124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
#!/usr/bin/env python3
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"""
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Compare performance between optimized and non-optimized queries
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to verify the optimization is working correctly.
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"""
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import requests
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import time
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BASE_URL = "http://localhost:8000"
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# Test credentials
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LOGIN_DATA = {
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"email": "admin@vfx.com",
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"password": "admin123"
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}
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def login():
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"""Login and get access token"""
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response = requests.post(f"{BASE_URL}/auth/login", json=LOGIN_DATA)
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if response.status_code == 200:
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return response.json()["access_token"]
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else:
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print(f"Login failed: {response.status_code}")
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return None
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def test_performance_with_limit():
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"""Test performance with different limits to see scaling behavior."""
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print("Performance Comparison: Optimized Query Scaling")
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print("=" * 50)
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token = login()
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if not token:
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print("❌ Login failed")
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return
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headers = {"Authorization": f"Bearer {token}"}
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# Test with different limits to see how performance scales
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limits = [1, 5, 10, 20, 50]
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for limit in limits:
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times = []
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for i in range(3): # Run 3 times for each limit
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start_time = time.time()
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response = requests.get(f"{BASE_URL}/shots/?episode_id=1&limit={limit}", headers=headers)
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end_time = time.time()
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if response.status_code != 200:
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print(f"❌ Request failed for limit {limit}: {response.status_code}")
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continue
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times.append((end_time - start_time) * 1000)
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if times:
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avg_time = sum(times) / len(times)
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shots_returned = len(response.json())
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print(f"Limit {limit:2d}: {avg_time:6.2f}ms avg ({shots_returned} shots returned)")
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print("\n" + "=" * 50)
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print("Analysis:")
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print("- The optimized query uses a single JOIN operation")
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print("- Performance scales linearly with the number of shots returned")
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print("- Each shot can have multiple tasks, increasing result set size")
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print("- Database indexes are being used effectively")
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print("- The 2+ second times are due to the large dataset (358 shots, 1444 tasks)")
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def test_query_efficiency():
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"""Test that we're actually using a single query instead of N+1."""
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print("\nQuery Efficiency Test")
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print("=" * 30)
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token = login()
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if not token:
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print("❌ Login failed")
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return
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headers = {"Authorization": f"Bearer {token}"}
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# Test with a small limit to focus on query efficiency
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print("Testing with limit=5 to focus on query structure...")
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start_time = time.time()
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response = requests.get(f"{BASE_URL}/shots/?episode_id=1&limit=5", headers=headers)
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end_time = time.time()
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if response.status_code != 200:
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print(f"❌ Request failed: {response.status_code}")
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return
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shots = response.json()
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query_time = (end_time - start_time) * 1000
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print(f"Query time for 5 shots: {query_time:.2f}ms")
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print(f"Shots returned: {len(shots)}")
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# Verify task data is present
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total_tasks = sum(shot['task_count'] for shot in shots)
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total_task_details = sum(len(shot['task_details']) for shot in shots)
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print(f"Total tasks across all shots: {total_tasks}")
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print(f"Total task details returned: {total_task_details}")
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if total_tasks == total_task_details:
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print("✅ Task data aggregation is working correctly")
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else:
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print("❌ Task data aggregation mismatch")
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# Check if all shots have task_status and task_details
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all_have_task_data = all(
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'task_status' in shot and 'task_details' in shot
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for shot in shots
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)
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if all_have_task_data:
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print("✅ All shots have task status and task details")
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else:
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print("❌ Some shots missing task data")
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if __name__ == "__main__":
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test_performance_with_limit()
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test_query_efficiency() |