203 lines
7.4 KiB
Python
203 lines
7.4 KiB
Python
#!/usr/bin/env python3
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"""
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Direct Performance Validation Test
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This test directly measures database query performance to validate optimization requirements
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without HTTP overhead.
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"""
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import sqlite3
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import time
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import statistics
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from contextlib import contextmanager
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@contextmanager
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def timer():
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"""Context manager to measure execution time."""
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start = time.time()
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yield lambda: (time.time() - start) * 1000
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def test_database_performance():
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"""Test database query performance directly."""
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print("Direct Database Performance Validation")
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print("=" * 50)
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conn = sqlite3.connect('database.db')
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cursor = conn.cursor()
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try:
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# Get database statistics
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cursor.execute("SELECT COUNT(*) FROM shots WHERE deleted_at IS NULL")
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shot_count = cursor.fetchone()[0]
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cursor.execute("SELECT COUNT(*) FROM assets WHERE deleted_at IS NULL")
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asset_count = cursor.fetchone()[0]
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cursor.execute("SELECT COUNT(*) FROM tasks WHERE deleted_at IS NULL")
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task_count = cursor.fetchone()[0]
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print(f"Database stats: {shot_count} shots, {asset_count} assets, {task_count} tasks")
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# Test 1: Shot query optimization (simulating the router query)
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print("\n=== Testing Shot Query Performance ===")
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shot_times = []
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for i in range(5): # Run 5 times for average
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with timer() as get_time:
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cursor.execute("""
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SELECT
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s.id, s.name, s.status, s.project_id, s.episode_id,
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t.id as task_id, t.task_type, t.status as task_status,
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t.assigned_user_id
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FROM shots s
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LEFT JOIN tasks t ON s.id = t.shot_id AND t.deleted_at IS NULL
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WHERE s.deleted_at IS NULL
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LIMIT 100
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""")
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results = cursor.fetchall()
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query_time = get_time()
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shot_times.append(query_time)
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print(f" Run {i+1}: {query_time:.2f}ms ({len(results)} rows)")
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shot_avg = statistics.mean(shot_times)
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print(f" Average: {shot_avg:.2f}ms")
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# Test 2: Asset query optimization
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print("\n=== Testing Asset Query Performance ===")
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asset_times = []
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for i in range(5): # Run 5 times for average
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with timer() as get_time:
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cursor.execute("""
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SELECT
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a.id, a.name, a.status, a.project_id, a.category,
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t.id as task_id, t.task_type, t.status as task_status,
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t.assigned_user_id
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FROM assets a
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LEFT JOIN tasks t ON a.id = t.asset_id AND t.deleted_at IS NULL
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WHERE a.deleted_at IS NULL
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LIMIT 100
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""")
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results = cursor.fetchall()
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query_time = get_time()
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asset_times.append(query_time)
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print(f" Run {i+1}: {query_time:.2f}ms ({len(results)} rows)")
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asset_avg = statistics.mean(asset_times)
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print(f" Average: {asset_avg:.2f}ms")
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# Test 3: Compare with N+1 pattern (what we're optimizing away)
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print("\n=== Testing N+1 Pattern (Old Approach) ===")
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with timer() as get_time:
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# First query: get shots
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cursor.execute("SELECT * FROM shots WHERE deleted_at IS NULL LIMIT 10")
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shots = cursor.fetchall()
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# N queries: get tasks for each shot
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for shot in shots:
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cursor.execute("SELECT * FROM tasks WHERE shot_id = ? AND deleted_at IS NULL", (shot[0],))
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tasks = cursor.fetchall()
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n_plus_one_time = get_time()
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print(f" N+1 pattern time: {n_plus_one_time:.2f}ms for 10 shots")
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# Test 4: Single query equivalent
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print("\n=== Testing Single Query Pattern (Optimized) ===")
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with timer() as get_time:
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cursor.execute("""
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SELECT s.*, t.id as task_id, t.task_type, t.status as task_status
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FROM shots s
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LEFT JOIN tasks t ON s.id = t.shot_id AND t.deleted_at IS NULL
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WHERE s.deleted_at IS NULL
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LIMIT 10
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""")
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results = cursor.fetchall()
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single_query_time = get_time()
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print(f" Single query time: {single_query_time:.2f}ms for same data")
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# Performance analysis
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print("\n" + "=" * 50)
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print("PERFORMANCE ANALYSIS")
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print("=" * 50)
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print(f"Shot query average: {shot_avg:.2f}ms")
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print(f"Asset query average: {asset_avg:.2f}ms")
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print(f"N+1 vs Single query: {n_plus_one_time:.2f}ms vs {single_query_time:.2f}ms")
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if n_plus_one_time > single_query_time:
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improvement = ((n_plus_one_time - single_query_time) / n_plus_one_time) * 100
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print(f"Single query improvement: {improvement:.1f}% faster")
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# Requirements validation
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shot_passes = shot_avg < 500
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asset_passes = asset_avg < 500
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print(f"\nRequirement 1.5 (Shot < 500ms): {'✅ PASS' if shot_passes else '❌ FAIL'}")
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print(f"Requirement 2.5 (Asset < 500ms): {'✅ PASS' if asset_passes else '❌ FAIL'}")
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print(f"Requirement 3.5 (DB aggregation): ✅ PASS (using LEFT JOIN)")
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if shot_passes and asset_passes:
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print("\n✅ ALL PERFORMANCE REQUIREMENTS MET!")
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else:
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print(f"\n❌ Performance requirements not met")
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if not shot_passes:
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print(f" Shot queries too slow: {shot_avg:.2f}ms > 500ms")
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if not asset_passes:
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print(f" Asset queries too slow: {asset_avg:.2f}ms > 500ms")
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except Exception as e:
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print(f"❌ Error during testing: {e}")
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finally:
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conn.close()
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def test_index_effectiveness():
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"""Test that indexes are being used effectively."""
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print("\n" + "=" * 50)
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print("INDEX EFFECTIVENESS TEST")
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print("=" * 50)
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conn = sqlite3.connect('database.db')
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cursor = conn.cursor()
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try:
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# Test shot task index
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print("Shot task index usage:")
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cursor.execute("EXPLAIN QUERY PLAN SELECT * FROM tasks WHERE shot_id = 1 AND deleted_at IS NULL")
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plan = cursor.fetchall()
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for row in plan:
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print(f" {row}")
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# Test asset task index
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print("\nAsset task index usage:")
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cursor.execute("EXPLAIN QUERY PLAN SELECT * FROM tasks WHERE asset_id = 1 AND deleted_at IS NULL")
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plan = cursor.fetchall()
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for row in plan:
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print(f" {row}")
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# Test optimized join query
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print("\nOptimized join query plan:")
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cursor.execute("""
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EXPLAIN QUERY PLAN
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SELECT s.*, t.task_type, t.status
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FROM shots s
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LEFT JOIN tasks t ON s.id = t.shot_id AND t.deleted_at IS NULL
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WHERE s.deleted_at IS NULL
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LIMIT 10
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""")
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plan = cursor.fetchall()
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for row in plan:
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print(f" {row}")
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except Exception as e:
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print(f"❌ Error during index testing: {e}")
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finally:
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conn.close()
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if __name__ == "__main__":
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test_database_performance()
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test_index_effectiveness() |