#!/usr/bin/env python3
"""
Test concurrent job handling in production API.
Verifies that multiple simultaneous requests work correctly.
"""
import asyncio
import httpx
import time
from datetime import datetime

API_BASE_URL = "http://localhost:8000"

# Test URLs (using the same URL is fine for testing)
TEST_URLS = [
    "https://www.google.com/maps/place/Soho+Factory/@54.6738155,25.2595844,17z/",
] * 5  # 5 concurrent jobs


async def submit_job(client: httpx.AsyncClient, url: str, job_num: int):
    """Submit a single scraping job"""
    print(f"[{datetime.now().strftime('%H:%M:%S')}] Job {job_num}: Submitting...")

    try:
        response = await client.post(
            f"{API_BASE_URL}/scrape",
            json={"url": url},
            timeout=10.0
        )

        if response.status_code == 200:
            data = response.json()
            job_id = data['job_id']
            print(f"[{datetime.now().strftime('%H:%M:%S')}] Job {job_num}: Started (ID: {job_id[:8]}...)")
            return job_id, job_num
        else:
            print(f"[{datetime.now().strftime('%H:%M:%S')}] Job {job_num}: Failed - {response.status_code}")
            return None, job_num

    except Exception as e:
        print(f"[{datetime.now().strftime('%H:%M:%S')}] Job {job_num}: Error - {e}")
        return None, job_num


async def monitor_job(client: httpx.AsyncClient, job_id: str, job_num: int):
    """Monitor a job until completion"""
    start_time = time.time()

    while True:
        try:
            response = await client.get(
                f"{API_BASE_URL}/jobs/{job_id}",
                timeout=5.0
            )

            if response.status_code == 200:
                job = response.json()
                status = job['status']

                if status == 'completed':
                    elapsed = time.time() - start_time
                    reviews = job.get('reviews_count', 0)
                    scrape_time = job.get('scrape_time', 0)
                    print(f"[{datetime.now().strftime('%H:%M:%S')}] Job {job_num}: ✅ COMPLETED - {reviews} reviews in {scrape_time:.1f}s (total: {elapsed:.1f}s)")
                    return True, elapsed, reviews

                elif status == 'failed':
                    elapsed = time.time() - start_time
                    error = job.get('error_message', 'Unknown error')
                    print(f"[{datetime.now().strftime('%H:%M:%S')}] Job {job_num}: ❌ FAILED - {error}")
                    return False, elapsed, 0

                elif status == 'running':
                    # Still running, wait and check again
                    await asyncio.sleep(2)
                else:
                    # Pending, wait longer
                    await asyncio.sleep(1)

        except Exception as e:
            print(f"[{datetime.now().strftime('%H:%M:%S')}] Job {job_num}: Monitor error - {e}")
            await asyncio.sleep(2)


async def test_concurrent_jobs():
    """Test multiple concurrent jobs"""
    print("=" * 70)
    print("Testing Concurrent Job Handling")
    print("=" * 70)
    print(f"Submitting {len(TEST_URLS)} jobs simultaneously...\n")

    overall_start = time.time()

    async with httpx.AsyncClient() as client:
        # Test 1: Check API is available
        try:
            response = await client.get(f"{API_BASE_URL}/", timeout=5.0)
            if response.status_code != 200:
                print("❌ API not available!")
                return
            print("✅ API is available\n")
        except Exception as e:
            print(f"❌ Cannot connect to API: {e}")
            print("\nPlease start the API server first:")
            print("  python api_server_production.py")
            return

        # Test 2: Submit all jobs concurrently
        print(f"Step 1: Submitting {len(TEST_URLS)} jobs in parallel...")
        print("-" * 70)

        submit_tasks = [
            submit_job(client, url, i+1)
            for i, url in enumerate(TEST_URLS)
        ]

        results = await asyncio.gather(*submit_tasks)
        job_ids = [(job_id, num) for job_id, num in results if job_id]

        print(f"\n✅ Submitted {len(job_ids)}/{len(TEST_URLS)} jobs successfully\n")

        if not job_ids:
            print("❌ No jobs were submitted successfully!")
            return

        # Test 3: Monitor all jobs concurrently
        print("Step 2: Monitoring jobs until completion...")
        print("-" * 70)

        monitor_tasks = [
            monitor_job(client, job_id, num)
            for job_id, num in job_ids
        ]

        completion_results = await asyncio.gather(*monitor_tasks)

        # Test 4: Analyze results
        print("\n" + "=" * 70)
        print("Results Summary")
        print("=" * 70)

        total_elapsed = time.time() - overall_start
        successful = sum(1 for success, _, _ in completion_results if success)
        failed = sum(1 for success, _, _ in completion_results if not success)

        avg_time = sum(elapsed for _, elapsed, _ in completion_results) / len(completion_results)
        total_reviews = sum(reviews for _, _, reviews in completion_results)

        print(f"Total jobs: {len(job_ids)}")
        print(f"Successful: {successful}")
        print(f"Failed: {failed}")
        print(f"Total reviews: {total_reviews}")
        print(f"Average job time: {avg_time:.1f}s")
        print(f"Total wall time: {total_elapsed:.1f}s")
        print()

        # Check if jobs ran in parallel
        if total_elapsed < avg_time * len(job_ids) * 0.8:
            print("✅ Jobs ran IN PARALLEL! (wall time < sum of job times)")
            speedup = (avg_time * len(job_ids)) / total_elapsed
            print(f"   Speedup: {speedup:.1f}x faster than sequential")
        else:
            print("⚠️  Jobs may have run SEQUENTIALLY")
            print(f"   Expected parallel time: ~{avg_time:.1f}s")
            print(f"   Actual time: {total_elapsed:.1f}s")

        print("\n" + "=" * 70)

        # Check memory/resource usage
        print("\n💡 Notes:")
        print("  - Each job runs a headless Chrome instance")
        print("  - Memory usage: ~500MB per concurrent job")
        print(f"  - Current test: {len(job_ids)} jobs = ~{len(job_ids) * 500}MB RAM")
        print("  - For production: Consider limiting concurrent jobs")
        print("    (Phase 2 adds Redis queue + worker pool for this)")


if __name__ == "__main__":
    try:
        asyncio.run(test_concurrent_jobs())
    except KeyboardInterrupt:
        print("\n\nTest interrupted by user")
    except Exception as e:
        print(f"\n❌ Test failed: {e}")
        import traceback
        traceback.print_exc()