pool browser"),
'new': logs.count("🆕 Creating new browser"),
}
async def hit_endpoint(client, url, payload, semaphore):
"""Single request with concurrency control."""
async with semaphore:
start = time.time()
try:
resp = await client.post(url, json=payload, timeout=60.0)
elapsed = (time.time() - start) * 1000
return {"success": resp.status_code == 200, "latency_ms": elapsed}
except Exception as e:
return {"success": False, "error": str(e)}
async def run_concurrent_test(url, payload, concurrent, total_requests):
"""Run concurrent requests."""
semaphore = asyncio.Semaphore(concurrent)
async with httpx.AsyncClient() as client:
tasks = [hit_endpoint(client, url, payload, semaphore) for _ in range(total_requests)]
results = await asyncio.gather(*tasks)
return results
def calculate_percentiles(latencies):
"""Calculate P50, P95, P99."""
if not latencies:
return 0, 0, 0
sorted_lat = sorted(latencies)
n = len(sorted_lat)
return (
sorted_lat[int(n * 0.50)],
sorted_lat[int(n * 0.95)],
sorted_lat[int(n * 0.99)],
)
def start_container(client, image, name, port):
"""Start container."""
try:
old = client.containers.get(name)
print(f"🧹 Stopping existing container...")
old.stop()
old.remove()
except docker.errors.NotFound:
pass
print(f"🚀 Starting container...")
container = client.containers.run(
image, name=name, ports={f"{port}/tcp": port},
detach=True, shm_size="1g", mem_limit="4g",
)
print(f"⏳ Waiting for health...")
for _ in range(30):
time.sleep(1)
container.reload()
if container.status == "running":
try:
import requests
if requests.get(f"http://localhost:{port}/health", timeout=2).status_code == 200:
print(f"✅ Container healthy!")
return container
except:
pass
raise TimeoutError("Container failed to start")
async def main():
print("="*60)
print("TEST 4: Concurrent Load Testing")
print("="*60)
client = docker.from_env()
container = None
monitor_thread = None
try:
container = start_container(client, IMAGE, CONTAINER_NAME, PORT)
print(f"\n⏳ Waiting for permanent browser init (3s)...")
await asyncio.sleep(3)
# Start monitoring
stop_monitoring.clear()
stats_history.clear()
monitor_thread = Thread(target=monitor_stats, args=(container,), daemon=True)
monitor_thread.start()
await asyncio.sleep(1)
baseline_mem = stats_history[-1]['memory_mb'] if stats_history else 0
print(f"📏 Baseline: {baseline_mem:.1f} MB\n")
url = f"http://localhost:{PORT}/html"
payload = {"url": "https://httpbin.org/html"}
all_results = []
level_stats = []
# Run load levels
for level in LOAD_LEVELS:
print(f"{'='*60}")
print(f"🔄 {level['name']} Load: {level['concurrent']} concurrent, {level['requests']} total")
print(f"{'='*60}")
start_time = time.time()
results = await run_concurrent_test(url, payload, level['concurrent'], level['requests'])
duration = time.time() - start_time
successes = sum(1 for r in results if r.get("success"))
success_rate = (successes / len(results)) * 100
latencies = [r["latency_ms"] for r in results if "latency_ms" in r]
p50, p95, p99 = calculate_percentiles(latencies)
avg_lat = sum(latencies) / len(latencies) if latencies else 0
print(f" Duration: {duration:.1f}s")
print(f" Success: {success_rate:.1f}% ({successes}/{len(results)})")
print(f" Avg Latency: {avg_lat:.0f}ms")
print(f" P50/P95/P99: {p50:.0f}ms / {p95:.0f}ms / {p99:.0f}ms")
level_stats.append({
'name': level['name'],
'concurrent': level['concurrent'],
'success_rate': success_rate,
'avg_latency': avg_lat,
'p50': p50, 'p95': p95, 'p99': p99,
})
all_results.extend(results)
await asyncio.sleep(2) # Cool down between levels
# Stop monitoring
await asyncio.sleep(1)
stop_monitoring.set()
if monitor_thread:
monitor_thread.join(timeout=2)
# Final stats
pool_stats = count_log_markers(container)
memory_samples = [s['memory_mb'] for s in stats_history]
peak_mem = max(memory_samples) if memory_samples else 0
final_mem = memory_samples[-1] if memory_samples else 0
print(f"\n{'='*60}")
print(f"FINAL RESULTS:")
print(f"{'='*60}")
print(f" Total Requests: {len(all_results)}")
print(f"\n Pool Utilization:")
print(f" 🔥 Permanent: {pool_stats['permanent']}")
print(f" ♨️ Hot: {pool_stats['hot']}")
print(f" ❄️ Cold: {pool_stats['cold']}")
print(f" 🆕 New: {pool_stats['new']}")
print(f"\n Memory:")
print(f" Baseline: {baseline_mem:.1f} MB")
print(f" Peak: {peak_mem:.1f} MB")
print(f" Final: {final_mem:.1f} MB")
print(f" Delta: {final_mem - baseline_mem:+.1f} MB")
print(f"{'='*60}")
# Pass/Fail
passed = True
for ls in level_stats:
if ls['success_rate'] < 99:
print(f"❌ FAIL: {ls['name']} success rate {ls['success_rate']:.1f}% < 99%")
passed = False
if ls['p99'] > 10000: # 10s threshold
print(f"⚠️ WARNING: {ls['name']} P99 latency {ls['p99']:.0f}ms very high")
if final_mem - baseline_mem > 300:
print(f"⚠️ WARNING: Memory grew {final_mem - baseline_mem:.1f} MB")
if passed:
print(f"✅ TEST PASSED")
return 0
else:
return 1
except Exception as e:
print(f"\n❌ TEST ERROR: {e}")
import traceback
traceback.print_exc()
return 1
finally:
stop_monitoring.set()
if container:
print(f"🛑 Stopping container...")
container.stop()
container.remove()
if __name__ == "__main__":
exit_code = asyncio.run(main())
exit(exit_code)